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Leaf disease recognition using image processing and deep learning techniques is currently a vibrant research area. Most studies have focused on recognizing diseases from images of whole leaves. This approach limits the resulting models’ ability to estimate leaf disease severity or identify multiple anomalies occurring on the same leaf. Recent studies have demonstrated that classifying leaf diseases based on individual lesions greatly enhances disease recognition accuracy. In those studies, however, the lesions were laboriously cropped by hand. This study proposes a semi-automatic algorithm that facilitates the fast and efficient preparation of datasets of individual lesions and leaf image pixel maps to overcome this problem. These datasets were then used to train and test lesion classifier and semantic segmentation Convolutional Neural Network (CNN) models, respectively. We report that GoogLeNet’s disease recognition accuracy improved by more than 15% when diseases were recognized from lesion images compared to when disease recognition was done using images of whole leaves. A CNN model which performs semantic segmentation of both the leaf and lesions in one pass is also proposed in this paper. The proposed KijaniNet model achieved state-of-the-art segmentation performance in terms of mean Intersection over Union (mIoU) score of 0.8448 and 0.6257 for the leaf and lesion pixel classes, respectively. In terms of mean boundary F1 score, the KijaniNet model attained 0.8241 and 0.7855 for the two pixel classes, respectively. Lastly, a fully automatic algorithm for leaf disease recognition from individual lesions is proposed. The algorithm employs the semantic segmentation network cascaded to a GoogLeNet classifier for lesion-wise disease recognition. The proposed fully automatic algorithm outperforms competing methods in terms of its superior segmentation and classification performance despite being trained on a small dataset.

Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic sorting. Yellow and rotten leaves are main types of abnormal leaves in hydroponic lettuce. This study aims to demonstrate a feasibility of detecting yellow and rotten leaves of hydroponic lettuce by machine learning models, i.e. Multiple Linear Regression (MLR), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). One-way analysis of variance was applied to reduce RGB, HSV, and L*a*b* features number of hydroponic lettuce images. Image binarization, image mask, and image filling methods were employed to segment hydroponic lettuce from an image for models testing. Results showed that G, H, and a* were selected from RGB, HSV, and L*a*b* for training models. It took about 20.25 s to detect an image with 3 024 × 4 032 pixels by KNN, which was much longer than MLR (0.61 s) and SVM (1.98 s). MLR got detection accuracies of 89.48% and 99.29% for yellow and rotten leaves, respectively, while SVM reached 98.33% and 97.91%, respectively. SVM was more robust than MLR in detecting yellow and rotten leaves of hydroponic. Thus, it was possible for abnormal hydroponic lettuce leaves detection by machine learning methods.

In laser powder bed fusion (LPBF) additive manufacturing, the melt pool (MP) characteristics are key indicators for process and part defects. For example, the laser scan location on the build plate...

Computer vision is nowadays is one of the promising & evolving areas in information technology. Image processing is increasingly used in several applications such as automotive, medical or aerospace. Every computer vision application involves capturing several images with the help of camera. Image acquisition, image transmission & image security are the three important aspects of image processing system. This paper includes development of complete image processing system including improved median filtering with better PSNR & operating frequency, Image compression module with better PSNR & compression ratio, Image Encryption module using Advanced Encryption Standard.

New method for Detection wheat stem rust disease using Image processing methods, Converting RGB image To Hue Saturation Value then setting HSV parameters for background deleting. Texture analyses used for feature extractions. Feature selections where used to accelerate accuracy of detection. Backpropagation neural network used for classifications.

Plants make up more than 80 percent of the human diet. As a result, they are critical for food security and ensuring that we all have access to enough, cheap, clean, and nutritional food to lead healthy and active living. This research focuses on plant diseases as they create a major threat to food security. Expert people are hired in traditional farming to physically evaluate line by line for host disease plants, which would be labor-intensive, time taking and potentially error-prone activity because it is done by people manually. This work focus on, supervised machine learning algorithms like RF, SVM, DT, KNN, NB, and KNN with image processing methods and also analysis algorithm results with each other and finds the best algorithm for plant disease classification. RF algorithm achieved 89 percent accuracy compared with other algorithms.

COVID-19 is one of the biggest pandemics that the world is facing today, and every day, we are coming up with new challenges in this area. Still, much research is already going on to overcome this pandemic, and we also get succeeded to some extent. Diverse sources such as MRI, CT scanning, blood samples, X-ray image, and many more are available to detect COVID-19. Thus, it can be easily said that through image processing, the classification of COVID-19 can be done. In this study, the COVID-19 detection is done by classifying with the use of a type of convolutional neural network termed a detail-oriented capsule network. Chest CT scan imaging for the prediction of COVID-19 and non-COVID-19 are classified in the present paper using a Detailed Oriented capsule network (DOCN). Accuracy, specificity, and sensitivity are parameters used for model evaluation. The proposed model has achieved 98% accuracy, 81% sensitivity, and 98.4% specificity.

