Adaptive Canny Edge Detection Research Articles

Stereo matching method based on high-precision occlusion-recovering and discontinuity-preserving

Stereo matching is the core of machine vision, such as 3D reconstruction, posture perception and driverless technology in the future etc. Currently, there are still great difficulties in improving the matching accuracy of disparity discontinuity and occlusion regions. In this paper, we propose a new high-precision occlusion-recovering and discontinuity-preserving method for stereo matching. We focus on disparity refinement, and present an adaptability-based disparity reconstruction that achieves accurate occlusion-recovering by fusing color aberration, distance, and disparity value. For discontinuity-preserving, disparity-based secondary guided filtering is proposed, then introduce adaptive image enhancement and adaptive Canny edge detection to correct errors in discontinuous regions. The experiments on Middlebury standard dataset show that our algorithm has high overall accuracy, especially in the disparity discontinuity and occlusion regions. This novel method is more direct, efficient, and less sensitive to the accuracy of disparity estimation, and the live-action shooting shows the robustness of our algorithm. The method has the prospect of civil and engineering use, and presents scientific reference for high-precision target identification, target tracking, 3D measurement, etc.

TireEye: Optical On-board Tire Wear Detection

Automotive tire tread depth has a major influence on a car's safety and thus has to be closely monitored. However, there is currently no on-board solution that can measure tire wear with an accuracy of less than 1 mm. TireEye is an optical device mounted inside the wheel well and facing towards the road. It records the cross-section of the longitudinal tread groove and extracts its outline by means of adaptive Canny edge detection. The tread wear indicators serve es means to calibrate the scale since they provide the minimum tread depth.

Robust Crater Detection Algorithm Based on Maximum Entropy Threshold Segmentation

For future lunar exploration and planetary missions, the digital elevation model (DEM) of the target object may not be well prepared before the mission, so developing a new robust crater detection algorithm (CDA) without prepared high-precision DEM is needed to meet the requirements of a high-reliability and high-precision detection and navigation system. In this paper, we presented a new robust lunar CDA method based on maximum entropy threshold segmentation. By calculating the entropy distribution of the ternary image, the threshold for retaining the maximum amount of image information is selected adaptively, a variety of evaluation indicators are proposed, and a multiple-indicator constraint matrix is constructed to realize the extraction and fitting of the craters. The proposed method has the following advantages: (1) it has strong robustness and is capable of extracting complete craters under multiple illumination conditions, which makes it suitable for the extraction of large-scale planetary and lunar images; (2) the extracted crater edges are clear and complete and do not merge with the surrounding environment edge; and (3) it avoids the problem of parameter sensitivity that is present in a traditional CDA algorithm. The proposed method was verified using an image taken by the Chang’e-2 lunar probe, and a comparison with the traditional method based on morphology and adaptive Canny edge detection shows that the number of craters detected increases by more than 35%, while the computational efficiency is improved by more than 40%.

Research on Automatic Color Scale Optimization Based Canny Identification Strategy for Underwater Fish

In order to solve the problem of the traditional Canny edge detection algorithm, due to the inaccurate selection of the threshold value, the recognition rate is low in the fish edge detection process. This paper proposes an adaptive Canny edge detection algorithm. The image enhancement is carried out by using automatic color scale, and the optimal recognition of underwater fish target is completed. The experimental results show that the improved algorithm and edge contour are more complete, which proves the effectiveness of the fish density recognition method.

Nonlinear RANSAC with crossline correction: An algorithm for vision-based curved cable detection system

Accurate curved line shape object detection is important for assistant Autonomous Underwater Vehicles)AUVs( to complete underwater cable inspection tasks. In this article an algorithm based on adaptive canny's edge detection and an improved random sample consensus (RANSAC) algorithm, called Crossline Correction Non-linear RANSAC (CCNL-RANSAC), is proposed to detect curved line object. The algorithm is featured on adaptive threshold in the canny's operator to improve correctness of edge detection, preliminary check and cross line correction introduced in a nonlinear RANSAC method to locate cables with better success rate and less execution time. Comprehensive underwater cable detection experiments were carried out by a range-gated imaging system in a big water tank. With the proposed CCNL-RANSAC algorithm, we increased success rate to 96% ~99% in line detection for the images of curved cable taken as far as 21 m long in clear water. The average execution time is reduced from 202 ms (using standard RANSAC) to 88 ms on average using CCNL-RANSAC, with 80% time reduction at most. With the help of CCNL-RSNSAC algorithm proposed in the paper, vision based AUVs is able to make auto inspection of curved cables and pipeline underwater, which will make the periodical checking of those marine structures more efficiently.

