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In the latest research progress, deep neural networks have been revolutionized by frameworks to extract image features more accurately. In this study, we focus on an attention model that can be useful in deep neural networks and propose a simple but strong feature extraction deep network architecture, W-Net. The architecture of our W-Net network has two mutually independent path structures, and it is designed with the following advantages. (1) There are two independent effective paths in our proposed network structure, and the two paths capture more contextual information from different scales in different ways. (2) The two paths acquire different feature images, and in the upsampling approach, we use bilinear interpolation thus reducing the feature map distortion phenomenon and integrating the different images processed. (3) The feature image processing is at a bottleneck, and a hierarchical attention module is constructed at the bottleneck by reclassifying after the channel attention module and the spatial attention module, resulting in more efficient and accurate processing of feature images. During the experiment, we also tested iSAID, a massively high spatial resolution remote sensing image dataset, with further experimental data comparison to demonstrate the generality of our method for remote sensor image segmentation. [ABSTRACT FROM AUTHOR]

Objects in a real scene often occlude each other and inferring a complete appearance from the visible part is an important and challenging task. In this paper, the authors propose a self‐supervised generative adversarial network GIGAN (GAN for generating the invisible), which can generate the complete appearance of objects without labelled invisible part information. The authors build two cycle transformation networks CycleIncomplete (CycleI) and CycleComplete (CycleC) that share parameters to improve the accuracy of mask completion. This design does not require well‐matched training images and can make better use of the limited labelled samples. In addition, the authors propose a conditional normalization module and combine it with the inferred complete mask output. The combination not only enhances the content recovery ability and obtains more realistic outputs, but also improves the efficiency of the generation process. Experimental results show that compared with existing self‐supervised learning models, our method achieves l1 error, mean intersection‐over‐union (mIOU), and Fréchet inception distance (FID) improvements on the COCOA and KINS datasets. [ABSTRACT FROM AUTHOR]

The intrinsic high parallelism and entanglement characteristics of quantum computing have made quantum image processing techniques a focus of great interest. One of the most widely used techniques in image processing is segmentation, which in one of their most basic forms can be carried out using thresholding algorithms. In this paper, a fault-tolerant quantum dual-threshold algorithm has been proposed. This algorithm has been built using only Clifford+T gates for compatibility with error detection and correction codes. Because fault-tolerant implementation of T gates has a much higher cost than other quantum gates, our focus has been on reducing the number of these gates. This has allowed adding noise tolerance, computational cost reduction, and fault tolerance to the state-of-the-art dual-threshold segmentation circuits. Since the dual-threshold image segmentation involves the comparison operation, as part of this work we have implemented two full comparator circuits. These circuits optimize the metrics T-count and T-depth with respect to the best circuit comparators currently available in the literature. [ABSTRACT FROM AUTHOR]

Food quality control is an important task in the agricultural domain at the postharvest stage for avoiding food losses. The latest achievements in image processing with deep learning (DL) and computer vision (CV) approaches provide a number of effective tools based on the image colorization and image-to-image translation for plant quality control at the postharvest stage. In this article, we propose the approach based on Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) techniques to use synthesized and segmented VNIR imaging data for early postharvest decay and fungal zone predictions as well as the quality assessment of stored apples. The Pix2PixHD model achieved higher results in terms of VNIR images translation from RGB (SSIM = 0.972). Mask R-CNN model was selected as a CNN technique for VNIR images segmentation and achieved 58.861 for postharvest decay zones, 40.968 for fungal zones and 94.800 for both the decayed and fungal zones detection and prediction in stored apples in terms of F1-score metric. In order to verify the effectiveness of this approach, a unique paired dataset containing 1305 RGB and VNIR images of apples of four varieties was obtained. It is further utilized for a GAN model selection. Additionally, we acquired 1029 VNIR images of apples for training and testing a CNN model. We conducted validation on an embedded system equipped with a graphical processing unit. Using Pix2PixHD, 100 VNIR images from RGB images were generated at a rate of 17 frames per second (FPS). Subsequently, these images were segmented using Mask R-CNN at a rate of 0.42 FPS. The achieved results are promising for enhancing the food study and control during the postharvest stage. [ABSTRACT FROM AUTHOR]

Vascular segmentation is a crucial task in biomedical image processing, which is significant for analyzing and modeling vascular networks under physiological and pathological states. With advances in fluorescent labeling and mesoscopic optical techniques, it has become possible to map the whole-mouse-brain vascular networks at capillary resolution. However, segmenting vessels from mesoscopic optical images is a challenging task. The problems, such as vascular signal discontinuities, vessel lumens, and background fluorescence signals in mesoscopic optical images, belong to global semantic information during vascular segmentation. Traditional vascular segmentation methods based on convolutional neural networks (CNNs) have been limited by their insufficient receptive fields, making it challenging to capture global semantic information of vessels and resulting in inaccurate segmentation results. Here, we propose SegVesseler, a vascular segmentation method based on Swin Transformer. SegVesseler adopts 3D Swin Transformer blocks to extract global contextual information in 3D images. This approach is able to maintain the connectivity and topology of blood vessels during segmentation. We evaluated the performance of our method on mouse cerebrovascular datasets generated from three different labeling and imaging modalities. The experimental results demonstrate that the segmentation effect of our method is significantly better than traditional CNNs and achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

• A new model was proposed to segment the intensity inhomogeneity image. • A prior constraint was added in the proposed for improving the robustness. • Based on an operator method, the proposed model can be efficiently solved. • Numerical implementations showed the robustness of the proposed model. Image segmentation is a fundamental task in computer vision and image processing. How to efficiently decrease the effect such as high noise, low resolution and intensity inhomogeneity is the key to improve the accuracy of segmentation. To this end, we propose a novel segmentation model by assuming the image to be the product of a piecewise-constant function and a smooth function and then the model can be proposed based on the suitable gradient spaces. To improve the robustness of the proposed model, we add a prior constraint for the smooth function and then analyse the existence of the solution based on the functional theory. Since the proposed model includes two nonsmooth terms, we use a heat kernel convolution to approximately replace the length regularization term and then the alternating direction method of multipliers can be employed to solve it. Numerical experiments on several testing images demonstrate that the effectiveness and the robustness of the proposed model compared to some state-of-the-art segmentation models. [ABSTRACT FROM AUTHOR]

