scholarly journals Visual Sensor Based Image Segmentation by Fuzzy Classification and Subregion Merge

2017 ◽  
Vol 2017 ◽  
pp. 1-15
Author(s):  
Huidong He ◽  
Xiaoqian Mao ◽  
Wei Li ◽  
Linwei Niu ◽  
Genshe Chen
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Color Image ◽  
Color Image Segmentation ◽  
Fuzzy Classification ◽  
Spatial Proximity ◽  
Visual Sensor ◽  
Detection Algorithms ◽  
Color Similarity ◽  
Different Color

The extraction and tracking of targets in an image shot by visual sensors have been studied extensively. The technology of image segmentation plays an important role in such tracking systems. This paper presents a new approach to color image segmentation based on fuzzy color extractor (FCE). Different from many existing methods, the proposed approach provides a new classification of pixels in a source color image which usually classifies an individual pixel into several subimages by fuzzy sets. This approach shows two unique features: the spatial proximity and color similarity, and it mainly consists of two algorithms: CreateSubImage and MergeSubImage. We apply the FCE to segment colors of the test images from the database at UC Berkeley in the RGB, HSV, and YUV, the three different color spaces. The comparative studies show that the FCE applied in the RGB space is superior to the HSV and YUV spaces. Finally, we compare the segmentation effect with Canny edge detection and Log edge detection algorithms. The results show that the FCE-based approach performs best in the color image segmentation.

scholarly journals Color Image Segmentation Based on Different Color Space Models Using Automatic GrabCut

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Dina Khattab ◽  
Hala Mousher Ebied ◽  
Ashraf Saad Hussein ◽  
Mohamed Fahmy Tolba
Keyword(s):  
Image Segmentation ◽  
Comparative Study ◽  
Color Image ◽  
Color Space ◽  
User Interaction ◽  
Color Image Segmentation ◽  
Space Representation ◽  
Color Spaces ◽  
Different Color ◽  
The Comparative Study

This paper presents a comparative study using different color spaces to evaluate the performance of color image segmentation using the automatic GrabCut technique. GrabCut is considered as one of the semiautomatic image segmentation techniques, since it requires user interaction for the initialization of the segmentation process. The automation of the GrabCut technique is proposed as a modification of the original semiautomatic one in order to eliminate the user interaction. The automatic GrabCut utilizes the unsupervised Orchard and Bouman clustering technique for the initialization phase. Comparisons with the original GrabCut show the efficiency of the proposed automatic technique in terms of segmentation, quality, and accuracy. As no explicit color space is recommended for every segmentation problem, automatic GrabCut is applied withRGB,HSV,CMY,XYZ, andYUVcolor spaces. The comparative study and experimental results using different color images show thatRGBcolor space is the best color space representation for the set of the images used.

Color Image Segmentation of Endoscopic and Microscopic Images for Abnormality Detection in Esophagus

2012 ◽  
pp. 165-193
Author(s):  
P. S. Hiremath ◽  
Iranna Y. Humnabad
Keyword(s):  
Image Segmentation ◽  
Human Body ◽  
Color Image ◽  
Color Image Segmentation ◽  
Microscopic Images ◽  
Region Detection ◽  
Endoscopic Images ◽  
Detection And Identification ◽  
Segmented Images ◽  
Different Color

The study of medical image analysis encompasses the various techniques for acquisition of images of biological structures pertaining to human body using radiations in different frequency ranges. The advancements in medical imaging over the past decades are enabling physicians to non-invasively peer inside the human body for the purpose of diagnosis and therapy. In this chapter, the objective is to focus on the studies relating to the analysis of endoscopic images of lower esophagus for abnormal region detection and identification of cancerous growth. Several color image segmentation techniques have been developed for automatic detection of cancerous regions in endoscopic images, which assists the physician for faster, proper diagnosis and treatment of the disease. These segmentation methods are evaluated for comparing their performances in different color spaces, namely, RGB, HSI, YCbCr, HSV, and CIE Lab. The segmented images are expected to assist the medical expert in drawing the biopsy samples precisely from the detected pathological legions. Further, various methods have been proposed for segmentation and classification of squamous cell carcinoma (SCC) from color microscopic images of esophagus tissue during pathological investigation. The efficacy of these methods has been demonstrated experimentally with endoscopic and microscopic image set and compared with manual segmentation done by medical experts. It is envisaged that the research in this direction eventually leads to the design and production of efficient intelligent computer vision systems for assisting the medical experts in their task of speedy accurate diagnosis of diseases and prescription of appropriate treatment of the patients.

