Showing papers on "Canny edge detector published in 2005"

Canny edge detection enhancement by scale multiplication

Abstract: The technique of scale multiplication is analyzed in the framework of Canny edge detection. A scale multiplication function is defined as the product of the responses of the detection filter at two scales. Edge maps are constructed as the local maxima by thresholding the scale multiplication results. The detection and localization criteria of the scale multiplication are derived. At a small loss in the detection criterion, the localization criterion can be much improved by scale multiplication. The product of the two criteria for scale multiplication is greater than that for a single scale, which leads to better edge detection performance. Experimental results are presented.

Detection and classification of edges in color images

Abstract: Up to now, most of the color edge detection methods are monochromatic-based techniques, which produce, in general, better than when traditional gray-value techniques are applied. In this overview, we focus mainly on vector-valued techniques because it is easy to understand how to apply common edge detection schemes to every color component. Opposed to this, vector-valued techniques are new and different. The second part of the article addresses the topic of edge classification. While edges are often classified into step edges and ramp edges, we address the topic of physical edge classification based on their origin into shadow edges, reflectance edges, orientation edges, occlusion edges, and specular edges. In the rest of this article we discuss various vector-valued techniques for detecting discontinuities in color images. Then operators are presented based on vector order statistics, followed by presentation by examples of a couple of results of color edge detection. We then discuss different approaches to a physical classification of edges by their origin.

Liver segmentation for CT images using GVF snake.

Abstract: Accurate liver segmentation on computed tomography(CT)images is a challenging task especially at sites where surrounding tissues (e.g., stomach, kidney) have densities similar to that of the liver and lesions reside at the liver edges. We have developed a method for semiautomatic delineation of the liver contours on contrast-enhanced CTimages. The method utilizes a snake algorithm with a gradient vector flow (GVF) field as its external force. To improve the performance of the GVF snake in the segmentation of the liver contour, an edge map was obtained with a Canny edge detector, followed by modifications using a liver template and a concavity removal algorithm. With the modified edge map, for which unwanted edges inside the liver were eliminated, the GVF field was computed and an initial liver contour was formed. The snake algorithm was then applied to obtain the actual liver contour. This algorithm was extended to segment the liver volume in a slice-by-slice fashion, where the result of the preceding slice constrained the segmentation of the adjacent slice. 551 two-dimensional liverimages from 20 volumetric images with colorectal metastases spreading throughout the livers were delineated using this method, and also manually by a radiologist for evaluation. The difference ratio, which is defined as the percentage ratio of mismatching volume between the computer and the radiologist’s results, ranged from 2.9% to 7.6% with a median value of 5.3%.

Driver assistance system for lane detection and vehicle recognition with night vision

Abstract: The objective of this research is to develop a vision-based driver assistance system to enhance the driver's safety in the nighttime. The proposed system performs both lane detection and vehicle recognition. In lane detection, three features including lane markers, brightness, slenderness and proximity are applied to detect the positions of lane markers in the image. On the other hand, vehicle recognition is achieved by using an evident feature which is extracted through three four steps: taillight standing-out process, adaptive thresholding, centroid detection, and taillight pairing algorithm. Besides, an automatic method is also provided to calculate the tilt and the pan of the camera by using the position of vanishing point which is detected in the image by applying Canny edge detection, Hough transform, major straight line extraction and vanishing point estimation. Experimental results for thousands of images are provided to demonstrate the effectiveness of the proposed approach in the nighttime. The lane detection rate is nearly 99%, and the vehicle recognition rate is about 91%. Furthermore, our system can process the image in almost real time.

Multi-scale edge detection and image segmentation

Abstract: In this paper, we propose a novel multi-scale edge detection and vector field design scheme. We show that using multiscale techniques edge detection and segmentation quality on natural images can be improved significantly. Our approach eliminates the need for explicit scale selection and edge tracking. Our method favors edges that exist at a wide range of scales and localize these edges at finer scales. This work is then extended to multi-scale image segmentation using our anisotropic diffusion scheme.

Evolving optimal feature extraction using multi-objective genetic programming: a methodology and preliminary study on edge detection

Abstract: In this paper we describe a generic methodology to create an "optimal" feature extraction pre-processing stage for pattern classification. Our aim is to map the input data into a new, one-dimensional feature space in which separability is maximized under a simple thresholding classification. We have used multi-objective genetic programming with Pareto strength-based ranking to bias the selection procedure. The methodology is applied to the edge detection problem in image processing; we make quantitative comparison with the pre-processing stages of the well-known Canny edge detector using synthetic and real-world edge data and conclude that the performance of our evolutionary-based method is much superior to the Canny algorithm based on the criterion of minimum Bayes risk.