The advancement of 6G networks is driven by the need for customer-centric communication and network control, particularly in applications such as intelligent transport systems. These applications rely on outdoor communication in extremely high-frequency (EHF) bands, including millimeter wave (mmWave) frequencies exceeding 30 GHz. However, EHF signals face challenges such as higher attenuation, diffraction, and reflective losses caused by obstacles in outdoor environments. To overcome these challenges, 6G networks must focus on system designs that enhance propagation characteristics by predicting and mitigating diffraction, reflection, and scattering losses. Strategies such as proper handovers, antenna orientation, and link adaptation techniques based on losses can optimize the propagation environment. Among the network components, aerial networks, including unmanned aerial vehicles (UAVs) and electric vertical take-off and landing aircraft (eVTOL), are particularly susceptible to diffraction losses due to surrounding buildings in urban and suburban areas. Traditional statistical models for estimating the height of tall objects like buildings or trees are insufficient for accurately calculating diffraction losses due to the dynamic nature of user mobility, resulting in increased latency unsuitable for ultra-low latency applications. To address these challenges, this paper proposes a deep learning framework that utilizes easily accessible Google Street View imagery to estimate building heights and predict diffraction losses across various locations. The framework enables real-time decision-making to improve the propagation environment based on users' locations. The proposed approach achieves high accuracy rates, with an accuracy of 39% for relative error below 2%, 83% for relative error below 4%, and 96% for both relative errors below 7% and 10%. Compared to traditional statistical methods, the proposed deep learning approach offers significant advantages in height prediction accuracy, demonstrating its efficacy in supporting the development of 6G networks. The ability to accurately estimate heights and map diffraction losses before network deployment enables proactive optimization and ensures real-time decision-making, enhancing the overall performance of 6G systems. [ABSTRACT FROM AUTHOR]

Automatic weed identification and detection are crucial for precision weeding operations. In recent years, deep learning (DL) has gained widespread attention for its potential in crop weed identification. This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL. Through an analysis of relevant literature from both within and outside of China, the author summarizes the development history, research progress, and identification and detection methods of DL-based weed identification technology. Emphasis is placed on data sources and DL models applied to different technical tasks. Additionally, the paper discusses the challenges of time-consuming and laborious dataset preparation, poor generality, unbalanced data categories, and low accuracy of field identification in DL for weed identification. Corresponding solutions are proposed to provide a reference for future research directions in weed identification. [ABSTRACT FROM AUTHOR]

In remote-sensing image processing tasks, images with higher resolution always result in better performance on downstream tasks, such as scene classification and object segmentation. However, objects in remote-sensing images often have low resolution and complex textures due to the imaging environment. Therefore, effectively reconstructing high-resolution remote-sensing images remains challenging. To address this concern, we investigate embedding context information and object priors from remote-sensing images into current deep learning super-resolution models. Hence, this paper proposes a novel remote-sensing image super-resolution method called Context-Guided Constrained Network (CGC-Net). In CGC-Net, we first design a simple but effective method to generate inverse distance maps from the remote-sensing image segmentation maps as prior information. Combined with prior information, we propose a Global Context-Constrained Layer (GCCL) to extract high-quality features with global context constraints. Furthermore, we introduce a Guided Local Feature Enhancement Block (GLFE) to enhance the local texture context via a learnable guided filter. Additionally, we design a High-Frequency Consistency Loss (HFC Loss) to ensure gradient consistency between the reconstructed image (HR) and the original high-quality image (HQ). Unlike existing remote-sensing image super-resolution methods, the proposed CGC-Net achieves superior visual results and reports new state-of-the-art (SOTA) performance on three popular remote-sensing image datasets, demonstrating its effectiveness in remote-sensing image super-resolution (RSI-SR) tasks. [ABSTRACT FROM AUTHOR]

Wearing medical masks of various materials and types will lead to different prevention and control the effect of virus spread. Segmentation to masks in image data sets will benefit analyzing the differences in prevention and control effects of different types of masks. Currently, there are many facial mask detection algorithms, but there is a lack of further segmentation. Therefore, this study proposes a facial mask segmentation method based on image processing and deep learning. An improved contrast-limited adaptive histogram equalization preprocessing method is proposed to reduce the poor effect of traditional preprocessing caused by dark part of the image through brightness adaptive adjustment. SSD network is used for mask pre-detection, and CA- Net is used for mask segmentation. With U-Net as the backbone network, CA-Net adds spatial attention module, channel attention module and scale attention module to realize the comprehensive attention guidance of space, channel and scale of feature map at the same time. The segmentation result is processed by binary image closing operation to get the final segmentation results. The Dice evaluation of preliminarily segmentation index can reach to 79.18%±3.44%, and the Dice coefficient is increased to 84.03%±2.81% by adding preprocessing and post-processing operations, indicating that the proposed method could significantly improve the visual segmentation results. [ABSTRACT FROM AUTHOR]

Cracks are fractures or breaks that occur in materials such as concrete, metals, rocks, and other solids. Various methods are used to detect and monitor cracks; among many of them, image-based methodologies allow fast identification of the distress and easy quantification of the percentage of cracks in the scene. Two main categories can be identified: classical and deep learning approaches. In the last decade, the tendency has moved towards the use of the latter. Even though they have proven their outstanding predicting performance, they suffer some drawbacks: a "black-box" nature leaves the user blind and without the possibility of modifying any parameters, a huge amount of labeled data is generally needed, a process that requires expert judgment is always required, and, finally, they tend to be time-consuming. Accordingly, the present study details the methodology for a new algorithm for crack segmentation based on the theory of minimal path selection combined with a region-based approach obtained through the segmentation of texture features extracted using Gabor filters. A pre-processing step is described, enabling the equalization of brightness and shadows, which results in better detection of local minima. These local minimal are constrained by a minimum distance between adjacent points, enabling a better coverage of the cracks. Afterward, a region-based segmentation technique is introduced to determine two areas that are used to determine threshold values used for rejection. This step is critical to generalize the algorithm to images presenting close-up scenes or wide cracks. Finally, a geometrical thresholding step is presented, allowing the exclusion of rounded areas and small isolated cracks. The results showed a very competitive F1-score (0.839), close to state-of-the-art values achieved with deep learning techniques. The main advantage of this approach is the transparency of the workflow, contrary to what happens with deep learning frameworks. In the proposed approach, no prior information is required; however, the statistical parameters may have to be adjusted to the particular case and requirements of the situation. The proposed algorithm results in a useful tool for researchers and practitioners needing to validate their results against some reference or needing labeled data for their models. Moreover, the current study could establish the grounds to standardize the procedure for crack segmentation with a lower human bias and faster results. The direct application of the methodology to images obtained with any low-cost sensor makes the proposed algorithm an operational support tool for authorities needing crack detection systems in order to monitor and evaluate the current state of the infrastructures, such as roads, tunnels, or bridges. [ABSTRACT FROM AUTHOR]

Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biological populations. It has efficient and parallel global optimization capabilities and strong optimization performance. In this paper, the ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, thimble colony algorithm, and other swarm intelligent optimization algorithms are deeply studied. The model, features, improvement strategies, and application fields of the algorithm in image processing, such as image segmentation, image matching, image classification, image feature extraction, and image edge detection, are comprehensively reviewed. The theoretical research, improvement strategies, and application research of image processing are comprehensively analyzed and compared. Combined with the current literature, the improvement methods of the above algorithms and the comprehensive improvement and application of image processing technology are analyzed and summarized. The representative algorithms of the swarm intelligence algorithm combined with image segmentation technology are extracted for list analysis and summary. Then, the unified framework, common characteristics, different differences of the swarm intelligence algorithm are summarized, existing problems are raised, and finally, the future trend is projected. [ABSTRACT FROM AUTHOR]