Recent transformer-based models, especially patch-based methods, have shown huge potentiality in vision tasks. However, the split fixed-size patches divide the input features into the same size patches, which ignores the fact that vision elements are often various and thus may destroy the semantic information. Also, the vanilla patch-based transformer cannot guarantee the information communication between patches, which will prevent the extraction of attention information with a global view. To circumvent those problems, we propose the Efficient Attention Pyramid Transformer (EAPT) in this paper. More specifically, we first propose Deformable Attention, which learns an offset for each position in patches. Therefore, even with split fixed-size patches, our method can still obtain non-fixed attention information that can cover various vision elements. Then, we design the Encode-Decode Communication module (En-DeC module), which can obtain communication information between all patches to get more complete global attention information. Finally, we also proposed a position encoding specifically for vision transformers, which can be used for patches of any dimensions and any lengths. Extensive experiments on the vision tasks of image classification, object detection, semantic segmentation demonstrate the effectiveness of our proposed model. Furthermore, we also conduct rigorous ablation studies to evaluate the key components of the proposed structure.

Recently, extraction of text or characters from images has become a wide field of research in the field of image analysis, neural networks, artificial intelligence and machine vision. The aim of character recognition is to extract and recognize the text from images and can be thus used to analyze contents, documents, or retrieve certain information etc. Image analysis has various processes and algorithms available for text extraction like edge detection, point detection, corner detection etc. for extracting the text from images. Many steps are involved in text recognition including pre-processing, segmentation, feature extraction, and classification. This paper addresses FAST algorithm for corner detection applied on the images for text recognition. The FAST algorithm detects and determines the presence of a corner by testing a small area around the potential center of the corner with the help of intensity value. This approach of text recognition can be efficiently used even with images with noise content or blurs.

The theme of this study is to provide a detailed description of its recent improvements in image segmentation and lesion classification in disease prognosis. Previous studies have shown that gray-white matter hyperintensities (GWMH) is one of the hallmarks of Nipah encephalitis, which sometimes occurs during the incubation period. Predicting this type of inflammation is a challenging task because it involves some unknown medical risk factors. A typical Magnetic Resonance Imaging (MRIs) is the best non-invasive system to analyze the anatomical structure of the brain. In-depth analysis of the defined pathological structure from isolated MR imaging leads to a reduction in the processing time of the prognostic model. Modern learning techniques such as Machine Learning, Computer Vision, and Deep Learning are the most promising techniques for determining the optimal outcome, computer can able to learn and extract useful information from historical data using various algorithms. Disease prognosis based on deep learning is sophisticated, so it can handle a variety of difficult tasks including image processing, classification, and feature extraction, noise and object detection. Diffusion-weighted imaging (DWI) in MRIs is a clinical prototype that can be used to diagnose brain abnormalities and to evaluate the microscopic architectural and molecular function of human organs or tissues. In this study, we summarize the results of diagnosing Nipah encephalitis using some publicly available brain encephalitis and encephalopathy databases.

High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imaging using deep learning (DL). This study conducts a comprehensive and insightful survey and analysis of recent developments in deep HDR imaging methodologies. We hierarchically and structurally group existing deep HDR imaging methods into five categories based on (1) number/domain of input exposures, (2) number of learning tasks, (3) novel sensor data, (4) novel learning strategies, and (5) applications. Importantly, we provide a constructive discussion on each category regarding its potential and challenges. Moreover, we review some crucial aspects of deep HDR imaging, such as datasets and evaluation metrics. Finally, we highlight some open problems and point out future research directions., Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), main and suppl. material

Semantic segmentation is paramount for autonomous vehicles to have a deeper understanding of the surrounding traffic environment and enhance safety. Deep neural networks (DNN) have achieved remarkable performances in semantic segmentation. However, training such a DNN requires a large amount of labelled data at pixel level. In practice, it is a labour-intensive task to manually annotate dense pixel-level labels. To tackle the problem associated with a small amount of labelled data, Deep Domain Adaptation (DDA) methods have recently been developed to examine the use of synthetic driving scenes so as to significantly reduce the manual annotation cost. Despite remarkable advances, these methods unfortunately suffer from the generalisability problem that fails to provide a holistic representation of the mapping from the source image domain to the target image domain. In this paper, we therefore develop a novel ensembled DDA to train models with different up-sampling strategies, discrepancy and segmentation loss functions. The models are, therefore, complementary with each other to achieve better generalisation in the target image domain. Such a design does not only improve the adapted semantic segmentation performance, but also strengthen the model reliability and robustness. Extensive experimental results demonstrate the superiorities of our approach over several state-of-the-art methods.

Human-machine interactive systems show increasing demand for analysing fast moving objects in high-frame-rate videos. Robust foreground detection, which is able to reduce large amount of redundant background data from high-frame-rate video, becomes the essence to achieve ultra-high-speed human-machine interactions. This paper proposes a local spatial propagation based background model generation, a local linear illumination correction based background model update, and a regional central coordinates and edge keypoints constrained foreground region reselection. The three proposals make up a robust and hardware-friendly foreground detection method. Experimental results prove that the proposed hardware-friendly algorithm achieves high accuracy and robustness on various kinds of challenging cases. Meanwhile, the hardware implementation utilizes little hardware resources and achieves realtime processing of high-frame-rate (784 frame/second) video with the delay less than 1 ms/frame in image processing core. In addition, a practical system is implemented by combing a PC, a high-speed camera and a field programmable gate array (FPGA) for realworld applications. This work will significatively promote the development and application of high-speed human machine interaction. A demo of the proposed vision system working at 784 FPS is available at https://wcms.waseda.jp/em/5f84f75136a6.

