Showing papers on "Canny edge detector published in 2007"

An Improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection

Abstract: Moving object detection is very important in intelligent surveillance. In this paper, an improved algorithm based on frame difference and edge detection is presented for moving object detection. First of all, it detects the edges of each two continuous frames by Canny detector and gets the difference between the two edge images. And then, it divides the edge difference image into several small blocks and decides if they are moving areas by comparing the number of non-zero pixels to a threshold. At last, it does the block-connected component labeling to get the smallest rectangle that contains the moving object. Experimental results show the improved algorithm overcomes the shortcomings of the frame difference method. It has a high recognition rate and a high detection speed, which has a broad market prospect.

Laplacian Operator-Based Edge Detectors

Abstract: Laplacian operator is a second derivative operator often used in edge detection. Compared with the first derivative-based edge detectors such as Sobel operator, the Laplacian operator may yield better results in edge localization. Unfortunately, the Laplacian operator is very sensitive to noise. In this paper, based on the Laplacian operator, a model is introduced for making some edge detectors. Also, the optimal threshold is introduced for obtaining a maximum a posteriori (MAP) estimate of edges

Wavelet-Based Pavement Distress Image Edge Detection with À Trous Algorithm

Abstract: Edge detection is an alternative method in the process for identifying and classifying pavement cracks for automated pavement evaluation systems. A number of edge detectors are widely used in image processing; most specify only a spatial scale for detecting edges. However, pavement surface images frequently have various details at different scales. Therefore, wavelet-based multiscale technique can be a candidate to extract edge information from pavement surface images. Instead of detecting edges in the space domain, wavelet analysis has the ability to describe both domains in time and in frequency. It was first applied in image edge detection in 1992, using the local maximum of the magnitude of the gradient to obtain edge representation. Nevertheless, this subsampling algorithm leads to a loss of translation variance and may produce many artifacts. In this paper, wavelet edge detection based on a trous algorithm (holes algorithm) is used in pavement distress segmentation. This algorithm is an undecimated ...

Development of a fully automatic scheme for detection of masses in whole breast ultrasound images.

Abstract: Ultrasonography has been used for breast cancer screening in Japan. Screening using a conventional hand-held probe is operator dependent and thus it is possible that some areas of the breast may not be scanned. To overcome such problems, a mechanical whole breast ultrasound (US) scanner has been proposed and developed for screening purposes. However, another issue is that radiologists might tire while interpreting all images in a large-volume screening; this increases the likelihood that masses may remain undetected. Therefore, the aim of this study is to develop a fully automatic scheme for the detection of masses in whole breast US images in order to assist the interpretations of radiologists and potentially improve the screening accuracy. The authors database comprised 109 whole breast US imagoes, which include 36 masses (16 malignant masses, 5 fibroadenomas, and 15 cysts). A whole breast US image with 84 slice images (interval between two slice images: 2 mm) was obtained by the ASU-1004 US scanner (ALOKA Co., Ltd., Japan). The feature based on the edge directions in each slice and a method for subtracting between the slice images were used for the detection of masses in the authors proposed scheme. The Canny edge detector was applied to detect edges in US images; these edges were classified as near-vertical edges or near-horizontal edges using a morphological method. The positions of mass candidates were located using the near-vertical edges as a cue. Then, the located positions were segmented by the watershed algorithm and mass candidate regions were detected using the segmented regions and the low-density regions extracted by the slice subtraction method. For the removal of false positives (FPs), rule-based schemes and a quadratic discriminant analysis were applied for the distribution between masses and FPs. As a result, the sensitivity of the authors scheme for the detection of masses was 80.6% (29/36) with 3.8 FPs per whole breast image. The authors scheme for a computer-aided detection may be useful in improving the screening performance and efficiency.

The Fast Multilevel Fuzzy Edge Detection of Blurry Images

Abstract: To realize the fast and accurate detection of the edges from the blurry images, the fast multilevel fuzzy edge detection (FMFED) algorithm is proposed. The FMFED algorithm first enhances the image contrast by means of the fast multilevel fuzzy enhancement (FMFE) algorithm using the simple transformation function based on two image thresholds. Second, the edges are extracted from the enhanced image by the two-stage edge detection operator that identifies the edge candidates based on the local characteristics of the image and then determines the true edge pixels using the edge detection operator based on the extremum of the gradient values. Experimental results demonstrate that the FMFED algorithm can extract the thin edges and remove the false edges from the image, which leads to its better performance than the Sobel operator, Canny operator, traditional fuzzy edge detection algorithm, and other multilevel fuzzy edge detection algorithms

Logarithmic edge detection with applications

Abstract: In real world machine vision problems, issues such as noise and variable scene illumination make edge and object detection difficult. There exists no universal edge detection method which works under all conditions. In this paper, we propose a logarithmic edge detection method. This achieves a higher level of scene illumination and noise independence. We present experimental results for this method, and compare results of the algorithm against several leading edge detection methods, such as Sobel and Canny. For an objective basis of comparison, we use Pratt's Figure of Merit. We further demonstrate the application of the algorithm in conjunction with Edge Detection based Image Enhancement (EDIE), showing that the use of this edge detection algorithm results in better image enhancement, as quantified by the Logarithmic AME measure.

