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The tradeoff among spatial, temporal, and spectral resolution of remote sensing (RS) images due to sensor properties limits the development of RS applications. Most image enhancement studies tend to focus on either spatio-temporal fusion or spatio-spectral fusion. As a more comprehensive solution, spatial–temporal-spectral fusion (STSF) is complicated but its potential is worth to be further explored. In this study, we propose a novel STSF method from the perspective of multi-temporal Pansharpening. Canny edge extraction is applied to Panchromatic (PAN) images to identify edges while avoiding the disruption of multi-temporal land cover changes. We then build a TemPanSharpening net (TPSnet) which only uses one high-spatio-low-spectra-temporal PAN and one low-spatio-high-spectra-temporal multispectral image as input. TPSnet follows a super-resolution structure and embeds two basic modules: residual-in-residual dense blocks (RRDB) and convolutional block attention module (CBAM). A series of interior ablation experiments were conducted on TPSnet and we also compared it with some representative spatio-temporal fusion, Pansharpening, and STSF algorithms. TPSnet presented satisfactory performance on complicated meter-level ground surfaces according to the quantitative evaluation result, and it demonstrated excellent robustness to land cover change. [ABSTRACT FROM AUTHOR]

In the process of facial expression recognition, face detection is the prerequisite, image preprocessing is the foundation, facial expression feature extraction is the key, and facial expression classification is the target. Effective feature extraction in this process can improve the accuracy of facial expression classifications. On the other hand, traditional facial expression recognition methods are not only complicated in the feature extraction process, but also unable to obtain more in-depth high-semantic features and deep features from the original image. To solve the above problems, this paper proposes a facial expression recognition method based on multi-channel fusion and lightweight neural network. First, a cascade classifier based on Haar features is used to detect the face region of the facial expression image. Second, the local binary pattern (LBP) is used to extract the local texture features from the face region. Third, face edge features are simultaneously obtained by performing edge detection in the face region based on the Canny edge detection algorithm. Fourth, the obtained face image, LBP texture feature image, and edge detection Canny image are fused, and the fused image is input into the constructed lightweight neural network for training and recognition. Experiments are carried out on the public image databases Facial Expression Recognition 2013 (Fer2013) and extended Cohn–Kanade (CK +) using the hold-out cross-validation method. The experimental results show that the proposed method effectively extracts more complete image features by combining traditional feature extraction algorithms with deep learning feature extraction algorithms, improves the accuracy and robustness of facial expression recognition, and has better recognition rate and generalization ability compared to other mainstream methods. [ABSTRACT FROM AUTHOR]

Since the collapse of the Soviet Union, the crop cultivation structure in the Aral Sea Basin has changed dramatically, and these changes are worth studying. However, historical crop remote sensing mapping at the watershed scale remains challenging, especially crop misclassification at the cropland edge due to mixed pixels. Therefore, we proposed a field segmentation approach to constrain field edges based on time‐series Sentinel‐2 remote sensing images and the Google Earth Engine platform and then employed the random forest algorithm to perform crop classification based on time series Landsat/Sentinel‐2 images and crop phenology information to produce historical crop maps in the Aral Sea Basin from the 1990s onward. The results showed that the intersection over union between the extracted field edges and in situ‐measured field size data was 0.65. The overall accuracy of crop mapping was 95.2% in 2019. Then, we extended our method to historical mapping over the 1991–2015 period with accuracies ranging from 82.8% to 91.3%. Moreover, our method applied to historical mapping works well in terms of accuracy and policy matching. These findings indicate that our method can accurately distinguish cropland edges to reduce classification errors due to mixed pixels. This method is promising for solving the cropland edge problem for historical crop mapping in the Aral Sea Basin and can potentially provide a reference for historical crop classification in other watersheds of the world. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has posed significant challenges in accurately diagnosing the disease, as severe cases may present symptoms similar to pneumonia. Real-Time Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) is the conventional diagnostic technique; however, it has limitations in terms of time-consuming laboratory procedures and kit availability. Radiological chest images, such as X-rays and Computed Tomography (CT) scans, have been essential in aiding the diagnosis process. In this research paper, we propose a deep learning (DL) approach based on Convolutional Neural Networks (CNNs) to enhance the detection of COVID-19 and its variants from chest X-ray images. Building upon the existing research in SARS and COVID-19 identification using AI and machine learning techniques, our DL model aims to extract the most significant features from the X-ray scans of affected individuals. By employing an explanatory CNN-based technique, we achieved a promising accuracy of up to 97 % in detecting COVID-19 cases, which can assist physicians in effectively screening and identifying probable COVID-19 patients. This study highlights the potential of DL in medical imaging, specifically in detecting COVID-19 from radiological images. The improved accuracy of our model demonstrates its efficacy in aiding healthcare professionals and mitigating the spread of the disease. [ABSTRACT FROM AUTHOR]