Purpose Leukaemia is diagnosed conventionally by observing the peripheral blood and bone marrow smear using a microscope and with the help of advanced laboratory tests. Image processing-based methods, which are simple, fast, and cheap, can be used to detect and classify leukemic cells by processing and analysing images of microscopic smear. The proposed study aims to classify Acute Lymphoblastic Leukaemia (ALL) by Deep Learning (DL) based techniques. Procedures The study used Deep Convolutional Neural Networks (DNNs) to classify ALL according to WHO classification scheme without using any image segmentation and feature extraction that involves intense computations. Images from an online image bank of American Society of Haematology (ASH) were used for the classification. Findings A classification accuracy of 94.12% is achieved by the study in isolating the B-cell and T-cell ALL images using a pretrained CNN AlexNet as well as LeukNet, a custom-made deep learning network designed by the proposed work. The study also compared the classification performances using three different training algorithms. Conclusions The paper detailed the use of DNNs to classify ALL, without using any image segmentation and feature extraction techniques. Classification of ALL into subtypes according to the WHO classification scheme using image processing techniques is not available in literature to the best of the knowledge of the authors. The present study considered the classification of ALL only, and detection of other types of leukemic images can be attempted in future research.

In robot visual servoing system, effective detected the region of interest area (ROI) in target images is the first problem should be solved, but the detection is susceptible to an unstructured environment. In this paper, an instance segmentation network algorithm based on Mask Region Convolutional Neural Networks (Mask R-CNN) framework is proposed for ROI image preprocessing. As instance segmentation technology can distinguish complex environmental information, so first with this advantage filter out message such as shape or similar area to target image, then with semantic segmentation technology add special category labels to the filtered images and distinguish different individual instances in similar categories. Finally through the series steps aforementioned, robot vision system can overcome the impact of environmental factors and identify the target image. The proposed method is used to detect target image under five constraints such as occlusion and reflection, result shows the algorithm can effectively deal with challenges that brought by complex constraints, and even can predict the location data of missing information based on some image information. In addition, based on the algorithm proposed in this paper, we used one seven axis robot visual servo platform, executed visual servoing experiment under different unstructured environments, further verifies the effectiveness of our proposed method.

The data processing of iris detection which was done by computer system were able to get better and eficient information. One of method that was used in digital image processing is edge detection. The function of this method is to identify the line or edge of the image so that the information of border line of image that can be highlighted and applied to identify the iris digital image pattern. In this research, the researcher has been tried an alternative to segment the system in order to identify the iris focused to image processing by edge detection and iris fitur extraction used Sobel detection. Iris image was from Chinese Academy of Science Institute of Automation (CASIA) version 1.0 database. The images were gray level images in BMP format and 320 x 280 pixel. It was consists of 756 images. As the results, 20 selected iris digital images randomly could not be identified by Sobel detection.

The Brain Tumor is one of the most serious scenarios associated with the brain where a cluster of abnormal cells grows in an uncontrolled fashion. The field of image processing has experienced remarkable growth in the area of biomedical applications with the invention of different techniques in deep learning. Brain tumor classification and detection is a subject of prime importance where Convolutional Neural Networks (CNN) find application. But the main drawback of the existing technology is that it is complex with a huge number of parameters contributing to high execution time and high system specifications for implementation. In this paper, a novel architecture for Brain tumor classification and tumor type object detection using the RCNN technique is proposed which has been analyzed using two publicly available datasets from Figshare ( Cheng et al., 2017 ) and Kaggle (2020) . Here we aim to decrease the execution time of a conventional RCNN architecture with the use of a low complex framework and propose a system for brain tumor analysis. We first use a Two Channel CNN, a low complex architecture to classify between Glioma and healthy tumor MRI samples which was successfully done with an accuracy of 98.21 percentage. Later this same architecture is used as the feature extractor of an RCNN to detect the tumor regions of the Glioma MRI sample that has been classified from the previous stage and the tumor region is bounded using bounding boxes. Also, this method has been extended to other two types of tumors Meningioma and Pituitary tumor. The methodology was able to achieve very low execution time as compared with the other existing architectures with an average confidence level of 98.83 percentage.

In the medical field, Medical diagnosis using Computed tomography (CT) has become increasingly popular due to their non-invasive approach and quick overall turnaround time. 3D visualization for CT Colonography involves the assessment and diagnosis of a patient to find the presence of cancerous polyps in the colon, by taking a Computed Tomography scan of the patient, and evaluating the reports. This technique reduces evaluation time by allowing the doctors themselves to analyze the CT scans without the need for a radiologist to generate an initial report. This technique also avoids an invasive procedure on the patient. This paper gives the insightsof developing computer aided system for the detection of Polyps in CT Colonography images using the principles of Image Processing and the Deep Learning, specifically an ensemble of Convolutional Neural Networks using GoogleNet architecture and 3D reconstruction of the same. The accuracy achieved by the proposed system for region classification, region 1 polyp detection and region 2 polyp detection are 98.75%, 93.75% and 94.03% respectively, and their F1 Scores are 0.88, 0.82 and 0.84 respectively.

