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• A novel water transparency calculation algorithm is introduced, employing deep neural network and image processing technology. This algorithm obviates the need for a water gauge, allowing the calculation of water transparency solely through the analysisof Secchi disk videos. Notably, it demonstrates heightened detection efficiency and success rates. • An enhanced Siamese tracker, termed SiamDCFF (Siamese tracker based on dual correlation feature fusion), is proposed for Secchi disk recognition. Distinguishing features of SiamDCFF include a dual correlation module and a feature fusion module. On the Secchi disk dataset, SiamDCFF achieves the highest success rate, outperforming other methods detailed in this paper. • The simplified 3D-ResNet [41] is leveraged to assess the state of the Secchi disk, transforming the critical position determination problem into an optimization challenge. The objective function involves maximizing the number of visible and invisible Secchi disk images on both sides of the critical position. This method facilitates adaptive critical position determination. • A novel Gated Recurrent Unit based on auto-regression (ARGRU) is proposed, taking into account the motion characteristics of the Secchi disk. ARGRU achieves a higher success detection rate for water transparency compared to the water gauge recognition method. Notably, ARGRU outperforms RNN [42], GRU, and LSTM [43] on the dataset presented in this paper. Water transparency is traditionally assessed through visual inspection using a lowered Secchi disk (SD) into the water, with the disappearance depth of the SD recorded as the measure of transparency. However, manual and visually-dependent frequency measurements of the SD render this process labor-intensive and time-consuming. This paper presents a comprehensive machine-vision-based algorithm designed for the automatic computation of water transparency using Secchi disk videos. The algorithm leverages multiple deep neural networks, including an enhanced Siamese tracking model, a 3D-ResNet, and an improved GRU network. Trained models directly identify the SD in the video, calculating water transparency by processing pixel information. Experimental results demonstrate the algorithm's efficacy in estimating water transparency across diverse natural environments, achieving commendable accuracy (MAE = 3.6 cm MSE = 21.5 cm RMSE = 4.6 cm), rapid processing speed (average 6.87 s), and robust stability. In comparison with the former water-gauge-based algorithm, our proposed algorithm exhibits heightened efficiency and a superior detection success rate. [ABSTRACT FROM AUTHOR]

Multi-exposure image fusion (MEF) is an important area in computer vision and has attracted increasing interests in recent years. Apart from conventional algorithms, deep learning techniques have also been applied to MEF. However, although many efforts have been made on developing MEF algorithms, the lack of benchmarking studies makes it difficult to perform fair and comprehensive performance comparison among MEF algorithms, thus hindering the development of this field significantly. In this paper, we fill this gap by proposing a benchmark of multi-exposure image fusion (MEFB), which consists of a test set of 100 image pairs, a code library of 21 algorithms, 20 evaluation metrics, 2100 fused images, and a software toolkit. To the best of our knowledge, this is the first benchmarking study in the field of MEF. This paper also gives a literature review on MEF methods with a focus on deep learning-based algorithms. Extensive experiments have been conducted using MEFB for comprehensive performance evaluation and for identifying effective algorithms. We expect that MEFB will serve as an effective platform for researchers to compare the performance of MEF algorithms. • A literature review on multi-exposure image fusion is presented. • A multi-exposure image fusion benchmark is proposed. • The benchmark integrates a test set, algorithms, evaluation metrics, and a toolkit. • Comprehensive performance comparison is conducted and presented. [ABSTRACT FROM AUTHOR]

Picture processing is applied in all kind of fields, such as space science research, medical imaging, photography art. Because the human vision system is a complex nonlinear dynamic system, the traditional image enhancement methods often can not meet the requirements of real-time in practical applications. In the face of a large number of images, it is of great practical significance to obtain practical information from these data. With the rapid development of computer CnTech, image compression coding CnTech has also made great progress. And through the Mask R-CNN algorithm for effective segmentation and image recognition, can help people face massive image information, to their own needs as the goal, to achieve efficient retrieval, analysis, induction. The traditional methods of image classification by manually selecting features or manually extracting template matching have some defects. In this paper, a Mask R-CNN image optimization processing CnTech is proposed, which can accurately identify images and has higher accuracy for image feature extraction. It is meaningful to achieve automatic and accurate segmentation of microscopic images. Through training on COCO data set, it is verified through experiments that there are problems in image segmentation and recognition. In Mask R-CNN model, resource consumption is reduced and the efficiency of image segmentation and recognition is improved. Compared with the current cutting-edge algorithms, the image object detection on the strength of Mask R-CNN model has significant advantages. [ABSTRACT FROM AUTHOR]

This paper proposes the planar image-space trajectory (PIST) planning algorithm for contour following using computer vision and a force control scheme. The PIST algorithm generates machining trajectories offset to a planar workpiece with an applied force direction so that a force controller can be utilized. The PIST algorithm starts with an image of the workpiece, corrects for the perspective of the camera, defines a contour following trajectory in image space, and then transfers the trajectory into the robot's workspace for playback. A case study is presented where the PIST algorithm is applied to deburring sheet metal parts for validation through experimentation. A comparison is performed between the PIST algorithm and CAD/CAM software using CAD data for path design and a laser scanner for localization. The PIST planner achieved a positional accuracy greater than 1.7 mm compared to the CAM/CAM accuracy of 1.2 mm. An admittance controller successfully accounted for the positional error to achieve satisfactory and equivalent surface finish results for both trajectory planning approaches. The PIST planner is a beneficial alternative for trajectory planning as it significantly reduced the setup and process time while adapting to deviations from the design dimensions and manufacturing imperfections. [ABSTRACT FROM AUTHOR]

The paper presents a computationally efficient method developed to automatically detect obstacles that appear at railroad-road crossings. The proposed algorithm works maintenance-free and automatically detects obstacles that appear and stay or are left at the crossing. These obstacles can pose a threat for the trains causing even a trail derailment. When a detected obstacle is a person it is crucial to generate a warning in order to react and safely remove the person before the train arrives. The proposed algorithm is based on live video stream analysis. The standard image processing methods were used in order to detect obstacles in the acquired images. However, several techniques were proposed to obtain the desired results dealing with image vibrations, heavy rain and snow. Such robustness was obtained by building and maintaining a fuzzy background frame and appropriate filtering. The obstacle detection system was implemented and tested. The results confirmed its correct operation and usefulness in real-world conditions. [ABSTRACT FROM AUTHOR]

