Color Image Region Growth Segmentation Integration of Normalized Cut

2010 ◽  
Vol 143-144 ◽  
pp. 139-142
Author(s):  
Xiao Ying Wu ◽  
Li Juan Ma ◽  
Zhao Feng Li ◽  
Shi Tao Yan
Keyword(s):  
Color Image ◽  
Image Features ◽  
Human Vision ◽  
Segmentation Result ◽  
Segmentation Method ◽  
Human Visual Perception ◽  
Normalized Cut ◽  
Image Region ◽  
Region Growth ◽  
Selection Of

This paper solves that image segmentation result is not consistent with human visual perception or too broken. First of all, based on the continuity of image features, appropriate human vision, calculated the similarity of color image pixel as Eq.2 in HSV space to grow region, then made the regional merge, using normalized-cut segmentation method as Eq.4 and Eq.5 to eliminate over-segmentation phenomenon. In this paper, experimental results shows that the segmentation can be achieved very good results as Fig.1, and parts of the method can be applied in other segmentation to solve over segmentation. This method on color images as the research object is different from other methods on gray images, the selection of seeds and achieves these automatic that differ from general algorithms, presents a new implementation to solve over-segmentation.

scholarly journals A Multi-Model Stereo Similarity Function Based on Monogenic Signal Analysis in Poisson Scale Space

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Jinjun Li ◽  
Hong Zhao ◽  
Chengying Shi ◽  
Xiang Zhou
Keyword(s):  
Color Image ◽  
Image Features ◽  
Image Feature ◽  
Similarity Function ◽  
Local Orientation ◽  
Stereo Images ◽  
Interest Points ◽  
Monogenic Signal ◽  
Local Color ◽  
Instantaneous Phase

A stereo similarity function based on local multi-model monogenic image feature descriptors (LMFD) is proposed to match interest points and estimate disparity map for stereo images. Local multi-model monogenic image features include local orientation and instantaneous phase of the gray monogenic signal, local color phase of the color monogenic signal, and local mean colors in the multiscale color monogenic signal framework. The gray monogenic signal, which is the extension of analytic signal to gray level image using Dirac operator and Laplace equation, consists of local amplitude, local orientation, and instantaneous phase of 2D image signal. The color monogenic signal is the extension of monogenic signal to color image based on Clifford algebras. The local color phase can be estimated by computing geometric product between the color monogenic signal and a unit reference vector in RGB color space. Experiment results on the synthetic and natural stereo images show the performance of the proposed approach.

scholarly journals Mean-Based Breakpoint Selection on Circular Histogram

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jiulun Fan ◽  
Jipeng Yang
Keyword(s):  
Selection Criteria ◽  
Color Image ◽  
Selection Criterion ◽  
Threshold Value ◽  
Simple Expression ◽  
Circular Data ◽  
Cumulative Distribution ◽  
Color Model ◽  
Hsi Color Model ◽  
Selection Of

Circular histogram represents the statistical distribution of circular data; the H component histogram of HSI color model is a typical example of the circular histogram. When using H component to segment color image, a feasible way is to transform the circular histogram into a linear histogram, and then, the mature gray image thresholding methods are used on the linear histogram to select the threshold value. Thus, the reasonable selection of the breakpoint on circular histogram to linearize the circular histogram is the key. In this paper, based on the angles mean on circular histogram and the line mean on linear histogram, a simple breakpoint selection criterion is proposed, and the suitable range of this method is analyzed. Compared with the existing breakpoint selection criteria based on Lorenz curve and cumulative distribution entropy, the proposed method has the advantages of simple expression and less calculation and does not depend on the direction of rotation.

scholarly journals Creating Color Image Features Using Local Contrast Method

2018 ◽  
Vol 7 (3) ◽  
pp. 367-376
Author(s):  
Ayman Al-Rawashdeh ◽  
Ziad Al-Qadi
Keyword(s):  
Image Recognition ◽  
Color Image ◽  
Digital Processing ◽  
Image Features ◽  
Local Contrast ◽  
Image Size ◽  
Data Types ◽  
Memory Space ◽  
Contrast Method ◽  
Image Pixels

Digital color images are now one of the most popular data types used in the digital processing environment. Color image recognition plays an important role in many vital applications, which makes the enhancement of image recognition or retrieval system an important issue. Using color image pixels to recognize or retrieve the image, but the issue of the huge color image size that requires accordingly more time and memory space to perform color image recognition and/or retrieval. In the current study, image local contrast was used to create local contrast victor, which was then used as a key to recognize or retrieve the image. The proposed local contrast method was properly implemented and tested. The obtained results proved its efficiency as compared with other methods.

