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Long-term visual place recognition (VPR) is challenging as the environment is subject to drastic appearance changes across different temporal resolutions, such as time of the day, month, and season. A wide variety of existing methods address the problem by means of feature disentangling or image style transfer but ignore the structural information that often remains stable even under environmental condition changes. To overcome this limitation, this article presents a novel structure-aware feature disentanglement network (SFDNet) based on knowledge transfer and adversarial learning. Explicitly, probabilistic knowledge transfer (PKT) is employed to transfer knowledge obtained from the Canny edge detector to the structure encoder. An appearance teacher module is then designed to ensure that the learning of appearance encoder does not only rely on metric learning. The generated content features with structural information are used to measure the similarity of images. We finally evaluate the proposed approach and compare it to state-of-the-art place recognition methods using six datasets with extreme environmental changes. Experimental results demonstrate the effectiveness and improvements achieved using the proposed framework. Source code and some trained models will be available at http://www.tianshu.org.cn.

Scale-invariant feature transform (SIFT) has been successfully used for optical image registration, but it cannot produce satisfying results when directly applied to synthetic aperture radar (SAR) images. In the present study, a novel method is proposed for registration between SAR and optical images. First, candidate keypoints are detected using SIFT algorithm. Then, Canny edge detection algorithm is adopted to remove the edge points which are wrong candidate points. Next, SIFT descriptors are generated from these correct keypoints. Last, the fast library for approximate nearest neighbors (FLANNs) algorithm is applied to search matching points in high-dimensional space. Experimental results show that the proposed approach is significantly more accurate and much faster than the original SIFT algorithm.

Traditional Canny edge detection algorithm has been widely used for the extraction of hyperbolic edges in ground penetrating radar (GPR). However, the Canny edge detection algorithm cannot adaptively determine the segmentation thresholds. Moreover, multiple hyperbolic edges are usually detected for each target, which increases the complexity of subsequent processing. In this letter, we propose a novel method that obtains only one hyperbolic edge for each target and automatically determines the segmentation threshold. Both simulation and real data results show that the proposed method is able to extract the target hyperbolic edges precisely and significantly improves the efficiency of target detection combined with the Hough transform in impulse GPR systems.

Sharing information through images is a trend nowadays. Advancements in the technology and user-friendly image editing tool make easy to edit the image and spread fake news through different social networking platforms. Forged image has been generated through an advanced image editing tool, so it is very challenging for image forensics to detect the micro discrepancy which distorted the micro pattern. This paper proposes an image forensic detection technique, which implies multi-level discrete wavelet transform to implement digital image filtering. Canny edge detection technique is implemented to detect the edge of the image to implement Otsu’s based enhanced local ternary pattern (OELTP), which can detect forgery-related artifact. DWT is implemented over Cb and Cr components of the image and using edge texture to improve the Otsu global threshold, which is used to extract features using ELTP technique. Support vector machine (SVM) is used for classification to find the image is forged or not. The performance of the work evaluated on three different open available data sets CASIA v1, CASIA v2, and Columbia. Our proposed work gives better results with some of the previous states of the work in terms of detection accuracy.

With the advancement of cloud computing technology, cloud servers are utilized to process large-scale data, especially multimedia data. However, concerns about leakage of private information prevent cloud computing from being further popularized. Thus, privacy-preserving computation for multimedia data is becoming increasingly important as a research hotspot. Edge detection plays an important role in image processing and computer vision. Different from previous researches on privacy-preserving computation, privacy-preserving edge detection faces new problems such as how to encrypt and represent edges. In this paper, we propose a privacy-preserving computation scheme for Canny edge detection. We first give an overview of our scheme, which involves one client and three cloud servers. Then, three key building blocks in the proposed scheme are put forward: pixel permutation; secure comparison and multiplication protocols; secure edge representation. Based on these building blocks, our scheme is carefully designed and constructed step by step. Furthermore, we analyze the correctness and the security of our scheme in detail. Finally, comparative experiments show that our scheme can maintain the quality of edge detection while meeting security requirements.

Chroma key foreground forgery is the most common forgery to manipulate the contents of a digital video. This kind of video can be presented in courts to show fake evidence, in political campaigns to misguide people and in the social media to distribute fake information. There are few techniques to discover this type of forgery. But a technique is required to detect the chroma key foreground forgery in the digital videos under various attacks to provide its robustness. In this paper, a passive approach is proposed for the detection of chroma key foreground forgery which is based on frame edge identification. The proposed approach generates difference frames for each frame pair of a digital video. The edges of each difference frame are identified in the proposed approach using canny edge detector. Then the proposed approach differentiates the edges pixels of each edge frame into large and small edge pixel difference values by applying threshold. After detection of the chroma key foreground forgery, the forged foreground is isolated from the authentic part of each edge frame in the digital video. Then the isolated forged foreground is localized and tracked within each frame of a forged digital video. The proposed approach is examined on the digital videos with different cases which shows high recall rate than precision rate in the experimental work. The proposed approach is then evaluated on the digital videos which are forged under the various attacks for its effective robustness. The proposed approach is also performed effectively on digital videos having realistic cases of chroma key foreground forgery with efficient results. The experimental results indicate higher detection accuracy, lower execution time and better robustness of the proposed approach than the other existing techniques.