Retinal Imaging, a non-invasive way to scan the back of the eye, provides a mean to extract different possible biomarkers, such as Artery and Vein Ratio (AVR). AVR is a well-known biomarker for various diseases, such as diabetes, glaucoma, hypertension, etc. The main objective of this paper to propose a fully automatic method to measure the AVR. The research hypothesis is that the system generated AVR is not significantly different from the ground truth. We have tested the system performance on publicly available INSPIRE-AVR (Iowa Normative Set for Processing Images of the REtina) dataset which contains 40 high-resolution colour fundus camera images and an AVR reference standard. The prerequisite for AVR measurement is the classification of retinal vessels (into arteries and veins) and the estimation of the vessel width. The images were classified into arteries and veins using Locally Consistent Gaussian Mixture Model (LCGMM) unsupervised classifier. The vessel width was estimated using the proposed Wavelet transform method from pre-processed images. Images pre-processing was performed using homomorphic filtering. Obtained results are compared with the vessel width calculated using the most common canny edge detector method. The calculated AVR was evaluated using two methods namely- Knudtson and Goatman, by utilizing the calculated vessel's widths. The system-generated AVR results were compared with the ground truth (manually annotated by observer 1 (Ob1)), and statistical analysis was performed using a Student's t-test. Furthermore, the validation of system-generated AVR values with respect to (w.r.t) the ground truth was done by utilizing a Bland–Altman (BA) plot. Student's t-test shows no significant difference in the AVR measured using Knudtson blue(p-value is 0.805 > 0.05) and Goatman (p-value is 0.652 > 0.05) methods w.r.t Observer 1 (Ob1) when vessel width was measured using Wavelet transform. However, there was a significant difference between the AVRs by Ob1 and the system (with Knudtson: p-value is 0.01 < 0.05 and with Goatman: p-value is 0.02 < 0.05) when the vessel width was measured using the Canny edge detector. Bland Altman's analysis shows that both the Ob1 and the system (with width calculated using Wavelet method and the AVR calculated using Knudtson and Goatman formula) have no substantial bias in AVR estimation. Furthermore, the observed bias between the AVR measurements was very low at 0.003. Further, from BA plot it has been seen that the limits of agreement for the system where width was obtained using canny was much wider as compared to the system when the wavelet transform was used to calculate the width. Further, our system generated average accuracy of 99.7% and 99.5% using Kundson and Goatman formula w.r.t Ob1. and outperform the existing method. [ABSTRACT FROM AUTHOR]

Summary: Aiming at current serious problems that the bolt looseness can cause heavy damages and the manual bolt looseness detection is ineffective, this study presents a bolt looseness detection method based on Canny edge detector. First, HSV (hue, saturation, value) color space is used to extract the area of mark bar. Then, after expanding, eroding and median filtering the image, Canny edge detector is used to segment the marker area to extract its edge. Last, the fitted slope of edge is used to judge whether the bolt is loose. The experimental results show that the proposed method has F1 score of 88.37%, contributing to the automated detection of bolt looseness. [ABSTRACT FROM AUTHOR]

