Showing papers on "Canny edge detector published in 2011"

Characterization of Signals From Multiscale Edges

Abstract: A multiscale Canny edge detection is equivalent to finding the local maxima of a wavelet transform. The authors study the properties of multiscale edges through the wavelet theory. For pattern recognition, one often needs to discriminate different types of edges. They show that the evolution of wavelet local maxima across scales characterize the local shape of irregular structures. Numerical descriptors of edge types are derived. The completeness of a multiscale edge representation is also studied. The authors describe an algorithm that reconstructs a close approximation of 1-D and 2-D signals from their multiscale edges. For images, the reconstruction errors are below visual sensitivity. As an application, a compact image coding algorithm that selects important edges and compresses the image data by factors over 30 has been implemented. >

Edge Enhancement Algorithm Based on the Wavelet Transform for Automatic Edge Detection in SAR Images

Abstract: This paper presents a novel technique for automatic edge enhancement and detection in synthetic aperture radar (SAR) images. The characteristics of SAR images justify the importance of an edge enhancement step prior to edge detection. Therefore, this paper presents a robust and unsupervised edge enhancement algorithm based on a combination of wavelet coefficients at different scales. The performance of the method is first tested on simulated images. Then, in order to complete the automatic detection chain, among the different options for the decision stage, the use of geodesic active contour is proposed. The second part of this paper suggests the extraction of the coastline in SAR images as a particular case of edge detection. Hence, after highlighting its practical interest, the technique that is theoretically presented in the first part of this paper is applied to real scenarios. Finally, the chances of its operational capability are assessed.

Overlapping model of beads and curve fitting of bead section for rapid manufacturing by robotic MAG welding process

Abstract: As a deposition technology, robotic metal active gas (MAG) welding has shown new promises for rapid prototyping (RP) of metallic parts. During the process of forming metal parts with the robotic MAG welding technology, the sectional geometry of single-pass bead and the overlap of the adjacent beads have critical effects on the dimensional accuracy and quality of metal parts. In this work, Canny edge detection of the robotic MAG beads was carried out and the data were smoothed with a Gaussian filter and fitted with Gaussian function, logistic function, parabola function and sine function, respectively. In addition, a mathematical model of bead section was developed to analyze the bead geometry. Based on ''surfacing of equivalent area'' method, the concept of overlapping coefficient and optimum-overlapping coefficient was put forward, and calculated model of overlapping was analyzed. Optimal overlapping coefficient was calculated to be 63.66% under experimental condition. The conclusion is that the edge detection of bead section with Canny operator is continuous and distinct, and as compared with Gaussian function, logistic function and parabola function, sine function has higher accuracy to fit the measured data, and ''surfacing of equivalent area'' method shows to be rational and feasible by the experiments.

The Canny Edge Detector Revisited

Abstract: Canny (IEEE Trans Pattern Anal Image Proc 8(6):679-698, 1986) suggested that an optimal edge detector should maximize both signal-to-noise ratio and localization, and he derived mathematical expressions for these criteria Based on these criteria, he claimed that the optimal step edge detector was similar to a derivative of a gaussian However, Canny's work suffers from two problems First, his derivation of localization criterion is incorrect Here we provide a more accurate localization criterion and derive the optimal detector from it Second, and more seriously, the Canny criteria yield an infinitely wide optimal edge detector The width of the optimal detector can however be limited by considering the effect of the neighbouring edges in the image If we do so, we find that the optimal step edge detector, according to the Canny criteria, is the derivative of an ISEF filter, proposed by Shen and Castan (Graph Models Image Proc 54:112---133, 1992) In addition, if we also consider detecting blurred (or non-sharp) gaussian edges of different widths, we find that the optimal blurred-edge detector is the above optimal step edge detector convolved with a gaussian This implies that edge detection must be performed at multiple scales to cover all the blur widths in the image We derive a simple scale selection procedure for edge detection, and demonstrate it in one and two dimensions

A Laplacian Energy for Document Binarization

Abstract: This paper describes a new algorithm for document binarization, building upon recent work in energy-based segmentation methods. It uses the Laplacian operator to assess the local likelihood of foreground and background labels, Canny edge detection to identify likely discontinuities, and a graph cut implementation to efficiently find the minimum energy solution of an objective function combining these concepts. The results of this algorithm place it near the top on both the DIBCO-09 and H-DIBCO assessments.