Exam Answer Detection using Proposed Nested Loop Pixel Checking Based on Image Processing

Tracking object is one of the most discussed problems in digital images processing. The objectives of tracking objects include the counting, correction, classification and so on. This study discusses how tracking objects, in this case the multiple choice chosen by students, is carried out on the exam answer sheet using the proposed nested loop pixel checking method. The answer sheet used here is the answer sheet for the exam from SMK Khoiriyah Hasyim Tebuireng Jombang because each school has a different exam answer sheet format so that the coordinate parameters of each answer sheet between schools must be different. Before the nested loop pixel checking method, preprocessing was carried out first after taking the image, namely cropping the Region of Interest (ROI), conversion to grayscale, filtering, and edge detection using the adaptive Canny edge detection method. The process of nested loop pixel checking is done by iterating successively with steps per answer option, per number of answers, and per column (10 numbers). In addition, a special case is given to find out whether this proposed method works as desired. The results show that this method can track answers for both normal and special cases.

Innovative lane detection method to increase the accuracy of lane departure warning system

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.

Research on Fault Detection Algorithm of Pantograph Based on Edge Computing Image Processing

Traditional pantograph detection technology has installed a large number of various types of sensors on the pantograph, which is not conducive to the flow characteristics of the pantograph. Traditional detection technology can neither monitor the operation status of the pantograph in real time, nor guarantee the safe operation of the entire train. The existing schemes are only manual solutions, and intelligent real-time monitoring is performed without error, and a lot of labor costs are required. Focusing on the shortcomings of traditional pantographs in sliding plate wear and sliding plate crack detection, an improved mean filtering algorithm is proposed to optimize the image of pantograph, and the adaptive Canny edge detection technology is used to accurately calculate the skateboard wear. Firstly, the improved image algorithm is used to pre-treat the pantograph slide wear and crack length, then the improved mean filtering algorithm is combined with adaptive Canny operator for image optimization detection, and finally the edge calculation is used for crack identification. An optimized real-time on-line detection technology is proposed, which can accurately measure the wear and tear of skateboards and cracks. For different experimental environments, the optimization algorithm proposed in this paper is used to detect pantographs in real time. The experimental results show that, compared with the traditional detection algorithm, the detection accuracy of sliding plate wear can reach 0.5 mm, and and that of sliding plate cracks can reach 0.4 mm. The crack recognition accuracy of skates is as high as 93%.

Image-based Pantograph Slide Wear Detection System

In order to realize the on-line detection of the pantograph slide wear when the locomotive is put into storage, the technology of the pantograph wear detection based on FPGA is proposed. Using the high-speed features of FPGA algorithm processing, the image processing algorithm is integrated into hardware description language to accelerate image processing. Firstly, the pantograph is captured by the detecting device to obtain the target image of the pantograph; Then, the image is denoised and enhanced by the steps of mean filtering and histogram equalization in the pantograph image; Third, combined with wavelet denoising and adaptive Canny edge detection, the edge of the pantograph is detected and the lower edge of the skateboard is calibrated by Hough transform, and the wear value of the skateboard is calculated. The experimental results show that the image-based pantograph wear detection method can effectively detect the edge of the skateboard and obtain the maximum wear of the skateboard, and realize the on-line detection of the pantograph slide.