An Improved Canny Algorithm for Edge Detection

Abstract: Because of the high similarity between the gradient magnitudes of low-contrast edge and noise,the conventional Canny algorithm for detection,which is based on the double-threshold to detect and connect edges,damages partly the low-contrast edge though it suppresses noise.An improved Canny algorithm is therefore proposed instead of the double-threshold one,which detects and connects the edges in accordance to the difference of the gradient direction between the edge and the noise,so as to protect efficiently the details of all low-contrast edges with the noise suppressed simultaneously.The new approach is thus superior to the conventional one.Simulation tests have verified the effectiveness of the improved Canny algorithm for edge detection.

Mathematical-morphology-based edge detectors for detection of thin edges in low-contrast regions

Abstract: A new edge detector based on mathematical morphology to preserve thin edge features in low-contrast regions as well as other apparent edges is proposed. A quad-decomposition edge enhancement process, a thresholding process, and a mask-based noise filtering process were developed and used to enhance thin edge features, extract edge points and filter out some meaningless noise points, respectively. Moreover, five bipolar oriented edge masks were also designed to remove most of the incorrectly detected edge features. Many experiments were conducted to evaluate and compare the performance of the proposed algorithm and several conventional ones. Pratt's figure of merit achieved by the proposed algorithm was as high as 92.5. The comprehensive experimental results show that the proposed algorithm is capable of preserving thin edge details successfully in low-contrast regions and is robust against noise.

Traffic Sign Recognition for Intelligent Vehicle/Driver Assistance System Using Neural Network on OpenCV

Abstract: Traffic Sign Recognition (TSR) is used to regulate traffic signs, warn a driver, and command or prohibit certain actions. A fast real-time and robust automatic traffic sign detection and recognition can support and disburden the driver and significantly increase driving safety and comfort. Automatic recognition of traffic signs is also important for automated intelligent driving vehicle or driver assistance systems. This paper presents a study to recognize traffic sign patterns using Neural Networks technique. The images are pre-processed with several image processing techniques, such as, threshold techniques, Gaussian filter, Canny edge detection, Contour and Fit Ellipse. Then, the Neural Networks stages are performed to recognize the traffic sign patterns. The system is trained and validated to find the best network architecture. The experimental results show the highly accurate classifications of traffic sign patterns with complex background images and the computational cost of the proposed method.

Edge detection in noisy images by neuro-fuzzy processing

Abstract: A novel neuro-fuzzy (NF) operator for edge detection in digital images corrupted by impulse noise is presented The proposed operator is constructed by combining a desired number of NF subdetectors with a postprocessor Each NF subdetector in the structure evaluates a different pixel neighborhood relation Hence, the number of NF subdetectors in the structure may be varied to obtain the desired edge detection performance Internal parameters of the NF subdetectors are adaptively optimized by training by using simple artificial training images The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors from the literature Simulation results indicate that the proposed NF operator outperforms competing edge detectors and offers superior performance in edge detection in digital images corrupted by impulse noise

The QR-code reorganization in illegible snapshots taken by mobile phones

Abstract: In this paper, we give an algorithm for the analysis and correction of the distorted QR barcode (QR-code) image The introduced algorithm is based on the code area finding by four corners detection for 2D barcode We combine Canny edge detection with contours finding algorithms to erase noises and reduce computation and utilize two tangents to approximate the right-bottom point Then, we give a detail description on how to use inverse perspective transformation in rebuilding a QR-code image from a distorted one We test our algorithm on images taken by mobile phones The experiment shows that our algorithm is effective

An Edge Detection Algorithm Based on Improved CANNY Operator

Abstract: Edge detection is an important aspect for image processing, and primary step of spatial data extraction in geography information system. For contour detection, this paper proposed the improved template algorithm, which is not only including the gradient directions of X and Y, but also the first order partial finite differences of directions 45 and 135 degree in calculating the amplitude values. These mostly improved the calculation accuracy of the amplitude values. In the non-maxima suppression process, the factor ratio of four quadrants of linear interpolation is improved to achieve better detection results. Experiments showed that this improved CANNY algorithm has better noise suppression and edge continuity.