Real‐time semantic segmentation is a crucial technology in automatic driving scenarios, which needs to meet both high precision and real‐time. The authors observe that learning complex correlations between object categories is vital in the real‐time semantic segmentation task. Moreover, image spatial detail information plays an important role in small object segmentation and preserving edges and textures. A Semantics Recalibration and Detail Enhancement Network for real‐time semantic segmentation based on BiSeNet V2 is proposed. On the one hand, a lightweight Semantics Recalibration module is designed to effectively extract global semantic contextual information, which combines pyramid segmentation and adaptive recalibration operations to learn the correlations between object categories. On the other hand, a Detail Enhancement module takes the feature maps of the shallow layers in the semantics branch as input and refines the feature maps to highlight the detail information. Finally, quantitative and qualitative analyses on Cityscapes and CamVid datasets demonstrate the effectiveness and generalisation of the proposed method. [ABSTRACT FROM AUTHOR]

Nuclei instance segmentation is an important task in medical image analysis involving cell‐level pathological analysis, which is of great significance for many biomedical applications. Nuclei segmentation is a challenging task due to edge adhesions and the distribution of numerous tiny dense nuclei. In this work, a nuclei instance segmentation framework, namely, the improved BlendMask is proposed. In this framework, in order to improve the performance of detection and segmentation of dense small objects and adhering nuclei, two components, including dilated convolution aggregation module (DCA) and context information aggregation module (CIA), are designed. The DCA constructs multi‐path parallel dilated convolution, which greatly increases the receptive field of the network and the ability to capture multi‐scale contextual information. The CIA reduces the information loss in the channel by endowing the network with high‐level multi‐scale spatial context information. In addition, a novel distributional ranking loss function is given that can effectively alleviate the imbalance between the target and the background. The proposed method is validated on the DSB2018 dataset. Compared to BlendMask, this network improves by 3.6% on AP segmentation metric, and the segmentation performance of this network is superior to that of several recent classic open‐source nuclei instance segmentation methods. [ABSTRACT FROM AUTHOR]

Aerial photography is a long-range, non-contact method of target detection technology that enables qualitative or quantitative analysis of the target. However, aerial photography images generally have certain chromatic aberration and color distortion. Therefore, effective segmentation of aerial images can further enhance the feature information and reduce the computational difficulty for subsequent image processing. In this paper, we propose an improved version of Golden Jackal Optimization, which is dubbed Helper Mechanism Based Golden Jackal Optimization (HGJO), to apply multilevel threshold segmentation to aerial images. The proposed method uses opposition-based learning to boost population diversity. And a new approach to calculate the prey escape energy is proposed to improve the convergence speed of the algorithm. In addition, the Cauchy distribution is introduced to adjust the original update scheme to enhance the exploration capability of the algorithm. Finally, a novel "helper mechanism" is designed to improve the performance for escape the local optima. To demonstrate the effectiveness of the proposed algorithm, we use the CEC2022 benchmark function test suite to perform comparison experiments. the HGJO is compared with the original GJO and five classical meta-heuristics. The experimental results show that HGJO is able to achieve competitive results in the benchmark test set. Finally, all of the algorithms are applied to the experiments of variable threshold segmentation of aerial images, and the results show that the aerial photography images segmented by HGJO beat the others. Noteworthy, the source code of HGJO is publicly available at https://github.com/Vang-z/HGJO. [ABSTRACT FROM AUTHOR]

Background: The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable. Results: An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed. Conclusion: The technical realization of "Phenomenon" allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time. [ABSTRACT FROM AUTHOR]

Digital transformation has brought radical changes in several domains. Particularly, image processing techniques have been generally used in medical, security, and monitoring applications. Image segmentation is a specific task where an image is partitioned in meaningful segments, containing similar features and properties. Its aim is to simplify the original image for easy analysis since relevant information is highlighted. These techniques are commonly used to support medical experts in detecting areas of interest in medical images. Level set method is a methodology for image segmentation, which works with minimizing energy for segmentation of the image by active contours. The areas inside each contour belong to distinct segments. In active contour-based models, the level of each contour changes according to the intensity values (region-based active contours) or the gradient variations (edge-based active contours). Here, a new edge–region level set algorithm for image segmentation is proposed which controls the curve movement based on both intensity and gradient values. Moreover, the original active contour model has been modified by considering both the mean and the variance values of the pixels' neighborhood, instead of the mean value only. Indeed, in homogeneous regions with the same mean value could be assigned to the same segment while belonging to different ones. Since the initial curve definition is crucial for level set methods, a new methodology for initial curve detection based on Canny edge detector has been proposed. Experiments have been conducted on brain tumor magnetic resonance imaging (MRI). Images from Whole Brain Atlas (Harvard University Medical School) datasets, part Neoplastic Disease (brain tumor) have been used. Results have shown that the suggested approach is able to accurately detect tumor regions in the images and to overcome the original active contour models such as CV, LBF, and LIF. Using semi-average filter in pre-processing stage can strengthen edges and it led to detecting more strong edges in Canny edge detector. [ABSTRACT FROM AUTHOR]

As a severe thermal damage, grinding burns reduce the surface quality and service life of high-performance gears. In this study, to realize accurate localization, classification, and grading of burn defects on gear grinding surfaces, a machine vision–based intelligent detection method was proposed. Different from image processing methods based on hand-designed features, the proposed method can minimize inspection conditions. To recognize individual gear functional surfaces and burn regions from random backgrounds, an image segmentation model was developed by using deep convolutional neural networks. For the scarcity of defect samples in industrial surface testing, a data-augmentation method combining deep learning methods with geometric and color-space transformations was proposed. The experimental results of the gear surface image dataset demonstrated that the proposed burn segmentation model has a precision and recall rate of 84.44% and 82.79%, respectively. This work provides fresh insights for realizing efficient and accurate detection of gear grinding burn defects. [ABSTRACT FROM AUTHOR]

Metallographic images or often called the microstructures contain important information about metals, such as strength, toughness, ductility, corrosion resistance, which are used to choose the proper materials for various engineering applications. Thus by understanding the microstructures, one can determine the behaviour of a component made of a particular metal, and can predict the failure of that component in certain conditions. Image segmentation is a powerful technique for determination of morphological features of the microstructure like volume fraction, inclusion morphology, void, and crystal orientations. These are some key factors for determining the physical properties of metal. Therefore, automatic micro-structure characterization using image processing is useful for industrial applications which currently adopts deep learning-based segmentation models. In this paper, we propose a metallographic image segmentation method using an ensemble of modified U-Nets. Three U-Net models having the same architecture are separately fed with color transformed imaged (RGB, HSV and YUV). We improvise the U-Net with dilated convolutions and attention mechanisms to get finer grained features. Then we apply the sum-rule-based ensemble method on the outcomes of U-Net models to get the final prediction mask. We achieve the mean intersection over union (IoU) score of 0.677 on a publicly available standard dataset, namely MetalDAM. We also show that the proposed method obtains results comparable to state-of-the-art methods with fewer number of model parameters. The source code of the proposed work can be found at https://github.com/mb16biswas/attention-unet. [ABSTRACT FROM AUTHOR]