Edlines: Real-time line segment detection by Edge Drawing (ed)

Abstract: We propose a linear time line segment detector that gives accurate results, requires no parameter tuning, and runs up to 11 times faster than the fastest known line segment detection algorithm in the literature; namely, the LSD by Gioi et al. The proposed algorithm also includes a line validation step due to the Helmholtz principle, which lets it control the number of false detections. Our detector makes use of the clean, contiguous (connected) chain of edge pixels produced by our novel edge detector, the Edge Drawing (ED) algorithm; hence the name EDLines. With its accurate results and blazing speed, EDLines will be very suitable for the next generation real-time computer vision applications.

An improved Prewitt algorithm for edge detection based on noised image

Abstract: In this paper, an improved Prewitt algorithm for edge detection is proposed for the reason that the traditional Prewitt edge detection algorithm is sensitive to the noise. The traditional Prewitt edge detection operator only has two templates with horizontal and vertical directions. While the edge is in a plurality of directions, so operator with eight templates of different directions is put forward and it can detect more edges. In order to improve the capability of resisting noise, this paper put forward three improvements. First of all, the mean value rather than the maximum value of the gradient magnitude of the eight directions is used as the final gradient magnitude. Secondly, OTSU automatic threshold is used to set the gradient magnitude threshold. Again, an 8-neighborhood template is proposed to remove the isolated single pixel noise. The experimental results show that the improved algorithm improves the anti noise performance greatly, and detects the edges of the random noised image effectively.

A bag-of-regions approach to sketch-based image retrieval

Abstract: This paper presents a system for retrieving photographs using free-hand sketched queries. Regions are extracted from each image by gathering nodes of a hierarchical image segmentation into a bag-of-regions (BoR) representation. The BoR represents object shape at multiple scales, encoding shape even in the presence of adjacent clutter. We extract a shape representation from each region, using the Gradient Field HoG (GF-HOG) descriptor which enables direct comparison with the sketched query. The retrieval pipeline yields significant performance improvements over the previous GF-HOG results reliant on single-scale Canny edge maps, and over leading descriptors (SIFT, SSIM) for visual search. In addition, our system enables localization of the sketched object within matching images.

An Improved Canny Algorithm for Edge Detection

Abstract: Edge detection is an important part of digital image processing. This paper discusses the basic theory of edge detection, its method based on the traditional Canny operator, and proposes an improved algorithm based on the eight neighborhood gradient magnitude to overcome the disadvantages of being sensitive to noise in the calculation of the traditional canny operator gradient. The two thresholds of the traditional Canny operator need manual setting, so there are some defects to different images. This paper puts forward an adaptive threshold calculation by OTSU method. The experimental results prove that this improved method can effectively detect the edge of the image. And the continuity of the edge is strong, and positioning accuracy is high. .

Mathematical morphological edge detection for remote sensing images

Abstract: Edge detection is a terminology in image processing and computer vision particularly in the areas of feature detection and extraction to refer to the algorithms which aims at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities. The need of edge detection is to find the discontinuities in depth, discontinuities in surface orientation, changes in material properties and variations in scene illumination. Remote sensing images are generally corrupted from noise. Mathematical morphology is a new technique for edge detection. It is a theory and technique for analysis and processing of geometrical structures, based on set theory. Mathematical morphology was originally developed for binary images, and later extends to grey scale functions and images. Basically the noise can be easily suppressed by mathematical morphology. So by using mathematical morphology the image can be enhanced and the edges can be detected. The result of edge detection using mathematical morphology will be compared with Sobel edge detector, Prewitt edge detector, laplacian of gaussian edge detector and Canny edge detector.

