Adaptive Dual-Threshold Edge Detection Based on Wavelet Transform

In order to solve the problems of local maximum modulus extraction and threshold selection in the edge detection of finite resolution digital images, a new wavelet transform based adaptive dual threshold edge detection algorithm is proposed. The local maximum modulus is extracted by linear interpolation in wavelet domain. With the analysis on histogram, the image is filtered with an adaptive dual threshold method, which effectively detects the contours of small structures as well as the boundaries of large objects. A wavelet domain's propagation function is used to further select weak edges. Experimental results have shown the self adaptivity of the threshold to images having the same kind of histogram, and the efficiency even in noise tampered images.

MIXED SCHEME FOR IMAGE EDGE DETECTION BASED ON WAVELET TRANSFORM

A mixed scheme based on Wavelet Transformation (WT) is proposed for image edge detection. The scheme combines the wavelet transform and traditional Sobel and LoG (Laplacian of Gaussian) operator edge-detection algorithms. The precise theory analysis is given to show that the wavelet transformation has an advantage for signal processing. Simulation results show that the new scheme is better than only using the Sobel or LoG methods. Complexity analysis is also given and the conclusion is acceptable, therefore the proposed scheme is effective for edge detection.

AN APPLIED RESEARCH ON APPROACH OF DYADIC WAVELET TRANSFORM FOR REMOTE SENSING IMAGE EDGE DETECTION

In the edge detection of Remote Sensing (RS) image, the useful detail losing and the spurious edge often appear. To solve the problem, the authors uses the dyadic wavelet to detect the edge of surface features by combining the edge detecting with the multi-resolution analyzing of the wavelet transform. Via the dyadic wavelet decomposing, the RS image of a certain appropriate scale is obtained, and the edge data of the plane and the upright directions are respectively figured out, then the gradient vector module of the surface features is worked out. By tracing them, the authors get the edge data of the object, therefore build the RS image which obtains the checked edge. This method can depress the effect of noise and examine exactly the edge data of the object by rule and line. With an experiment of an RS image which obtains an airport, the authors certificate the feasibility of the application of dyadic wavelet in the object edge detection.

Human Body Image Edge Detection Based on Wavelet Transform

Human dresses are different in thousands way. Human body image signals have big noise, a poor light and shade contrast and a narrow range of gray gradation distribution. The application of a traditional grads method or gray method to detect human body image edges can't obtain satisfactory results because of false detections and missed detections. According to the peculiarity of human body image, dyadic wavelet transform of cubic spline is successfully applied to detect the face and profile edges of human body image and Mallat algorithm is used in the wavelet decomposition in this paper.

Image Edge Detection Based on Wavelet Transform

Based on the multiresolution decomposition and local time-frequency analysis of the wavelet transform, the image edge detection by wavelet transform is studied. Two methods are dealt with, which are the channel exclusive-OR operation and the high frequency energy-conserving edge detection. In accordance with the contradictory between antinoise ability and detection accuracy, the mutual-energy cross technique for noise suppression is proposed. By computer simulation, the experimental results are obtained on a test image and Lena image. The noise supressing ability is improved and the signal-noise ratio is increased by more than 3dB.

ADAPTIVE MULTISCALE EDGE DETECTION BASED ON VISION FEATURE OF EDGE AND WAVELET TRANSFORM

A new rnultiscale edge detection method is presented, which is based on an effective edge measure. The effective edge measure, used to adaptively adjust the scales of wavelet transform, is defined using the novel features of image edge obtained from human being vision characteristics. Finally, two experiments show that the proposed algorithm appears to work well.

Use of the Wavelet Transform for Digital Terrain Model Edge Detection (Special Issue—Wavelet Analysis)

The purpose of this work is to analyze the feasibility of using the wavelet transform in the edge detection of digital terrain models (DTM) obtained by Laser Scanner. The Haar wavelet transform and the edge detection method called Wavelet Transform Modulus Maxima (WTMM), both implemented in Matlab language, were used. In order to validate and verify the efficiency of WTMM, the edge detection of the same DTM was performed by the Roberts, Sobel-Feldman and Canny methods, chosen due to the wide use in the scientific community in the area of Image Processing and Remote Sensing. The comparison of the results showed superior performance of WTMM in terms of processing time.