• The two change detection algorithms proposed in this paper use KFCM gradient clustering algorithm. • KFCM algorithm is a classic clustering algorithm in unsupervised clustering, which can effectively cluster most of the data. • We will study how to use the special features of the image to complete the clustering of the difference maps. In recent years, computer vision, especially deep learning, has been widely used in various fields. Through the deep learning aerial image detection gradient clustering algorithm automatic recognition, it can solve the limitations of manual shooting by humans, can shoot from a high altitude to a panoramic view of a specific area, and provide a more comprehensive solution. The traditional forest resource management and management work is mainly carried out by forestry personnel to carry out a large number of investigations and investigations on the forest. This method not only consumes a lot of manpower and material resources, but also does not have real-time nature. It is difficult to deal with all kinds of forest management. Problems, causing unnecessary losses. In this regard, this paper proposes an aerial image change detection algorithm based on H-KFCM, and designs related experiments to verify and demonstrate the performance of the algorithm. In this paper, we conduct a parallel study based on deep learning on the gradient clustering algorithm of deep learning in aerial image processing. By using CUDA (Compute Unified Device Architecture) to perform large-scale parallel processing of aerial data. Can greatly shorten the time to obtain results, improve the efficiency of relevant personnel. Experiment analysis. It can be seen from the results that the deep learning parallelization program implemented in this paper has a faster calculation speed and uses less time in high-resolution images, and has a good acceleration ratio compared to the CPU. [ABSTRACT FROM AUTHOR]

In order to satisfy the requirement of precise pin-point landing mission in Mars exploration, this paper proposes an innovative visual navigation algorithm based on feature lines extraction and matching to estimate the attitude and position of the lander during descent phase. Currently, common navigation landmarks are feature points and craters. But, the number of feature points extracted from landing image is so large that the image process is complex and time-consuming, and they do not usually have absolute coordinate information to absolute navigation. Meanwhile, crater is matched difficultly. Hence, ridges, fissures and other surface textures similar to lines are most common visual features of Mars and can be used as navigation landmarks for visual absolute and relative navigation. Firstly, that feature lines matching based on the EDLine algorithm remains invariant to scaling, rotation, and illumination, etc. provides the basis for visual navigation by using feature line. Secondly, feature lines selected as navigation landmarks can be used to absolute navigation and relative navigation respectively, which can implement integrated navigation. In this paper, a novel integrated visual navigation has been designed to finish the precise pin-point landing and overcome the accumulated errors of relative navigation and the limited application range of absolute navigation. Experimental simulation results show that the integrated navigation algorithm solves the limitation of single navigation mode and improves the effectiveness and stability of the algorithm. • A navigation algorithm using feature line for pinpoint landing is proposed. • The lander's absolute and relative pose are estimated during descent phase. • Integrated navigation overcomes the limitation of single navigation. [ABSTRACT FROM AUTHOR]

• Optimized incremental bit-quads count in component trees. • The intuition of incremental bit-quad count in component trees correctness. • A Fast method to compute area, perimeter and Euler Number in grayscale images. • A Fast algorithm to compute continuous perimeter approximation in grayscale images. • Software applications were rewritten using attributes computed by bit-quads. Component tree is a full image representation which encodes all connected components from upper (resp. lower) level sets of a given image through the inclusion relation. Information from this representation can be used in many image processing and computational vision applications, e.g. connected filtering, image segmentation, feature extraction, among others. In general, each node of a component tree represents a connected component of a level set and stores attributes which describes features of this connected component. This paper presents a review of a previously published method to compute attributes such as area, perimeter, and number of Euler by incrementally counting patterns while traversing nodes of a component tree. This method foundation is further detailed in this paper by presenting a novel theoretical background and algorithm correctness intuition. We also present a novel approach for this algorithm showing improvements for run-time execution and precision analysis. [ABSTRACT FROM AUTHOR]

Single image dehazing is a fundamental but challenging task in image processing. Various deep learning-based methods have achieved great dehazing performance. However, there are still hazy residues, even color distortion and texture loss when removing haze from complex outdoor images in dense hazy scenes. A densely integrated multi-branch attentive net for image dehazing is proposed in the paper to address the above problems. The network includes a multi-scale feature extraction module and a dense-feature fusion module. The multi-scale feature extraction module adopts a multi-branch structure composed of residual channel attention blocks, which can expand the receptive field and filter the extracted features of diverse scales by weighting for fusion. It raises network learning accuracy. The dense-feature fusion module contains a multi-level feature fusion module for front and back layers, a color information renovation module, and a feature enhancement module. It achieves dynamically adjusting the channel weights of features at diverse scales, learning rich context information and suppressing redundant information, and compensating for the lack of color and texture information. The dense-feature fusion module bolsters the generalization capability of the network. The proposed method achieves superior objective and subjective evaluation results via quantitative and qualitative experiments on synthetic hazy images and natural hazy images, with better generalization ability and dehazing effect than the current SOTA dehazing algorithms, and effectively ameliorates the color distortion and incomplete dehazing. [ABSTRACT FROM AUTHOR]

Highlights • In this paper, a camera calibration method is proposed to acquire the checkerboard image by adding polarizer before the camera lens. • By introducing the Stokes variable feedback to obtain the optimal polarization angle of the checkerboard image to remove or suppress the high optical region influence in the image acquisition, the optimal polarization angle chessboard image can be used to improve the coordinate precision of corner point. • The accuracy of sub-pixel coordinate extraction makes the result of camera calibration more accurate. • Compared with the previous methods to improve the accuracy of corner extraction considers the idea of improving angle extraction accuracy from the algorithm level. • This paper focuses on the image processing of corner image acquisition, and has the relative innovation in the way and method. Abstract The accurate estimation of camera internal and external parameters is one of the keys to high-precision camera calibration. At present, the mainstream Zhang's calibration method requires the use of a checkerboard to achieve high-precision calibration result. Therefore, the precise extraction of the pixel coordinates at the corner points on the checkerboard is one of the key technologies. The traditional methods are to improve the corner extraction algorithm, but ignore the imaging process of the calibration images. It is difficult to detect and eliminate the high-light regions in the process of calibrating image, and the existence of high-light regions will cause low accuracy of camera calibration. Therefore, a camera calibration method based on optimal polarization information is proposed in this paper. The method first uses a Stokes variable linear feedback through a CCD camera equipped with a polarizer to obtain a checkerboard calibration image at an optimal polarization angle in different spatial positions. Then use the sub-pixel level detection algorithm and Gaussian fitting method to locate the corner points in the image and to solve the sub-pixel coordinates of the corner points precisely. At last, the two-dimensional pixel coordinates of the corner points in each checkerboard image are extracted to perform camera calibration. The experimental results show that the coordinates of corner points of calibration plate image based on the optimal polarization angle have about 1 pixel difference compared with the coordinates of the calibration plate corner points under natural illumination, and the slopes between the parallel lines of the fitted are closer, meet the high precision calibration requirement of camera. [ABSTRACT FROM AUTHOR]