Introduction. Machine vision systems are increasingly used in industrial production, particularly for monitoring the quality of electronic components. Radiographic (Х-ray) inspection is currently one of the most popular types of non-destructive testing. Electronic components are typically characterized by a small size, hence, their radiographic inspection should be based on obtaining images and their further enlargement. X-ray equipment for performing such studies is designed such that there are relatively small input doses of X-ray radiation in the plane of the receiver, which leads to a higher image noise than that using conventional X-ray devices.Aim. To develop a method for automated object recognition on microfocus X-ray images.Materials and methods. A method for segmentation of X-ray images is proposed. In the first step, adaptive median filtering is performed followed by correction of the image background by subtracting the distorting function. Next, the contours of the objects in the image are identified using the Canny edge detector followed by recognition of the objects on the resulting image.Results. The developed method was tested for quality control of the installation of microcircuits and for determining the number of electronic components. The experiments confirmed the accuracy of the proposed method. When monitoring the quality of microcircuit installation, the number of detected defects differed from that verified by the operator by less than 10 %. The average error of the proposed method was less than 0.1% when determining the number of electronic components.Conclusion. The proposed method for object recognition on microfocus X-ray images demonstrated sufficient accuracy in typical tasks of non-destructive testing of electronic components.

In recent years, accelerative aging society is meeting the short supply of young and middle-aged labor. Particularly, most young people are reluctant to work in the construction industry, which has caused the labor cost of floor tiling to rise year by year. In addition, floor tiling requires workers to continuously bend over or lean over to work, which greatly jeopardizes the physical health of them. Therefore, advanced technology applied in floor tiling is highly demanded to replace the traditional manual method. On the context, the automatic method of floor tiling may promote the transformation and upgrading of the industry. Although a few robots for floor tiling have been developed, the automation of existing systems is still at a low level. This paper proposes a robot floor-tiling control strategy based on visual measurement feedback and finite-state machine, in which the calculation of tile position information in limited field of vision is obtained by an improved Canny edge detection and a Hough linear transformation. Moreover, an algorithm for complementing tile position information based on visual measurement is proposed, and the quality of tile laying is evaluated online. To evaluate the effect of the proposed control strategy, the experimental verifications are given. The experimental results indicate that the proposed method can complete the automatic floor tiling with high accuracy.

Diabetes is a serious threat to human health. In 2016, non-communicable diseases including Diabetes accounted for 70% of the total causes of death in the world. Diabetes if left unchecked will cause complications that can attack other organs to cause blindness called Diabetic Retinopathy (DR). Ophthalmologists make a grouping of diabetic characteristics of retinopathy by observing the retinal images of the eye taken using a fundus camera. This method requires a long time in the observation that allows errors in making observations, so image processing is needed to detect and classify the stage of diabetic retinopathy suffered by the patient. Thus, this research aims to help the process of early treatment of patients with diabetic retinopathy so as not to cause blindness. The data used in this study is DB0 Diaret data with a pixel size of 128 x 104 and the amount of data is 131. The methods used in this system include Canny Edge Detection, Prewitt, and stadium readings using Artificial Neural Network Algorithms. In this study the highest accuracy results obtained on the Canny Edge Detection method with a value of 90% while the Prewitt method has a 79% result. So, we get the conclusion that Canny Edge Detection is considered better.

Radiation therapy requires clinical linear accelerators to be mechanically and dosimetrically calibrated to a high standard. One important quality assurance test is the Winston-Lutz test which localises the radiation isocentre of the linac. In the current work we demonstrate a novel method of analysing EPID based Winston-Lutz QA images using a deep learning model trained only on synthetic image data. In addition, we propose a novel method of generating the synthetic WL images and associated ‘ground-truth’ masks using an optical path-tracing engine to ‘fake’ mega-voltage EPID images. The model called DeepWL was trained on 1500 synthetic WL images using data augmentation techniques for 180 epochs. The model was built using Keras with a TensorFlow backend on an Intel Core i5-6500T CPU and trained in approximately 15 h. DeepWL was shown to produce ball bearing and multi-leaf collimator field segmentations with a mean dice coefficient of 0.964 and 0.994 respectively on previously unseen synthetic testing data. When DeepWL was applied to WL data measured on an EPID, the predicted mean displacements were shown to be statistically similar to the Canny Edge detection method. However, the DeepWL predictions for the ball bearing locations were shown to correlate better with manual annotations compared with the Canny edge detection algorithm. DeepWL was demonstrated to analyse Winston-Lutz images with an accuracy suitable for routine linac quality assurance with some statistical evidence that it may outperform Canny Edge detection methods in terms of segmentation robustness and the resultant displacement predictions.