Application and Analysis of Wavelet Transform in Image Edge Detection

For the image processing technology, technicians have been looking for a convenient and simple detection method for a long time, especially for the innovation research on image edge detection technology. Because there are a lot of original information at the edge during image processing, thus, we can get the real image data in terms of the data acquisition. The usage of edge is often in the case of some irregular geometric objects, and we determine the contour of the image by combining with signal transmitted data. At the present stage, there are different algorithms in image edge detection, however, different types of algorithms have divergent disadvantages so It is diffi cult to detect the image changes in a reasonable range. We try to use wavelet transformation in image edge detection, making full use of the wave with the high resolution characteristics, and combining multiple images, in order to improve the accuracy of image edge detection.

Edge detection based on object tree image representation and wavelet transform

In applications such as image retrieval and recognition, precise edge detection for interested regions plays a decisive role. Existing methods generally take little care about local characteristics, or become time consuming if every detail is considered. In the paper, a new method is put forward based on the combination of effective image representation and multiscale wavelet analysis. A new object tree image representation is introduced. Then a series of object trees are constructed based on wavelet transform modulus maxima at different scales in descending order. Computation is only needed for interested regions. Implementation steps are also given with an illustrative example.

Multiscale edge detection of noisy images using wavelets

The classical edge detectors work fine with the high quality pictures, but often are not good enough for noisy images because they cannot distinguish edges of different significance. The paper presented a novel approach to multiscale edge detection for noisy images using wavelet transforms based on Lipschitz regularity coefficients and a cascade algorithm. The relationship between wavelet transform and Lipschitz regularity was established. The proposed wavelet based edge detection algorithm combined the coefficients of wavelet transforms along with a cascade algorithm which significantly improves the result. The comparison between the proposed method and the classical edge detectors was carried out. The algorithm was applied to various images and its performance was discussed. The results of edge detection of contaminated images using the proposed algorithm show that it works better than the classical edge detectors.

Sub-pixel edge detection method for miniature parts in microscopic field of view

In order to improve the edge detection precision of miniature parts in microscopic field of viewa sub-pixel edge detectionalgorithm combining non-orthogonal quadratic B-spline wavelet transform algorithm and Zernike moment algorithm is proposed.Non-orthogonal quadratic B-spline wavelet transform algorithm is adopted to get the pixel edge of miniature parts?andthe moment invariant of Zernike moment algorithm is used for refining the pixel edge to get sub-pixel edges.A real-time detectionsystem based on the proposed algorithm for miniature parts is established.The general system structure and operational principle are given,the real-time image acquisition and detection are completed,the results of edge detection are analyzed and the detection precision is evaluated.The results show that parts size can be0.01-10mm and the detection precision reaches0.01%-0.1%.Therefore,the edge of the miniature parts can be accurately identified and the detection precision can be improved to sub-pixel level,which meets the requirements of miniature parts precision detection.

Contrast Enhancement of Radiographs Using Shift Invariant Wavelet Transform

A novel approach using shift invariant wavelet transform is presented for the contrast enhancement of radiographs. By exploiting cross-scale correlation among wavelet coefficients, edge information of radiographic images is extracted and protected, while noise is smoothed out in the wavelet domain. Radiographs are then reconstructed from the transform coefficients modified at multi-scales by nonlinear enhancement operator. The method can achieve effectively contrast enhancement and edge-preserved denoising simultaneously, yet it is capable of giving visually distinct images and offering considerable benefits in medical diagnosis.

Multiscale Product for Edge Detection

Many methods have been proposed to extract the most relevant areas of an image. This article explores the method of edge detection by the multiscale product （MP） of the wavelet transform. The wavelet used in this work is the first derivative of a bidimensional Gaussian function. InitiaRy, we construct the wavelet, then we present the MP approach which is applied to binary and grey levels images. This method is compared with other methods without noise and in the presence of noise. The experiment results show fhht the MP method for edge detection outPerforms conventional methods even in noisy environments.