Highlights • Latent sharp image and the blur PSF are estimated jointly via a multi-functional optimization problem addressed by a first-order primal-dual solver. • This approach generates a multi-resolution pyramid of the input blurred image. • We introduce a PSF initiation method that benefits from the salient structure obtained via an l 0 gradient smoothing of blurred image. • A first-order primal-dual solution to the l 0 gradient smoothing problem is introduced in this paper. Abstract In this paper, a blind deblurring algorithm that is well converged towards the latent sharp image and the blur PSF is proposed. The proposed algorithm employs our recently proposed ringing suppression regularizer in a solution to the blind deblurring problem. Latent sharp image and the blur PSF are estimated jointly via a multi-functional optimization problem addressed by a first-order primal-dual solver. This approach generates a multi-resolution pyramid of the input blurred image and propagates the estimation results of the lower resolution levels to the higher resolution ones. By considering the PSF initiation as an important step in the blind deblurring process, we introduce a PSF initiation method that benefits from the salient structure obtained via an l 0 -gradient smoothing of blurred image. A first-order primal-dual solution to the l 0 -gradient smoothing problem is introduced in this paper. Experimental results show that the proposed deblurring algorithm significantly enhances the results favorably against other state-of- the-art image deblurring methods. [ABSTRACT FROM AUTHOR]

Driving in the wrong direction is one of the main reasons that cause road accidents in Thailand. To efficiently detect wrong direction driving vehicles, we proposed a system that can track those moving vehicles from CCTVs using deep learning and image processing techniques. Our proposed system is composed of two main algorithms: Road Lane Boundary detection from CCTV algorithm (RLB-CCTV) and Majority-Based Correct Direction Detection algorithm (MBCDD). In this paper, we introduced the RLB-CCTV algorithm and the enhanced version of MBCDD algorithms for detecting road boundaries and wrong-direction driving vehicles. Based on our experiments, our system can identify the road lane boundary and track vehicles driving in the wrong direction on each lane with an accuracy of 96.61%. [ABSTRACT FROM AUTHOR]

This paper proposes a robust deblurring algorithm for noisy images with just noticeable blur (JNB). Natural images are usually accompanied by JNB and noise when viewed under pixel scale resolution, but are easily neglected. In this paper, we describe the JNB phenomenon and briefly analyze its causes. An image-gradient-related constrained factor is introduced in our algorithm based on the prior of image noise’s low gradient distribution. Richardson-Lucy method is also adopted to reduce algorithm’s time cost. For large-size cellphone images, we put forward an effective image segmentation model suitable for space-variant blur. Experiments show that our method obtains high-quality deblurring images and are competitive to state-of-the-art deblurring algorithms. [ABSTRACT FROM AUTHOR]

A large amount of data, provided in the form of video data, is acquired during manned inspections flights of electric power lines. This data is analyzed by expert human inspectors to detect faults in the power lines infrastructure and prepare the inspection reports. This process is extremely time consuming, very expensive and prone to human error. In this paper, we present PoLIS: the Power Line Inspection Software, which has been developed with the objective of assisting the analysis of the data acquired during inspection flights. PoLIS is based on the cooperation between computer vision and machine learning techniques to automatically process video sequences acquired during inspection flights, resulting in a set of representative images per electric tower which we call Key Frames. These representative images can then be used for inspection purposes, leading to a drastic reduction of the human operators’ workload. At the core of the strategy lies an electric tower detector, which is in charge of estimating the location of the towers within the images based on the combination of a sliding window search technique and a supervised classifier. The location of the tower is then tracked using a tracking-by-registration algorithm based on direct methods, estimating the position of the tower in different images. Finally, different criteria are applied for defining whether the image corresponds to a Key Frame image or not. Extensive evaluation of the proposed strategy is conducted using videos acquired during manned helicopter inspections. The videos constituting this database contain several thousand frames representing both medium and high voltage power transmission lines in the infra-red (IR) and visible spectra. The obtained results show that the proposed strategy can reduce the large amount of data present in the inspection videos to a few Key Frames for each tower. It is also demonstrated that the learning-based approach proposed in PoLIS is appropriate for detecting electric towers, a process which is made faster and more robust by coupling it with a tower tracking algorithm. A Graphical User Interface allowing the application of PoLIS to user-provided videos is also presented in this paper, illustrating the whole process and the automated generation of an inspection report. [ABSTRACT FROM AUTHOR]

In the current techno-savvy world, cybersecurity is a prime concern. Biometrics are being extensively used for authentication and authorization. Face recognition(FR) is a class of biometrics which has proved to be one of the most effective methods for identification and verification, which works even when the subject is unaware of being scanned. This paper gives insight into the use of Image Processing in Robotic Applications. It discusses the use of computer vision by surveillance robots. Surveillance applications prefer unsupervised learning over supervised learning as unsupervised learning doesn’t require true labels. The burgeoning demand for unsupervised learning in surveillance applications proffered the nub of this project. The paper coalesces the well-known similarity algorithm SSIM with Spectral Clustering to produce prodigious results. SSIM surpasses other techniques like MSE by extracting structural features from images. This leads to a significant improvement in performance because humans also extract structural information from visuals. SSIM eliminates the effect of illumination and then uses the attributes that depict the structure of objects to gain the desired structural information. The performance of the proposed model was compared with other similarity measures on the ORL(Olivetti Research Laboratory Cambridge), Caltech and Faces96 datasets. An accuracy of 89.5% was achieved on the ORL and 88.3% on the Caltech and 86.7% on the Faces96 dataset. [ABSTRACT FROM AUTHOR]