Over the last few years, the study of characteristics of particles present in the environment becomes an interesting research area for scientists. Simulation of physical and dynamic characteristics of particulate matter (PM) is a prominent area for researchers. For the implementation of particle image velocimetry (PIV) for complex particulate matter with overlapping boundaries, it is necessary to remove non-physical measurements. These non-physical measurements such as unsteady surface and inaccurate edge detection lead to the spurious velocity of particles. In this note, a Canny edge detector is employed to identify the edge of particles. For unsteady surfaces, a special process is followed as follows: (a) find the gradient magnitude in the particles image velocimetry frame from the Canny edge detected frame to optimize particle detection, (b) classify a large high-intensity area in view to extract the particles, (c) find out the rough surface area which contains these particles with their reflections, (d) finally eliminate these particle's reflections. Finally, after this, PIV is implemented on these extracted processed frames from the video dataset to measure the motion of the particles. In this paper, a Canny edge detector with particle image velocimetry (PIV) algorithm is proposed to simulate the dynamic behavior of particulate matter present in particles stock video footage (PSVF) dataset of particles. The proposed model is trained to estimate the motion of particles, and the result showed an accuracy of 92.97% for the particles stock video footage dataset over the other existing methods. [ABSTRACT FROM AUTHOR]

The concept of the “smart city” has emerged with the advancement of technology, but some facilities are not sufficiently intelligent, such as parking lots. Hence, this paper proposes an inexpensive and plug-to-play camera-based smart parking system for airports. The system utilizes inverse perspective mapping (IPM) to provide an aerial view image of the parking lot, which is then processed to extract parking space information. The system also includes a guidance system to assist drivers in finding available parking spaces. The system is simulated on a 3D scene based on the parking lot of Macao International Airport. In the experiment, our system achieved an accuracy rate of 97.03% and a mean distance error of 8.59 pixels. This research study shows the potential of enhancing parking lots using only cameras as data collectors, and the results show that the system is capable of providing accurate and useful information. It performs well in parking lots with open space, in particular. Moreover, it is an economical solution for implementing a smart parking lot.

Breast cancer is very prevalent and because of its death rate is taken into deliberation to be the second dangerous disease in the world. There is a relentless effort to create more effective techniques for an early and reliable diagnosis. Classical approaches require oncologists to investigate breast lesions to detect and classify different cancer stages. Such manual attempts in many cases are time-consuming and inefficient. Hence there is a requirement for effective methods to diagnose cancer cells with high accuracy without human involvement. A "Moon Phase Wavelet Chain Rule Model" has been proposed in this research we introduced Moon Light Dimming Illumination Technique and Smart Recon Techniques. Thus, it overcomes the dense mass accumulation by providing a clear view of fat density, heterogeneous density, tumour size and thus it reduces the beam hardening problems. Our work has initiated Modified Segmented Stationary Wavelet Transform and Multivariable Chain Rule-Based Back Propagation Neural Network, to improvised the features extraction and classifying the phases of breast cancer by avoiding the under and overfitting problems. The proposed model reduces the dense mass accumulation, beam hardening, and obtains a segmented feature image for feature extraction.The Accuracy, Sensitivity, Specificity, Recall, Precision, prevalence performances of 98.62%, 98.25%, 97.52%, 98.25%, 97.25%, and 25.03% respectively. Hence, the outcome of the proposed model has been showing that our system is a promising and robust method for detecting breast cancer. [ABSTRACT FROM AUTHOR]

Lane marking detection is fundamental for both advanced driving assistance systems and traffic surveillance systems. However, detecting lane is highly challenging when the visibility of a road lane marking is low, obscured or often invisible due to real-life challenging environment and adverse weather. Most of the lane detection methods suffer from four types of challenges: (i) light effects i.e. shadow, glare of light, reflection etc. created by different light sources like streetlamp, tunnel-light, sun, wet road etc.; (ii) Obscured visibility of eroded, blurred, dashed, colored and cracked lane caused by natural disasters and adverse weather (rain, snow etc.); (iii) lane marking occlusion by different objects from surroundings (wiper, vehicles etc.); and (iv) presence of confusing lane like lines inside the lane view e.g., guardrails, pavement marking, road divider etc. In this paper, we proposed a simple, real-time, and robust lane detection and tracking method to detect lane marking considering the abovementioned challenging conditions. In this method, we introduced three key technologies. First, we introduce a comprehensive intensity threshold range (CITR) to improve the performance of the canny operator in detecting different types of lane edges e.g., clear, low intensity, cracked, colored, eroded, or blurred lane edges. Second, we propose a two-step lane verification technique, the angle-based geometric constraint (AGC) and length-based geometric constraint (LGC) followed by Hough Transform, to verify the characteristics of lane marking and to prevent incorrect lane detection. Finally, we propose a novel lane tracking technique, to predict the lane position of the next frame by defining a range of horizontal lane position (RHLP) along the x axis which will be updated with respect to the lane position of previous frame. It can keep track of the lane position when either left or right or both lane markings are partially and fully invisible. To evaluate the performance of the proposed method we used the DSDLDE (Lee and Moon, 2018) and SLD (Borkar et al., 2009) dataset with $1080\times 1920$ and $480\times 720$ resolutions at 24 and 25 frames/sec respectively where the video frames containing different challenging scenarios. Experimental results show that the average detection rate is 97.55%, and the average processing time is 22.33 msec/frame, which outperforms the state-of-the-art method.