Identification of Sources of Potential Fields with Directional Wavelet Transform:Application to Mineral Exploration

For the sustainable supply of mineral resources, blind deposits are becoming the emphasis of exploration after long-period exploitation of exposed deposits.The collection and analysis of gravity or magnetic data represents one of the cheapest forms of large-scale geophysical exploration.With the identification of potential fields,we can get the map of worms or skeletonizations showing the three-dimension structure of shallow crust,and find the

Identification of Potential Mineral Fields with Directional Wavelet Transform:Application to Mineral Exploration

For the sustainable supply of mineral resources, blind deposits are becoming the emphasis of exploration after long-period exploitation of exposed deposits.The collection and analysis of gravity or magnetic data represents one of the cheapest ways of large-scale geophysical exploration.With the identification of potential mineral fields,we can get the map of worms or skeletonizations showing

Lifting Scheme and Its Application to Edge Detection

Lifting scheme is a useful and very general technique for constructing wavelet decomposition. The paper adapts the lifting into redundant lifting to obtain shift invariant wavelet transform. In prediction and update stages of the lifting morphological operator is adopted for preserving local maxima of a signal over several scales, which is particularly useful in wavelet\|based signal detec tion. The new transform presented in the paper is applied in multiresoluti on edge detection of medical image and experim ent results are given to show better performance and applicable potentiali ty.

Parallel Implementation of a Multiscale Edges Detection algorithm

We present in this paper an implementation of a multiscale edges detection algorithm on multiprocessor using SYnDEx which is a programming environment to generate optimized distributed real-time executives. The implementation has been done on three TMS320C40 and the acceleration in comparison with one processor is 2.2.

Edge detection of magnetic tile cracks,based on wavelet

In order to extract the defect edge information on the magnetic tile surface with low contrast and textured background,an edge detection algorithm based on image weighted information entropy and wavelet modulus maxima is proposed.At first,a new Butterworth high pass filter(BHPF) with adaptive cutoff frequency is produced,because the clarity and complexity of the textured background are described by the weighted information entropy of the image gradient variance quantitatively,and the filter can change its parameters through matching the non-linear relationship between the information entropy and the cutoff frequency.And then,the best decomposition scale is obtained by the level determination function to prevent edge information from missing.At last,edge points are got by double threshold after obtaining the wavelet modulus maxima,and then the edge image is linked by the edge points to ensure the edge continuity and veracity.Experiment results indicate that the proposed algorithm outperforms the conventional Canny and Sobel algorithm,and the edge detection algorithm can also detect other defects,and lays the foundation for defecting auto- recognition.

Multiscale B-spline wavelet for edge detection

Based on the theory of B-spline, a new family of multiscale wavelet transforms has been presented. The edge of signals can be efficiently represented and detected through its zero-crossing or modulus maxima. For B-spline of order n, the fast algorithms for decomposition and reconstruction have been derived. Also the impulse and frequency responses of the corresponding decomposition and reconstruction filters are given explicitly. In terms of time/frequeny localization it has been proved that cubic B-spline is nearly optimal for most applications. The results have also laid a basis for further applications to stereo vision matching, denoising, etc.

Image edge detection based on pulse coupled neural network and modulus maxima in non-subsampled contourlet domain

Edge is the intrinsic geometric structure of an image. Edge detection methods are the key technologies in the lleld of image processing. In this paper, a multi-scale image edge detection method is proposed to effectively extract image geometric features. A source image is decomposed into the high frequency directional sub-bands coefficients and the low frequency sub-bands coefficients by non-subampled contourlet transform （NSCT）. The high frequency sub-bands coefficients are used to detect the abundant details of the image edges by the modulus maxima （MM） algorithm. The low frequency sub-band coefficients are used to detect the basic contour line of the image edges by the pulse coupled neural network （PCNN）. The final edge detection image is reconstructed with detected edge information at different scales and different directional sub-bands in the NSCT domain. Experimental results demonstrate that the proposed method outperforms several state-of-art image edge detection methods in both visual effects and objective evaluation.