Two-Stage Image Segmentation Scheme Based on Inexact Alternating Direction Method

2016 ◽  
Vol 9 (3) ◽  
pp. 451-469 ◽  
Author(s):  
Zhanjiang Zhi ◽  
Yi Sun ◽  
Zhi-Feng Pang
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Fundamental Problem ◽  
Brain Mri ◽  
Alternating Direction Method ◽  
Natural Images ◽  
Clustering Method ◽  
Two Stage ◽  
Alternating Direction

AbstractImage segmentation is a fundamental problem in both image processing and computer vision with numerous applications. In this paper, we propose a two-stage image segmentation scheme based on inexact alternating direction method. Specifically, we first solve the convex variant of the Mumford-Shah model to get the smooth solution, the segmentation are then obtained by apply the K-means clustering method to the solution. Some numerical comparisons are arranged to show the effectiveness of our proposed schemes by segmenting many kinds of images such as artificial images, natural images, and brain MRI images.

A Divided-and-Conquer Algorithm Based on Guided Selection for Two-View Motion Segmentation

2010 ◽  
Vol 20-23 ◽  
pp. 452-458
Author(s):  
Hui Jing Wang ◽  
Kai Chen ◽  
Yi Zhou ◽  
Yan Zhang ◽  
Hai Bing Guan
Keyword(s):  
Computer Vision ◽  
Expectation Maximization ◽  
Random Sampling ◽  
Fundamental Problem ◽  
Motion Segmentation ◽  
Dynamic Scene ◽  
Clustering Method ◽  
Cluster Process ◽  
Selection For ◽  
Segmentation Problem

Motion segmentation for dynamic scene is a fundamental problem in computer vision due to its well-known chicken-and-egg character. The key issue is to estimate both numbers and parameters of motions simultaneously. Different from global clustering method and random sampling scheme, in this paper, we propose a divided-and-conquer algorithm to solve the motion segmentation problem. A guided selection is used to choose the most creditable hypothetical motion as a candidate seed and then make it grow larger. Compared to previous works such as expectation maximization and factorization approaches, there is no need for any pre-knowledge of the number of motions. To global non-parametric clustering method, it is fast because each time we only do cluster process in a partitioned sub-set. Experiments have shown that the proposal method can give a satisfying result for motion segmentation.

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.

scholarly journals Fusing Multiple Multiband Images

2018 ◽  
Vol 4 (10) ◽  
pp. 118 ◽  
Author(s):  
Reza Arablouei
Keyword(s):  
State Of The Art ◽  
Likelihood Estimation ◽  
Alternating Direction Method ◽  
Natural Images ◽  
Hyperspectral Images ◽  
Superior Performance ◽  
Method Of Multipliers ◽  
Alternating Direction ◽  
Abrupt Changes ◽  
Fused Image

High-resolution hyperspectral images are in great demand but hard to acquire due to several existing fundamental and technical limitations. A practical way around this is to fuse multiple multiband images of the same scene with complementary spatial and spectral resolutions. We propose an algorithm for fusing an arbitrary number of coregistered multiband, i.e., panchromatic, multispectral, or hyperspectral, images through estimating the endmember and their abundances in the fused image. To this end, we use the forward observation and linear mixture models and formulate an appropriate maximum-likelihood estimation problem. Then, we regularize the problem via a vector total-variation penalty and the non-negativity/sum-to-one constraints on the endmember abundances and solve it using the alternating direction method of multipliers. The regularization facilitates exploiting the prior knowledge that natural images are mostly composed of piecewise smooth regions with limited abrupt changes, i.e., edges, as well as coping with potential ill-posedness of the fusion problem. Experiments with multiband images constructed from real-world hyperspectral images reveal the superior performance of the proposed algorithm in comparison with the state-of-the-art algorithms, which need to be used in tandem to fuse more than two multiband images.

A Segmentation Method of Traffic Marking Based on Average Gray Approximating the Optimal Threshold

2014 ◽  
Vol 556-562 ◽  
pp. 3510-3513 ◽  
Author(s):  
Zu Sheng Chen ◽  
You Fu Wu
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Crucial Role ◽  
Road Traffic ◽  
Image Database ◽  
Identification Problem ◽  
Optimal Threshold ◽  
Segmentation Method ◽  
Local Average

Image segmentation technique was used widely for computer vision and image processing. A robust technique of image segmentation plays a crucial role in identification problem. In this paper, a nonparametric and unsupervised method of automatic threshold for segmenting image was proposed, i.e. the optimal threshold is approximated by global average gray and local average gray, and this method was compared with other methods by using standard image. The experimental results show that our method proposed in this paper is robust. In addition, an image database of road traffic marking (www.ananth.in/RoadMarkingdetection.html) is provided to do this experiment for testing our method, the results show that our method is excellent.

scholarly journals Texture classification using gene expression programming

2021 ◽  
Author(s):  
Fereshteh Mahvarsayyad
Keyword(s):  
Gene Expression ◽  
Image Processing ◽  
Computer Vision ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Texture Classification ◽  
Gene Expression Programming ◽  
Simple Arithmetic ◽  
Image Characteristics ◽  
Processing Step