Cell attachment is of paramount importance in implant design, bioreactor design, tissue engineering and the design of non-fouling surfaces. Surface roughness is a significant factor that affects cell attachment. To explore the impact of roughness characteristics, micromachining approaches can be used to fabricate surfaces with controlled microscale topography. When optical microscopy is employed to study cell attachment to optically opaque micropatterned surfaces, one needs to separate the area of an image coated with cells from the background. Manual cell counting can be used to assess the amount of attached cells. However, this process is very time consuming, when the studied surface is larger than several square millimeters. This paper describes an approach for the automatic estimation of the area of cells attached to the surfaces of micro-patterned optically opaque platforms. Saccharomyces cerevisiae yeast cells were used to test the developed approach. The approach uses image registration and segmentation tools available in MathWorks MATLAB R2020b Image Processing Toolbox. The factors that affect the accuracy of the developed approach (magnification, contrast and focus) as well as the ways of improving the results are discussed. [ABSTRACT FROM AUTHOR]

Semantic segmentation is a growing topic in high-resolution remote sensing image processing. The information in remote sensing images is complex, and the effectiveness of most remote sensing image semantic segmentation methods depends on the number of labels; however, labeling images requires significant time and labor costs. To solve these problems, we propose a semi-supervised semantic segmentation method based on dual cross-entropy consistency and a teacher–student structure. First, we add a channel attention mechanism to the encoding network of the teacher model to reduce the predictive entropy of the pseudo label. Secondly, the two student networks share a common coding network to ensure consistent input information entropy, and a sharpening function is used to reduce the information entropy of unsupervised predictions for both student networks. Finally, we complete the alternate training of the models via two entropy-consistent tasks: (1) semi-supervising student prediction results via pseudo-labels generated from the teacher model, (2) cross-supervision between student models. Experimental results on publicly available datasets indicate that the suggested model can fully understand the hidden information in unlabeled images and reduce the information entropy in prediction, as well as reduce the number of required labeled images with guaranteed accuracy. This allows the new method to outperform the related semi-supervised semantic segmentation algorithm at half the proportion of labeled images. [ABSTRACT FROM AUTHOR]

Video summarization offers a summary version that conveys the primary information of a longer video. The main challenges of video summarization are related to keyframe extraction and saliency mapping. Thus, this work proposes a sparse coding model for keyframe extraction and saliency mapping applications. Specifically, the minimax concave penalty (MCP) is utilized as a sparse regularization scheme and the regularized non‐convex MCP problem is solved by decomposing MCP into two convex functions and the convex function's algorithm difference is relied on to solve the resulting sub‐problems. The experimental results demonstrate higher compressed keyframes and saliency maps than current state‐of‐the‐art algorithms. In particular, the model attains a lower summary length of 34% and 19% compared to sparse modeling representation selection (SMRS) and sparse modeling using the determinant sparsity measure (SC‐det), respectively. In addition, the developed scheme has a shorter computation time, requiring 82% and 33% less time than the ITTI and the dense and sparse reconstruction (DSR) methods. [ABSTRACT FROM AUTHOR]

In the U.S., peanut farmers receive premium prices for crops with high seed grades. One component of seed grade is the proportion of seed weight to that of pod hulls and other matter. Seed weight and size are also important traits for food processors. Current methods for evaluating peanut seed grade require the opening of the pod and are time-consuming and labor-intensive. In this study, a non-destructive and efficient method to determine peanut seed weights was investigated. X-ray images of a total of 513 peanut pods from three commercial cultivars, each representing three market types, were taken using a soft X-ray imaging system. The region of interest of each image, the seeds, was extracted two ways, manually and with a differential evolution segmentation algorithm. The comprehensive attenuation index (CAI) value was calculated from the segmented regions of interest. Lastly, linear regression models were established between peanut seed weights and the CAI. The results demonstrated that the X-ray imaging technology, coupled with the differential evolution segmentation algorithm, may be used to estimate seed weights efficiently from intact peanut pods. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) in general and laser powder bed fusion (PBF-LB/M) in particular are becoming increasingly important in the field of production technologies. Especially the high achievable accuracies and the great freedom in design make PBF-LB/M interesting for the manufacturing and repair of gas turbine blades. Part repair involves building AM-geometries onto an existing component. To minimise the offset between component and AM-geometry, a precise knowledge of the position of the component in the PBF-LB/M machine is mandatory. However, components cannot be inserted into the PBF-LB/M machine with repeatable accuracy, so the actual position will differ for each part. For an offset-free build-up, the actual position of the component in the PBF-LB/M machine has to be determined. In this paper, a camera-based position detection system is developed considering PBF-LB/M constraints and system requirements. This includes finding an optimal camera position considering the spatial limitations of the PBF-LB/M machine and analysing the resulting process coordinate systems. In addition, a workflow is developed to align different coordinate systems and simultaneously correct the perspective distortion in the acquired camera images. Thus, position characteristics can be determined from images by image moments. For this purpose, different image segmentation algorithms are compared. The precision of the system developed is evaluated in tests with 2D objects. A precision of up to 30μm in translational direction and an angular precision of 0.021∘ is achieved. Finally, a 3D demonstrator was built using this proposed hybrid strategy. The offset between base component and AM-geometry is determined by 3D scanning and is 69μm. [ABSTRACT FROM AUTHOR]

The failure or debonding of CEIs (Concrete-Epoxy Interfaces) in Fiber-Reinforced Polymer concrete (FRP) systems occurs in one or a combination of three modes: CC (Cohesive failure in Concrete), CE (Cohesive failure in Epoxy), and IF (Interfacial Failure). These failure modes are usually identified, and their relationships are established by human intuition, which is prone to subjectivity. This study proposes a novel method based on image processing techniques to analyze CEI fracture surfaces and evaluate their failure modes. The failure modes of CEI fracture surfaces of specimens from a 3PB (Three-Point Bending) experiment were assessed using an HVS, CIE L*a*b*, YCbCr, or RGB color space image segmentation-based image processing technique on the preprocessed images of the CEI failure sides. A manual approach was adopted to validate the accuracy of the proposed method. Comparing the failure mode (CE) obtained using the manual and the proposed methodology, an RMSE (Root Means Square Error) of 0.19, 0.10, 0.23, and 0.26 was obtained for HVS, CIE L*a*b*, YCbCr, or RGB color space, respectively. The epoxy area selected with CIE L*a*b* color space produced the most accurate evaluation of the failure modes. This study provides an accurate method of quantifying the failure modes of CEI fracture surfaces. The methodology proposed in this study is recommended for forensic investigations to understand better the possible causes of failure in externally bounded fiber-reinforced polymers. [ABSTRACT FROM AUTHOR]

