Computer Vision Algorithms for Image Segmentation, Motion Detection, and Classification

2020 ◽  
pp. 119-138
Author(s):  
Mehrdad Ghaziasgar ◽  
Antoine Bagula ◽  
Christopher Thron
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Motion Detection

Hybrid Image Segmentation Within a Computer Vision Hierarchy

1993 ◽  
Vol 30 (1) ◽  
pp. 51-64
Author(s):  
Ray Thomas ◽  
Fariborz Zahedi
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Vision System ◽  
Critical Role ◽  
Generic Model ◽  
Test Results ◽  
Segmentation Approach ◽  
Edge Based

Hybrid image segmentation within a computer vision hierarchy A generic model of a computer vision system is presented which highlights the critical role of image segmentation. A hybrid segmentation approach, utilising both edge-based and region-based techniques, is proposed for improved quality of segmentation. An image segmentation architecture is outlined and test results are presented and discussed.

scholarly journals Image Segmentation Methods for Flood Monitoring System

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1825
Author(s):  
Nur Muhadi ◽  
Ahmad Abdullah ◽  
Siti Bejo ◽  
Muhammad Mahadi ◽  
Ana Mijic
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Digital Images ◽  
Rapid Development ◽  
Jaccard Index ◽  
Monitoring Systems ◽  
Dice Similarity Coefficient ◽  
Hybrid Technique ◽  
Segmentation Methods ◽  
Flood Monitoring

Flood disasters are considered annual disasters in Malaysia due to their consistent occurrence. They are among the most dangerous disasters in the country. Lack of data during flood events is the main constraint to improving flood monitoring systems. With the rapid development of information technology, flood monitoring systems using a computer vision approach have gained attention over the last decade. Computer vision requires an image segmentation technique to understand the content of the image and to facilitate analysis. Various segmentation algorithms have been developed to improve results. This paper presents a comparative study of image segmentation techniques used in extracting water information from digital images. The segmentation methods were evaluated visually and statistically. To evaluate the segmentation methods statistically, the dice similarity coefficient and the Jaccard index were calculated to measure the similarity between the segmentation results and the ground truth images. Based on the experimental results, the hybrid technique obtained the highest values among the three methods, yielding an average of 97.70% for the dice score and 95.51% for the Jaccard index. Therefore, we concluded that the hybrid technique is a promising segmentation method compared to the others in extracting water features from digital images.

A Novel Neutrosophic Image Segmentation Based on Improved Fuzzy C-Means Algorithm (NIS-IFCM)

2019 ◽  
Vol 34 (05) ◽  
pp. 2055011
Author(s):  
Jing Zhao ◽  
Xiaoli Wang ◽  
Ming Li
Keyword(s):  
Computer Vision ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Classical Problem ◽  
Local Optimum ◽  
The Novel ◽  
Swarm Optimization ◽  
Novel Method ◽  
Fuzzy C Means Algorithm ◽  
Novel Algorithm

Image segmentation is a classical problem in the field of computer vision. Fuzzy [Formula: see text]-means algorithm (FCM) is often used in image segmentation. However, when there is noise in the image, it easily falls into the local optimum, which results in poor image boundary segmentation effect. A novel method is proposed to solve this problem. In the proposed method, first, the image is transformed into a neutrosophic image. In order to improve the ability of global search, a combined FCM based on particle swarm optimization (PSO) is proposed. Finally, the proposed algorithm is applied to the neutrosophic image segmentation. The results of experiments show that the novel algorithm can eliminate image noise more effectively than the FCM algorithm, and make the boundary of the segmentation area clearer.

SCREEN IMAGE SEGMENTATION AND CORRECTION FOR A COMPUTER DISPLAY

2014 ◽  
Vol 28 (04) ◽  
pp. 1454002 ◽  
Author(s):  
YUNG-SHENG CHEN ◽  
KUN-LI LIN
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Affine Transformation ◽  
Physical World ◽  
Screen Image ◽  
Digital World ◽  
The Look ◽  
The Given ◽  
Deformation Correction ◽  
Vision Application

Perception of content displayed on the screen of a computer display using computer vision is a challenging topic if the treated target is changed from physical world to digital world. Screen area from the given computer display image should be segmented and corrected primarily before perceiving the content displayed on the screen. An automatic approach is proposed to the segmentation and deformation correction of screen area for a computer display image. Due to some inherent characteristics existing on ordinary computer displays, the segmentation can be performed by contour tracing. After contouring the screen area, its four corner locations can be readily identified. By approximating the obtained corners to the closest normal screen region, the deformed screen image can be further restored with affine transformation. As a computer vision application on the "look at" screen image, the effectively segmented screen region can be fixed after a little time. The experiments demonstrate that about 70% cases can be fixed under 33 processed frames, others under 51 processed frames, and thus confirm the feasibility of the proposed approach.

scholarly journals Markov Models for Image Labeling

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
S. Y. Chen ◽  
Hanyang Tong ◽  
Carlo Cattani
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Discrete Optimization ◽  
Belief Propagation ◽  
Markov Models ◽  
Optimization Methods ◽  
Image Labeling ◽  
Markov Random ◽  
Basic Concepts ◽  
Fundamental Formulation

Markov random field (MRF) is a widely used probabilistic model for expressing interaction of different events. One of the most successful applications is to solve image labeling problems in computer vision. This paper provides a survey of recent advances in this field. We give the background, basic concepts, and fundamental formulation of MRF. Two distinct kinds of discrete optimization methods, that is, belief propagation and graph cut, are discussed. We further focus on the solutions of two classical vision problems, that is, stereo and binary image segmentation using MRF model.

scholarly journals Implementasi Canny Edge Detection Pada Aplikasi Pendeteksi Jalur Lalu Lintas

2021 ◽  
Vol 5 (1) ◽  
pp. 164
Author(s):  
Ratna Salkiawati ◽  
Allan Desi Alexander ◽  
Hendarman Lubis
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Image Understanding ◽  
Canny Edge Detection ◽  
Traffic Lane ◽  
Sense Of Security ◽  
Canny Edge

Based on the traffic accident report, it was found that there were 41,771 (Forty-one thousand seven hundred and seventy-one) incidents caused by disorderly drivers. (POLRI, 2018). One of these disorders is by driving a motorized vehicle outside the traffic lane. In this study, researchers developed computer vision using sensor methods and image processing. The stages in computer vision are the image acquisition process, the image segmentation process, and the image understanding process. This study aims to develop an application using computer vision to warn drivers of disorderly traffic or to increase the alertness of motorized vehicle drivers by detecting the condition of the driver's path. It is hoped that this research will provide a sense of security for motorized vehicle drivers, as well as provide applications that are expected to increase driver awareness to avoid traffic accidents

scholarly journals A REVIEW ON IMAGE SEGMENTATION USING GPU

2016 ◽  
Vol 15 (10) ◽  
pp. 7160-7163
Author(s):  
Gurpreet Kaur ◽  
Sonika Jindal
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
System Resources ◽  
Cuda Architecture ◽  
Graphics Processing

Image Segmentations play a heavy role in areas such as computer vision and image processing due to its broad usage and immense applications. Because of the large importance of image segmentation a number of algorithms have been proposed and different approaches have been adopted. Segmentation divides an image into distinct regions containing each pixel with similar attributes. The objective of apportioning is to simplify and/or alter the representation of an image into something that is more meaningful and more comfortable to break down. This paper discusses the various techniques implemented for image segmentation and discusses the various Computations that can be performed on the graphics processing unit (GPU) by means of the CUDA architecture in order to achieve fast performance and increase the utilization of available system resources.

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