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Development of energy-efficient and high-performance bioreactors requires progress in methods for assessing the key parameters of the biosynthesis process. With a wide variety of approaches and methods for determining the phase contact area in gas–liquid flows, the question of obtaining its accurate quantitative estimation remains open. Particularly challenging are the issues of getting information about the mass transfer coefficients instantly, as well as the development of predictive capabilities for the implementation of effective flow control in continuous fermentation both on the laboratory and industrial scales. Motivated by the opportunity to explore the possibility of applying classical and non-classical computer vision methods to the results of high-precision video records of bubble flows obtained during the experiment in the bioreactor vessel, we obtained a number of results presented in the paper. Characteristics of the bioreactor's bubble flow were estimated first by classical computer vision (CCV) methods including an elliptic regression approach for single bubble boundaries selection and clustering, image transformation through a set of filters and developing an algorithm for separation of the overlapping bubbles. The application of the developed method for the entire video filming makes it possible to obtain parameter distributions and set dropout thresholds in order to obtain better estimates due to averaging. The developed CCV methodology was also tested and verified on a collected and labeled manual dataset. An onwards deep neural network (NN) approach was also applied, for instance the segmentation task, and has demonstrated certain advantages in terms of high segmentation resolution, while the classical one tends to be more speedy. Thus, in the current manuscript both advantages and disadvantages of the classical computer vision method (CCV) and neural network approach (NN) are discussed based on evaluation of bubbles' number and their area defined. An approach to mass transfer coefficient estimation methodology in virtue of obtained results is also represented. [ABSTRACT FROM AUTHOR]

The bolt-loosening detection method using vision-based technique is summarized and a new gray gradient enhancement method is proposed to improve the stability of the nut edge detection and bolt-loosening detection. The Influence of the thresholds in Canny edge detector is studied and a new gray gradient enhancement method is proposed to enhance the gray gradient at the pixels on the outer boundary of the nut. Meanwhile, the suggestion on the high threshold value in Canny edge detector is given and can be used to reduce the subjectivity of parameter determination in Canny detector and the interference of uninterested edge lines. The proposed method is verified using a bolt connection in laboratory. The results show that the proposed method can effectively highlight the outer boundary of the nut and improve the stability of the nut edge detection and bolt-loosening detection. [ABSTRACT FROM AUTHOR]

With the penetration of artificial intelligence technology into various fields the combination of artificial intelligence and art design provides a broader prospect for the intelligent design of clothing. As an active topic of deep learning of artificial intelligence style transfer starts to be used in the fields of clothing pattern design and art painting. At present there are many technical shortcomings in using style transfer technology for clothing pattern design. When style transfer based on the convolutional neural network is applied to clothing pattern design the problems of monotonous color simple texture and inability to remove redundant backgrounds arise. Therefore this study explores an integration of Gram matrix and Canny edge detector to solve the problem of multi-style fusion and background segmentation in style transfer. In this study in order to realize multi-style transfer we first input multiple style images into the VGG-19 model so that the layers designated as style output can extract the features of each style image and output them separately. We calculate the Gram matrix of each image separately and weight all the obtained Gram matrices to form a new matrix. Therefore the co-occurrence and correlation of each channel in the new matrix can represent the fusion style. In order to deal with the redundant backgrounds generated in the style transfer process and the non-rendered areas due to the features of swimsuit styles we adopt the Canny edge detector algorithm and the OpenCV image processing library to perform operations such as rendering segmentation of images using the HSV interval differences of different rendered areas of swimsuit and finally obtain the swimsuit pattern design drawings. Compared with other convolutional neural networks whose style transfer can only extract the style of one image we optimize the structure of the Gram matrix and can extract the style of multiple images to transfer at the same time. In the processing of the image generated by style transfer by analyzing the structure and design features of swimsuit a clothing image segmentation model applicable to swimsuits is established. In order to verify the effectiveness of this study in the field of clothing pattern design we compare the effect drawings generated in this study with those generated by the style transfer method of other convolutional neural networks using the three metrics of questionnaire score PSNR and SSIM and the results show that the method of this study obtains higher evaluation in all the three metrics. This study by combining the painting art style with swimsuit pattern design is able to design a large number of swimsuit patterns with multi-style fusion features at a very low cost and has great application prospects. There is still room for improvement in the accuracy of swimsuit image segmentation in this study and further research will be conducted in this area. [ABSTRACT FROM AUTHOR]