Edge Detection with sub-pixel accuracy based on approximation of Edge with Erf function

Abstract: Edge detection is an often used procedure in digital image processing. For some practical applications it is desirable to detect edges with sub-pixel accuracy. In this paper we present edge detection method for 1-D images based on approximation of real image function with Erf function. This method is verified by simulations and experiments for various numbers of samples of simulated and real images. Results of simulations and experiments are also used to compare proposed edge detection scheme with two often used moment-based edge detectors with sub-pixel precision.

Feature reconstruction in inverse problems

Abstract: A strategy for the derivation of fast, accurate and stable algorithms for combining the reconstruction and the feature extraction step for solving linear ill-posed problems in just one method is presented. The precomputation of special reconstruction kernels with optimized parameters for the combination of the two tasks allows for fast implementations and better results than separate realizations. The concept of order optimality is generalized to the solution of feature reconstruction and to Banach spaces in order to find criteria for the selection of suitable mollifiers. Results from real data in different tomographic modalities and scanning geometries are presented with the direct calculation of derivatives, as in Canny edge detectors, and the Laplacian of the solution used in many segmentation algorithms. The method works also when the searched-for solution is not smooth or when the data are very noisy. This shows the versatility of the approach.

A distributed Canny edge detector and its implementation on FPGA

Abstract: Edge detection is one of the key stages in image processing and object recognition. The Canny edge detector is one of the most widely-used edge detection algorithms due to its good performance. In this paper, we present a distributed Canny edge detection algorithm that results in significantly reduced memory requirements, decreased latency and increased throughput with no loss in edge detection performance as compared to the original Canny algorithm. The new algorithm uses a low-complexity 8-bin non-uniform gradient magnitude histogram to compute block-based hysteresis thresholds that are used by the Canny edge detector. Furthermore, an FPGA-based hardware architecture of our proposed algorithm is presented in this paper and the architecture is synthesized on the Xilinx Virtex-5 FPGA. Simulation results are presented to illustrate the performance of the proposed distributed Canny edge detector. The FPGA simulation results show that we can process a 512×512 image in 0.287ms at a clock rate of 100 MHz.

Content based Image Retrieval for Medical Images using Canny Edge Detection Algorithm

Abstract: The rapid expansion of digital data content has led to the need for rich descriptions and efficient Retrieval Tool. To develop this, content based image Retrieval method has played an important role in the field of image retrieval. This paper aims to provide an efficient medical image data Retrieval from a huge content of medical database using one of the images content such as image shape, because, efficient content-based image Retrieval in the medical domain is still a challenging problem. The main objective of this paper is to provide an efficient tool which is used for efficient medical image retrieval from a huge content of medical image database and which is used for further medical diagnosis purposes.

Automatic measurement of cup to disc ratio based on line profile analysis in retinal images

Abstract: Retinal image examination is useful for early detection of glaucoma, which is a leading cause of permanent blindness. In order to evaluate the presence of glaucoma, ophthalmologists may determine the cup and disc areas and diagnose glaucoma using a vertical cup-to-disc ratio. However, determination of the cup area based on computation algorithm is very difficult, thus we propose a method to measure the cup-to-disc ratio using a vertical profile on the optic disc. The edge of optic disc was then detected by use of a Canny edge detection filter. The profile was then obtained around the center of the optic disc. Subsequently, the edges of the cup area were determined by classification of the profiles based on zero-crossing method. Lastly, the vertical cup-to-disc ratio was calculated. Using forty five images, including twenty three glaucoma images, the AUC of 0.947 was achieved with this method.

An edge detection approach of image fusion based on improved Sobel operator

Abstract: Anti-noise ability and edge continuity of Sobel edge detection algorithm are poor. In order to solve these problems, an improved method of Sobel operator is given in this paper. In addition, making use of fusion technology, a kind of method combined with improved Sobel operator, wavelet transform, Canny algorithm and Prewitt operator is put forward, which keeps their respective advantages. Experiments show that the fusion image effectively improves the accuracy of edge detection and gets a quite ideal edge detection effect.

