scholarly journals Study of Object Detection in Sonar Image using Image Segmentation and Edge Detection Methods

Author(s):  
U. Anitha ◽  
S. Malarkkan ◽  
J. Premalatha ◽  
P. Grace Kanmani Prince
Author(s):  
Shouvik Chakraborty ◽  
Mousomi Roy ◽  
Sirshendu Hore

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.


2020 ◽  
Vol 17 (4) ◽  
pp. 172988142093609
Author(s):  
Yuanyuan Tian ◽  
Luyu Lan ◽  
Haitao Guo

The sonar image segmentation is needed such as in underwater object orientation and recognition, in collision prevention and navigation of underwater robots, in underwater investigation and rescue, in seafloor object seeking, in seafloor salvage, and in marine military affairs like torpedo detection. The wavelet-based methods have the ability of multiscale and multiresolution, and they are apt at edge detection and feature extraction of images. The applications of these methods to the sonar image segmentation are increasingly raised. The contents of the article are to classify the sonar image segmentation methods with wavelets and to describe main ideas, advantages, disadvantages, and conditions of use of every method. In the methods for sonar image region (or texture) segmentation, the thought of multiscale (or multiresolution) analysis of the wavelet transform is usually combined with other theories or methods such as the clustering algorithms, the Markov random field, co-occurrence matrix, Bayesian theory, and support vector machine. In the methods for sonar image edge detection, the space–frequency local characteristics of the wavelet transform are usually utilized. The wavelet packet-based and beyond wavelet-based methods can usually reach more precise segmentation. The article also gives 12 directions (or development trends predicted) of the sonar image segmentation methods with wavelets which should be researched deeply in the future. The aim of writing this review is to make the researchers engaged in sonar image segmentation learn about the research works in the field in a short time. Up to now, the similar reviews in this field have not been found.


2014 ◽  
Vol 998-999 ◽  
pp. 929-933
Author(s):  
Lu Yi Li ◽  
Jun Yong Ye

In the segmentation algorithms of the depth image, because the object and its support surface are continuous in the depth data ,the traditional method of edge detection methods can’t detect the edge between the object and its support surface. To solve this problem, the segmentation algorithm of the depth image is studied in this paper. Firstly, we use canny operator to detect the edge the of depth image of the scene. Then the depth image of the scene is transformed into points of a 3-D space coordinate and normal vector is calculated for each point. The method of calculation the direction of the normal vector is used to determine the point of which belongs to the support surface area, which determine the support surface area of the scene. Finally, we detect image edge of the image that the support surface area is extracted, and fuse the result of canny operator edge detection and edge of the image that the support surface area is extracted. Experiments show that the segmentation algorithm works well, which the problem of detection the edge between the support surface area and the object and can also achieve a good depth image segmentation.


2021 ◽  
Vol 7 (5) ◽  
pp. 77
Author(s):  
Wesley T. Honeycutt ◽  
Eli S. Bridge

Few object detection methods exist which can resolve small objects (<20 pixels) from complex static backgrounds without significant computational expense. A framework capable of meeting these needs which reverses the steps in classic edge detection methods using the Canny filter for edge detection is presented here. Sample images taken from sequential frames of video footage were processed by subtraction, thresholding, Sobel edge detection, Gaussian blurring, and Zhang–Suen edge thinning to identify objects which have moved between the two frames. The results of this method show distinct contours applicable to object tracking algorithms with minimal “false positive” noise. This framework may be used with other edge detection methods to produce robust, low-overhead object tracking methods.


2021 ◽  
Vol 5 (6) ◽  
pp. 1062-1069
Author(s):  
Shoffan Saifullah ◽  
Andiko Putro Suryotomo ◽  
Yuhefizar

This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values ​​of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.


Image segmentation is the method to fragment a given image into a number of Regions or objects. The level of detail to which the partition is carried depends on the problem being solved. Edge detection is mostly used techniques in digital image processing. Edge detection will preserve the structural properties of an image and filter out unwanted dsata. In this paper, Edge detection methods such as Sobel, Prewitt, Robert, Canny, and Laplacian of Gaussian (LOG) are used. These methods are used in image segmentation. Edge detection can be enhanced by combining with denoised image. Wiener filter, Gaussian Filter and Median Filters are used for noise reductionS. The results of various methods are analyzed by implemented in MATLAB.


2018 ◽  
pp. 1686-1708 ◽  
Author(s):  
Shouvik Chakraborty ◽  
Mousomi Roy ◽  
Sirshendu Hore

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.


2016 ◽  
Vol 3 (2) ◽  
pp. 26
Author(s):  
HEMALATHA R. ◽  
SANTHIYAKUMARI N. ◽  
MADHESWARAN M. ◽  
SURESH S. ◽  
◽  
...  

Author(s):  
M. N. Favorskaya ◽  
L. C. Jain

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.


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