Summary of image edge detection

Abstract: Edge is one of the most fundamental and significant features. Edge detection is always one of the most classical studying projects of computer vision and image processing field. The fist step of image analysis and understanding is edge detection. The goal of edge detection is to recover information about shapes and reflectance or transmittance in an image. It is one of the fundamental steps in image processing, mage analysis, image patter recognition, and computer vision, as well as in human vision. The correctness and reliability of its results affect directly the comprehension machine system made for objective world. The summary for basic edge detection methods was made. It involved the detection methods only but not filtering, edge location, analysis of algorithm complexity and functional evaluation about a detector.

A new fuzzy approach for edge detection

Abstract: An edge detection is one of the most important tasks in image processing. Image segmentation, registration and identification are based on edge detection. In the literature, there is some techniques developed to achive this task such as Sobel, Prewitt, Laplacian and Laplacian of Gaussian. In this paper, a novel knowledge-based approach which have been used to realize control techniques for past years is proposed for edge detection. Some of the classical techniques are used with certain parameters such as threshold and σ to implement edge detection process. The another restricts about classial approach, results generally have fixed edge thickness. The rule-based approach offers most advantages such as giving permission to adapt some parameters easily. The edges thickness can be changed easily by adding new rules or changing output parameters. That is to say rule-based approach has flexible structure which can be adapted any time or any where easily and new fuzzy approach produces nice result as well as classical techniques at least.

Automated Edge Detection Technique for Pap Smear Images Using Moving K-Means Clustering and Modified Seed Based Region Growing Algorithm

Abstract: In previous studies, conventional seed based region growing (SBRG) has successfully been used to detect the edges of certain regions of interest on digital images. The SBRG algorithm offers several advantages over other conventional edge detection algorithms based on gradient decision; the edges of regions found are perfectly thin and fully connected, and the algorithm is very stable with respect to noise. However, two parameters of the SBRG algorithm, which are threshold value and initial seed point location, must be determined manually. Thus, it is timeconsuming and the edge detection performance is highly subjective to the user. Besides that, the SBRG algorithm cannot avoid trapped seed point problem, which causes incomplete edge detection process. The SBRG algorithm also can only detect the edge of one region of interest in one time. To avoid those problems, the current study proposed an automated edge detection technique. The proposed technique consists of moving k-means clustering and SBRG algorithm. However, the current study modified the SBRG algorithm to enhance its capability in edge detection process. The modified seed based region growing (MSBRG) algorithm is able to detect edges of more than one regions of interest as well as differentiate those edges and can avoid incomplete edge detection process as compared to conventional SBRG algorithm. In the proposed automated edge detection technique, firstly, moving k-means clustering algorithm is used to find the thresholds values automatically. After that, based on the thresholds values, the proposed MSBRG algorithm is applied to detect the edges of regions of interest. Then, the proposed technique is applied to Pap smear images to detect the cytoplasm and nucleus edges of cervical cells. The results obtained show that the proposed automated edge detection technique produce better edge detection performance as compared to conventional SBRG, Cubic Spline, Frei Chen, Kirsch, Laplacian, Prewitt, Roberts, Robinson and Sobel algorithms.

Insignificant shadow detection for video segmentation

Abstract: To prevent moving cast shadows from being misunderstood as part of moving objects in change detection based video segmentation, this paper proposes a novel approach to the cast shadow detection based on the edge and region information in multiple frames. First, an initial change detection mask containing moving objects and cast shadows is obtained. Then a Canny edge map is generated. After that, the shadow region is detected and removed through multiframe integration, edge matching, and region growing. Finally, a post processing procedure is used to eliminate noise and tune the boundaries of the objects. Our approach can be used for video segmentation in indoor environment. The experimental results demonstrate its good performance.

A contour based image segmentation algorithm using morphological edge detection

Abstract: In this paper, a novel approach for edge-based image segmentation is proposed. Image segmentation and object extraction play an important role in supporting content-based image coding, indexing, and retrieval. However, it's always a tough task to partition an object in a graph-based image. We proposed an image segmentation algorithm by integrating mathematical morphological edge detector with region growing technique. The images are first enhanced by morphological closing operations, and then detect the edge of the image by morphological dilation residue edge detector. Moreover, we deploy growing seeds into the edge image that obtained by the edge detection procedure. By cross comparing the growing result and the detected edges, the partition lines of the image are generated. In this paper, we presented the theoretical backgrounds and procedure illustrations of the proposed algorithm. Furthermore, the proposed algorithm is implemented in C++ language and evaluate on several images with promising results.