In computer vision, segmentation refers to the process of subdividing a digital image into constituent regions with homogeneity in some image characteristics. Image segmentation is considered as a pre-processing step for object recognition. The problem of segmentation, being one of the most difficult tasks in image processing, gets more complicated in the presence of random textures in the image. This paper focuses on texture classification, which is defined as supervised texture segmentation with prior knowledge of textures in the image. We investigate a classification method using Gene Expression Programming (GEP). It is shown that GEP is capable of evolving accurate classifiers using simple arithmetic operations and direct pixel values without employing complicated feature extraction algorithms. It is also shown that the accuracy of classification is related to the fact that GEP can detect the regularities of texture patterns. As part of this project, we implemented a Photoshop plug-in that uses the evolved classifiers to identify and select target textures in digital images.

Role of Clustering Techniques in Effective Image Segmentation

2018 ◽  
pp. 128-160
Author(s):  
Bhavneet Kaur ◽  
Meenakshi Sharma
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Object Detection ◽  
Real Time ◽  
Vision Research ◽  
Medical Equipment ◽  
Additional Advantage ◽  
Clustering Techniques

Image segmentation is gauged as an essential stage of representation in image processing. This process segregates a digitized image into various categorized sections. An additional advantage of distinguishing dissimilar objects can be represented within this state of the art. Numerous image segmentation techniques have been proposed by various researchers, which maintained a smooth and easy timely evaluation. In this chapter, an introduction to image processing along with segmentation techniques, computer vision fundamentals, and its applied applications that will be of worth to the image processing and computer vision research communities has been deeply studied. It aims to interpret the role of various clustering-based image segmentation techniques specifically. Use of the proposed chapter if made in real time can project better outcomes in object detection and recognition, which can then later be applied in numerous applications and devices like in robots, automation, medical equipment, etc. for safety, advancement, and betterment of society.

An Infrared Image Segmentation Algorithm Based on Spatial Correlative Information

2010 ◽  
Vol 44-47 ◽  
pp. 3274-3278
Author(s):  
Ke Yong Wang ◽  
Cheng Tian Song ◽  
Jia Hao Deng
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Segmentation ◽  
Adaptive Algorithm ◽  
Infrared Image ◽  
Infrared Images ◽  
Background Condition ◽  
Complex Background ◽  
Infrared Image Segmentation ◽  
Image Segmentation Algorithm

Image segmentation is an important technique for image processing and computer vision. The principles of 1-D Otsu’s algorithm and thresholding through index of fuzziness are described. Since the infrared images of tank have low object-background contrasts and blurred boundaries in the complex background condition, an adaptive algorithm for image thresholding through index of fuzziness, which is combined with the spatial correlative information, is proposed. The new method makes full use of the spatial correlation of pixels, so that it can extract the detail of the image from the complex background effectively, and improve the accuracy of the segmentation. The results of experiments prove that the presented algorithm has better performance and better robustness against noise.

scholarly journals Brain Tumor Detection and Classification Using Image Processing Techniques

2021 ◽  
pp. 32-38
Author(s):  
P. Sankar Ganesh ◽  
T. Selva Kumar ◽  
Mukesh Kumar ◽  
Mr. S. Rajesh Kumar
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Human Body ◽  
Brain Mri ◽  
X Rays ◽  
Abnormal Growth ◽  
Brain Functions ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

At present, processing of medical images is a developing and important field. It includes many different types of imaging methods. Some of them are Computed Tomography scans (CT scans), X-rays and Magnetic Resonance Imaging (MRI) etc. These technologies allow us to detect even the smallest defects in the human body. Abnormal growth of tissues in the brain which affect proper brain functions is considered as a brain tumor. The main goal of medical image processing is to identify accurate and meaningful information using images with the minimum error possible. MRI is mainly used to get images of the human body and cancerous tissues because of its high resolution and better quality images compared with other imaging technologies. Brain tumor identifications through MRI images is a difficult task because of the complexity of the brain. MRI images can be processed and the brain tumor can be segmented. These tumors can be segmented using various image segmentation techniques. The process of identifying brain tumors through MRI images can be categorized into four different sections; pre-processing, image segmentation, feature extraction and image classification.

scholarly journals Interactive Natural Image Segmentation and Foreground Extraction

2018 ◽  
Vol 7 (3.27) ◽  
pp. 70
Author(s):  
Y David Solomon Raju ◽  
D Krishna Reddy
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Green's Function ◽  
Experimental Results ◽  
Natural Images ◽  
Natural Image ◽  
Method Performance ◽  
Foreground Extraction ◽  
Interactive Image Segmentation ◽  
Benchmark Datasets

Interactive image segmentation is very practical and important problem in computer vision.  In this paper a regressive based Green’s function is employed to formulate the problem of segmentation. The method is incorporated with different clustering approaches intended to extract the foreground regions from the natural images. The method performance is improved with proper labeling of foreground and background regions, and with more number of cluster regions. The method is evaluated with two standard benchmark datasets and found that the experimental results were promising.  

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