Chrominance Edge Preserving Grayscale Transformation with Approximate First Principal Component for Color Edge Detection

Abstract: Edges that are visible in color images may not be detected in the corresponding grayscale image. This is due to the neighboring objects having different hues but the same intensities. Hence, a color edge preserving grayscale conversion algorithm is proposed that helps detect color edges using only the luminance component. The algorithm calculates an approximation to the first principal component to form a new set of luminance coefficients instead of using the conventional luminance coefficients. This method can be directly applied to all existing grayscale edge detectors for color edge detection. Processing only one channel instead of three channels results in lower computational complexity compared to other color edge detectors. Experimental results on test images show similar edge detection capabilities to typical color edge detectors at reduced complexity levels.

Thermal and Visual Image Registration in Hough Parameter Space

Abstract: This paper introduces a new approach in image registration, that is a multisensor registration in Hough parameter space. Visual and thermal images of building fronts were aimed to be aligned in order to inspect thermal properties of buildings. Some preprocessing of visible images was necessary to be comparable to their thermal counterparts, namely downsampling and color space conversion from RGB to grayscale intensity. For each image pair, edges were detected with Canny edge detector and, as a result, binary edge images were obtained. These images were further processed by Hough transform which extracted all linear image segments. We decided for linear segments, because they are the most frequent feature appearing in the images of buildings. In the Hough parameter space the rotation and translation of the linear segments can be recovered using the line correspondence analysis. The method was verified first on synthetic images with only translation, only rotation, and also both the rotation and translation together. Finally, a verification on real images was done. The method was able to correctly register both type of images, synthetic and the real ones. In general, our algorithm can cope with rotated and translated images if only a few linear segments are detectible.

Edge Detection Based on Spiking Neural Network Model

Abstract: Inspired by the behaviour of biological receptive fields and the human visual system, a network model based on spiking neurons is proposed to detect edges in a visual image. The structure and the properties of the network are detailed in this paper. Simulation results show that the network based on spiking neurons is able to perform edge detection within a time interval of 100 ms. This processing time is consistent with the human visual system. A firing rate map recorded in the simulation is comparable to Sobel and Canny edge graphics. In addition, the network can separate different edges using synapse plasticity, and the network provides an attention mechanism in which edges in an attention area can be enhanced.

A family of quadratic snakes for road extraction

Abstract: The geographic information system industry would benefit from flexible automated systems capable of extracting linear structures from satellite imagery. Quadratic snakes allow global interactions between points along a contour, and are well suited to segmentation of linear structures such as roads. However, a single quadratic snake is unable to extract disconnected road networks and enclosed regions. We propose to use a family of cooperating snakes, which are able to split, merge, and disappear as necessary. We also propose a preprocessing method based on oriented filtering, thresholding, Canny edge detection, and Gradient Vector Flow (GVF) energy. We evaluate the performance of the method in terms of precision and recall in comparison to ground truth data. The family of cooperating snakes consistently outperforms a single snake in a variety of road extraction tasks, and our method for obtaining the GVF is more suitable for road extraction tasks than standard methods.

Carotid ultrasound segmentation using DP active contours

Abstract: Ultrasound provides a non-invasive means for visualizing various tissues within the human body. However, these visualizations tend to be filled with speckle noise and other artifacts, due to the sporadic nature of high frequency sound waves. Many techniques for segmenting ultrasound images have been introduced in order to deal with these problems. One such technique is the active contouring. In this paper, two proposed alterations to the dynamic programming parametric active contour model (or snake) are introduced. The first alteration allows the snake to converge to the one-response result of a modified Canny edge detector. The second provides a function that allows a user to preset apriori knowledge about a given object being detected, by means of curve fitting and energy modification. The results yield accurate segmentations of crosssectional transverse carotid artery ultrasound images that are validated by an independent clinical radiologist. Utilizing the proposed alterations leads to a reduction of clinician interaction time while maintaining an acceptable level of accuracy for varying measures such as percent stenosis.

An Optimal Initialization Technique for Improving the Segmentation Performance of Chan-Vese Model

Abstract: In level set method, initialization mode not only influences evidently the implementation efficiency but also relates directly to the final results. The paper presents an new initialization scheme for improving the segmentation performance of Chan-Vese model. The proposed initialization scheme consists of two stages. The first stage computes rough edges by using canny edge detection operator. The second stage removes noise edges and redundant edges by a morphological filter, and generates closed contours by iteratively connecting edge points according to a local cost function. In comparison with the primal Chan-Vese model, experimental data show that the Chan-Vese model equipped with our initialization scheme provides superior segmentation results and takes less computational cost.

