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Edge is basically the symbol and reflection of partial image discreteness. It is one of the most commonly used operations in image processing and pattern recognition, it contains a wealth of internal information leading to strong interpretation of image. Resisting against noise, illumination and extracting appropriate features from an image is a great challenge in many computer vision applications. Indeed this topic participates to reduce the handled information and focuses on those related to existing objects. Efficient and accurate edge detection will lead to increase in the performance of many computer vision applications, including image segmentation, object-based image coding and image retrieval. Contour detection contributes to locate pixel sets which correspond to sudden intensities variation, these unstable properties of the given image commonly suggest to important events on going in the scene. In this paper, we present in the first time a novel and robust method for edge detection based on joint and conditional entropy when we highlight a Shannon theory, the second part of this paper is dedicated to decision making of edge pixels membership by intelligent method based on fuzzy logic tool. [ABSTRACT FROM AUTHOR]

In computer vision, automatically identifying and locating text in images or videos is an important task. Traditional text detection methods are not effective in detecting complex scenes in irregular rectangular areas such as text bending, spacing, and special shapes, which are mainly reflected in the fragmentation of text detection areas. In this paper, a text detection method based on edge attention mechanism is proposed to better adapt to complex scenes. The proposed method takes Encoder–Decoder as the core idea. First of all, an edge attention module is designed, including global attention and local attention. The global attention module is used to perceive the features of text regions and nontext regions, while the local attention module is used to learn the information of text boundaries. Then a multi-scale feature fusion process is designed, which can strengthen the edge information and key information of text regions. Finally, the model outputs probability maps and threshold maps, and generates high-precision binary maps of text regions. After experimental verification, the proposed method on the public data set significantly reduces the fragmentation of the detection area, improves the detection accuracy of the text area, and has better robustness for text detection scenes with unconventional rectangular areas. [ABSTRACT FROM AUTHOR]

Image segmentation (IE) in several disciplines of image processing and computer vision is an essential topic. Segmentation splits a picture into the areas or items that it constitutes. Image segmentation may be achieved with many approaches, some easier than others because of sophisticated programming requirements. The most common technique for segmenting pictures is edge detection (ED) based on sudden (locomotive) intensity fluctuations. This paper aims to study edge detection approaches for the division of images and acquired experimental findings, Sobel, Prewitt, Robert, CannyLoG (Laplacian of Gaussian). It is vital to ensure that picture segmentation algorithms deliver correct results quickly and efficiently for computer vision to reach its full potential. Computer vision approaches require more investigation in hierarchical architectural IoT networks created for seeing the world. In this work, the new way to provide joint image detection (JID) algorithm is to provide multi-scaling approaches for edge detection and segmentation using IoT edge computing (EC). This JID-EC method avoids the requirement to choose and track the edge explicitly. This study provides an overview of fundamental ideas, techniques, and algorithms common to segment images and edge detection, focusing on the segmentation and visualization of joint-articular cartilage images. The reason for this failure is that it is an image noise-sensitive high pass filter. The need for improved algorithms to meet a suitable value of low and high thresholds should thus be stressed for picture noise such as a canny edge, and the performance is achieved with an efficiency of 95.2%. [ABSTRACT FROM AUTHOR]

Edge detection is one of the most fundamental fields in computer vision. With the rapid development of the combination of Convolutional Neural Network and Multi-Scale Representation of image, significant progress has been made in this field. However, most of them have a huge size, which makes it hard to apply in reality, and a huge number of parameters may lead to waste of computing resources. In this paper, we focus on qualitative analysis of the role of each part in the network, and propose a modified light-weight architecture based on our result and the study of former works. Our new architecture is composed of residual-blocks, max-pooling layers and batch normalization layers. Compared with the previous models, the new architecture performs better in memory, convergence and computation efficiency with similar model size. Moreover, the new architecture can achieve better accuracy with smaller model size. When evaluating our model on the well-known BSDS500 benchmark, we achieve ODS F-measure of 0.769 with parameters less than 0.3 M, which shows a better property than the state-of-the-art result 0.766 at this level. [ABSTRACT FROM AUTHOR]

Edges are prominent features in images. The detection and analysis of edges are key issues in image processing, computer vision and pattern recognition. Wavelet provides a powerful tool to analyze the local regularity of signals. Wavelet transform has been successfully applied to the analysis and detection of edges. A great number of wavelet-based edge detection methods have been proposed over the past years. The objective of this paper is to give a brief review of these methods, and encourage the research of this topic. In practice, an image is usually of multi-structure edge, the identification of different edges, such as steps, curves and junctions play an important role in pattern recognition. In this paper, more attention is paid on the identification of different types of edges. We present the main idea and the properties of these methods. [ABSTRACT FROM AUTHOR]