Light Field (LF) cameras are able to capture both the intensity and the direction of light rays from the scene. This rich information demands a certain amount of memory and bandwidth for storage and transmission and, to alleviate this requirement, the LF content is processed and compressed. These operations often add degradations to the LF content that may affect their visual quality, requiring the use of methods to estimate the visual quality as perceived by the end consumer. In this paper, we propose a no-reference LF image quality assessment (LF-IQA) method that is based on a two-stream CNN architecture. The two-stream CNN extracts rich distortion-related spatial and angular binocular characteristics of LF contents to estimate their quality. More specifically, the first stream extracts angular information by processing Canny maps of Epipolar Plane Images (EPIs) generated from the corresponding LF contents, while the second stream extracts spatial information by processing mean canny maps generated from canny maps of sub-aperture images (SAIs). We also propose a novel approach to generate multiple epipolar-plane images - the MultiEPL. Results show that the proposed LF-IQA method outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has posed significant challenges in accurately diagnosing the disease, as severe cases may present symptoms similar to pneumonia. Real-Time Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) is the conventional diagnostic technique; however, it has limitations in terms of time-consuming laboratory procedures and kit availability. Radiological chest images, such as X-rays and Computed Tomography (CT) scans, have been essential in aiding the diagnosis process. In this research paper, we propose a deep learning (DL) approach based on Convolutional Neural Networks (CNNs) to enhance the detection of COVID-19 and its variants from chest X-ray images. Building upon the existing research in SARS and COVID-19 identification using AI and machine learning techniques, our DL model aims to extract the most significant features from the X-ray scans of affected individuals. By employing an explanatory CNN-based technique, we achieved a promising accuracy of up to 97% in detecting COVID-19 cases, which can assist physicians in effectively screening and identifying probable COVID-19 patients. This study highlights the potential of DL in medical imaging, specifically in detecting COVID-19 from radiological images. The improved accuracy of our model demonstrates its efficacy in aiding healthcare professionals and mitigating the spread of the disease.

In the case of real-time image processing, it is necessary to determine the computational complexity of the mathematical operations used. Reduction of computational complexity of 2D discrete convolution can be achieved by using a separable convolution. In this article, we focus on the application of a canny edge detector for different types of images. The main goal was to speed up the process of applying the kernel matrix to a given image using a separable convolution. By applying a separable convolution, we compared the duration of the Gaussian filter application, edges detection and the Hysteresis threshold level. Applying a separable convolution should speed up the duration of the 2D Gaussian filter as well as the edge detection. The main variable that interested us was time, but an important factor in the application of the filter and edge detection is the number of operating cycles. The use of a separable convolution should significantly reduce the number of computational cycles and reduces the duration of filter application and detection.

In the present work, an integrated modified canny detector and an active contour were proposed for automated medical image segmentation. Since the traditional canny detector (TCD) detects only the edge’s pixels, which are insufficient for labelling the image, a shape feature was extracted to select the initial region of interest ‘IROI’ as an initial mask for the active contour without edge (ACWE), using a proposed modified canny detector (MCD). This procedure overcomes the drawback of the manual initialization of the mask location and shape in the traditional ACWE, which is sensitive to the shape of region of region of interest (ROI). The proposed method solves this problem by selecting the initial location and shape of the IROI using the MCD. Also, a post-processing stage was applied for more cleaning and smoothing the ROI. A practical computational time is achieved as the proposed system requires less than 5 minutes, which is significantly less than the required time using the traditional ACWE. The results proved the ability of the proposed method for medical image segmentation with average dice 87.54%.