The combination of edge detection and region extraction in nonparametric color image segmentation

1996 ◽  
Vol 92 (1-4) ◽  
pp. 277-294 ◽  
Author(s):  
Naoko Ito ◽  
Ryu Kamekura ◽  
Yoshihisa Shimazu ◽  
Teruo Yokoyama ◽  
Yutaka Matsushita
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Color Image ◽  
Color Image Segmentation ◽  
Region Extraction

scholarly journals Wavelet adaptive quantization based color image segmentation

2020 ◽  
Author(s):  
Anand Swaminathan ◽  
K.Venkata Subramaniyan ◽  
Tiruppathirajan G. ◽  
Rajkumar J
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Region Growing ◽  
Color Image Segmentation ◽  
Modern World ◽  
New Approach ◽  
Processing Step ◽  
Segmentation Algorithms ◽  
Color Similarity ◽  
Homogeneous Regions

Image segmentation is an important pre-processing step towards higher level tasks such as object recognition, computer vision or image compression. Most of the existing segmentation algorithms deal with grayscale images only. But in the modern world, color images are extensively used in many situations. A new approach for color image segmentation is presented in this paper. There are many ways to deal with image segmentation problem and in these techniques; a particular class of algorithms traces their origin from region-based methods. These algorithms group homogeneous pixels, which are connected to primitive regions. They are easy to implement and are promising. Therefore, here one of the most efficient region-based segmentation algorithms is explained. The color image is quantized adaptively, using a wavelet transform. Then the region growing process is adopted. As preprocess, before actual region merging, small regions are eliminated by merging them with neighbor regions depending upon color similarity. After this, homogeneous regions are merged to get segmented output.

scholarly journals Interactive Color Image Segmentation using HSV Color Space

2019 ◽  
Vol 7 (1) ◽  
pp. 37-41
Author(s):  
D. Hema ◽  
◽  
Dr. S. Kannan ◽  
Keyword(s):  
Image Segmentation ◽  
Object Detection ◽  
Color Image ◽  
Research Work ◽  
Color Space ◽  
Color Image Segmentation ◽  
Hsv Color Space ◽  
Space Model ◽  
Detection Algorithms ◽  
Rgb Image

The primary goal of this research work is to extract only the essential foreground fragments of a color image through segmentation. This technique serves as the foundation for implementing object detection algorithms. The color image can be segmented better in HSV color space model than other color models. An interactive GUI tool is developed in Python and implemented to extract only the foreground from an image by adjusting the values for H (Hue), S (Saturation) and V (Value). The input is an RGB image and the output will be a segmented color image.

scholarly journals Fuzzy Magnetic Color Image Segmentation and Advanced Edge Detection

2009 ◽  
Author(s):  
Christy George
Keyword(s):  
Fuzzy Logic ◽  
Image Segmentation ◽  
Edge Detection ◽  
Color Image ◽  
Color Images ◽  
Color Image Segmentation ◽  
Color Information ◽  
Sobel Operator ◽  
Logic Inference ◽  
The Given

In this paper, color image segmentation based on fuzzy logic has been studied. Anefficient Fuzzy logic inference engine based on magnetic fields has been implemented in this project so as to intelligently extract color information in the given image and classify it into the predominant color pyramid with the help of artificial color magnets. This also forms the basis for the priority based edge detection. Sobel operator is used in this project which is intelligently fused with the result of the above mentioned method to enhance the output so as to obtain a priority based enhanced edge detection output. Experimental results have demonstrated the effectiveness and superiority of the proposed method after extensive set of color images was tested.

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