Feature detection plays a crucial role in image registration. There exists quite a few feature detection algorithms in literature like BRISK, FAST, SURF etc [14]. Each of these algorithms has its own advantages and disadvantages. BRISK is rotation and scale invariant, but it takes more time to detect the feature points. On the other hand FAST, as the name suggests, takes less time to detect the key points, but it is not scale invariant. To overcome the demerits of BRISK and FAST feature detection algorithms, this paper proposes a hybrid feature detection algorithm, which consumes less time to detect the feature key points and it is also rotation and scale invariant. This paper also focus on a comparative analysis of BRISK, FAST and proposed algorithm in terms of time to detect feature points. This paper has taken five feature key points in every Remote-sensing images and also deals with feature detection using the above three algorithms. It can be observed from the results and tables that in case of hybrid feature detector, it takes less time to detect five feature points. [ABSTRACT FROM AUTHOR]

Computerized image analysis methods for dermoscopy are primarily of great interest and benefit, as it provides significant information about the lesion, which can be of pertinence for the clinicians and a stand-alone warning implement. Computer-based diagnostic systems require dedicated image processing algorithms to provide mathematical descriptions of the suspected regions, such systems hold a great potential in oncology. In this paper, we have performed a review of the state of art techniques used in computer-aided diagnostic systems, by giving the domain aspects of melanoma followed by the prominent techniques used in each of the steps. The steps include dermoscopic image pre-processing, segmentation, extraction and selection of peculiar features, and relegation of skin lesions. The paper also presents cognizance to judge the consequentiality of every methodology utilized in the literature, in addition to the corresponding results obtained in this perspective. The inadequacies and the future research directions are accentuated. [ABSTRACT FROM AUTHOR]

The analysis of two-dimensional images enables the detection of structures, objects and their further classification. In the Internet of Things, images are data obtained from cameras or projections of signals. In this paper, we propose a hybridization of the classic RANSAC approach with a selected heuristic algorithm like Grey Wolf Optimizer. This solution allows for increasing the ability to adjust images by analyzing the detected key points of the image. Such hybridization is interesting in terms of the possibility of obtaining stitching accuracy. Moreover, we have introduced the automation of parameter selection to increase the accuracy and speed of the real-time algorithm. For this purpose, an analysis of the results of the algorithm for combining video frames into one large image allowed for a significant reduction in the amount of data processing time. The proposed algorithm was tested on various heuristic algorithms, where their differences and impact on the operation of the method were indicated and discussed. Moreover, the proposition was tested in practical application by automatically creating an image based on video frames recorded by the drone. • Hybridization of RANSAC with a heuristic solution for image stitching purposes. • A method of automating the selection of parameters in the algorithm. • Framework for minimizing the number of calculations in smart devices. • Practical application of the proposed method in IoT solution using a drone. [ABSTRACT FROM AUTHOR]

Dictionary learning has played an important role in the success of sparse representation. Although several dictionary learning approaches have been developed for image classification, discriminative dictionary pair learning, i.e., jointly learning a synthesis dictionary and an analysis dictionary, is still in its infant stage. In this paper, we proposed a novel model of Fisher discrimination dictionary pair learning (FDDPL), in which Fisher discrimination information is embedded into analysis representation, analysis dictionary, and synthesis dictionary representation. With the proposed Fisher-like discrimination term, discrimination of both synthesis dictionary representation and analysis dictionary representation is introduced into the dictionary pair learning model. An iterative algorithm to efficiently solve the proposed FDDPL and a FDDPL based classifier are also presented in this paper. The experiments on face recognition, scene categorization, gender classification, and action recognition clearly show the advantage of the proposed FDDPL. [ABSTRACT FROM AUTHOR]

The term “iris” refers to the highly textured annular portion of the human eye that is externally visible. An iris recognition system exploits the richness of these textural patterns to distinguish individuals. Iris recognition systems are being used in a number of human recognition applications such as access control, national ID schemes, border control, etc. To capture the rich textural information of the iris pattern regardless of the eye color, traditional iris recognition systems utilize near-infrared (NIR) sensors to acquire images of the iris. This, however, restricts the iris image acquisition distance to close quarters (less than 1 m). Over the last several years, there have been numerous attempts to design and implement iris recognition systems that operate at longer standoff distances ranging from 1 m to 60 m. Such long range iris acquisition and recognition systems can provide high user convenience and improved throughput. This paper reviews the state-of-the-art design and implementation of iris-recognition-at-a-distance (IAAD) systems. In this regard, the design of such a system from both the image acquisition (hardware) and image processing (algorithms) perspectives are presented. The major contributions of this paper include: (1) discussing the significance and applications of IAAD systems in the context of human recognition, (2) providing a review of existing IAAD systems, (3) presenting a complete solution to the design problem of an IAAD system, from both hardware and algorithmic perspectives, (4) discussing the use of additional ocular information, along with iris, for improving IAAD accuracy, and (5) discussing the current research challenges and providing recommendations for future research in IAAD. [ABSTRACT FROM AUTHOR]

The method based on machine vision image processing is used to detect the surface defects of Si 3 N 4 bearing roller. Owing to the variety of defects, small area and low contrast, it is easy to miss or error detection. In this paper, an adaptive update template defect enhancement algorithm based on Gaussian model is proposed. First, a large number of surface images of Si 3 N 4 bearing roller are collected to obtain the non-defect background statistical feature, and the background characteristic curve is fitted by Gaussian model. Further, the initial background template is gained according to the Gaussian curve. Then, combined with the gray distribute of defect images and initial background template, unique adaptive update template can be established. Finally, subtraction operation and nonlinear enhancement are used to improve the comparison of defect information and background. Through inverse sorting, adaptive threshold segmentation and Canny operation, the precise positioning of defects is realized. The enhancement algorithm can effectively enhance the contrast and eliminate the influence of noise. The average detection time is 0.84s, and the detection accuracy is 96.2%. [ABSTRACT FROM AUTHOR]