In recent decades, tool wear monitoring has played a crucial role in the improvement of industrial production quality and efficiency. In the machining process, it is important to predict both tool cost and life, and to reduce the equipment downtime. The conventional methods need enormous quantities of human resources and expert skills to achieve precise tool wear information. To automatically identify the tool wear types, deep learning models are extensively used in the existing studies. In this manuscript, a new model is proposed for the effective classification of both serviceable and worn cutting edges. Initially, a dataset is chosen for experimental analysis that includes 254 images of edge profile cutting heads; then, circular Hough transform, canny edge detector, and standard Hough transform are used to segment 577 cutting edge images, where 276 images are disposable and 301 are functional. Furthermore, feature extraction is carried out on the segmented images utilizing Local Binary Pattern (LBPs) and Speeded up Robust Features (SURF), Harris Corner Detection (HCD), Histogram of Oriented Gradients (HOG), and Grey-Level Co-occurrence Matrix (GLCM) feature descriptors for extracting the texture feature vectors. Next, the dimension of the extracted features is reduced by an Improved Dragonfly Optimization Algorithm (IDOA) that lowers the computational complexity and running time of the Deep Belief Network (DBN), while classifying the serviceable and worn cutting edges. The experimental evaluations showed that the IDOA-DBN model attained 98.83% accuracy on the patch configuration of full edge division, which is superior to the existing deep learning models. [ABSTRACT FROM AUTHOR]

This work tackles the issue of noise removal from images, focusing on the well-known DCT image denoising algorithm. The latter, stemming from signal processing, has been well studied over the years. Though very simple, it is still used in crucial parts of state-of-the-art “traditional” denoising algorithms such as BM3D. For a few years however, deep convolutional neural networks (CNN), especially DnCNN, have outperformed their traditional counterparts, making signal processing methods less attractive. In this paper, we demonstrate that a DCT denoiser can be seen as a shallow CNN and thereby its original linear transform can be tuned through gradient descent in a supervised manner, improving considerably its performance. This gives birth to a fully interpretable CNN called DCT2net. To deal with remaining artifacts induced by DCT2net, an original hybrid solution between DCT and DCT2net is proposed combining the best that these two methods can offer; DCT2net is selected to process non-stationary image patches while DCT is optimal for piecewise smooth patches. Experiments on artificially noisy images demonstrate that two-layer DCT2net provides comparable results to BM3D and is as fast as DnCNN algorithm. [ABSTRACT FROM AUTHOR]

The Hand Gestures Recognition (HGR) System can be employed to facilitate communication between humans and computers instead of using special input and output devices. These devices may complicate communication with computers especially for people with disabilities. Hand gestures can be defined as a natural human-to-human communication method, which also can be used in human-computer interaction. Many researchers developed various techniques and methods that aimed to understand and recognize specific hand gestures by employing one or two machine learning algorithms with a reasonable accuracy. This work aims to develop a powerful hand gesture recognition model with a 100% recognition rate. We proposed an ensemble classification model that combines the most powerful machine learning classifiers to obtain diversity and improve accuracy. The majority voting method was used to aggregate accuracies produced by each classifier and get the final classification result. Our model was trained using a self-constructed dataset containing 1600 images of ten different hand gestures. The employing of canny's edge detector and histogram of oriented gradient method was a great combination with the ensemble classifier and the recognition rate. The experimental results had shown the robustness of our proposed model. Logistic Regression and Support Vector Machine have achieved 100% accuracy. The developed model was validated using two public datasets, and the findings have proved that our model outperformed other compared studies. [ABSTRACT FROM AUTHOR]