Number Plate Recognition for Indian Cars Using Morphological Dilation and Erosion with the Aid Of Ocrs

Abstract: Vehicle number plate recognition(VNPR) has been intensively studied in many countries. Due to the different types of number plates being used, the requirements of an automatic number plate recognition system is different for each country. In this paper, a number plate localization and recognition system for vehicles in Tamilnadu(India) is proposed. This system is developed based on digital images and can be easily applied to commercial car park systems for the use of documenting access of parking services, secure usage of parking houses and also to prevent car theft issues. The proposed algorithm is based on a combination of morphological operation with area criteria tests for number plate localization. Segmentation of the plate characters was achieved by the application of edge detectors, labeling and fill hole approach. The character recognition was accomplished with the aid of optical characters by the process of Template matching. The system was experimented with four different edge detectors namely Sobel, Canny, Prewitt, LOG. A comparative analysis on the success rate of the proposed system showed overall better success rate of 96.8% by using canny edge detector.

Restoration of blurred images using Blind Deconvolution Algorithm

Abstract: Image restoration is the process of recovering the original image from the degraded image. Aspire of the project is to restore the blurred/degraded images using Blind Deconvolution algorithm. The fundamental task of Image deblurring is to de-convolute the degraded image with the PSF that exactly describe the distortion. Firstly, the original image is degraded using the Degradation Model. It can be done by Gaussian filter which is a low-pass filter used to blur an image. In the edges of the blurred image, the ringing effect can be detected using Canny Edge Detection method and then it can be removed before restoration process. Blind Deconvolution algorithm is applied to the blurred image. It is possible to renovate the original image without having specific knowledge of degradation filter, additive noise and PSF. To get the effective results, the Penalized Maximum Likelihood (PML) Estimation Technique is used with our proposed Blind Deconvolution Algorithm.

A Study on Hand Gesture Recognition Technique

Abstract: Hand gesture recognition system can be used for interfacing between computer and human using hand gesture. This work presents a technique for a human computer interface through hand gesture recognition that is able to recognize 25 static gestures from the American Sign Language hand alphabet. The objective of this thesis is to develop an algorithm for recognition of hand gestures with reasonable accuracy. The segmentation of gray scale image of a hand gesture is performed using Otsu thresholding algorithm. Otsu algorithm treats any segmentation problem as classification problem. Total image level is divided into two classes one is hand and other is background. The optimal threshold value is determined by computing the ratio between class variance and total class variance. A morphological filtering method is used to effectively remove background and object noise in the segmented image. Morphological method consists of dilation, erosion, opening, and closing operation. Canny edge detection technique is used to find the boundary of hand gesture in image. A contour tracking algorithm is applied to track the contour in clockwise direction. Contour of a gesture is represented by a Localized Contour Sequence (L.C.S) whose samples are the perpendicular distances between the contour pixels and the chord connecting the end-points of a window centered on the contour pixels. These extracted features are applied as input to classifier. Linear classifier discriminates the images based on dissimilarity between two images. Multi Class Support Vector Machine (MCSVM) and Least Square Support Vector Machine (LSSVM) is also implemented for the classification purpose. Experimental result shows that 94.2% recognition accuracy is achieved by using linear classifier and 98.6% recognition accuracy is achieved using Multiclass Support Vector machine classifier. Least Square Support Vector Machine (LSSVM) classifier is also used for classification purpose and shows 99.2% recognition accuracy.

Genetic programming for edge detection: A global approach

Abstract: Edge detection is an important task in computer vision. This paper describes a global approach to edge detection using genetic programming (GP). Unlike most traditional edge detection methods which use local window filters, this approach directly uses an entire image as input and classifies pixels directly as edges or non-edges without preprocessing or postprocessing. Shifting operations and common standard operators are used to form the function set. Precision, recall and true negative rate are used to construct the fitness functions. This approach is examined and compared with the Laplacian and Sobel edge detectors on three sets of images providing edge detection problems of varying difficulty. The results suggest that the detectors evolved by GP outperform the Laplacian detector and compete with the Sobel detector in most cases.