In this paper, five classical edge detection operators are compared, and then a traffic signal light detection and recognition scheme that can be used for intelligent connected vehicles is implemented. Firstly, the image to be processed is obtained by detecting the traffic signal light through the vision sensor. The image is preprocessed: the color space of the image is converted from RGB space to HSV space. Through the grayscale, histogram equalization, image binarization processing, using the morphological closure operation, the five operators are compared in noise sensitivity, positioning accuracy and signal-to-noise ratio, the Canny edge detection operator is selected for image edge detection, and the target recognition area is obtained. Finally, using the histogram drawn, the number of red, green and yellow pixel points in the histogram can be clearly counted, and the color with the largest number of pixel points can be identified as the color of the identified traffic signal light, and the identification of the traffic signal light can be completed. The actual pictures are simulated on the MATLAB, which verifies the feasibility of the proposed method of traffic signal light recognition method based on Canny operator in this paper, which can correctly identify the color of the traffic signal light.

The problem of edge detection plays a crucial role in almost all research areas of image processing. If edges are detected accurately, one can detect the location of objects and the parameters such as shape and area can be measured more precisely. In order to overcome the above problem, a feature-based image registration (FBIR) method in combination with an improved version of canny with fuzzy logic is proposed for accurate detection of edges. The major contributions of the present work are summarized in three steps. In the first step, a restoration-based enhancement algorithm is proposed to get a fine image from a distorted noisy image. In the second step, two versions of input images are registered using a modified FBIR approach. In the third step, to overcome the drawback of canny edge detection algorithm, each step of the algorithm is modified. The output is then fed to a “fuzzy inference system”. The “fuzzy rule-based technique”, when applied to the problem of “edge detection”, is very “efficient” because the thickness of the edges can be controlled by simply changing “rules and output parameters”. The domain of the images under consideration is various well-known image databases such as Berkeley and USC-SIPI databases, whereas the proposed method is also suitable for other types of both indoor and outdoor images. The robustness of the proposed method is analysed, compared and evaluated with seven image assessment quality (IAQ) parameters. The performance of the proposed method is compared with some of the state-of-the-art edge detection methods in terms of the seven IAQ parameters.

Leaves are one of the parts of plants that can be applied in the process of identifying species images of leaves. The process of classification of leaf imagery can be done by identifying the image of the leaf shape that can be done by identifying the pattern of the leaf by recognizing the structural characteristics of the leaf such as the shape and texture of the leaf. The classification of this leaf image is based on the canny edge detection and gray-level co-occurrence matrix (GLCM) features using the k-Nearest Neighbor (kNN) classification method. The data used is as many as 350 leaf imagery with seven different species. The results show that from the two extraction features used, the Canny feature gets an accuracy of 80% and, the GLCM features gets an accuracy of 93.3%. And the merging of the two features resulted in an increased accuracy of 98%. It can be concluded that this research has produced good accuracy in identifying leaf imagery based on canny edge detection features and Gray-Level Co-occurrence Matrix (GLCM) features and k-Nearest Neighbor classifier method. Key words -- Leaf, canny edge detection, gray-level co-occurrence matrix (GLCM), the k-nearest neighbor (kNN)

Biometric authentication is one of the most quickly increasing innovations in today's world; this promising technology has seen widespread use in a variety of fields, including surveillance services, safe financial transfers, credit-card authentication. in biometric verification processes such as gender, age, ethnicity is iris recognition technology is considered the most accurate compared to other vital features such as face, hand geometry, and fingerprints. Because the irises in the same person are not similar. In this work, the study of gender classification using Artificial Neural Networks (ANN) based on iris recognition. The eye image data were collected from the IIT Delhi IRIS Database. All datasets of images were processed using various image processing techniques using the neural network. The results obtained showed high performance in training and got good results in testing. ANN's training and testing process gave a maximum performance at 96.4% and 97% respectively.

In the industry of agricultural farming, defected fruits are the major reason for financial calamities across the globe. It affects both the quality and competence of the fruits. Quality detection is a post-harvest process that requires highly skilled labor and time. Therefore, the automatic detection of quality of fruits is an important step in the harvesting process which helps to save the man power and time consumption. Different systems have been designed using image processing and learning techniques that detect the quality of fruits and classify them. To speed up the fruit sorting process, a system has been designed using machine learning and deep learning techniques. The proposed system works with total nine types of fruits: apple, banana, pear, guava, grape, mango, pomegranate, orange and tomato. Recurrent neural network is used as a proposed deep learning classifier which is trained using good and bad extracted features through implementation of principal component analysis. A simple contrast enhancement technique, preceded by grayscale conversion, has been used for balancing the unstable light in the input fruit image that can suppress the object definition. In the segmentation phase, canny edge detection is used for discovering the boundaries of the fruits. The comparative analysis with existing multi-fruit or single-fruit systems clearly shows that the proposed general purpose system overshadows them by achieving better accuracy (98.47%), precision (98.93%), recall (75.44%) and mean square error (1.53%) values.

Excited by ground-breaking progress in automatic code generation, machine translation, and computer vision, further simplify web design workflow by making it easier and productive. A Model architecture is proposed for the generation of static web templates from hand-drawn images. The model pipeline uses the word-embedding technique succeeded by long short-term memory (LSTM) for code snippet prediction. Also, canny edge detection algorithm fitted with VGG19 convolutional neural net (CNN) and attention-based LSTM for web template generation. Extracted features are concatenated, and a terminal LSTM with a SoftMax function is called for final prediction. The proposed model is validated with a benchmark based on the BLUE score, and performance improvement is compared with the existing image generation algorithms.