The Canny algorithm is one of the most commonly used edge detectors due to its superior performance, especially in noisy environments. Its main limitation is that it is time consuming due to its multistage nature and the use of complex computational operations, primarily hysteresis thresholding. For this reason, many efficient implementations of the Canny edge detector have been developed on different accelerating platforms, such as ASICs, FPGAs and GPUs. The two main limitations of the GPU implementations developed to date are the bottleneck caused by the hysteresis process, and the use of fixed hysteresis thresholds. To overcome these issues, a novel GPU-based unsupervised and distributed Canny edge detector is proposed in this paper. Experimental evaluation showed that our Canny edge detector fully satisfies real time requirements, as it only requires 0.35 ms on average to detect edges on 512 × 512 images, and that it is faster than existing GPU and FPGA implementations. [ABSTRACT FROM AUTHOR]

Light Field (LF) cameras capture spatial and angular information of a scene, generating a high-dimensional data that brings several challenges to compression, transmission, and reconstruction algorithms. One research area that has been attracting a lot of attention is the design of Light Field images quality assessment (LF-IQA) methods. In this paper, we propose a No-Reference (NR) LF-IQA method that is based on reference-free distortion maps. With this goal, we first generate a synthetically distorted dataset of 2D images. Then, we compute SSIM distortion maps of these images and use these maps as ground error maps. We train a GAN architecture using these SSIM distortion maps as quality labels. This trained model is used to generate reference-free distortion maps of sub-aperture images of LF contents. Finally, the quality prediction is obtained performing the following steps: 1) perform a non-linear dimensionality reduction with a isometric mapping of the generated distortion maps to obtain the LFI feature vectors and 2) perform a regression using a Random Forest Regressor (RFR) algorithm to obtain the LF quality estimates. Results show that the proposed method is robust and accurate, outperforming several state-of-the-art LF-IQA methods.

Registration of aerial images to enrich 3-D light detection and ranging (LiDAR) points with radiometric information can enhance the capability of object detection, scene classification, and semantic segmentation. However, airborne LiDAR data may not always come with on-board optical images collected during the same flight mission. Indirect georeferencing can be adopted, if ancillary imagery data are found available. Nevertheless, automatic recognition of control primitives in LiDAR and imagery datasets becomes challenging, especially when they are collected on different dates. This article proposes a generic registration mechanism based on using the phase congruency (PC) model and scene abstraction to overcome the stated challenges. The approach relies on the use of a PC measure to compute the image moments that determine the study scene's edges. Potential candidate points can be identified based on thresholding the image moments' values. A shape context descriptor is adopted to automatically pair symmetric candidate points to produce a final set of control points. Coordinate transformation parameters between the two datasets were estimated using a least squares adjustment for four registration models: first- (affine), second-, third-order polynomials, and direct linear transform models. Datasets covering different urban landscapes were used to examine the proposed workflow. The root-mean-square error of the registration is between one and two pixels. The proposed workflow is found to be computationally efficient especially with small-sized datasets, and generic enough to be applied in registering various imagery data and LiDAR point clouds.

In this paper, the application of the neural network based watermarking framework is considered in the area of transformations. The logo information is embedded in the edge of the Contourlet transform. The canny edge detection is applied to detect the edge associated with the transform coefficients. The genetic algorithm has been used to choose the transform level and watermarking intensity. The genetic algorithm selects the best transform level and watermarking intensity based on the lowest error in extracting logo information for different default attacks. Of course, it should be noted that depending on the capacity of the logo, the number of subbands will be selected. In this paper, two methods of the differential and neural network are used to extract the logo and then the two methods of extraction are compared in terms of error extraction. The approaches of the embedding and the de-embedding in case of learning algorithm of the neural network via individual training data set are considered in the present research to carry out a series of experiments with different scenarios for the purpose of verifying the proposed techniques, obviously. [ABSTRACT FROM AUTHOR]

The timely diagnosis of skin lesion diseases is highly difficult for people living in rural or far flung areas due to dearth of qualified dermatologists. However, the dermatologists can diagnose skin lesion diseases by carefully examining the high-quality images at their clinics or from a distance area. Further, the computerized automatic diagnostic system may assist primary health professionals for quick and accurate diagnosis of these skin diseases. Thus, there is a need for medical image processing and analysis of skin lesion images to enhance their visibility properties. An efficient and effective skin lesion detection and identification software tool will provide a better classification system and may enhance the automation of skin lesion diagnosis. In this work, detection of skin lesions from human skin images is conducted by utilizing three image processing segmentation methodologies namely—Edge Detection using Ant Colony Optimization, Color Space-based Thresholding, Genetic Algorithm-based Segmentation and FCM-Based Image Segmentation. In order to quantitatively collate the working of three techniques, the entropy values of skin lesion images are considered. Application of FCM-based Segmentation yields in far better attribute of skin lesion images as compared to Genetic Algorithm-based Segmentation, Edge Detection using Ant Colony Optimization and Color Space-based Thresholding. [ABSTRACT FROM AUTHOR]