Wind turbine blade leading edge erosion is a major source of power production loss and early detection benefits optimization of repair strategies. Two machine learning (ML) models are developed and evaluated for automated quantification of the areal extent, morphology and nature (deep, shallow) of damage from field images. The supervised ML model employs convolutional neural networks (CNN) and learns features (specific types of damage) present in an annotated set of training images. The unsupervised approach aggregates pixel intensity thresholding with calculation of pixel-by-pixel shadow ratio (PTS) to independently identify features within images. The models are developed and tested using a dataset of 140 field images. The images sample across a range of blade orientation, aspect ratio, lighting and resolution. Each model (CNN v PTS) is applied to quantify the percent area of the visible blade that is damaged and classifies the damage into deep or shallow using only the images as input. Both models successfully identify approximately 65% of total damage area in the independent images, and both perform better at quantifying deep damage. The CNN is more successful at identifying shallow damage and exhibits better performance when applied to the images after they are preprocessed to a common blade orientation. [ABSTRACT FROM AUTHOR]

Medical images exhibit multi‐granularity and high obscurity along boundaries. As representative work, the U‐Net and its variants exhibit two shortcomings on medical image segmentation: (a) they expand the range of reception fields by applying addition or concatenate operators to features with different reception fields, which disrupts the distribution of the essential feature of objects; (b) they utilize the downsampling or atrous convolution to characterize multi‐granular features of objects, which can obtain a large range of reception fields but leads to blur boundaries of objects. A Shuffling Atrous Convolutional U‐Net (SACNet) for circumventing those issues is proposed. The significant component of SACNet is the Shuffling Atrous Convolution (SAC) module, which fuses different atrous convolutional layers together by using a shuffle concatenate operation, so that the features from the same channel (which correspond to the same attribute of objects) are merged together. Besides the SAC modules, SACNet utilizes an EP module during the fine and medium levels to enhance the boundaries of objects, and utilizes a Transformer module during the coarse level to capture an overall correlation of pixels. Experiments on three medical image segmentation tasks: abdominal organ, cardiac, and skin lesion segmentation demonstrate that, SACNet outperforms several state‐of‐the‐art methods and facilitates easy transplant to other semantic segmentation tasks. [ABSTRACT FROM AUTHOR]

Image segmentation is an essential step in vision sensing and image processing. It enables the understanding of the object's classes, spatial locations, and extents in the scene, which can be used to support a wide range of construction applications such as progress monitoring, safety management, and productivity analysis. The recent ground-breaking achievements of deep learning-based approaches for semantic segmentation are at the cost of expensive large-scale training datasets annotated at the pixel level. Although building information modeling (BIM) has been leveraged to alleviate labeling costs using automatically generated, color-coded images as semantic labels, the differences between the BIM models and the real-world scenes make it difficult to apply networks trained on BIM-generated labels to real images. Furthermore, it takes nontrivial efforts to reduce such differences. To address these problems, this paper proposes a weakly supervised segmentation approach that uses inexpensive image-level labels. The missing boundary information in image-level labels is compensated by BIM-extracted object information. The proposed method consists of three modules: (1) detect initial object locations from image-level labels; (2) extract object information from BIM as prior knowledge; and (3) incorporate the prior knowledge into the network to enhance the detected object locations. Three extensive experiments are designed to evaluate the effectiveness of the proposed method. Results show that the proposed method substantially improves the detected object areas by using prior knowledge of target objects from BIM and outperforms the state-of-the-art weakly supervised methods. [ABSTRACT FROM AUTHOR]

In the food and beverage industry, the existing recognition of code characters on the surface of complex packaging usually suffers from low accuracy and low speed. This work presents an efficient and accurate inkjet code recognition system based on the combination of the deep learning and traditional image processing methods. The proposed system mainly consists of three sequential modules, i.e., the character’s region extraction by modified YOLOv3-tiny network, the character processing by the traditional image processing methods such as binarization and the modified character projection segmentation, and the character recognition by a Convolutional recurrent neural network (CRNN) model based on a modified version of MobileNetV3. In this system, only a small amount of tag data has been made and an effective character data generator is designed to randomly generate different experimental data for the CRNN model training. To the best of our knowledge, this report for the first time describes that deep learning has been applied to the recognition of codes on complex background for the real-life industrial application. Experimental results have been provided to verify the accuracy and effectiveness of the proposed model, demonstrating a recognition accuracy of 0.986 and a processing speed of 100 ms per bottle in the end-to-end character recognition system. [ABSTRACT FROM AUTHOR]

Generation of random fields (GRF) for image segmentation represents partitioning an image into different regions that are homogeneous or have similar facets of the image. It is one of the most challenging tasks in image processing and a very important pre-processing step in the fields of computer vision, image analysis, medical image processing, pattern recognition, remote sensing, and geographical information system. Many researchers have presented numerous image segmentation approaches, but still, there are challenges like segmentation of low contrast images, removal of shadow in the images, reduction of high dimensional images, and computational complexity of segmentation techniques. In this review paper, the authors address these issues. The experiments are conducted and tested on the Berkely dataset (BSD500), Semantic dataset, and our own dataset, and the results are shown in the form of tables and graphs. [ABSTRACT FROM AUTHOR]

Image segmentation is imperative for image processing applications. Thresholding technique is the easiest way of partitioning an image into different regions. Mostly, entropy-based threshold selection methods are used for multilevel thresholding. However, these methods suffer from their dependencies on spatial distribution of gray values. To solve this issue, a novel segmentation error minimization (SEM)-based method for multilevel optimal threshold selection using opposition equilibrium optimizer (OEO) is suggested. In this contribution, a new segmentation score (SS) (objective function) is derived while minimizing the segmentation error function. Our proposal is explicitly free from gray level spatial distribution of an image. Optimal threshold values are achieved by maximizing the SS (fitness value) using OEO. The key to success is the maximization of score among classes, ensuring the sharpening of the shred boundary between classes, leading to an improved threshold selection method. It is empirically demonstrated how the optimal threshold selection is made. Experimental results are presented using standard test images. Standard measures like PSNR, SSIM and FSIM are used for validation The results are compared with state-of-the-art entropy-based technique. Our method performs well both qualitatively and quantitatively. The suggested technique would be useful for biomedical image segmentation. [ABSTRACT FROM AUTHOR]