The Circle Hough Transform (CHT) is one of the popular circle detection algorithm in image processing and machine vision application, favored for its tolerance to noise. Nevertheless, it involves huge computation and excessive memory requirements. Because of its drawbacks, various modifications have been suggested to increase its performances. In this paper, we present a new modification of the CHT method developed for an automatic biometric iris recognition system. The novelty of this method resides on the use of the incremental property in order to reduce the resources requirement and the parallel property for decreasing the computation time. The incremental property is obtained using the approximation of the used trigonometric functions, while the parallel property is achieved by calculating, at the same time, several point coordinates of the circle. The software implementation and the validation have been done in C++ and MATLAB on real images. The errors analysis and the performances of the proposed method against the basic CHT method are presented in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithmic framework for some basic problems on binary images. Algorithms for binary images such as one of extracting a connected component containing a query pixel and that of connected components labeling play basic roles in image processing. Those algorithms usually use linear work space for efficient implementation. In this paper we propose algorithms for several basic problems on binary images which are efficient in time and space, using space-efficient algorithms for grid graphs. More exactly, some of them run in O ( n log n ) time using O ( 1 ) work space and the others run in O ( n ) or O ( n log n ) time using O ( n ) work space for a binary image of n pixels stored in a read-only array. [ABSTRACT FROM AUTHOR]

The task of segmenting small infrared targets, which have few pixels and weak features, has been a difficult problem in the field of small target image processing. Small targets not only exist in general images, but also widely exist in drone cameras, communication base station cameras, rescue cameras, and vehicle cameras. Research on small target segmentation algorithm is very important for analyzing and utilizing these images, and it also has important application value in security, traffic, and rescue. Traditional small target segmentation algorithms are able to segment objects with simple target contour edges and large differences in signal strength. The traditional algorithm often has a high false detection rate and missed detection rate when facing several targets with weak signal strength. It does not perform well in complex scenes. In this paper, we introduce an infrared small target segmentation scheme facing multiple types and numbers of targets. This paper also produces an infrared UAV and pedestrian dataset for validation. Experiments show that the optimized algorithm has a better detection effect on targets in complex scenes. [ABSTRACT FROM AUTHOR]

Layout analysis is essential step in information extraction from scanned document images. In this paper we propose an algorithm for aligning layouts generated with different OCR engines. The main requirement is to always generate the same layout for the given document image regardless of OCR engine used for image processing. In that way information extraction from scanned documents, that is heavily dependent on fields positions in the document, does not depend on specific OCR engine. In other words, it is sufficient to maintain universal extractor knowledge and not necessary to train extractor explicitly with samples processed on specific OCR engine. The proposed algorithm can handle administrative documents with complex layout. [ABSTRACT FROM AUTHOR]

The pandemic of Covid-19 has caused a shift of paradigm of education, from face-to-face to e-learning. E-learning leads to an escalation in digitalization of handwritten documents because it requires submission of homework and assignments through online. To help teachers in checking digitalized handwritten homework, this paper proposes an automatic checking system based on a convolutional neural network (CNN) for handwritten numeral recognition. The CNN is used to recognize four arithmetic operations in mathematical questions consisting of addition, deduction, multiplication and division. The performance CNN in handwritten numeral recognition have been optimized in terms of activation function and gradient descent algorithm. The proposed CNN is also trained and tested with the MNIST handwritten data set. The experimental results show that the recognition accuracy the improved CNN improves to a certain extent as compared to before optimization. [ABSTRACT FROM AUTHOR]

Image segmentation is the process of dividing image into homogenous regions by some charasteristics and is widely used in medical diagnostics. Segmentation algorithms are used for anatomical features extraction from medical images. The Hybrid Ant Colony Optimization (ACO) – k-means and Grub Cut image segmentation algorithms for MRI images segmentation are considered in this paper. The proposed algorithms and sub-system for the medical image segmentation have been implemented. As there is no universal algorithm for medical image segmentation, image segmentation is still a challenging problem in image processing and computer vision in many real time applications and hence more research work is required. The experimental results show that the proposed algorithm has good accuracy in comparison to Grub cut. [ABSTRACT FROM AUTHOR]

Deciding a rational number of clusters for the problems to be solved and determining the parameters associated with the clustering algorithms are two critical issues in the configuration of data clustering. Usually, a manual trial-and-error manner is used to induce a feasible configuration. This paper presents an automated clustering method, which determines the clustering configuration automatically. The proposed method is based on the techniques of support-vector machines with a local-search strategy. It starts with running one-class support-vector machines (OCSVM) to partition input data into a random number of clusters. When a result is obtained, the “local-search” mechanism launches several rounds of OCSVM each of which works with a new clustering configuration. Each new configuration is from the current configuration with incrementally modifications. The clustering results obtained from the local searches are post-evaluated by specific clustering validity index and the best one is retained. The clustering configuration of the best result is used by OCSVM for the clustering afterwards. Such a clustering process iterates until no better result can be obtained. This paper describes the clustering algorithm and compares three clustering validity indices, i.e. distance-based index, Davies–Bouldin index, and Xie–Beni index, on their effectiveness. The performance of the proposed method is demonstrated on the segmentation of several aerial images. Experimental results show that the proposed approach is feasible and effective for image segmentation. [ABSTRACT FROM AUTHOR]

Due to the ill-posed nature of image restoration, seeking a meaningful image prior is still a great challenge in the field of image processing. The total variation with overlapping group sparsity (OGS-TV) has been successfully applied for image denoising/deblurring. In this paper, we further study the overlapping group sparsity of the image gradient. The sparsity is measured by the ℓ q quasi-norm (0 < q < 1). The proposed regularizer comes down to the well-known hyper-Laplacian prior if the overlapping group size is 1. Although it seems to be a simple extensive study compared with the previous works, its regularization capability and corresponding mathematical problems are still in demand for imaging science. To solve the non-convex and non-smooth minimization problem, we use the alternating direction method of multipliers as the main algorithm framework. The difficult inner subproblem is tackled by the majorization-minimization method with the sophisticatedly derived majorizer. We carry out some numerical experiments to demonstrate the effectiveness of the proposed regularizer in terms of PSNR and SSIM values. [ABSTRACT FROM AUTHOR]

Fibre reinforced filament fabrication is a potential additive manufacturing method for certain load-bearing parts in aerospace and automotive products. In current practice, printing paths are planned from part geometry without considering loading conditions. This paper presents a new method for optimizing printing paths to align with the principal stress field of parts in use. Because of the powerful processing capabilities, for the first time grayscale image was adopted to represent the irregular vector field, which can be robustly processed into sub-regions for generating regular printing paths. Preliminary bending test achieved promising increase in tensile strength compared with conventional methods. [ABSTRACT FROM AUTHOR]