Dynamic Granularity Matrix Space Based Adaptive Edge Detection Method for Structured Light Stripes

Structured light has been widely applied to 3D shape measurement with the capabilities of rapidness, high-accuracy, and noncontact. Because of uneven illumination and noise, it is difficult to distinguish the light stripes and background of the image, which reduces the measurement accuracy. In this paper, an adaptive Canny edge detection method with two phases is proposed for structured light stripes. Firstly, the idea of dynamic granularity is introduced and the dynamic granularity matrix space is established, in which image segmentation problems are described as the transformations and jumping of image at different granularity layers. The hierarchical structure and optimal granularity layer of image are obtained as the basis of adaptive edge detection. Secondly, a quantum-inspired group leader hybrid algorithm is adopted to calculate the optimal threshold from the optimal granularity layer, which is taken as the adaptive threshold for Canny operator. Finally, experimental tests and comparisons have been conducted to verify the effectiveness of the adaptive method proposed. The experiments show that the proposed method achieves high segmentation accuracy, improves the segmentation efficiency, and has strong robustness to noise.

Ship infrared image edge detection based on an improved adaptive Canny algorithm

Influenced by light reflection and water fog interference, ship infrared images are mostly blurred and have low signal-to-noise ratio. In this paper, an improved adaptive Canny edge detection algorithm for infrared image of ship is proposed, which aims to solve the threshold of the traditional Canny cannot be adjusted automatically and the shortcomings of sensitivity to noise. The contrast limited adaptive histogram equalization algorithm is adopted to enhance the infrared image, the morphological filter replaces the Gauss filter to smooth the image, and the OTSU algorithm is utilized to adjust the high and low thresholds dynamically. The experimental results show that the improved Canny algorithm, which can not only improve the contrast of the image and automatically adjust the threshold but also reduce the background sea clutter and false edges, is an effective edge detection method.

Delineation of Carpal Bones From Hand X-Ray Images Through Prior Model, and Integration of Region-Based and Boundary-Based Segmentations

Image segmentation is critical and challenging in computer vision and medical image analysis. Despite decades of research, existing segmentation algorithms are still subject to typical segmentation problems, such as over-segmentation, under-segmentation, and non-closed and spurious edges. In this paper, taking the carpal bones from hand X-ray images as the foreground regions, we propose a segmentation approach to integrate segmentations from region-based and boundary-based methods to tackle these typical segmentation problems. First, adaptive local thresholding and adaptive Canny edge detection are explored to extract foreground regions and the edge map. Second, the integration of the edge map and foreground regions by XORing is proposed, to tackle the over-segmentation by adding a background boundary from the edge map near the carpal bone boundary so as to break the connection between the foreground and the over-segmented background, to handle under-segmentation by adding a foreground boundary from the edge map near the carpal bone boundary so as to enclose the missing foreground due to under-segmentation, and to complement non-closed edge and spurious edge from the edge map through the carpal bone regions from the local adaptive thresholding. Optionally, marker-controlled watershed segmentation or an active contour-based method is employed to refine the integrated segmentation. The segmented foreground regions are identified through exploring a prior model. The proposed approach has been validated on 30 representative X-ray hand images for non-overlapping carpal bones to yield an average Dice coefficient of 0.976 ± 0.006. The proposed method could provide a theory or tool for accurately segmenting images with discernible boundaries and non-uniform grayscale distribution both within the background and foreground.

Automatic detection of particle size distribution by image analysis based on local adaptive canny edge detection and modified circular Hough transform

To obtain size distribution of nanoparticles, scanning electron microscope (SEM) and transmission electron microscopy (TEM) have been widely adopted, but manual measurement of statistical size distributions from the SEM or TEM images is time-consuming and labor-intensive. Therefore, automatic detection methods are desirable. This paper proposes an automatic image processing algorithm which is mainly based on local adaptive Canny edge detection and modified circular Hough transform. The proposed algorithm can utilize the local thresholds to detect particles from the images with different degrees of complexity. Compared with the results produced by applying global thresholds, our algorithm performs much better. The robustness and reliability of this method have been verified by comparing its results with manual measurement, and an excellent agreement has been found. The proposed method can accurately recognize the particles with high efficiency.