In recent history, Convolutional Neural Networks have attained breakthroughs in addressing many intractable problems in the domain of image processing. But its performance builds upon its chosen hyper parameters and it is a tedious job to manually fine tune these hyper parameters. Hence, in this research, an Automated Hyper-parameter Optimized Convolution Neural Net is proposed which is further applied to uncover the class of skin cancer. The method has utilized a Grey Wolf Optimization algorithm for optimizing the hyper parameters of CNN, by adopting a proper encoding scheme. The effectiveness of the model is verified by comparing it with the performance of Particle Swarm Optimization and Genetic algorithm based hyper-parameter optimized CNN applied on the International Skin Imaging Collaboration skin lesion multi class data set. Simulation results infer that the proposed model is able to produce a testing accuracy up to 98.33% which is around 4% and 1% more compared to PSO and GA based models respectively. Similarly with the proposed model, the testing loss realized is around 0.17% which is 39.2% and 15% less compared to PSO and GA based models respectively. The experimental results clearly demonstrate that the proposed model performs competitively compared to other reported models.

With the growing demand for geospatial data, challenging aerial images with high spatial, spectral, and temporal resolution achieve excellent development. Currently, deep Convolutional Neural Network (CNN) structures are applied widely for object detection. Nevertheless, existing deep CNN-based models consist of complex network structures and require immense amounts of graphics processing unit (GPU) computation power with high energy consumption. Thus, achieving efficient real-time object detection for limited memory and processing capacity embedded device is a major challenge. This paper proposes a feasible and lightweight object detection model based on deep CNN where a mobile inverted bottleneck module is adopted in the backbone structure. Moreover, an enhanced spatial pyramid pooling is adopted to increase the receptive field in the network by concatenating the multi-scale local region features. The experimental results demonstrated that the proposed model achieved higher average precision and required the smallest memory storage compared to previous works. Moreover, the proposed model offers the best trade-offs in terms of detection accuracy, model size, and detection time which has excellent potential to be deployed on limited capacity embedded device.

In this article, a periodic self-triggered impulsive (PSTI) control scheme is proposed to achieve synchronization of neural networks (NNs). Two kinds of impulsive gains with constant and random values are considered, and the corresponding synchronization criteria are obtained based on tools from impulsive control, event-driven control theory, and stability analysis. The designed triggering protocol is simpler, easier to implement, and more flexible compared with some previously reported algorithms as the protocol combines the advantages of the periodic sampling and event-driven control. In addition, the chaotic synchronization of NNs via the presented PSTI sampling is further applied to encrypt images. Several examples are also utilized to illustrate the validity of the presented synchronization algorithm of NNs based on PSTI control and its potential applications in image processing.

Data protection is a severe constraint in the heterogeneous IoT era. This article presents a Hardware-Software Co-Simulation of AES-128 bit encryption and decryption for IoT Edge devices using the Xilinx System Generator (XSG). VHDL implementation of AES-128 bit algorithm is done with ECB and CTR mode using loop unrolled and FSM-based architecture. It is found that AES-CTR and FSM architecture performance is better than loop unrolled architecture with lesser power consumption and area. For performing the Hardware-Software Co-Simulation on Zedboard and Kintex-Ultra scale KCU105 Evaluation Platform, Xilinx Vivado 2016.2 and MATLAB 2015b is used. Hardware emulation is done for grey images successfully. To give a practical example of the usage of proposed framework, we have applied it for Biomedical Images (CTScan Image) as a case study. Security analysis in terms of the histogram, correlation, information entropy analysis, and keyspace analysis using exhaustive search and key sensitivity tests is also done to encrypt and decrypt images successfully., Comment: The manuscript is accepted in IEEE Consumer Electronics Magazine . 6 pages in double column format. A total of 4 figures

The assurance of secret data transmitted over the Internet and confining its access to explicit grouped information has become a significant security and protection issue. To hide the presence of such information, a robust and blind color image steganography algorithm, using fractal cover images, Singular Value Decomposition (SVD), Integer Wavelet Transform (IWT), and Discrete Wavelet Transform (DWT) is proposed in this paper. The proposed algorithm conceals the color secret image intelligently inside a fractal cover image thus creating a unique stego-image that is virtually a replica of the original cover image. The novelty of this proposed blind image hiding method lies in the fact that a small file size of a unique fractal image can be used to conceal a large amount of information payload. The fractal cover images can be implemented without debasing its quality to make the shrouded information sufficiently strong against image processing attacks. The algorithm possesses features like robustness, imperceptibility, security, and greater hiding capacity due to the application of fractal compression, DWT-SVD, and IWT-SVD hybrid transform schemes. The secret image is inserted in U and V components of the fractal cover with a scaling factor based on the HVS model and a stego-image with sufficiently large keyspace is created. Furthermore, the proposed work compares the performance analysis and security offered by IWT- SVD and DWT- SVD hybrid combinations for different fractal covers using certain metrics like PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), SSIM (Structural Similarity Index), BRISQUE (Blind/ Reference less Image Spatial Quality Evaluator) score and Bits per pixel (BPP). The results prove that image degradation is very less (less than 0.6%) in the proposed algorithm even when the secret image file size is more than double the file size of the fractal cover image which also saves the bandwidth and storage requirements.

The speckle noise is the major area of concern in ultrasound imaging. Speckle degrades the speed and accuracy of subsequent image processing tasks such as segmentation, description etc. But reduction of speckle may cause blurring or loss of edges and important features. In our work, we have employed a novel thresholding rule based on wavelet transform for speckle reduction from ultrasound images. The wavelet transform performs multi-scale analysis of the given image by treating different frequency components present in an image separately. The experiment results exhibit that the proposed thresholding rule gives better results for speckle reduction, edge preservation and feature preservation for medical ultrasound images, as compared with the existing thesholding rules.