This paper presents a partially connected Multilayer Perceptron (PCM) neural network as an optimal new MLP with a supervised algorithm and three hidden layers to detect face emotions. Compared with the traditional MLP, the proposed network shows improvements in speed, accuracy, and computational time. Six emotions have been considered in this study, namely anger, surprise, fear, sadness, normal, and happiness. The image dataset with the fixed background is used to test and train the network. Canny edge detection algorithm is employed to separate regions of the face including eyes, mouth, and eyebrows from the background. A binary image is extracted to represent the areas of eye, mouth, and eyebrow. The feature extraction is carried out by Legendre coefficients of Legendre polynomials which are selected for the best accuracy. Results show that the partial network is faster and has a lower computational complexity compared with the full connection network. It also has a faster convergence with higher accuracy.

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.

The rapid development of artificial intelligence technology makes it widely used in various fields. In order to more scientifically assist teenagers in physical training, this paper develops a set of teenagers’ physical training system based on artificial intelligence technology. Firstly, the experimental platform is built, and the sensor nodes are connected with the test host through the serial port to collect data to the experimental platform. The system consists of target detection module, data analysis module, and human posture estimation module. The background modeling method based on vibe model is used to form the target detection module, and the canny edge detection algorithm is used to form the data analysis module. Finally, the posture auxiliary index is established to estimate the human posture. This paper makes a systematic application test on a youth sports team. The experimental group was trained with artificial intelligence-based physical training system, while the control group was trained with traditional training methods. Before the experiment, the physical fitness of the two groups of subjects were evaluated, including standing long jump, 50 meters sprint, 30 s single swing rope skipping, pull-up, and squat 1RM. After 3 and 6 weeks of training, the physical fitness was evaluated again. The experimental results show that the intelligent assistant system established in this paper can accurately show that the physiological load of the athlete is in line with the law of physiological function change. After six weeks of training, the standing long jump of the experimental group has been improved by 20.97 cm, the 50 meters dash has been accelerated by 1.21 s, the 30 second single swing rope has been increased by 13.76, the pull-up has been increased by 1.41, and the squat 1RM has been increased by 15.16. This shows that the auxiliary training system based on artificial intelligence can help young athletes improve their physical quality and enhance their sports skills.

Most of the landing systems are based on GPS and radar altimeter, sonar, infrared. But in urban environments buildings and other obstacles disturb the GPS signal and can even cause loss of signal. In such case it will be beneficial to have independent control of navigation and landing assistance system. So the main aim is to design a software system that will assist helicopter or Unmanned Aerial Vehicle accurately under all-weather. The software system takes height parameter and images from helicopter or Unmanned Aerial Vehicle as an input. After applying number of processing techniques like edge detection, RGB to Gray scale on the image, the image is compared with the HSV dataset to find the free space. For edge detection Canny edge detection algorithm is used. From the number of free spaces nearest patch is selected by taking vehicle dimension and landing orientation of the vehicle into consideration. Performance of the system depends on the accuracy and the speed of the system. This system also resolves the potentially dangerous problem of GPS denial.

This paper suggests two novel presented cancellable biometric realization approaches recognition and template protection. In the suggested scheme, the A Trous Transform (AT) algorithm is applied on the face images. Then the AT divides the image into seven subbands. The resultant map is encrypted with the Homomorphic Filtering Masking (HFM) encoding algorithm is utilized for cancelable face recognition system. Then the second HFM utilized is produced from the image. This technique can be used to advance a frequency domain procedure for making this system for biometric template protection. The second algorithm presents a new technique to detect the features for Facial Expression Recognition (FER). This technique is established on segmentation process by Canny Edge Detection (CD) and Hough Transform (HT) with the number of feature points as the key parameters for classification. This algorithm is set up analysis the FER with the number of HT feature points as the key parameters for classification. Simulation results using evaluation metrics False Positive Rate (FPR), False Negative Rate (FNR), Equal Error Rate (EER), Receiver Operating Characteristic (ROC) and Area under ROC (AROC) prove that the first proposed cancelable biometric technique with the second key are best with comparing the other keys.The obtained results clear that the second suggested technique has sucesseded in detection the features of FER for sad, happy, neutral, angry, disgust, fear, surprise cases and the face positions.

The accurate classification of the fragments is a critical step in the restoration of the Terracotta Warriors. However, the traditional manual-based method is time-consuming and labor-intensive, and the accuracy mainly depends on the archeologist’s experience. In this paper, we present a novel classification framework for the 3D Terracotta Warriors fragments. The core of our framework is a dual-modal based neural network, which can incorporate geospatial and texture information of the fragments and output the category of each fragment. The geospatial information is extracted from the point cloud directly. At the same time, a method based on the 3D mesh model and improved Canny edge detection algorithm is proposed to extract the texture information. As to the real-world data experiments, the dataset includes 800 pieces of the arm, 810 pieces of the body, 810 pieces of head and 830 pieces of leg, and the mean accuracy rate is 91.41%, which is better than other existing methods, which only based on geospatial information or texture information. We hope our framework can provide a useful tool for the virtual restoration of the Terracotta Warriors.