In the case of real-time image processing, it is necessary to determine the computational complexity of the mathematical operations used. Reduction of computational complexity of 2D discrete convolution can be achieved by using a separable convolution. In this article, we focus on the application of a canny edge detector for different types of images. The main goal was to speed up the process of applying the kernel matrix to a given image using a separable convolution. By applying a separable convolution, we compared the duration of the Gaussian filter application, edges detection and the Hysteresis threshold level. Applying a separable convolution should speed up the duration of the 2D Gaussian filter as well as the edge detection. The main variable that interested us was time, but an important factor in the application of the filter and edge detection is the number of operating cycles. The use of a separable convolution should significantly reduce the number of computational cycles and reduces the duration of filter application and detection. [ABSTRACT FROM AUTHOR]

Some evidence has demonstrated that focal vibration (FV) plays an important role in the mitigation of spasticity. However, the research on developing the FV system to mitigate the spasticity effectively has been seldom reported. To relieve post-stroke spasticity, a new pneumatic FV system has been proposed in this paper. An image processing approach, in which the edge of vibration actuator was identified by the Canny edge detector, was utilized to quantify this system’s parameters: the frequency ranging from 44 Hz to 128 Hz and the corresponding amplitude. Taking one FV protocol with the frequency of 87 Hz and the amplitude 0.28 mm of this system as an example, a clinical experiment was carried out. In the clinical experiment, FV was applied over the muscle belly of the antagonist of spastic muscle for twelve chronic spastic stroke patients. Spasticity was quantified by the muscle compliance and area under the curve for muscle (AUC_muscle). The result has demonstrated that, in the state of flexion of spastic muscle, the AUC_muscle and muscle compliance of the spastic muscle significantly increased immediately after FV compared with before-FV, illustrating the mitigation of the spasticity. This study will not only provide a potential tool to relieve post-stroke spasticity, but also contribute to improving the sensory and motor function of patients with other neurological diseases, e.g. spinal cord injury, multiple sclerosis, Parkinson and dystonia, etc. [ABSTRACT FROM AUTHOR]

Low-level and high-level feature extraction methods and algorithms for the image formation of a rectangular object were considered. The algorithm for object detection based on correlation analysis, as well as the algorithm containing the use of Canny edge detector, Hough and Radon transform for lines detection, and then, depending on the properties of the object lines combining in the rectangular area, were explored. The algorithms were tested on the base of 1000 passports for the problem of accurate photo edges detection.

In recent decades, tool wear monitoring has played a crucial role in the improvement of industrial production quality and efficiency. In the machining process, it is important to predict both tool cost and life, and to reduce the equipment downtime. The conventional methods need enormous quantities of human resources and expert skills to achieve precise tool wear information. To automatically identify the tool wear types, deep learning models are extensively used in the existing studies. In this manuscript, a new model is proposed for the effective classification of both serviceable and worn cutting edges. Initially, a dataset is chosen for experimental analysis that includes 254 images of edge profile cutting heads; then, circular Hough transform, canny edge detector, and standard Hough transform are used to segment 577 cutting edge images, where 276 images are disposable and 301 are functional. Furthermore, feature extraction is carried out on the segmented images utilizing Local Binary Pattern (LBPs) and Speeded up Robust Features (SURF), Harris Corner Detection (HCD), Histogram of Oriented Gradients (HOG), and Grey-Level Co-occurrence Matrix (GLCM) feature descriptors for extracting the texture feature vectors. Next, the dimension of the extracted features is reduced by an Improved Dragonfly Optimization Algorithm (IDOA) that lowers the computational complexity and running time of the Deep Belief Network (DBN), while classifying the serviceable and worn cutting edges. The experimental evaluations showed that the IDOA-DBN model attained 98.83% accuracy on the patch configuration of full edge division, which is superior to the existing deep learning models.