In image processing, smoke may degrade visibility and deteriorate the performance of high-level vision applications. Therefore, single image smoke removal is crucial for computer vision. Currently, existing smoke removal algorithms mainly leverage handcrafted priors. Moreover, these methods usually apply haze removal methods to perform smoke removal due to the similarity between smoke and haze. However, these methods cannot sufficiently address the degradation of thick smoke and may suffer from residual smoke and color distortion problems due to the non-global and non-homogeneous distribution of smoke. In this paper, to solve the aforementioned problems, an end-to-end deep neural network called DesmokeNet is proposed. We construct a two-stage recovered pipeline to remove the smoke in different thicknesses. The light and thick smoke is first removed locally by the smoke removal network (SRN). The missing pixels in the thick smoke are then recovered by the pixel compensation network (PCN). Moreover, we proposed the thickness-aware pixel loss and the dark channel loss to suppress the residual smoke. To further increase the discriminative ability of the DesmokeNet, we proposed self-attentive feature consensus loss and multi-level contrastive regularization loss to improve the performance of smoke removal. Finally, to train the proposed method, we construct the first large-scale dataset containing synthetic and real-world data. Extensive experiments show that the proposed method outperforms favorably against other state-of-the-art methods quantitatively and qualitatively.

Consistent distribution of shadows on the surface of the shaded PV array enhances the array's harvested capacity. Therefore, in this paper, an Image processing inspired Chaos Map reconfiguration is proposed to mitigate the effect of shading. The proposed reconfiguration is aimed at achieving uniform row current when compared with the other conventional configurations with physical relocation of panels without changing the electrical connections. The efficacy of the proposed method is tested on Short Wide, Short Narrow, Long Wide and Long Narrow shading conditions on the basis of both software and hardware results. The proposed method enhanced output metrics under partially shaded conditions and gives most effective dispersion of shading over entire PV array. This proposed method is also compared with other static existing reconfiguration techniques for the above said shading conditions and the results show tremendous increment in output power under both hardware and software platforms. The results replicate that the proposed configuration can be generalized for any size of an SPV Array and output parameters can be enhanced by physical relocation under any sort of shading conditions.

Most compute-intensive applications demand approaches to optimize energy efficiency in CMOS design. Configurable architectures and approximate computing are alternatives to meet the quality and energy requirements in multimedia processing during the runtime. This work proposes a 2D Gaussian filter design to provide multiple quality-power-performance points of operation. The main goal is to design an accuracy-configurable 2D Gaussian filter accelerator at the runtime with a low overhead cost when a new configuration is dynamically selected. The power consumption results show reductions ranging from 9.87% to 58.96%, with an up to 14.8% increase in the hardware area.

A noniterative fractional-order (FO) two-dimensional (2-D) adaptive filtering mechanism is proposed in this article. Quartiles-based adaptive textural segmentation is employed for the calculation of texture-dependent FO. Statistically, the quartiles/quintiles/quantiles are cut points for dividing the range of a probability distribution into continuous intervals with equal probabilities. For the purpose of texture-based isolation of the spatial regions, a 2-D textural map is framed by evaluating spatial entropy by considering a pixel-wise local circular neighborhood. A novel end-to-end framework is proposed for FO texture-dependent image sharpening in an independent manner without influencing the other classes of textural regions. A novel inclusion of texture-wise adaptive gamma correction is also proposed in this article by drafting a mechanism where different kinds of textural regions can be separately processed in an independent manner. A novel quintiles-based multiscale Retinex (MSR) inspired approach for reflectance computation is coined in this article for suppressing environmental artifacts and unbalanced/nonuniform illumination. In this context, various scales required for MSR are themselves computed through quartiles-based intensity levels. The proposed model is highly modular. So, it can also be pipelined in a parallel manner, along with any well-established state-of-the-art contrast enhancement approach. Also, this approach is noniterative and highly robust. It can be proposed as an add-on for several possible (as well as pre-existing) image processing procedures. Rigorous comparative evaluations are performed so that the excellence of the proposed approach can be underlined.

Defect detection of trains is of great significance for operation safety and maintenance efficiency for railway maintenance. Nowadays, China railway system utilizes high-speed line scan cameras to capture images of critical parts of moving trains. The visual inspection on the images still heavily relies on manual interpretation. To reduce the labor requirements, we propose a novel two-stage deep learning based framework for component defect detection of moving trains. The proposed framework is composed of two major successive stages: detecting train components by using our proposed hierarchical object detection scheme (HOD), and detecting component defects based on multiple neural networks and image processing methods. Our proposed HOD can effectively detect and localize train components from large to small in a hierarchical way. Furthermore, a gated feature fusion method that can extract and combine the hierarchical contextual features and spatial contexts is also proposed to improve the performance. To the best of our knowledge, it is the first time in the literature that component defect detection of moving trains is systematically analyzed. Extensive experiments on real images from China railway system have demonstrated that our framework outperforms the state-of-the-art baselines significantly.