• A linear time algorithm is given to find relative orthogonal convex hull. • The algorithm is combinatorial in nature. • The algorithm is useful for image processing algorithms and shape analysis of digital objects. A linear time combinatorial algorithm to construct the relative orthogonal convex hull of the inner isothetic cover of a digital object (hole-free) with respect to its outer isothetic cover is presented in this paper. The algorithm first constructs the inner and the outer isothetic covers of the digital object which is the input to the algorithm and then constructs the relative orthogonal convex hull from the inner isothetic cover and the outer isothetic cover. The algorithm can also be used to construct the relative orthogonal convex hull of one orthogonal polygon with respect to another orthogonal polygon which encloses the first polygon entirely where the input will be two orthogonal polygons one containing the other instead of the digital object. The proposed algorithm first finds the concavities of the inner polygon and checks for intruded concavities of the outer polygon. A combinatorial technique is used to obtain the relative orthogonal convex hull. The efficiency and the correctness of the algorithm are verified as it is tested on various types of digital objects. [ABSTRACT FROM AUTHOR]

The paper focuses on the opportunity to use star sensors to help space situational awareness and space surveillance. Catalogs of orbiting satellites around Earth are usually established on ground-based measurements that rely on optical or radar data provided by instruments on Earth. However, space-based observations offer new opportunities, as they are unrelated to the weather and the circadian rhythm to which the ground system is subjected. Consequently, space-based optical observation systems can provide data without atmospheric distortion from different observer-to-target distances, enhancing the possibility to detect and catalog resident space objects, i.e. satellites and space debris. This work deals with the feasibility study of an innovative strategy, which consists in the use of a star sensor with a dedicated algorithm to run directly on-board. This approach minimizes the impact on the platform so that, from a theoretical point of view, every satellite with a star sensor can be used as a space surveillance observer. The output of the algorithm consists of a database of pre-processed objects, which are then post-processed on ground. Numerical tests on synthetic images provided by a high-fidelity simulator demonstrate the applicability of the proposed approach. The study has been conducted within the framework of the Italian Space Agency project SPOT - Star sensor image Processing for orbiting Objects deTection. • A space-based detection of resident space objects (RSOs) is proposed using star sensors. • A novel on-board algorithm for RSOs detection is described. • An analysis of the performances is reported using a high fidelity simulator. • Radial, tangential and orthogonal observations have been compared. • Results show the feasibility of the described approach. [ABSTRACT FROM AUTHOR]

Image synthesis is currently one of the most addressed image processing topic in computer vision and deep learning fields of study. Researchers have tackled this problem focusing their efforts on its several challenging problems, e.g. image quality and size, domain and pose changing, architecture of the networks, and so on. Above all, producing images belonging to different domains by using a single architecture is a very relevant goal for image generation. In fact, a single multi-domain network would allow greater flexibility and robustness in the image synthesis task than other approaches. This paper proposes a novel architecture and a training algorithm, which are able to produce multi-domain outputs using a single network. A small portion of a dataset is intentionally used, and there are no hard-coded labels (or classes). This is achieved by combining a conditional Generative Adversarial Network (cGAN) for image generation and a Meta-Learning algorithm for domain switch, and we called our approach MetalGAN. The approach has proved to be appropriate for solving the multi-domain label-less problem and it is validated on facial attribute transfer, using CelebA dataset. [ABSTRACT FROM AUTHOR]

This paper introduces a novel generalized multifrequency fusion method for defect detection in Eddy Current NDE. The proposed technique provides an ability to highlight small defect signals in presence of noise or larger signals from structural irregularities. This paper contains a complete mathematical formulation of the method and its applications to simulated and experimental data arising from two typical ECT problems: detection of the small cracks in the tube wall near tube support region of steam generator tubes, and detection of small cracks in the bottom layer of two-layer aluminum panels riveted with steel fasteners. [ABSTRACT FROM AUTHOR]

This paper introduces the wavelet image two-line (Wi2l) coding algorithm for low complexity compression of images. The algorithm recursively encodes an image backwards reading only two lines of a wavelet subband, which are read in blocks of 512 bytes from flash memory. It thus only requires very little memory, i.e., a memory array for two wavelet subband lines, an array to store intermediate tree level data, and an array for writing binary data. A picture of 256×256 pixels would require 1152 bytes of memory. Computation time for the coding is derived analytically and measured on a real system. The times on a low-cost microcontroller for 256×256 grayscale pictures are measured as 0.25–0.6 s for encoding and 0.22–0.77 s for decoding. The algorithm can thus realize a low complexity system for compression of images when combined with a customized scheme for the wavelet transform; low complexity here refers to low memory, minimum write access to flash memory, usage of integer operations only, and low conceptual complexity (ease of implementation). As demonstrated in this paper, a compression performance similar to JPEG 2000 and the more recent Google WebP picture compression is achieved. The compression system uses flash memory (SD or MMC card) and a small camera sensor thus building an image communication system. It is also suitable for mobile devices or satellite communication. The underlying C source code is made publicly available. [ABSTRACT FROM AUTHOR]

A stencil is a thin sheet of material, such as paper, plastic, or metal, with certain patterns cut from it. Applying a pigment through the cut-out holes produces a design on an underlying surface. Using multiple overlapping stencil layers, artists can create intricate, yet reproducible imagery on a variety of surfaces. Traditionally, artists have to design not only the final appearance, but also each individual stencil layer. A stencil layer needs to be connected, geometrically simple, and physically stable. Taking all these constraints into account during the design process is difficult and unintuitive even for skilled artists. In this paper, we propose a system which separates the artistic design stage from the complex and tedious task of stencil creation. For a given user design, our algorithm automatically generates a set of stencil layers satisfying all required properties. The task is formulated as a constrained energy optimization problem and solved efficiently. Experiments, including a user study, are carried out to examine the complete algorithm as well as each individual step. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multiple-parameter fractional Fourier transform with its transform order being a real vector, based on which a high-security image encryption scheme is also given. This novel fractional Fourier transform has removed the restriction on the dimension of transform order and highly enhances the security of image encryption scheme proposed in this paper without increasing the computational complexity and hardware cost. The numerical results verify the efficacy and security of this image encryption method. The vector power multi-parameter fractional Fourier transform is a generalized form of the classical fractional Fourier transform with all the previous fractional Fourier transform as its special cases and has theoretical significance in information processing and information security. [ABSTRACT FROM AUTHOR]