Dynamic Thresholding based Adaptive Canny Edge Detection

this paper, a method for adaptive Canny edge detection algorithm is proposed. Adaptive Canny algorithm is used to increase the accuracy of output objects. In traditional Canny need to set two threshold values manually, so there are some defects to different images but this paper puts faorward an adaptive threshold values based on mean and median values. Our proposed adaptive Canny edge detection method can detect edges successfully which is divided into several steps. First, Gaussian filter is used to smooth and remove noise. Second, gradient magnitude is computed. Third, non- maximum suppression is applied in which the algorithm removes pixels that are not part of an edge. Finally, hysteresis thresholding is applied which uses two threshold values, upper and lower. A pixel will be marked as an edge if it's gradient lies in between of lower and upper threshold values. A pixel will be discarded if it's gradient below the lower or beyond the upper threshold values. Eventually, the pixels gradient is between the two threshold values will be connected as marked edge. The experimental results show the efficacy of the proposed method.

Hardware Design and Implementation of Adaptive Canny Edge Detection Algorithm

In this paper, a hardware system for adaptive Canny edge detection algorithm is designed and simulated for a 128 pixel, 8-bit monochrome linescan camera. The system is designed to detect objects as they move along a conveyor belt in a manufacturing environment, the camera observe dark objects on a light conveyor belt . Here adaptive Canny algorithm is used to increase the accuracy of output objects. In traditional Canny need to set two threshold values manually, so there are some defects to different images but this paper puts forward an adaptive threshold values base on mean and median values. The output result of adaptive Canny proves its accuracy is high. There are multiple steps to implement adaptive Canny. First, Gaussian filter is used to smooth and remove noise. Second, compute the gradient magnitude. Third, nonmaximum suppression is applied in which the algorithm removes pixels that are not part of an edge. Hysteresis uses two threshold values, upper and lower. A pixel will be marked as an edge if it’s gradient lies in between of lower and upper threshold values. A pixel will be discarded if it’s gradient below the lower or beyond the upper threshold values. Eventually, the pixels gradient is between the two threshold values will be connected as marked edge. General Terms Noise Reduction of Images, Compute Gradient Magnitude and Angle, Non-Maximum Suppression, Hysteresis Thresholding, Adaptive Canny Edge Detection Algorithms

Weighted elliptic curve-fitting method with sub-pixel level in the light-probe vision measuring system

In a light-probe vision measuring system, we first extract a rough contour of the marking points based on adaptive canny edge detection, in order to improve the extracting accuracy of the image co-ordinate of the marking points’ centre. This paper shows that the edge of rough contour may be sub-pixel extracted with the method of the grey moment. This method can be extrapolated to any position in the image plane. Finally, the image co-ordinates of marking points’ centre can be obtained with the weighted least-squares curve-fitting. In this paper, we extend the existing technique and provide a comprehensive evaluation of the proposed method. The results show that this method has higher stability precision than the exiting one. The extraction accuracy of this technique can achieve as low as 0.01 pixels. We have incorporated these ideas into the actual vision measurement of the Canny Elevator Co., Ltd.

Quantitative Analysis of Feature Detection Using Adaptive Canny Edge Detector and Enhanced Ant Colony Optimization

Feature detection is a fundamental technique in broad fields of image processing, pattern recognition and computer vision. A digital image in general contains objects, edges, noises and background. Critical changes in properties of objects can be captured via detecting sharp variations in image brightness. The edges can be detected via numerous approaches on a basis of image intensity changes. Edge broken and false detection are typical problems using classical methods, which will result in information loss and feature deformity. The notion of optimization is thus introduced into edge detection. The Canny edge detector and Ant Colony Optimization (ACO) detector are among the most successful and effective approaches for edge detection. The Canny edge detector is designed to capture edges by searching local optima of the gradient of the intensity. It is susceptible to noises presenting on the raw images, so details of images could be slightly changed when Gaussian smoothing is applied. To improve accuracy, the adaptive edge tracing scheme is proposed. On the other hand, artificial intelligence has also been introduced. Being one of metaheuristic optimization approaches, the evolutionary computing oriented ACO becomes a promising approach for feature capturing without necessity of smoothing filters. Selection of maximum intensity difference as the path visibility function for ACO will contribute better to generate true edges and avoid false edges. Both the adaptive Canny edge detection and enhanced ACO are proposed in this article. Comparative studies are also conducted to evaluate the edge detection qualities. The outcomes are analyzed and evaluated from both qualitative and quantitative points of view, where merits and drawbacks of the two schemes have been indicated.