The limitation to the most commonly used histogram equalization (HE) technique is the inconsideration of the neighborhood info near each pixel for contrast enhancement. This gives rise to noise in the output image. To overcome this effect, a novel joint histogram equalization (JHE) based technique is suggested. The focus is to utilize the information among each pixel and its neighbors, which improves the contrast of an image. The suggested method is developed in a truly two-dimensional domain. The joint histogram is constructed using the original image and its average image. Further, it does not require a target uniform distribution for generating the output. The two-dimensional cumulative distribution function (CDF) is utilized as a mapping function to get the output pixel intensity. Extensive experiments are performed using 300 test images from BSD database. The experimental analysis indicates that the procedure produces better results than the state-of-the-art HE based contrast enhancement algorithms. More significantly, it produces the best results even for images having a narrow dynamic range. The implementation simplicity of the proposed algorithm may attract researchers to explore the idea for new applications in image processing.

Plant height and width is an essential phenotypic parameter that can be used not only as an indicator of overall plant growth but also used to estimate the advanced parameters such as the design of agricultural machines, estimation of yield, and site-specific applications. Presently, chili plant height and width are mostly measured manually, which is laborious and time-consuming process. The goal of this study was to develop and evaluate a real-time phenotyping system using an image processing approach to measure chili plant height and width under field conditions. The image processing algorithm was developed and compiled in the open-source computer vision library (OpenCV) and Python language using PyCharm as an integrated development environment (IDE). The developed image processing algorithm was evaluated in both static and field conditions in two plots of chili plants. The developed system was able to capture a valid image of the chili plant under field conditions and accurately estimate the height and width of the plant with a RMSE in the ranges of 0.30-0.60 cm. The height and width measured by the proposed image processing algorithm were strongly correlated (R2 = 0.80–0.95) with manually measured values. Furthermore, the image processing approach has much more advanced features to measure the more complex geometric traits of plants.

Internet of things (IoT) becomes the backbone of the advanced countries and it has a real contribute to exchange the traditional style or way of practical life, even personal life into smart style, with (IoT) technology the life become more and more easy and professional. internet of things achieves various applications coordinate with sensors and standard protocols to apply what is called machine -to- machine connection (M2M), in this paper we will talk more about the concept of (M2M), the main component of internet of things and finally the common protocols that is used in network, in addition to that this work present an IOT operation with processing system using camera for capturing image and Xilinx system generator(XSG)models for designing image processing algorithms and the result of the processing is an image with black and white for edge detection and Thresholding models and gray color image for gray enhancement model.

We demonstrate use of a complex constraint based on the interaction of x-rays with matter for reconstructing images from coherent X-ray diffraction. We show the complementary information provided by the phase and magnitude of the reconstructed wavefield greatly improves the quality of the resulting estimate of the transmission function of an object without the need for a priori information about the object composition.

In the automatic classification of colored natural textures, the idea of proposing methods that reflect human perception arouses the enthusiasm of researchers in the field of image processing and computer vision. Therefore, the color space and the methods of analysis of color and texture, must be discriminating to correspond to the human vision. Rock images are a typical example of natural images and their analysis is of major importance in the rock industry. In this paper, we combine the statistical (Local Binary Pattern (LBP) with Hue Saturation Value (HSV) and Red Green Blue (RGB) color spaces fusion) and frequency (Gabor filter and Discrete Cosine Transform (DCT)) descriptors named respectively Gabor Adjacent Local Binary Pattern Color Space Fusion (G-ALBPCSF) and DCT Adjacent Local Binary Pattern Color Space Fusion (D-ALBPCSF) for the extraction of visual textural and colorimetric features from direct view images of rocks. The textural images from the two G-ALBPCSF and D-ALBPCSF approaches are evaluated through similarity metrics such as Chi2 and the intersection of histograms that we have adapted to color histograms. The results obtained allowed us to highlight the discrimination of the rock classes. The proposed extraction method provides better classification results for various direct view rock texture images. Then it is validated by a confusion matrix giving a low error rate of 0.8% of classification.

Wi-Fi fingerprint-based indoor localization is one of the most practical localization methods, which does not require extra infrastructure and special hardware. However, we need to acquire a dataset with a high-density dataset in the target environment in this framework. To overcome the data acquisition cost problem, we propose a brand new data augmentation for Wi-Fi indoor localization named Between-Location data augmentation (BL data augmentation). We generate the fingerprint data for the whole target environment with high density by only using the sparsely sampled data. Between-Class learning, which is the origin of BL data augmentation and the latest powerful data augmentation method for sound recognition and image processing, mixes two data linearly with normalization; however, this mixing does not make sense in indoor localization because mixed fingerprint has no meaning and the label of indoor localization is not categorical information but physically correlated information. To overcome these two problems, we propose the generative model based on neural networks installed the physical relationship of labels and Wi-Fi fingerprint property. BL data augmentation enables us to reduce data sampled locations while keeping the localization accuracy even if some target locations have no data. From the experimental results, indoor localization methods with BL data augmentation outperform the state-of-the-art data augmentation method on several indoor localization models, whatever the data collection location is dense or sparse. Moreover, the localization with BL data augmentation using 10 % sampled location achieves the same accuracy with localization without data augmentation using all sampled locations.