A method is proposed, which uses the digital image processing technique to identify cloud boundaries from scanning Ka-band (~35.29 GHz) radar imagery dataset. In this method, a cloud is considered as an uninterrupted region of radar echoes with radar reflectivity higher than -34 dBZ and area greater than 3 km2. The proposed algorithm involves 1) conversion of radar RGB image to grayscale by removing white background and noise, 2) identification of cloud boundaries by canny edge detection, and 3) estimation of cloud cross-section area (CCSA) and cloud top height (CTH) based on the pixel width. This method is effectively applied to Ka-band radar images collected over Mandhardev, a high altitude scanning station in the Western Ghats (WGs), India to derive CTH and CCSA. CTH distribution shows three peaks, one at about 2 km with others at about 7-8 km, and 12 km. The cloud occurrence shows an apparent diurnal variation with a maximum in the afternoon hours while a semidiurnal variation is observed in CCSA. The proposed method shows consistent statistics with Global Precipitation Measurement Ka-band radar observations and thus suitable to build robust cloud climatology over the WGs. Such cloud statistics are essential to validate the representation of clouds in weather and climate models.

Gacha games has tedious activities need to be done by the players to progress the game besides using real money. The usage of bots and macros to automate the game are not uncommon nowadays with the high demand of grinding within the game. This research purposes is to create a bot that can replace the user task to click buttons on game stages, thus helping users in their grinding. The automatic clicker applied image recognition to find the area to be clicked. Firstly, the template image is manually taken by the user to be used as the matching. The screen image, which captured on the go, and the template image are pre-processed using three variations techniques to find the best precision of the coordinate area. Afterwards, the cursor will be moved to the coordinate position returned from the matching between template image and screen image. The coordinate returned from the matching is randomized using Gaussian Distributions but still within the matched positions between template image and screen image. The experiment shows that Canny Edges gives the good coordinate position. On the other hand, the grayscale gives a poor result on giving the coordinate position. Canny Edge and combination of Canny Edge and grayscale always return the target’s position. With this bot, user does not need to supervise the game every time the game stage has finished and restarting the stage.

Accurate identification of the shape and position of organs and abnormal objects (e.g., tumors) in medical images plays an important role in surgical planning as well as in the diagnosis and prognosis of diseases. However, this is difficult to achieve from two-dimensional medical images as these images present inaccurate and ambiguous organ boundaries. Further, traditional image processing-based boundary detection methods such as the Canny edge detector and Sobel operator exhibit poor boundary detection performance for images with substantial noise. Recently, the use of deep learning has resulted in improvements in semantic segmentation in medical images. In this paper, we propose a generic boundary-aware loss function to facilitate the effective discernment of the boundaries of organs and abnormal objects in medical images. Specifically, the proposed loss function introduces a boundary area and assigns higher weights to the loss of pixels located in the boundary area than to those in the non-boundary areas, thereby promoting effective learning in the boundary area. The results of experiments conducted using public medical datasets comprising colon polyp, skin lesion, and chest X-ray data indicate that the standard loss functions, such as cross-entropy loss and Dice loss, combined with the proposed boundary-aware loss function, achieve comparable or better performance than those without the boundary-aware loss function.

In recent years, due to its balance mechanical and tribological properties, hybrid materials have occupied a dominant position in the automotive industry and aerospace industry. However, in the study of mechanical and tribological properties, it is necessary to perform metallographic characterization of hybrid composites. The discovery of digital image processing technique as a next generation technology for metallographic characterization results in time saving and error free solution. This automation technology includes multiple stages, such as image enhancement. Perform spatial filtering to remove noise, edge detection, and watershed conversion. One of the key factors of digital image processing technique is the application of statistical analysis in edge detection stage. This signify, edge detection stage plays the crucial role in digital image processing technique which provide a good quality microstructure from an unfavorable image. As per our comparative study between the edge operators like Sobel operator, Robert operator, Prewitt operator and Canny operator, we recommend the results of canny edge detectors for watershed transformation to calculate the grain edge to find the average grain size and results of statistical analysis method validate the same. The results also confirmed the formation of distinct edge of grain after watershed transformation.

Image analysis is an important technique that can help specialists localize, detect, and segment objects in different types of medical images such as MRI, CTs, and ultrasounds (US). In this research, we use US images to identify and segment the enlarged veins in the pampiniform venous plexus, which is called varicocele. The proposed method aims to determine whether a potential patient is affected or not. This method was evaluated using 90 US images that were taken of the left testicles of 90 patients in the Supine position. This system analyzes US images in three stages which are; preprocessing, processing, and edge detection. The Region Of Interest (ROI) of the pampiniform plexus area was extracted using Otsu segmentation with different parameters (0.1, 0.2, 0.17) and different color modes (Grayscale, YCbCr, RGB). In the processing stage, different denoising filters were used. Eventually, in the edge detection stage, four edge detectors were applied which are Canny, Soble, Prewitt, and Roberts. Results showed that the best accuracy in detecting varicocele was 78% when the YCbCr color mode yellow (y) channel was used with 0.1 Otsu segmentation and the Canny edge detector. The system also showed a Sensitivity of 91%, as the test was able to detect 91% of the people with Varicocele, and the Specificity value was 39%.