Color Lesion Boundary Detection Using Live Wire

Abstract: The boundaries of oral lesions in color images were detected using a live-wire method and compared to expert delineations. Multiple cost terms were analyzed for their inclusion in the final total cost function including color gradient magnitude, color gradient direction, Canny edge detection, and Laplacian zero crossing. The gradient magnitude and direction cost terms were implemented so that they acted directly on the three components of the color image, instead of using a single derived color band. The live-wire program was shown to be considerably more accurate and faster compared to manual segmentations by untrained users.

A new edge detection method in image processing

Abstract: The edge detection methods based on difference operation are used widely in image processing. It could detect the variation of gray levels, but it is sensitive to noise. In order to improve the ability of noise rejection, this paper proposed a ratio of gray levels between 2 successive image points to denote the variation of gray levels. Furthermore, the paper defined a ratio and an integer logarithm ratio of gray levels. Based on the integer logarithm ratio of gray levels, a new edge detection method was proposed. The advantage of the proposed detection method is that the sensitivity of edge detection can be adjusted by selecting parameter a easily. The experiment results have shown that the effectiveness of edge detection and the ability of noise rejection of the proposed edge detection method are better than that of the traditional ones based on the difference operation.

Airport Detection Base on Support Vector Machine from A Single Image

Abstract: Airport is one of the key transportation targets Airport detection is very important in military and civil fields A novel method to detect airports from a single image is proposed in this paper It combines texture features with shape features, and uses support vector machine as a classification function Canny edge detector is firstly used, then short lines and curves are removed, and long straight lines are detected by Hough transform, at last the airport runways are discriminated by support vector machine The experimental results demonstrate the efficacy of the proposed automatic airport-detection method

Palm-line detection

Abstract: Palm lines, which consist of principal lines and wrinkles, are stable and essential traits for palmprint-based individual identification and can be extracted in low-resolution images. However, the research on palm-line detection has done little. Due to special properties of palmprint, in addition to the structure feature, width of the palm-line, which generally reflects strength information, is important to identify palms especially when various palmprints have similar structures. In this paper, a palm-line detection approach is proposed to simultaneously extract structure and strength features of palm lines by minimizing a local image area which is of similar brightness to each individual pixel. The presented method has been tested on the PolyU palmprint database and compared with the canny edge detector and SUSAN edge finder. Experimental results illustrate the effectiveness of this approach.

An edge detection method by combining fuzzy logic and neural network

Abstract: An edge detection method by combining fuzzy logic and neural network is proposed in this paper. First, the distance measures between the feature vector in 4 directions and the six edge prototype vectors for each pixel are taken as input pattern and fed into input layer of the self-organizing competitive neural network. Classifying the type of edge through this network, the thick edge image is obtained. After classifying, we utilize the competitive rule to thin the thick edge image in order to get the fine edge image. At last, the speckle edges are discarded from the edge image, thus the final optimal edge image is got. We compared the edge images got from our method with that from Canny's one and Sobel's one in our experiments. The experimental results show that the effect of our method is superior to other two methods and the robustness of our method is better.

Demosaicing with improved edge direction detection

Abstract: A new edge adaptive color demosaicing method for Bayer pattern images of single-sensor digital cameras is presented in this paper. An edge direction detector for narrow edges is proposed by making full use of inter-channel correlation to determine edge directions within the smallest detection radius. A combinative criterion is then formulated to cater for the diversity of edge occurrence in real-world scenes. The improvement of the proposed demosaicing scheme is achieved by an effective detection on edge directions, emphasizing on green channel restoration and the overall refinement. Experimental results show that the new scheme preserves better edge details, reduces color aliasing artifacts, and achieves both higher signal fidelity and higher visual image quality as compared with some existing schemes.

Gradient-threshold edge detection based on the human visual system

Abstract: We present an improved method that is suitable for gradient-threshold edge detectors. The method takes into account the basic characteristics of the human visual system and masks the gradient image with the luminance and the activity of local image before edge labeling. An implementation of this method on a Canny detector is described as an example. The results show that the edge images obtained by our algorithm are more consistent with the perceptive edge images.

Bi-lateral filtering based edge detection on hexagonal architecture

Abstract: Edge detection plays an important role in image processing but is still an open problem. This paper presents a novel edge detection method based on bi-lateral filtering which achieves better performance than single Gaussian filtering. In this form of filtering, both spatial closeness and intensity similarity of pixels are considered in order to preserve important visual cues provided by edges and reduce the sharpness of transitions in intensity values as well. In addition, the edge detection method proposed in this paper is achieved on hexagonally sampled images. Due to the compact and circular nature of the hexagonal lattice, a better quality edge map is obtained on hexagonal architecture than common edge detection on square architecture. Experimental results using our proposed method in this paper exhibit encouraging performance.