Median filter is one of the predominant filters that are used to suppress impulse noise. Its simplicity and ability to maintain edges has led to an extensive application in the domain of image processing and computer vision. However, challenges such as moderate to high running time of the standard median filter algorithm and relatively poorer performance when the image is highly corrupted with impulse noise, have led to the design of several variations of the algorithm. One set of variation of the algorithm concentrates on generating quality outputs, while the other set focuses on reducing running time. Among the set targeting the reduction of the running time of the median filter is the DP approximated median filter. However, DP performs poorly when images are corrupted with moderate to high levels of noise. This paper therefore proposes an Improved Approximation Median Filtering Algorithms (IAMFA-I & IAMFA-II) based on DP to generate a better output. The introduction of Mid-Value-Decision-Median in DP reduces the chances of selecting corrupted pixel for denoised image. Experimental results indicate that the IAMFA-II has better running time and equivalent output compared with DP, while IAMFA-I generates better output and has equivalent running time when compared with DP.

Time-series data are widespread in real-world industrial scenarios. To recover and infer missing information in real-world applications, the problem of time-series prediction has been widely studied as a classical research topic in data mining. Deep learning architectures have been viewed as next-generation time-series prediction models. However, recent studies have shown that deep learning models are vulnerable to adversarial attacks. In this study, we prospectively examine the problem of time-series prediction adversarial attacks and propose an attack strategy for generating an adversarial time series by adding malicious perturbations to the original time series to deteriorate the performance of time-series prediction models. Specifically, a perturbation-based adversarial example generation algorithm is proposed using the gradient information of the prediction model. In practice, unlike the imperceptibility to humans in the field of image processing, time-series data are more sensitive to abnormal perturbations and there are more stringent requirements regarding the amount of perturbations. To address this challenge, we craft an adversarial time series based on the importance measurement to slightly perturb the original data. Based on comprehensive experiments conducted on real-world time-series datasets, we verify that the proposed adversarial attack methods not only effectively fool the target time-series prediction model LSTNet, they also attack state-of-the-art CNN-, RNN-, and MHANET-based models. Meanwhile, the results show that the proposed methods achieve a good transferability. That is, the adversarial examples generated for a specific prediction model can significantly affect the performance of the other methods. Moreover, through a comparison with existing adversarial attack approaches, we can see that much smaller perturbations are sufficient for the proposed importance-measurement based adversarial attack method. The methods described in this paper are significant in understanding the impact of adversarial attacks on a time-series prediction and promoting the robustness of such prediction technologies.

Recently, electrocardiogram (ECG) signals have received a high level of attention as a physiological signal in the field of biometrics. It has presented great possibilities for its strength against counterfeit. However, the ECG feature templates are irreplaceable, and a compromised template implies a permanent loss of identity. Therefore, several studies have been introduced biometric template protection techniques such as cancelable techniques to protect the original template in case it is stolen or lost. In this research, a cancelable ECG approach is proposed to protect the ECG feature template for human authentication. In our system, we first employed some image processing techniques for preprocessing the input ECG signals. Then, a deep transfer learning approach is employed to extract the deep ECG features. Later, the proposed cancelable approach based on DNA and amino acid is applied to protect the deep feature templates. Lastly, a Support Vector Machine (SVM) is employed for authentication. Extensive experiments on two commonly used datasets coupled with comprehensive theoretical analysis demonstrate the highest accuracy of the proposed system and the strong resilience of the system to various security and privacy attacks. Results show that the proposed cancelable method meets all requirements of cancelable biometrics such as irreversibility, revocability, and unlinkability.

Smart wheelchairs (SW) technology is one of the solutions to help disabled people who do not have a hand or people to help them. Apart from able to move on its own, a smart wheelchair needs to be safe to use. One of the ways to increase SW safety is the ability to detect obstacles. In this study, we tried to create obstacle detection that can classify the stairs descent and floor based on image processing. To achieve our purpose, Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to increase image contrast. After that, Gray Level Co-occurrence Matrix (GLCM) is used to extract features from the image. Finally, Back Propagation Neural Network (BPNN) is used to classify the image. Based on the test result, BPNN achieves results with 95% Accuracy, 95% Sensitivity, 95% Specificity with an average computation time of 0.0035 s.

Traditional fault diagnosis methods are easy to be affected by different working conditions. This paper proposed a motor fault diagnosis method based on visual knowledge, to reduce the impact of changes in working conditions and improve the feature extraction ability. The mapping relationship between actual faults and image intuitive features by symmetrized dot pattern (SDP) and scale invariant feature transform (SIFT) is established in this paper. The fault state is obtained by statistics of the matching point with the dictionary templates generated from signals of normal and unnormal motors. Compared with other machine learning algorithms, this method does not need too much data training and learning. The efficiency of this method is validated by experiments, and the data image processing technology has great industrial application value in the field of motor fault detection or monitoring in the age of intelligence.

Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) observation, has been an active research topic in the area of image processing in recent decades. Particularly, deep learning-based super-resolution (SR) approaches have drawn much attention and have greatly improved the reconstruction performance on synthetic data. However, recent studies show that simulation results on synthetic data usually overestimate the capacity to super-resolve real-world images. In this context, more and more researchers devote themselves to develop SR approaches for realistic images. This article aims to make a comprehensive review on real-world single image super-resolution (RSISR). More specifically, this review covers the critical publicly available datasets and assessment metrics for RSISR, and four major categories of RSISR methods, namely the degradation modeling-based RSISR, image pairs-based RSISR, domain translation-based RSISR, and self-learning-based RSISR. Comparisons are also made among representative RSISR methods on benchmark datasets, in terms of both reconstruction quality and computational efficiency. Besides, we discuss challenges and promising research topics on RSISR.