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.

Detection of brain of tumor is a laborious task as it involves identification, segmentation followed by detection of the tumor. It is a very challenging task to envisage uncommon structures in the image of human brain[15]. An Image processing concept called MRI can be used to visualize different structures of human body. The Magnetic Resonance images (MRI) are used to detect the uncommon portions of human brain. This paper explores different noise removal methods accompanied by Balance-contrast enhancement technique (BCET) which results in increased accuracy. Segmentation followed by canny edge detection is performed on the improved images to detect the fine edges of the abnormalities present. The model attained an accuracy of at most 98% in detecting the tumor or the abnormality in a human brain which determines the effectiveness of the proposed model.

The purpose of this research is to find a suitable method for detecting the edges of noisy digital images by eliminating the noise effects. The image will be partitioned into equal partitions and the initial threshold of that image partition will be calculated. By applying all these thresholds into the self-organized map (SOM) neural network input optimized for learning and training based optimization algorithm (TLBO), threshold clustering will be performed. The partitioned image will be edge detected by entropy method. Choosing the threshold for image segmentation is of great importance. The mean of the brightness of digital noise images is not a good representative of the initial threshold. Noise causes the mean intensity of the brightness to take distance from the main range of the intensity of the image so the resulting edge detected image will be severely noisy and truncated. By determining the highest frequency of brightness intensity instead of the mean brightness, the above-mentioned weaknesses will be eliminated. This method outperforms many current methods, such as Tsallis entropy, Singh and Kiani and even Canny Edge Detection which demonstrates the effectiveness of the proposed method, In the Table 1 the PSNR of image 5 of the proposed method is 61.4896, but Singh method which is 55.61, Tsallis method which is 53.9234, Kiani method which is 53.9315 the proposed method is less than the other methods.

Lane detection is part of the advanced driver assistance system (ADAS) equipped in intelligent vehicles. The system provides the driver with significant geometric information of the road ahead. Numerous deep learning techniques have been employed in lane detection because of the simplicity, ease, and efficiency of these techniques in learning discriminative features from RGB (red, green, and blue) images. However, existing works have rarely considered detecting lane markings during bad weather conditions, which could reduce lane detection performance. Hence, this paper proposed a Fully Convolutional Network (FCN) model with RGB and Canny edge detection used as the model’s spatial input. The proposed platform was developed using two scenarios: FCN-RGB-edge and FCN-edge. The model development was divided into three stages, namely data acquisition, platform development, and benchmarking against existing methods and data. Both scenarios using the proposed method yielded a 4% improvement compared to the original FCN-RGB images (i.e., the previous method). The Canny edge detection method successfully extracted necessary information from the images and neglected the water drops in rainy conditions by treating them as noise. In summary, the proposed method has the potential to boost the performance of the ADAS system in detecting lane markings in rainy conditions.

Lane departure warning is one important feature in Advanced Driver Assistance Systems (ADAS), which aims to improve overall safety on the road. However, challenges such as inconsistent shadows and fading lane markings often plague the road surface and cause the lane detection system to produce false warnings. Users are aggravated by the warning and tend to disable this safety feature. This paper proposes an efficient Gabor filtering-based lane detection method to overcome the aforementioned conditions and improves the accuracy of lane departure warning system. Furthermore, it serves as a cost-effective solution to a lane departure warning problem, allowing it to be widely deployed. It is heuristically found that lane marking has a general directional property, which can be further enhanced by Gabor filter while suppressing inconsistent road shadows and road markers. Enhanced lane markings are then subjected to adaptive canny edge detection to extract distinct edge markings. Lastly, Hough transformation is applied to label the correct lane candidates on the road surface. Furthermore, we generate a dataset of Malaysia road with various driving conditions. As a proof of concept, a lane departure warning system is built based on the proposed lane detection method, which is able to achieve an accuracy of 93.67% for lane detection and 95.24% for lane departure warning tested on our challenging dataset. The codes are implemented on Raspberry pi 3B and installed in a vehicle for real-time application. The codes are multithreaded and found to achieve a desirable frame speed of 20 fps at 75% CPU utilization.

To enhance the invisibility and the robustness of the watermarking algorithm, a robust and imperceptible watermarking scheme is presented by combining Canny edge detection, contourlet transform with singular value decomposition. The host image is firstly decomposed by the contourlet transform. Then the low frequency sub-band obtained by contourlet transform is partitioned into 4×4 non-overlapping blocks and the singular value decomposition is carried out for the specific blocks selected by the Canny edge detection. Finally, the watermark is embedded into the coefficient of matrix U. The embedded watermark could be recovered blindly in the watermark extraction stage. And the robustness and the imperceptibility are efficiently guaranteed with an optimal threshold k selected by the least-square curve fitting. Experimental results demonstrate that the proposed watermarking scheme is superior in terms of imperceptibility and robustness against common attacks.