Morphological Edge Detection Algorithm Based on Multi-Structure Elements of Different Directions

Abstract: Edge detection is one of the important pre-processing steps in image analysis. Edges characterize boundaries and edge detection is one of the most difficult tasks in image processing hence it is a problem of fundamental importance in image processing. Edges in images are areas with strong intensity contrasts and a jump in intensity from one pixel to the next can create major variation in the picture quality. Edge detection of an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Conventionally, mathematical morphology edge detection methods use single and symmetrical structure elements. But they are difficult to detect complex edge feature, because they are only sensitive to image edge which has the same direction of structure elements. This paper proposed a novel edge detection algorithm based on multi-structure elements morphology of eight different directions. The eight different edge detection results are obtained by using morphological gradient algorithm respectively, and final edge results are obtained by using synthetic weighted method. The experimental results showed that the proposed algorithm is more efficient for edge detection than conventional mathematical morphological edge detection algorithms and differential edge detection operators.

Iris recognition based on lbp and combined lvq classifier

Abstract: Iris recognition is considered as one of the best biometric methods used for human identification and verification, this is because of its unique features that differ from one person to another, and its importance in the security field. This paper proposes an algorithm for iris recognition and classification using a system based on Local Binary Pattern and histogram properties as a statistical approaches for feature extraction , and Combined Learning Vector Quantization Classifier as Neural Network approach for classification, in order to build a hybrid model depends on both features. The localization and segmentation techniques are presented using both Canny edge detection and Hough Circular Transform in order to isolate an iris from the whole eye image and for noise detection .Feature vectors results from LBP is applied to a Combined LVQ classifier with different classes to determine the minimum acceptable performance, and the result is based on majority voting among several LVQ classifier. Different iris datasets CASIA, MMU1, MMU2, and LEI with different extensions and size are presented. Since LBP is working on a grayscale level so colored iris images should be transformed into a grayscale level. The proposed system gives a high recognition rate 99.87 % on different iris datasets compared with other methods.

A Method for Detection and Classification of Glass Defects in Low Resolution Images

Abstract: This paper presents a novel method for detection and recognition of glass defects in low resolution images. First, the defect region is located by the method of Canny edge detection, and thus the smallest connected region (rectangle) can be found. Then, the binary information of the core region can be obtained based on a specific filter. After noises are removed, a novel Binary Feature Histogram (BFH) is proposed to describe the characteristic of the glass defect. Finally, the AdaBoost method is adopted for classification. The classifiers are designed based on BFH. Experiments with 800 bubble images and 240 non-bubble images prove that the proposed method is effective and efficient for recognition of glass defects, such as bubbles and inclusions.

A study of the edge detection for road lane

Abstract: This article presents an investigation of an optimum algorithm for edge detection in order to use in the road lane detection process. The main issues, including the speed, the accuracy, and the limited resources, were taken to consider for the realization on the FPGA technology. The edge detection algorithms of Canny, Prewitt, Sobel and Roberts were compared using MATLAB. A number of road images were captured by a video camera with the image size of 640x480 pixels and the frame rate of 30 fps. In addition, a mask filter was applied to remove red, green, and blue values to help the edge detection process be more efficient. From the experimental results, the Canny algorithm was the most time consuming process, and gave too many lines outside the road lane. Among these, the Roberts algorithm is not only the smallest size, but also gained the fastest speed (3.14 times faster than the Canny algorithm) and the most accurate one to detect the lines of the actual road lanes.

Common Carotid Artery Lumen Segmentation in B-mode Ultrasound Transverse View Images

Abstract: To evaluate atherosclerosis, common carotid artery (CCA) lumen segmentation requires outlining the intima contour on transverse view of B-mode ultrasound images. The lumen contours are automatically segmented using a morphology method in this paper. The proposed method is based on self-adaptive histogram equalization, non-linear filtering, Canny edge detector and morphology methods. Experiments demonstrated that the merit (FOM) value of lumen segmentation is 0.705. The comparison between proposed method and manual contours on 180 transverse images of the CCA showed a mean absolute error of 0.47±0.13 mm and mean max distance of 2.08± 0.63 mm respectively. These results compare favorably with a clinical need for reducing use variability.