As a fundamental but difficult topic in computer vision, 3D object segmentation has various applications in medical image analysis, autonomous vehicles, robotics, virtual reality, lithium battery image analysis, etc. In the past, 3D segmentation was performed using hand-made features and design techniques, but these techniques could not generalize to vast amounts of data or reach acceptable accuracy. Deep learning techniques have lately emerged as the preferred method for 3D segmentation jobs as a result of their extraordinary performance in 2D computer vision. Our proposed method used a CNN-based architecture called 3D UNET, which is inspired by the famous 2D UNET that has been used to segment volumetric image data. To see the internal changes of composite materials, for instance, in a lithium battery image, it is necessary to see the flow of different materials and follow the directions analyzing the inside properties. In this paper, a combination of 3D UNET and VGG19 has been used to conduct a multiclass segmentation of publicly available sandstone datasets to analyze their microstructures using image data based on four different objects in the samples of volumetric data. In our image sample, there are a total of 448 2D images, which are then aggregated as one 3D volume to examine the 3D volumetric data. The solution involves the segmentation of each object in the volume data and further analysis of each object to find its average size, area percentage, total area, etc. The open-source image processing package IMAGEJ is used for further analysis of individual particles. In this study, it was demonstrated that convolutional neural networks can be trained to recognize sandstone microstructure traits with an accuracy of 96.78% and an IOU of 91.12%. According to our knowledge, many prior works have applied 3D UNET for segmentation, but very few papers extend it further to show the details of particles in the sample. The proposed solution offers a computational insight for real-time implementation and is discovered to be superior to the current state-of-the-art methods. The result has importance for the creation of an approximately similar model for the microstructural analysis of volumetric data. [ABSTRACT FROM AUTHOR]

Image semantic segmentation is one of the core tasks for computer vision. It is widely used in fields such as unmanned driving, medical image processing, geographic information systems, and intelligent robots. Aiming at the problem that the existing semantic segmentation algorithm ignores the different channel and location features of the feature map and the simple method when the feature map is fused, this paper designs a semantic segmentation algorithm that combines the attention mechanism. First, dilated convolution is used, and a smaller downsampling factor is used to maintain the resolution of the image and to obtain its detailed information. Secondly, the attention mechanism module is introduced to assign weights to different parts of the feature map, which reduces the accuracy loss. The design feature fusion module assigns weights to the feature maps of different receptive fields obtained by the two paths and merges them together to obtain the final segmentation result. Finally, through experiments, it was verified on the Camvid, Cityscapes, and PASCAL VOC2012 data sets. Mean intersection over union (MIoU) and mean pixel accuracy (MPA) are used as metrics. The method in this paper can make up for the loss of accuracy caused by downsampling while ensuring the receptive field and improving the resolution, which can better guide the model learning. And the proposed feature fusion module can better integrate the features of different receptive fields. Therefore, the proposed method can significantly improve the segmentation performance compared to the traditional method. [ABSTRACT FROM AUTHOR]

Experts have used ultrasound imaging to manually determine follicle count and perform measurements, especially in cases of polycystic ovary syndrome (PCOS). However, due to the laborious and error-prone process of manual diagnosis, researchers have explored and developed medical image processing techniques to help with diagnosing and monitoring PCOS. This study proposes a combination of Otsu's thresholding with the Chan–Vese method to segment and identify follicles in the ovary with reference to ultrasound images marked by a medical practitioner. Otsu's thresholding highlights the pixel intensities of the image and creates a binary mask for use with the Chan–Vese method to define the boundary of the follicles. The acquired results were compared between the classical Chan–Vese method and the proposed method. The performances of the methods were evaluated in terms of accuracy, Dice score, Jaccard index and sensitivity. In overall segmentation evaluation, the proposed method showed superior results compared to the classical Chan–Vese method. Among the calculated evaluation metrics, the sensitivity of the proposed method was superior, with an average of 0.74 ± 0.12. Meanwhile, the average sensitivity for the classical Chan–Vese method was 0.54 ± 0.14, which is 20.03% lower than the sensitivity of the proposed method. Moreover, the proposed method showed significantly improved Dice score (p = 0.011), Jaccard index (p = 0.008) and sensitivity (p = 0.0001). This study showed that the combination of Otsu's thresholding and the Chan–Vese method enhanced the segmentation of ultrasound images. [ABSTRACT FROM AUTHOR]

Deep learning methods have achieved outstanding results in many image processing and computer vision tasks, such as image segmentation. However, they usually do not consider spatial dependencies among pixels/voxels in the image. To obtain better results, some methods have been proposed to apply classic spatial regularization, such as total variation, into deep learning models. However, for some challenging images, especially those with fine structures and low contrast, classical regularizations are not suitable. We derived a new regularization to improve the connectivity of segmentation results and make it applicable to deep learning. Our experimental results show that for both deep learning methods and unsupervised methods, the proposed method can improve performance by increasing connectivity and dealing with low contrast and, therefore, enhance segmentation results. [ABSTRACT FROM AUTHOR]

More than 50% of the images captured by optical satellites are covered by clouds, which reduces the available information in the images and seriously affects the subsequent applications of satellite images. Therefore, the identification and segmentation of cloud regions come to be one of the most important problems in current satellite image processing. Due to the complexity and variability of satellite images, especially when the ground is covered with snow, the boundary information of cloud regions is difficult to be accurately identified. The fast and accurate segmentation of cloud regions is a difficult point in the current research. We propose a lightweight convolutional neural network. Firstly, channel attention is used to optimize the effective information in the feature maps as a way to improve the network's ability to extract semantic information at each scale. Then, we fuse high and low-dimensional feature maps to enhance the network's ability to obtain small-scale semantic information. In addition, the feature aggregation module automatically adjusts the input multi-level feature weights to highlight the details of different features. Finally, we design the fully connected conditional random field to solve the problem that some noise in the input image and local minima during training is passed to the output layer resulting in the loss of edge features. Experimental results show that the proposed method achieves 0.9695 and 0.8218 for overall accuracy and recall, respectively, which has higher segmentation accuracy with the shortest time consumption compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Steel plays an important role in industry, and the surface defect detection for steel products based on machine vision has been widely used during the last two decades. This paper attempts to review state‐of‐art of vision‐based surface defect inspection technology of steel products by investigating about 170 publications. This review covers the overall aspects of vision‐based surface defect inspection for steel products including hardware system, automated vision‐based inspection method, existing problems and latest development. The types of steel product surface defects composition of visual inspection system are briefly described, and image acquisition system is introduced as well. The image processing algorithms for surface defect detection of steel products are reviewed, including image pre‐processing, region of interest (ROI) detection, image segmentation for ROI, feature extraction and selection and defect classification. The important problems such as small sample and real time of steel surface defect detection are discussed. Finally, the challenge and development trend of steel surface defect detection are prospected. [ABSTRACT FROM AUTHOR]

Image segmentation is an important task in image processing and analysis but due to the same ground object having different spectra and different ground objects having similar spectra, segmentation, particularly on high-resolution remote sensing images, can be significantly challenging. Since the spectral distribution of high-resolution remote sensing images can have complex characteristics (e.g., asymmetric or heavy-tailed), an innovative image segmentation algorithm is proposed based on the hierarchical Student's-t mixture model (HSMM) and spatial constraints with adaptive smoothing. Considering the complex distribution of spectral intensities, the proposed algorithm constructs the HSMM to accurately build the statistical model of the image, making more reasonable use of the spectral information and improving segmentation accuracy. The component weight is defined by the attribute probability of neighborhood pixels to overcome the influence of image noise and make a simple and easy-to-implement structure. To avoid the effects of artificially setting the smoothing coefficient, the gradient optimization method is used to solve the model parameters, and the smoothing coefficient is optimized through iterations. The experimental results suggest that the proposed HSMM can accurately model asymmetric, heavy-tailed, and bimodal distributions. Compared with traditional segmentation algorithms, the proposed algorithm can effectively overcome noise and generate more accurate segmentation results for high-resolution remote sensing images. [ABSTRACT FROM AUTHOR]