Nowadays, due to the rapid technological growth of information technology billions of data transferred through the network around the world in a fraction of seconds. To protect the transferred information from unauthorized access, security mechanism is required. At this point, significant role is performed by a technology called information security. So, to provide security, various techniques have been evolved such as cryptography, steganography and digital watermarking. Cryptography converts the message into scrambled format whereas in steganography the secret message is buried in the digital media. In this paper, both cryptography and steganography is combined to safeguard the information. First, the message is encrypted with the help of revised RSA algorithm to convert the message into secret message. Second, the cover image is segmented into edge and non-edge pixels using Canny edge detection technique and then N1(two) and N2 (four) bits of the secret messages are buried into the edge and non-edge pixel areas using LSB technique. Finally, the essential parameters like N1, N2 and the length values are implanted into the last four pixels of the cover-image to obtain the stego-image. At the recipient side, the hidden data are extracted from the stego-image after extracting N1, N2 and length information. Then, the extracted secret data is decrypted using revised RSA algorithm. Modified RSA algorithm improves the security by using two pairs of keys. At last, the various performance metrics parameters such as entropy, MSE, peak signal to noise ratio and histogram are measured. It is observed that the proposed system attains improved efficiency, security and imperceptibility properties of information hiding from the results.

Human iris and recognition patterns have been recognized as the best biometric marking ever found, owing to the uniqueness of iris and the textured iris patterns tend to remain natural, unchangeable and recognizable through existence. Mathematical analyses of the special stable patterns formed within the iris include Iris detection methods and a comparative analysis is carried out utilizing an established database. In this document, a clean electoral system is created to build a fraud-free ID list of electors. To find the Iris and Eyes, the algorithm of canny edge detection is used, Dougman's normalization procedure is used, object filters are added and finally the corresponding process is conducted for the Euclidian set. Biometric authentication confirms our identification by being a simple and increasingly secure method. We implement a weighted, majority voting process for all biometric authentication systems utilizing a bit wise contrast between inscription and biometric models to resolve this problem and to enable Iris identification in less than ideal images. We also observed that the approach outdoes the current majority and efficient bit sorting strategies through a set of tests with the database CASIA iris. Our approach is an easy and efficient way to boost the accuracy of established iris detection systems.

A wide variety of security applications like computers, laptops, mobile phones, ATM machines, physical access control to property etc. are using biometrics based system for human identification. Today’s Biometrics authentication system can provide highest level of security to the user of the service and the service providers. Today’s uni-modal biometric systems has become mature system and being used for low level to medium level security applications. But, for extremely higher level of security requirements like military base, nuclear plants, research laboratories etc., Finger knuckle image and Retina image based combination can be used, as this combinations provides the highest level of accuracy, low error rates, universality etc. Also the security of Multi-modal biometric templates is very importance as biometric template store vital information of the user. In this research work, Multi-modal Biometric system for higher level security using Finger Knuckle image and Retina image with templates security is developed, experiments are carried out and results are calculated using PolyU database for Finger Knuckle image and DRIVE for Retina images. A Finger Knuckle Image processing starts with Median Filtering, CLAHE method and Canny edge detection. The features extraction is carried using SURF feature method. Once the finger knuckle image template is generated, it is encrypted using RSA algorithm and stored in database. The Retina Image Processing is carried out by applying Green channel followed by Gaussian filtering, CLAHE method is applied on outcome of Gaussian filtering. SURF features set is extracted from retina ROI. Retina image template is generated which is later encrypted using RSA algorithm. The final experiment shows that genuine acceptance rate = 100% is achieved for threshold value 0.35 and False Acceptance Rate is only 1%. The scientific contribution of proposed research work is a multi-biometric system with higher accuracy (GAR is 100%), low error rate (FAR is 1%), better template protection by RSA encryption and efficient hybrid model using finger knuckle image and retina image.

Edge detection is the front-end processing stage in most computer vision and image understanding systems. Among various edge detection techniques, Canny edge detector is the one of most commonly used. In this paper a modified Canny edge detection technique focusing on change of the Sobel operator is proposed. Instead of convolution kernels, the weighted least squares method is utilized to calculate the horizontal and vertical gradient. Experimental results show that the new detector can detect some edges which are not observed in the results using the Canny edge detector.

In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the road in all the sections of the country. Vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the developing system is consist of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny Edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to recognize and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully identified and recognized multi_style Iraqi license plates using different image situations and it was evaluated based on different metrics performance, achieving an overall system performance of 91.99%. This results shows the effectiveness of the proposed method compared with other existing methods, whose average recognition rate is 86% and the average processing time of one image is 0.242s which proves the practicality of the proposed method.

Nowadays user devices such as phones, tablets etc. allows processing the images with help of high-end applications and softwares developed. Most of the times, the images are downscaled to make them compatible with these end devices. This leads to the loss of image quality. This loss of information on downscaling an image results in distortion of edges and while zoomed in results into a blurred image. As the edge detection is a basic step for many image processing applications such as object detection, object segmentation, object recognition, etc. It is necessary to know the impact of edge detection on downscaled image. In this paper, we are using Canny Edge detection method to detect the edges. The original images are downscaled using different interpolation methods. Canny Edge detection is applied on original images and downscaled images to compare the distortion in the edges. We used Structural Similarity Index Method for comparison. We are also comparing execution time taken by Canny Edge Detection on different interpolation methods to check for optimal interpolation method. We observed that the distortion in edges and time efficiency differ for different interpolation methods which are detailed below in the result section. As blurring is also a disadvantage of downscaling, we are applying Gaussian Blur on the images to compare the blurring due to Gaussian blur technique and blurring due to downscaling.

