Multiscale Edge Detection Using First-Order Derivative of Anisotropic Gaussian Kernels

Accurate extraction of structural edge information of aluminum foam is an important method to study the complex structural properties of aluminum foam, but the conventional single-scale edge detection method is difficult to achieve complete extraction of structural edge information of aluminum foam. However, the multi-scale fusion edge detection method based on Gaussian smoothing also has some problems, such as strong edge diffusion, weak edge degradation, edge pixel movement and so on. In order to solve the shortcomings of the above methods, this paper proposes a multi-scale edge fusion algorithm based on texture suppression, which can extract the edge information of aluminum foam structure more accurately and completely. Firstly, preprocess the image. The illumination component of the image is extracted by the multi-scale fusion method, and the luminance of the image is corrected by the adaptive luminance correction method based on the two-dimensional gamma function. Secondly, construct multi-scale space. It is proposed to construct the guiding image of the guiding filtering by using the bilateral texture filtering and construct the multi-scale space by changing the scale factor of the guiding filtering. Both bilateral filtering and guided filtering have the function of suppressing the texture information of the image while maintaining the structural edge features of the image. Finally, extract edge seeds and fuse multi-scale edges. A new multi-scale image edge fusion algorithm is proposed, which uses seed edges as a medium to gradually merge multi-scale image edges. In order to extract the edge information of the foam aluminum cross-section structure more accurately and completely, the algorithm further optimizes the edge using gradient direction consistency and non-maximum suppression. In order to verify the feasibility of the proposed algorithm, this paper uses the dataset to test the proposed algorithm and a variety of existing algorithms, and compare the results of various algorithms by quantitative analysis. The experimental results show that the proposed algorithm is feasible and effective, and its performance is better than the comparison algorithm. INDEX TERMS Multi-scale space, multi-scale image processing, multi-scale edge fusion, edge extraction, aluminum foam, seed edges.

Section: Results On the Bsds500 Datasetmentioning

confidence: 89%

Application of New Multi-Scale Edge Fusion Algorithm in Structural Edge Extraction of Aluminum Foam

Zhang

2020

“…Zhang and his colleagues proposed a noise-robust image edge detector using automatic an-isotropic Gaussian kernels [16]. Wang and his colleagues proposed a multiscale edge detector based on first-order derivative of an-isotropic Gaussian kernels [17]. Undoubtedly, Canny operator [18] is one of the most classical methods for edge detection in such kind of algorithm.…”

Section: Related Workmentioning

confidence: 99%

Contour Detection by Simulating the Curvature Cell in the Visual Cortex and its Application to Object Classification

Chen

Cai

2020

This paper addresses contour detection by simulating the human visual system and its application to visual object classification. Unlike previously designed bioinspired contour detection algorithms, we consider contour to be the salience of an edge image, and we extract the salience by simulating the endstopped cell and curvature cell in the visual cortex. Generally, we follow a local-to-global feed-forward architecture, in which the size of the receptive field (RF) increases from the primary visual cortex to the higher visual cortex. Edges are first detected by simple cells in small RFs, where textural details are suppressed by non-classical receptive fields (NCRFs) and sparse coding. Second, edges are integrated into local segments by complex cells. Afterwards, they are combined into the salience of edge images by endstopped cells and curvature cells and are ultimately the core of the final contour. In addition, we also apply the bioinspired contour detection algorithm to visual object classification tasks. Experiments on contour extraction show that, compared with state-of-the-art bioinspired algorithms, our algorithm makes a considerable improvement on contour detection. Experiments on visual object classification show that the contours produced by our proposal are powerful representations of the original images, which implies that our proposal is both biologically plausible and technologically useful. INDEX TERMS Contour detection, curvature cell, image processing, machine vision, biologically inspired computation, visual object classification.

“…This requires the use of multi-scale thinking when detecting image edges, and wavelet multi-resolution analysis of image signals is very suitable for extracting local features of signals, so wavelet is a powerful tool for image edge detection [16,17]. Wavelet edge detection selects a larger scale to filter noise to identify edges, selects a smaller scale to achieve accurate positioning of edges, synthesizes edge images at different scales to obtain detection results [18][19][20]. The basic idea of morphological edge detection is to use structural elements with a certain shape to measure and extract the corresponding shape in the image to achieve the purpose of image analysis and recognition [21,22].…”

Section: Introductionmentioning

confidence: 99%

Image Edge Recognition of Virtual Reality Scene Based on Multi-Operator Dynamic Weight Detection

Liu

Chen

et al. 2020

Image edge detection in virtual reality scenes is one of the most important technologies in the field of image processing and computer vision. It occupies an important position in the image processing system and is a key factor that affects the performance of the entire system. The quality of the algorithm directly affects the performance of the computer's visual system. This paper briefly describes the basic theoretical methods of image edge detection. The basic theory and processing flow of image edge detection are analyzed, and several commonly used edge detection preprocessing methods and their basic principles are elaborated in detail. The existing traditional edge detection methods are briefly introduced, and the traditional edge detection methods are compared. This paper studies the adaptive multi-scale edge detection method based on Canny algorithm, and through theoretical analysis, compares the advantages and disadvantages of various algorithms in image edge detection. In the process of acquiring image edges, the local maximum value of the modulus is calculated along the gradient direction and the threshold is adaptively selected based on the image blocking principle. Comparing with the traditional modulus maximum edge detection method, this algorithm overcomes the contradiction that can suppress the interference of noise and obtain better edge detection effect to a certain extent. Experiments show that the method in this paper not only suppresses the interference of impulsive noise to the image edge detection, but also greatly reduces the possibility of false edges, and obtains a satisfactory accurate binary edge image. INDEX TERMS edge detection; multiple operators; adaptive dynamic weights; virtual reality scenes.