Visual illusion is the fallacious perception of reality or some actually existing object. In this paper, we imitate the mechanism of Ehrenstein illusion, neon color spreading illusion, watercolor illusion, Kanizsa illusion, shifted edges illusion, and hybrid image illusion using the Open Source Computer Vision Library (OpenCV). We also imitate these illusions using Cellular Neural Networks (CNNs). These imitations suggest that some illusions are processed by high-level brain functions. We next apply the morphological gradient operation to anomalous motion illusions. The processed images are classified into two kinds of images, which correspond to the central drift illusion and the peripheral drift illusion, respectively. It demonstrates that the contrast of the colors plays an important role in the anomalous motion illusion. We also imitate the anomalous motion illusions using both OpenCV and CNN. These imitations suggest that some visual illusions may be processed by the illusory movement of animations. [ABSTRACT FROM AUTHOR]

This paper proposes a wavelet based multilevel edge detection method that exploits spline dyadic wavelets and a frame work similar to that of Canny's edge detector.2 Using the recently proposed dyadic lifting schemes by Turghunjan et al.1 spline dyadic wavelet filters have been constructed, which are characterized by higher order of regularity and have the potential of better inherent noise filtering and detection results. Edges are determined as the local maxima in the subbands at different scales of the dyadic wavelet transform. Comparison reveals that our method performs better than Mallat's and Canny's edge detectors. [ABSTRACT FROM AUTHOR]

In computer vision and image processing, edge detection concerns the localization of significant variations of the grey level image and the identification of the physical phenomena that originated them. It is difficult to design a general edge detection algorithm which performs well in many contexts and captures the requirements of subsequent processing stages. Consequently, over the history of digital image processing, a variety of edge detectors have been devised which differ in their mathematical and algorithmic properties. In this paper, we briefly describe some existing edge detectors. We investigate the wavelet transform with direction which is related to the Canny and Marr–Hildreth's edge detectors. This wavelet transform has a remarkable feature that plays an important role in image processing applications. We discuss its applications to edge and target detections. The direction effect enables the wavelets to analyze the directional features of images. Algorithms and experiments for edge and target detections have been developed based on the theory. And, very promising results have been shown in image processing. [ABSTRACT FROM AUTHOR]

Edge detection is a major step in several computer vision applications. Edges define the shape of objects to be used in a recognition system, for example. In this work, we introduce an approach to edge detection inspired by a challenge for artists: the Speed Drawing Challenge. In this challenge, a person is asked to draw the same figure in different times (as 10 min, 1 min and 10 s); at each time, different levels of details are drawn by the artist. In a short time stamp, just the major elements remain. This work proposes a new approach for producing images with different amounts of edges representing different levels of relevance. Our method uses superpixel to suppress image details, followed by Globalized Probability of Boundary (gPb) and Canny edge detection algorithms to create an image containing different number of edges. After that, an edge analysis step detects whose edges are the most relevant for the scene. The results are presented for the BSDS500 dataset and they are compared to other edge and contour detection algorithms by quantitative and qualitative means with very satisfactory results. [ABSTRACT FROM AUTHOR]

Edge detection is a very important area in the field of Computer Vision. Edge detectors behave very poorly, their behavior may fall within tolerance in specific situations and have difficulty in adapting to different situations. Human vision is inherently a multiscale phenomenon and is sensitive to orientation and elongation. This work proposes the hardware implementation of efficient fuzzy logic based algorithm, which is used to detect the edges of an image without determining the threshold value. Edge detection in software is not suited for strong real-time applications. This problem is resolved by using hardware implementation on field programmable gate arrays (FPGAs). Fuzzy inference system is developed with four input pixel containing two fuzzy sets (FSs) one for white and another for black and one output pixel containing three FSs for white, black and edge. Fuzzy if-then rules are used to modify the membership functions. Finally, Mamdanidefuzzifier method is used to form the final edge image. For comparison, the same work was implemented using sobel operator. The hardware part is developed by using Verilog language. The FPGA implementation is targeted on Virtex5 Starter kit (xc5vlx50tff1136-1) and Virtex7 starter kit (xc7vx485tffq1157-1) using the updated Xilinx PlanAhead within the ISE 13.4 development suite. The edge thickness can be changed easily by adding new rules or changing output parameters. That is, rule-based approach has flexible structure that can be easily adapted to any time or anywhere and the new fuzzy approach produces better result than sobel operator. Experimental results show the ability and high performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Edge detection is one of the most commonly used operations in image processing and computer vision areas. Edges correspond to the boundaries between regions in an image, which are useful for object segmentation and recognition tasks. This work presents a novel method for 3D edge detection based on Boolean functions and local operators, which is an extension of the 2D edge detector introduced by Vemis et al. [ Signal Processing 45(2), 161-172 (1995)] The proposed method is composed of two main steps. An adaptive binarization process is initially applied to blocks of the image and the resulting binary map is processed with a set of Boolean functions to identify edge points within the blocks. A global threshold, calculated to estimate image intensity variation, is then used to reduce false edges in the image blocks. The proposed method is compared to other 3D gradient filters: Canny, Monga-Deriche, Zucker-Hummel and Sobel operators. Experimental results demonstrate the effectiveness of the proposed technique when applied to several 3D synthetic and real data sets. [ABSTRACT FROM AUTHOR]