A new edge detection algorithm for image corrupted by White-Gaussian noise

Abstract: Because corruption of image by White-Gaussian noise is a frequently encountered problem in acquisition, transmission and processing of image, and classical edge detection operators such as Roberts, Sobel, Prewitt and LOG operator have the deficiency of being sensitive to White-Gaussian noise, this paper proposes a new edge detection algorithm for Image corrupted by White-Gaussian noise that can reasonably consider White-Gaussian noise reduction and correct location of edge, and provides its specific arithmetic process. Finally, the comparison based on principle of new edge detection algorithm and classical edge detection operator is done, the experimental results indicate that the performance of new edge detection algorithm is better than that of classical edge detection operator.

Self-adaptive Canny operator edge detection technique

Abstract: When edge detection is performed using a Canny algorithm,the gradient image should be processed with "non-maximum module suppression" and then double thresholds evaluated to detect edgesHowever,the double thresholds are greatly affected by personal experienceExperiments show that the results of edge detection for different images are obviously different if the identical threshold is employed,which restricted the use of Canny algorithm in practiceTo solve this problem,an algorithm is proposed which can adaptively determine the double thresholds based on gradient histogram and minimum interclass varianceWith this algorithm,it can self-adaptively calculate the double thresholds for different images without the necessity to setup any parameter artificiallyFuzzy algorithm is adopted to choose edge pixelsTheory and experiments show that the algorithm is effective and correct

Detecting Coins with Different Radii based on Hough Transform in Noisy and Deformed Image

Abstract: Hough transform is a general technique for identifying the locations and orientations of certain types of features in a digital image and used to isolate features of a particular shape within an image. Because it requires that the desired features are specified in some parametric form, classical Hough transform is the most commonly used for the detection of regular curves such as lines, circles, ellipses, etc. A generalized Hough transform can be employed in applications where a simple analytic description of features is not possible. In this paper, Canny edge detector is used in conjunction with Hough transform to detect many coins with different radii. In this case, applying threshold make a significant and determinant factor in coins detection. Also, this paper shows that the Hough transform is an effective tool for coins detection even in the presence of noise such as salt and pepper, Gaussian and speckle noise. Also Hough transform succeeded in detecting blurring coins and deforming coins.

Medical Image Edge Detection Based on Omni-directional Multi-Scale Structure Element of Mathematical Morphology

Abstract: Based on mathematical morphology of omni directional multi-scale element, an algorithm of image edge- detection was proposed. Mathematical morphology of omni directional multi-scale element is defined in order to suppress noise and adapt to different edge in the image. An approach of image edge detection based on morphology of omni directional multi-scale element is constructed by power adding combination of morphological operation. The results of simulation in medical image demonstrate that the method performs better not only in edge detection but also in noise-suppression than classical edge detection operator.

Time-Varying Segmentation for Mapping of Land Cover Changes

Abstract: Among all satellite imaging systems, Synthetic Aperture Radar (SAR) is useful for monitoring purposes, as they provide data at any time under all weather conditions. Today, three spaceborne SAR systems are operational, while by the end of 2007 three further SAR instruments will be launched, thereby allowing to obtain an almost continuous monitoring of the Earth coverage. In order to translate these multi-temporal data into information in an unsupervised (automated) and reliable way, sophisticated algorithms must be available. In the past years several approaches - primarily based on texture analysis and statistical scene estimation - have been proposed for multi-temporal SAR data analysis. Such methods, based on probability density functions, perform well under strictly controlled conditions, but they are often limited with respect to sensor synergy where complex joint probability density functions must be considered - and to the temporal aspect. To address these limitations, an original set of algorithms for the unsupervised multi-temporal SAR data analysis is proposed. To this end, several issues have been tackled: filtering, edge detection, and image segmentation. Moreover, condition sine qua non for the system design, was that i) it is sensor independent, and ii) data from different SAR systems can be ingested without any a-priori knowledge about the probability density function. Basically, the proposed time varying segmentation involves four independent steps. In a first step, a multi-temporal anisotropic non-linear diffusion filter is applied to filter the images which ultimately allow feature extraction. Subsequently, an extension of Canny edge detection algorithm for multi-temporal edge detection is applied, hence obtaining an edge map consistent across the whole sequence of temporal images. In a third step, closed regions are obtained using a two-part coding scheme with the edge map (as side information) and region growing technique (using the multi-temporal stack). Finally, the number of underlying spectral class composing a segment histogram at every frame is estimated, thus detecting changes due to temporal land cover fluctuations. Results are presented based on a set of 16 ENVISAT ASAR Alternating Polarization and Radarsat-1 Fine Beam images acquired between November 2004 and July 2005 for an agricultural (maize and sun flower) area in South Africa.