Line following and lane tracking are robotic navigation techniques that use lines as a guide. The techniques can be applied to mobile robots in the industry. This research applied the Braitenberg controller and image processing to control and obtain line information around the mobile robot. The robot was implemented using Arduino Uno as a controller. A webcam was connected to a computer that performs image processing using canny edge detection and sends the data to the robot controller via serial communication. The robot can navigate on the side of the line, and the success rate of the system is 100 % at a turn of 135 ° and 80 % at a turn of 90 °.

In this paper, a novel region-based multi-focus color image fusion method is proposed, which employs the focused edges extracted from the source images to obtain a fused image with better focus. At first, the edges are obtained from the source images, using two suitable edge operators (Zero-cross and Canny). Then, a block-wise region comparison is performed to extract out the focused edges which have been morphologically dilated, followed by the selection of the largest component to remove isolated points. Any discontinuity in the detected edges is removed by consulting with the edge detection output from the Canny edge operator. The best reconstructed edge image is chosen, which is later converted into a focused region. Finally, the fused image is constructed by selecting pixels from the source images with the help of a prescribed color decision map. The proposed method has been implemented and tested on a set of real 2-D multi-focus image pairs (both gray-scale and color). The algorithm has a competitive performance with respect to the recent fusion methods in terms of subjective and objective evaluation.

Experts notice and classify various Regions of Interest (ROI) manually for identification, analysis, and development of a treatment. To overcome errors and discrepancies of the data in this state, automated analysis is utilized. A novel method for the classification of MRI brain tumor is proposed in this paper using the saliency driven image representation and CNN based classification with optimization. Initially, the preprocessing on MRI images is carried out using canny edge detection algorithm followed by saliency driven image representation using modified minimum barrier distance and nonlinear diffusion at multiple level. Finally, feature extraction and image classification is carried out by CNN and the optimization by ADAM optimizer. The implementation is carried out and the results are evaluated which outperforms the earlier methods.

In the World’s Cultural Heritage, stone monuments are a valuable treasure. The alarming increase of weathering damage on an ancient monument is leading to complete or partial destruction of heritage structures in the world. Early detection of such decaying is essential to preserve the monuments. A non-destructive technique is used for the analysis of monument decay. Digital image processing is one of the powerful methods of non-destructive techniques. This paper presents non-invasive detection of moss and crack in monuments using Integrated K-means clustering and Canny edge detection. Moss is detected using integrated clustering and compared with K-means image segmentation. In this study, mean square error and power signal to noise ratio are the two parameter metrics of moss detection. PSNR and MSE of proposed method is about 15.42 and 1882 improves than LBP-Mean with other methods. The crack is detected using integrated canny edge detection and the crack properties such as perimeter, orientation, and minor and major axis length are measured. Area of moss and cracks are determined. The proposed results improve the performance of detection than the existing method.

The role of preprocessing and segmentation are vital in image processing and computer vision. The medical images are prone to noise and the filtering algorithms are used for noise removal. In this paper, the fast bilateral filter is employed for noise removal and it has good edge preservation capacity. The segmentation algorithms are used to extract the region of interest and edge detection is a classical algorithm for tracing the contours of objects in an image. The canny edge detector is efficient when compared with the conventional edge detectors. The fast bilateral filter is proposed in this paper has the computation complexity of O(1) per pixel, while the classical bilateral filter has the computation complexity of O(W) operations per pixel, where W is the kernel size. The algorithms were implemented in Raspberry Pi using Open CV software package. The algorithms were tested on real time medical images.

Real-time traffic monitoring and parking are very important aspects for a better social and economic system. Python-based Intelligent Parking Management System (IPMS) module using a USB camera and a canny edge detection method was developed. The current situation of real-time parking slot was simultaneously checked, both online and via a mobile application, with a message of Parking “Available” or “Not available” for 10 parking slots. In addition, at the time entering in parking module, gate open and at the time of exit parking module, the gate closes automatically using servomotor and sensors. Results are displayed in figures with the proposed method flow chart.

The detection of markers or reflectors within point cloud data (PCD) is often used for 3-D scan registration, mapping, and 3-D environmental modeling. However, the reliable detection of such artifacts is diminished when PCD is sparse and corrupted by detection and spatial errors, for example, when the sensing environment is contaminated by high dust levels, such as in mines. In the radar literature, constant false alarm rate (CFAR) processors provide solutions for extracting features within noisy data; however, their direct application to sparse, 3-D PCD is limited due to the difficulty in defining a suitable noise window. Therefore, in this article, CFAR detectors are derived, which are capable of processing a 2-D projected version of the 3-D PCD or which can directly process the 3-D PCD itself. Comparisons of their robustness, with respect to data sparsity, are made with various state-of-the-art feature detection methods, such as the Canny edge detector and random sampling consensus (RANSAC) shape detection methods.