• Semantic-based segmentation (Semseg) methods are presented in deep learning architecture. • A comparative study is presented for reviewed paper based on performance. • Existing challenges, problems in DL-based semantic segmentation approaches are discussed. • The challenges of Semseg applications are mentioned for other researchers. • Prospect for future work in this area for stable medical image segmentation. Semantic-based segmentation (Semseg) methods play an essential part in medical imaging analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is classified into an instance, where each class is corresponded by an instance. In particular, the semantic segmentation can be used by many medical experts in the domain of radiology, ophthalmologists, dermatologist, and image-guided radiotherapy. The authors present perspectives on the development of an architectural, and operational mechanism of each machine learning-based semantic segmentation approach with merits and demerits. In this regard, researchers have proposed different Semseg methods and examined their performance in a variety of applications such as medical image analysis (e.g., medical image classification and segmentation). A review of recent advances in Semseg techniques are presented in this paper by applying computational image processing and machine learning methods. This article is further presented a comprehensive investigation on how different architectures are helpful for medical image segmentation. Finally, advantages, open challenges, and possible future directions are elaborated in the discussion part, beneficial to the research community to understand the significance of the available medical imaging segmentation technology based on Semseg and thus deliver robust segmentation solutions. Fig. 3: An overview of a latest trend to show the methodological evolution in the development of semantic-based segmentation models for medical images [Display omitted] [ABSTRACT FROM AUTHOR]

Automatic brain tumor detection in MR Images is one of the basic applications of machine vision in medical image processing, which, despite much research, still needs further development. Using multiple machine learning techniques as an ensemble system is one of the solutions that can be effective in achieving this goal. In this paper, a novel method for diagnosing brain tumors by combining data mining and machine learning techniques has been proposed. In the proposed method, each image is initially pre-processed to eliminate its background region and identify brain tissue. The Social Spider Optimization (SSO) algorithm is then utilized to segment the MRI Images. The MRI Images segmentation allows for a more precise identification of the tumor region in the image. In the next step, the distinctive features of the image are extracted using the SVD technique. In addition to removing redundant information, this strategy boosts the speed of the processing at the classification stage. Finally, a combination of the algorithms Naïve Bayes, Support vector machine and K-nearest neighbor is used to classify the extracted features and detect brain tumors. Each of the three algorithms performs feature classification individually, and the final output of the proposed model is created by integrating the three independent outputs and voting the results. The results indicate that the proposed method can diagnose brain tumors in the BRATS 2014 dataset with an average accuracy of 98.61%, sensitivity of 95.79% and specificity of 99.71%. Additionally, the proposed method could diagnose brain tumors in the BTD20 database with an average accuracy of 99.13%, sensitivity of 99% and specificity of 99.26%. These results show a significant improvement compared to previous efforts. The findings confirm that using the image segmentation technique, as well as the ensemble learning, is effective in improving the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Malaria is a pressing medical issue in tropical and subtropical regions. Currently, the manual microscopic examination remains the gold standard malaria diagnosis method. Nevertheless, this procedure required highly skilled lab technicians to prepare and examine the slides. Therefore, a framework encompassing image processing and machine learning is proposed due to inconsistencies in manual inspection, counting, and staging. Here, a standardized segmentation framework utilizing thresholding and clustering is developed to segment parasites' stages of P. falciparum and P. vivax species. Moreover, a multi-stage classifier is designed for recognizing parasite species and staging in both species. Experimental results indicate the effectiveness of segmenting thick smear images based on Phansalkar thresholding garnered an accuracy of 99.86%. The employment of variance and new transferring process for the clustered members, enhanced k-means (EKM) clustering has successfully segmented all malaria stages with accuracy and an F1-score of 99.20% and 0.9033, respectively. In addition, the accuracies of parasite detection, species recognition, and staging obtained through a random forest (RF) accounted for 86.89%, 98.82%, and 90.78%, respectively, simultaneously. The proposed framework enables versatile malaria parasite detection and staging with an interactive result, paving the path for future improvements by utilizing the proposed framework on all others malaria species. [ABSTRACT FROM AUTHOR]

Background: Medical image processing tasks represented by multi-object segmentation are of great significance for surgical planning, robot-assisted surgery, and surgical safety. However, the exceptionally low contrast among tissues and limited available annotated data makes developing an automatic segmentation algorithm for pelvic CT challenging. Methods: A bi-direction constrained dual-task consistency model named PICT is proposed to improve segmentation quality by leveraging free unlabeled data. First, to learn more unmarked data features, it encourages the model prediction of the interpolated image to be consistent with the interpolation of the model prediction at the pixel, model, and data levels. Moreover, to constrain the error prediction of interpolation interference, PICT designs an auxiliary pseudo-supervision task that focuses on the underlying information of non-interpolation data. Finally, an effective loss algorithm for both consistency tasks is designed to ensure the complementary manner and produce more reliable predictions. Results: Quantitative experiments show that the proposed PICT achieves 87.18%, 96.42%, and 79.41% mean DSC score on ACDC, CTPelvic1k, and the individual Multi-tissue Pelvis dataset with gains of around 0.8%, 0.5%, and 1% compared to the state-of-the-art semi-supervised method. Compared to the baseline supervised method, the PICT brings over 3–9% improvements. Conclusions: The developed PICT model can effectively leverage unlabeled data to improve segmentation quality of low contrast medical images. The segmentation result could improve the precision of surgical path planning and provide input for robot-assisted surgery. [ABSTRACT FROM AUTHOR]

Rock-art has been scratched, carved, and pecked into rock panels all over the world resulting in a huge number of engraved figures on natural rock surfaces that record ancient human life and culture. To preserve and recognize these valuable artifacts of human history, 2D digitization of rock surfaces has become a suitable approach due to the development of powerful 2D image processing techniques in recent years. In this article, we present a novel systematical framework for the segmentation of different petroglyph figures from 2D high-resolution images. The novel boundary enhancement with Gaussian loss (BEGL) function is proposed aiming at refining and smoothing the rock-arts boundaries in the basic UNet architecture. Several experiments on the 3D-pitoti dataset demonstrate that our proposed approach can achieve more accurate boundaries and superior results compared with other loss functions. The comprehensive framework of petroglyph segmentation from 2D high-resolution images provides the foundation for recognizing multiple petroglyph marks. The framework can then be extended to other cultural heritage digital protection domain easily. [ABSTRACT FROM AUTHOR]