An algorithm for edge detection of gray-scale image based on mathematical morphology

Abstract: On the basis of mathematical morphology,an improved edge detection operator is proposed that uses morphological operations such as dilation,erosion, opening,closing and their combination.The method can detect the edge efficiently and keep the detected edge smooth.Also introduced was the concept of multi-scale.In order to obtain an ideal image edge under the circumstances of existing noise,the size of structuring elements was adjusted.Experiments demonstrate that compared with traditional edge detectors,this edge detector has a good performance of noise reduction and requires fewer calculations,enhancing its practicality.

Feature-based composing for 3D MR angiography images

Abstract: Multiple volumes that are to be aligned to form a single volume are processed. The system and method use an equalization step, a edge detection step and a correlation step to determine the overlapping positions between the first volume and the second volume of a volume pair having a maximum correlation value, and the best alignment of the first volume and the second volume of the volume pair is determined by the correlation value. A coarse correlation step using lower resolution volumes can be performed first followed by a fine correlation step using higher resolution images to save processing time. Initial preprocessing steps such as volume shearing can be performed. Equalization involves equalizing voxel size and edge detection can be performed using a Canny edge detector.

Edge detection using quantitative combination of multiple operators

Abstract: Although a number of diverse edge detection techniques can be found in many image processing publications, there is no single detection method that performs well in every possible image context. Information that could be missed by one detector may be captured by another. The purpose of this paper is to describe a new framework which allows us to quantitatively combine the methods of different edge detection operators in order to yield improved results for edge detection in an image. The so called receiver operating characteristics (ROC) analysis is employed in a novel fashion to form an optimum edge map that matches the outcomes of a preselected set of edge detectors. The results of applying the above ROC analysis technique are demonstrated and compared with individual edge detection methods.

A Model-based Algorithm for Mass Segmentation in Mammograms

Abstract: A novel mass segmentation algorithm is proposed in this paper. It establishes two mass models to represent the various masses, uses iterative thresholding to extract the suspicious area, and applies a DWT-based approach to locate the masses. And then, a region growing process restricted by Canny edge detection is carried out to extract the rough mass regions, and finally active contour model is used to segment the masses accurately. The clinical experiment has demonstrated that our algorithm has higher performance than conventional methods

An improved morphological gradient edge detection algorithm

Abstract: Morphological gradient is a nonlinear edge detector that can overcome some drawbacks of the linear edge detectors. Wide edges in edge image detected by morphological gradient cause the low edge resolution. According to the inherent properties of the impulsive noise and reasons that wide edges produce, an edge detection algorithm is presented that the gradient image is segmented in two orthogonal orientations and local maxima are derived from the section curves. The algorithm's steps are presented in detail. The experimental results show our algorithm can improve the edge resolution and insensitivity to noise.

Fast and robust diaphragm detection and tracking in cardiac x-ray projection images

Abstract: A number of image analysis tasks of the heart region have to cope with both the problem of respiration and heart contraction. While the heart contraction status can be estimated based on the ECG, respiration status estimation must be based on the images themselves, unless additional devices for respiration measurements are used. Since diaphragm motion is closely linked to respiration, we describe a method to detect and track the diaphragm in x-ray projections. We model the diaphragm boundary as being approximately circular. Diaphragm detection is then based on edge detection followed by a Hough transform for circles. To avoid that the detection algorithm is misled by high frequency image content, we first apply a morphological multi-scale top hat operator. A Canny edge detector is then applied to the top hat filtered images. In the edge images, the circle corresponding to the diaphragm boundary is found by the Hough transform. To restrict the search in the 3D Hough parameter space (parameters are circle center coordinates and radius), prior anatomical knowledge about position and size of the diaphragm for the given image acquisition geometry is taken into account. In subsequent frames, diaphragm position and size are predicted from previous detection and tracking results. For each detection result, a confidence measure is computed by analyzing the Hough parameter space with respect to the goodness of the peak giving the circle parameters and by analyzing the coefficient of variation of the pixel that form the circle described by the maximum in Hough parameter space. If the confidence is not sufficiently high -- indicating a poor fit between the Hough circle and true diaphragm boundary -- the detection result is optionally refined by an active contour algorithm.

Mean Shift-Based Edge Detection for Color Image

Abstract: Edge detection is an important process in low level image processing With the advent of powerful computers, it is now possible to move to the more computationally intensive realm of color image understanding There are many benefits in doing so including the increased amount of information for object location and processing However, many proposed methods for color edge detection are computational expensive and are not very robust to the image noise In this paper, a new method based on mean shift algorithm to detect edge in color images is presented The gradient-ascent mean shift localizes edges accurately in the presence of noise and provides a good computational performance, being based on local operators Experimental results show the effectiveness and robustness of propose method