Data in the timeline of social media users consists of data in the form of text, images, audio, and video. Large and unstructured data in social media can be processed using various techniques such as text processing or image processing. In this study, the processed text data is used to classify Twitter users’ personality based on the DISC framework. Out of the initial collected 292 users, we semi-automatically filtered them for only personal accounts with Indonesian language posts. For being able to observe and assess a user’s personality out of their tweets choice of words, we made relevant keyword vocabularies corresponding to DISC framework and theory. There are four experiment scenarios done in this study, with variations on whether the keywords and text data are stemmed or not, and the keywords frequency calculation being weighted or not. Weighting the keywords using the current number in calculation based on their level does not show positive results, neither does stemming as the best results are shown by the not stemmed and not weighted scenario. This study is a preliminary research for an automatic profiling system which employs a combination of Natural Language Processing and Machine Learning approaches.

Modifying the existing models of classifiers’ operation is primarily aimed at increasing the effectiveness as well as minimizing the training time. An additional advantage is the ability to quickly implement a given solution to the real needs of the market. In this paper, we propose a method that can implement various classifiers using the federated learning concept and taking into account parallelism. Also, an important element is the analysis and selection of the best classifier depending on its reliability found for separated datasets extended by new, augmented samples. The proposed augmentation technique involves image processing techniques, neural architectures, and heuristic methods and improves the operation in federated learning by increasing the role of the server. The proposition has been presented and tested for the fruit image classification problem. The conducted experiments have shown that the described technique can be very useful as an implementation method even in the case of a small database. Obtained results are discussed concerning the advantages and disadvantages in the context of practical application like higher accuracy.

As we know, the fractal theory is an effective method for irregular image processing and analysis, which is difficult to implement using other baseline methods. However, Extreme Learning Machine (ELM) algorithm in combination with fractal theory can provide a better solution than the single fractal theory in image processing, transformation, data prediction, and so on. The features including fractal dimension, area ratio, and average perimeter of hematite, magnetite, calcium silicate, calcium ferrite, and stomata in the microstructure of irregular pellet images are extracted by artificial intelligence (AI) technologies. An AL-based ELM model suitable for the features of pellet images was constructed, the ELM model was optimized by regularization, and the feature parameters of pellet images were predicted by improved ELM algorithm, the optimal number of neuron nodes in the hidden layer was obtained for every mineral. The results show that the efficiency of model training and prediction in the feature for irregular pellet images is highly comparable to baseline methods. The method can be used as a reference in the intelligent prediction, control, and optimization of pellet image quality.

Image classification is a core field in the research area of image proces- sing and computer vision in which vehicle classification is a critical domain. The purpose of vehicle categorization is to formulate a compact system to assist in real-world problems and applications such as security, traffic analysis, and self- driving and autonomous vehicles. The recent revolution in the field of machine learning and artificial intelligence has provided an immense amount of support for image processing related problems and has overtaken the conventional, and handcrafted means of solving image analysis problems. In this paper, a combina- tion of pre-trained CNN GoogleNet and a nature-inspired problem optimization scheme, particle swarm optimization (PSO), was employed for autonomous vehi- cle classification. The model was trained on a vehicle image dataset obtained from Kaggle that has been suitably augmented. The trained model was classified using several classifiers; however, the Cubic SVM (CSVM) classifier was found to out- perform the others in both time consumption and accuracy (94.8%). The results obtained from empirical evaluations and statistical tests reveal that the model itself has shown to outperform the other related models not only in terms of accu- racy (94.8%) but also in terms of training time (82.7 s) and speed prediction (380 obs/sec).

Synthetic aperture radar (SAR), due to its inherent characteristics, will produce speckle noise, which results in the deterioration of image quality, so the removal of speckle in SAR image is very important for the subsequent high-level image processing. In order to balance the relationship between denoising and texture preservation, we propose a multiscale residual dense dual attention network (MRDDANet) for SAR image denoising. This algorithm can effectively suppress the speckle while fully retaining the texture details of the image. In MRDDANet, shallow features are extracted from the noisy images by multiscale modules with different kernel sizes, and then, the extracted shallow features are mapped to the residual dense dual-attention network to obtain the deep features of SAR image. Finally, the final denoising image is generated through global residual learning. MRDDANet has advantages of both multiscale blocks and residual dense dual attention networks. The dense connection can fully extract features in the image, and the dual-channel attention enables MRDDANet to pay more attention to noise information, which is beneficial to remove noise and keep the details of the original image at the same time. Compared with state-of-the-art algorithms, the results of the experiment indicate that our method not only improves various objective indicators but also shows great advantages in visual effects.

Change detection is a key technology in the field of polarimetric synthetic aperture radar (PolSAR) image processing. The current research on the change detection mainly focuses on studying PolSAR images with the same angle or small angle difference, and the angle problem is not considered. However, when the angle difference occurs, especially a large angle difference, some pixels might be falsely detected because the angle difference can affect the polarimetric characteristics. In this letter, we propose a multilevel information fusion-based (MIFB) method, which is suitable for extracting change information from PolSAR images with angle difference. In particular, the proposed method first adopts data resolution correction, then applies an improved feature-based registration algorithm, and finally, incorporates weighted graph theory with the superpixel segmentation algorithm to extract and merge pixel-based and object-based change areas to eliminate false alarms. Experimental results for multitemporal and multiangle PolSAR images reveal that the MIFB method can effectively eliminate false detection caused by angle differences and improve the detection accuracy.