This research study compares the performance of different digital image processing algorithms based on computer vision segmentation methods to process satellite multispectral images of Normalized Difference Vegetation Index (NDVI) to estimate agricultural cropland yield as a proposal for supporting spatial planning decisions. NDVI multispectral images were collected from Sentinel-2 L2-A satellite with distinctive features to be processed through these algorithms implemented in an owned and friendly software interface developed in MATLAB App Designer. These are based on image color detection, using three techniques: rectangular thresholding method, simple thresholding method, and segmentation through Mahalanobis discriminant classifier. The segmented images were used to estimate cropland yields as a function of NDVI variations and the characteristics of each analyzed image, employing a linear model that assigned a yield to each segmented area as a function of a specific NDVI range. Algorithm accuracy was determined as a function of expected cropland yield. Results show that the rectangular thresholding method tends to average cropland yield value in slightly non-uniform images. In contrast, thresholding by pixel and Mahalanobis methods performed better on highly non-uniform NDVI images, with deviations less than 8% compared with the expected cropland yield. The rectangular thresholding method could be a more straightforward tool regarding computational cost since, e.g., the demarcation of rectangular areas is easier in any cultivated area, facilitating the implementation of spatial support plans for farmers. The proposal is to use the rectangular thresholding method as a planning tool, as the other methods may be used for more accurate estimations. [ABSTRACT FROM AUTHOR]

Multi-level thresholding image segmentation divides an image into multiple regions of interest and is a key step in image processing and image analysis. Aiming toward the problems of the low segmentation accuracy and slow convergence speed of traditional multi-level threshold image segmentation methods, in this paper, we present multi-level thresholding image segmentation based on an improved slime mould algorithm (ISMA) and symmetric cross-entropy for global optimization and image segmentation tasks. First, elite opposition-based learning (EOBL) was used to improve the quality and diversity of the initial population and accelerate the convergence speed. The adaptive probability threshold was used to adjust the selection probability of the slime mould to enhance the ability of the algorithm to jump out of the local optimum. The historical leader strategy, which selects the optimal historical information as the leader for the position update, was found to improve the convergence accuracy. Subsequently, 14 benchmark functions were used to evaluate the performance of ISMA, comparing it with other well-known algorithms in terms of the optimization accuracy, convergence speed, and significant differences. Subsequently, we tested the segmentation quality of the method proposed in this paper on eight grayscale images and compared it with other image segmentation criteria and well-known algorithms. The experimental metrics include the average fitness (mean), standard deviation (std), peak signal to noise ratio (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM), which we utilized to evaluate the quality of the segmentation. The experimental results demonstrated that the improved slime mould algorithm is superior to the other compared algorithms, and multi-level thresholding image segmentation based on the improved slime mould algorithm and symmetric cross-entropy can be effectively applied to the task of multi-level threshold image segmentation. [ABSTRACT FROM AUTHOR]

Medical image segmentation is a crucial stage in computer vision and image processing to help the later-stage diagnosis process become more accurate. Because medical image segmentation, such as X-ray, can extract tissue, organs, and pathological structures. However, medical image processing, primarily in the segmentation process, has significant challenges regarding feature representation. Because medical images have different characteristics than other images related to contrast, blur, and noise. This study proposes the use of lung segmentation on chest X-ray images based on deep learning with the FCA-Net (Fully Convolutional Attention Network) architecture. In addition, attention modules, namely spatial attention and channel attention, are added to the Res2Net encoder so that it is expected to be able to represent features better. This research was conducted on chest X-ray images from Qatar University contained in the Kaggle repository. A chest x-ray image measuring 256 × 256 pixels and as many as 1500 images were then divided into 10% testing data and 90% training data. The training data will then be processed in K-Fold Cross validation from K = 2 until K = 10. The experiment was conducted with scenarios that used spatial attention, channel attention, and a combination of spatial and channel attention. The best test results in this study were using a variety of spatial attention and channel attention in the division of K-Fold with a value of K = 5 with a DSC (Dice Similarity Coefficient) value in the testing data of 97.24% and IoU (Intersection over Union) in the testing data of 94.66%. This accuracy result is better than the UNet++, DeepLabV3+, and SegNet architectures. [ABSTRACT FROM AUTHOR]

The vision system provides an important way for construction robots to obtain the type and spatial location information of the object. The characteristics of the construction environment, construction object, and robot structure are jointly examined in this paper to propose an approach of object segmentation by spraying the robot based on multi-layer perceptron. Firstly, the hand-eye system experimental platform is built through establishing the mathematical model of the system and calibrating the parameters of the model. Secondly, effort is made to carry out research on image preprocessing algorithms and related experiments, and compare the effects of different binocular stereo-matching algorithms in the actual engineering environment. Finally, research and an experiment are conducted to identify the applicability and effect of the depth image object segmentation algorithm based on multi-layer perceptron. The experimental results prove that the application of multi-layer perceptron to object segmentation by spraying robots can meet the requirement on solution accuracy and is suitable for the object segmentation of complex projects in real life. This approach not only overcomes the shortcomings of the existing recognition methods that are poor in accuracy and difficult to be used widely, but also provides basic data for the subsequent three-dimensional reconstruction, thus making a significant contribution to the research of image processing by spraying robots. [ABSTRACT FROM AUTHOR]

In image segmentation and in general in image processing, noise and outliers distort contained information posing in this way a great challenge for accurate image segmentation results. To ensure a correct image segmentation in presence of noise and outliers, it is necessary to identify the outliers and isolate them during a denoising pre-processing or impose suitable constraints into a segmentation framework. In this paper, we impose suitable removing outliers constraints supported by a well-designed theory in a variational framework for accurate image segmentation. We investigate a novel approach based on the power mean function equipped with a well established theoretical base. The power mean function has the capability to distinguishes between true image pixels and outliers and, therefore, is robust against outliers. To deploy the novel image data term and to guaranteed unique segmentation results, a fuzzy-membership function is employed in the proposed energy functional. Based on qualitative and quantitative extensive analysis on various standard data sets, it has been observed that the proposed model works well in images having multi-objects with high noise and in images with intensity inhomogeneity in contrast with the latest and state-of-the-art models. [ABSTRACT FROM AUTHOR]

The density peaks clustering (DPC) algorithm can identify clusters with various shapes and densities in the underlying dataset. However, the DPC algorithm cannot exactly find the true quantity of clustering centers when computing the local density, and it is difficult to handle non‐convex datasets. Moreover, the DPC algorithm is difficult to identify boundary points and outliers without a reasonable allocation strategy when dealing with low‐density points. To solve these limitations, a novel deviation density peaks clustering (DeDPC) algorithm is proposed. First, the local deviation of the spatial distance of datasets with different structures is utilised to replace the local density to generate a more reasonable clustering center decision graph. Second, a threshold is defined to further divide and process low‐density points. Finally, outliers in low‐density points can be accurately found to accurately cluster the dataset. To evaluate the performance of the DeDPC algorithm, experiments are conducted on synthetic and real‐world datasets and the DeDPC is compared with other clustering methods. The DeDPC is also applied to medical image segmentation to further demonstrate its capability for medical image processing. The simulation results show that the DeDPC method has good validity and utility for both non‐convex datasets and medical image segmentation. [ABSTRACT FROM AUTHOR]