Nowadays, image processing problems are becoming increasingly important due to development of the aerospace Earth monitoring systems, radio and sonar systems, medical devices for early diagnosis etc. But the most of the image processing works deal with images defined on rectangular two-dimensional grids or grids of higher dimension. In some practical situations, images are set on a cylinder. For example, images of a section of pipelines, worms, blood vessel, parts during turning, etc. The peculiarity of the domain for specifying such images requires its consideration in their models and processing algorithms. In the present paper, autoregressive models of cylindrical images are considered, expressions of the correlation function depending on the autoregression parameters are given. To represent heterogeneous images with random heterogeneities, «doubly stochastic» models are used in which one or more images control the parameters of the resulting image. The spiral scan of a cylindrical image can be considered as a quasiperiodic process due to the correlation of image rows. Pseudo-gradient algorithms for the modal identification are proposed. The statistical modeling showed that these algorithms give good model identification. [ABSTRACT FROM AUTHOR]

• Automated selection of image segmentation algorithms. • Computational cost reduction in image segmentation tasks. • Meta-learning outperformed any single algorithm explored over the whole dataset. • Machine Learning and Gradient-based image segmentation algorithms were studied. • Experiments were conducted over a widely known image dataset with 2100 examples. Image segmentation is a key issue in image processing. New image segmentation algorithms have been proposed in the last years. However, there is no optimal algorithm for every image processing task. The selection of the most suitable algorithm usually occurs by testing every possible algorithm or using knowledge from previous problems. These processes can have a high computational cost. Meta-learning has been successfully used in the machine learning research community for the recommendation of the most suitable machine learning algorithm for a new dataset. We believe that meta-learning can also be useful to select the most suitable image segmentation algorithm. This hypothesis is investigated in this paper. For such, we perform experiments with eight segmentation algorithms from two approaches using a segmentation benchmark of 300 images and 2100 augmented images. The experimental results showed that meta-learning can recommend the most suitable segmentation algorithm with more than 80% of accuracy for one group of algorithms and with 69% for the other group, overcoming the baselines used regarding recommendation and segmentation performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose two algorithms of the Nonnegative Tucker decomposition that contain manifold regularization terms used for capturing the underlying geometric structures, which are applied in clustering of image databases. In general, based on Tucker model in the form of least square estimation, that is to find interpretable basis factors, and achieve the compressed of original data. It usually supposes the structures of the processing databases are global linearity. However, in practical applications, such as the processing arguments of image, video sequence and Electroencephalography data, they are usually within richness structures of nonlinear. Therefore, it is necessary to uncover the underlying structures for achieving an effective application. Hence, in this article, we incorporate Laplacian Eigenmaps i.e., LE and Locally Linear Embedding i.e., LLE as the manifold regularization terms into the least square form of Tucker model. In addition, for the physical meaning of reasonable interpretation consistent with original data, thus, the basis factors and core tensor are constrained to be nonnegative. Finally, the numerical results have demonstrated both two our proposed algorithms can achieve an effective performance among state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• Performance analysis of different video-based eye blinking detection. • Development of a low-cost imaging acquisition system. • Quality metrics analysis of proposed algorithms. • Performance comparison between RGB and Grayscale images. • The red channel of the RGB camera achieves in our experiments a 100 % eye blink detection rate for event detection. The eye is considered a rich source for gathering information on our daily lives. In addition, the necessity to classify the eye as open or closed and detect eye blinks is increasing in various fields. For instance, it has been proven that eye blinks can be related to different ophthalmological conditions. Nevertheless, accurate, and robust blink estimation poses significant challenges to the efficacy of real-time applications due to the variability of eye images and light conditions. In this paper eye blinking has been monitored using a chin rest and an iOS smartphone attached to a cartesian machinery. Five video-based eye blink algorithms to classify frames have been analyzed and compared in terms of performance. To evaluate the results, different widely used statistical metrics have been employed. The results show that the proposed techniques successfully detect eye blinks, whereas the frame classifier shows different scores depending on the algorithm used. [ABSTRACT FROM AUTHOR]

Underwater images acquired in scattering environments are generally of poor quality because of the attenuation and backscattering of light when it passes through water with scattering particles. Polarimetric de-scattering methods can be used to significantly enhance the imaging quality in such a so-called turbid water. However, due to the complexity of polarimetric de-scattering algorithms, it is hard to achieve real-time de-scattering output from a polarimetric camera. In this paper, in order to efficiently increase the computational efficiency, the polarimetric de-scattering algorithm is optimized and a multi-threading framework is developed that enables the algorithm to run in real-time on ordinary laptops for the same polarimetric camera. We demonstrate the imaging performance by using the underwater polarimetric de-scattering system we proposed. We analyze the algorithm under different scattering conditions and discuss its optimal parameters. We find that there is a significant increase in the number of SIFT (i.e., scale invariant feature transform) feature points extracted from the processed image compared to the original image, showing that our system has potential applications in pattern recognition, computer vision, etc. [ABSTRACT FROM AUTHOR]

Abstract: Similarity measure plays an important role in many image processing fields. This paper introduces the medium mathematic systems, and establishes a novel image medium similarity measure between two pixels and that of between two image sets based on the measure of medium truth degree. Moreover, an image edge detection algorithm, an image matching algorithm and an image medium fidelity measure method governed by the medium similarity measure are discussed in this paper. Experimental results show that the proposed similarity measure is effective. [Copyright &y& Elsevier]

Abstract: This paper proposes food image classification methods exploiting both local appearance and global structural information of food objects. The contribution of the paper is threefold. First, non-redundant local binary pattern (NRLBP) is used to describe the local appearance information of food objects. Second, the structural information of food objects is represented by the spatial relationship between interest points and encoded using a shape context descriptor formed from those interest points. Third, we propose two methods of integrating appearance and structural information for the description and classification of food images. We evaluated the proposed methods on two datasets. Experimental results verified that the combination of local appearance and structural features can improve classification performance. [Copyright &y& Elsevier]

In recent decades, methods to find invariant points in digital images, called fiducial points, have gained great attention, mainly due to the demands of several applications in computer vision and image processing, such as the geometric matching of global structures, objects or specific regions. Among the most well known approaches are algorithms like SIFT, HOG, SURF, and their variations as A-SIFT, PCA-SIFT, surrounded by many others. Though the number of researches demonstrating the efficiency of such methods is undoubtedly enormous, the vast majority compares their performances only on pairs of images with little changes in view perspectives, objects or specific regions of the scenes. Thereby, the study of this type of technique under large viewpoint changes, called here LVC, has received little attention from the researchers. On the other hand, with the aim of filtering points of interest, most techniques have used the traditional extensive statistics. However, methods for image processing based on a new type of statistics, called non-extensive statistics, have shown to be efficient in several applications. In this paper, we present a new method, called q -SIFT, based on the non-extensive Tsallis statistics, to find fiducial points in a sequence of frames of videos under large viewpoints changes. We experimentally show the efficiency of the proposed method in video databases and propose new measurement metrics for this type of algorithm. • We present a method for Fiducial Point Description based on non-extensive physical systems. • We test the proposed method under what we call severe conditions, little tested in the literature. • We compare the proposed method with the main methods found in the literature, showing that the proposed method surpasses all others. • We present a new methodology to measure the performance of the studied algorithms. • We show that the database used represents non-extensive physical systems. [ABSTRACT FROM AUTHOR]

• A novel non-rigid registration method for medical images is proposed by introducing the image field into Demons algorithm. • Introduce the orientation field for registration and make use of the directional information. • There is no extraction of feature points and manual intervention in the proposed method. • Experiments on BrainWeb and FIRE databases show the effectiveness of the proposed registration method for medical images. In medical imaging application, non-rigid registration is an important step that requires both accuracy and efficiency to align the deformed query image with the reference image. For non-rigid registration, Demons is generally used as an effective algorithm. However, since the computational model of Demons uses only the gradient information, the directional information of the image has not been fully utilized. In this paper, we proposed a novel non-rigid registration method for processing medical images by introducing the image field to the Demons algorithm. As direction information is very important for spatial transformation in registration and the image fields contain the direction of the image, by combing image fields with the traditional model based registration algorithm, we introduce the orientation field directly to the model for estimating the distortion, and thus make a better use of the direction information of images and simplify the deformation model. Different performance measures are used to evaluate the qualitative measures of the proposed scheme. Experiments on brain images and fundus images show the improvement of the proposed algorithm compared to the other existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Highlights • Propose indirect diffusion model for extraction of deeply concave edges and sharp corners. • Model is defined as two PDEs, one driving level set motion and one smoothing indirectly level set. • Level set function can be initialized to zero function, so allowing for flexible initialization. • It is easy to set a termination criterion for the proposed algorithm. Abstract In this paper, we put forward an idea of indirect diffusion and further develope an indirect diffusion-based level set model for image segmentation. This model is based on the dynamic process of diffusion that is posed indirectly on level set function by way of auxiliary function, coupled with a transition region-based force that exhibits the desired sign-changing property. It is formulated as a coupled system of two evolution equations, in which the first equation drives the motion of zero level set toward the object edges and makes it possible to set a termination criterion on the algorithm, while the second equation (indirect diffusion) smoothens the auxiliary function and keeps the auxiliary function as close to the level set function as possible. The derived model can effectively be solved purely by the simplest explicit finite difference. Experimental results show that the proposed model not only has the strong capability of noise immunity, but it also can much better conduce to extraction of deeply concave edges and preservation of sharp corners, compared with the direct diffusion-based counterpart. [ABSTRACT FROM AUTHOR]

Highlights • We present a sparse tensor denoising model for magnetic resonance images. • The convergence of proposed scheme is proved. • The optional solution is proved. • Numerical algorithm is developed. • Comparison with experimental results is covered. Abstract In recent years, image denoising based on sparse tensors has been one promising technique for denoising magnetic resonance images or video processing. This paper aims at developing a new sparse tensor model based on reweighted regularization of factor matrices for magnetic resonance images denoising. An improved Split-Bregman scheme is proposed which is simple in implementation and efficient in computation. Additionally, the convergence of proposed scheme is proved. Experiments show that the proposed algorithm is efficient, and the denoising results are better than the state-of-the-art image denoising methods. The average computational time of our method is slightly longer than the others under the same iteration, except LPGPCA and model in Ruru and Zhixun (2018) [22]. [ABSTRACT FROM AUTHOR]

Highlights • Speckle noise reduction algorithm for parallel phase-shift digital holography (PPSDH) is proposed. • It enables reducing speckle noise in on-line. • Principle of speckle noise reduction is demonstrated. • It is based on characteristic of PPSDH and novel interpolation is adapted. • Experimental results show that speckle noise is suppressed. Abstract Digital holography is the best tool for measuring object information data. Parallel phase-shift digital holography (PPSDH) is particularly suitable for measuring moving objects. This method generates images of objects, but the image quality is diminished because they contain abundant speckle noise. In this paper, we propose an interpolation-based algorithm to reduce the levels of speckle noise in images generated by PPSDH, thus enhancing their quality. [ABSTRACT FROM AUTHOR]

Highlights • The proposed framework is an effective and flexible tool for structure-texture decomposition smoothing. • Exhibits good response to the image which has low-contrast details. • The computational time is competitive to the others state-of-the-art algorithms. Graphical abstract A comparison of the proposed algorithm's results using different guided edge-preserving filters. Abstract Structure-texture decomposition smoothing has been extensively studied due to its wide range of applications in computational photography and image processing. In this paper, we propose a new structure-texture decomposition algorithm which is based on two fundamental ideas: (1) guidance image and (2) iterative smoothing. The guidance image is generated by mitigating high-frequency oscillatory components in the original image. The result is then incorporated in a new generic iterative framework which makes use of well-known guided edge-reserving filters such as bilateral filter (BF), guided filter (GF), domain transform filter (DTF), and the extended Bayesian model averaging filter (BMA) called guided Bayesian model averaging filter (GBMA) to achieve texture smoothing. We have presented a detailed study of the proposed algorithm including: guidance image generation, an evaluation of the guided edge-preserving filters which are incorporated in the proposed iterative framework, the number of iterations for the proposed iterative structure, and the selection of guided edge-preserving filter. We demonstrate that the proposed method is a flexible and effective tool for a wide range of image editing applications including: image abstraction, color pencil drawing, content-aware image resizing, and texture editing. In particular, the proposed approach has the best performance in structure-texture decomposition for an image with low-contrast features. [ABSTRACT FROM AUTHOR]