Research of Remote Sensing Image Segmentation Based on Mean Shift and Region Merging

2011 ◽  
Vol 90-93 ◽  
pp. 2836-2839 ◽  
Author(s):  
Jian Cui ◽  
Dong Ling Ma ◽  
Ming Yang Yu ◽  
Ying Zhou
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Large Scale ◽  
Mean Shift ◽  
Image Features ◽  
Small Scale ◽  
Region Merging ◽  
Segmentation Method ◽  
Shift Method ◽  
Scale Parameters

In order to extract ground information more accurately, it is important to find an image segmentation method to make the segmented features match the ground objects. We proposed an image segmentation method based on mean shift and region merging. With this method, we first segmented the image by using mean shift method and small-scale parameters. According to the region merging homogeneity rule, image features were merged and large-scale image layers were generated. What’s more, Multi-level image object layers were created through scaling method. The test of segmenting remote sensing images showed that the method was effective and feasible, which laid a foundation for object-oriented information extraction.

Color Image Segmentation Method Based on Parameter-Controlled MSPCNN and SLIC

2021 ◽  
Vol 58 (2) ◽  
pp. 0210023
Author(s):  
李新颖 Li Xinying ◽  
冉思园 Ran Siyuan ◽  
廉敬 Lian Jing
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation ◽  
Segmentation Method

Algorithm for Petro-Graphic Color Image Segmentation Used in Oil Exploration

2014 ◽  
pp. 187-195 ◽  
Keyword(s):  
Image Segmentation ◽  
Thin Section ◽  
Heuristic Algorithm ◽  
Color Image ◽  
Color Image Segmentation ◽  
Oil Exploration ◽  
Segmentation Method ◽  
Graphic Images ◽  
Oil Gas ◽  
Automatic Algorithm

A new heuristic algorithm for porosity segmentation for the colored petro-graphic images is proposed. The proposed algorithm automatically detects the porosities that represent the presence of oil, gas, or even water in the analyzed thin section rock segment based on the colour of the porosity area filled with dies in the analyzed sample. For the purpose of the oil exploration, the thin section fragments are died in order to emphasize the porosities that are analyzed under the microscope. The percentage of the porosity is directly proportional to the probability of the oil, gas, or even water presence in the area where the drilling is performed (i.e. the increased porosity indicates the higher probability of oil existence in the region). The proposed automatic algorithm shows better results than the existing K-means segmentation method.

scholarly journals Image Deblurring Using Multi-Stream Bottom-Top-Bottom Attention Network and Global Information-Based Fusion and Reconstruction Network

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3724
Author(s):  
Quan Zhou ◽  
Mingyue Ding ◽  
Xuming Zhang
Keyword(s):  
Deep Learning ◽  
Spatial Information ◽  
Structural Similarity ◽  
Image Deblurring ◽  
Image Features ◽  
Human Vision ◽  
Global Information ◽  
Attention Network ◽  
Multi Scale ◽  
Blind Image Deblurring

Image deblurring has been a challenging ill-posed problem in computer vision. Gaussian blur is a common model for image and signal degradation. The deep learning-based deblurring methods have attracted much attention due to their advantages over the traditional methods relying on hand-designed features. However, the existing deep learning-based deblurring techniques still cannot perform well in restoring the fine details and reconstructing the sharp edges. To address this issue, we have designed an effective end-to-end deep learning-based non-blind image deblurring algorithm. In the proposed method, a multi-stream bottom-top-bottom attention network (MBANet) with the encoder-to-decoder structure is designed to integrate low-level cues and high-level semantic information, which can facilitate extracting image features more effectively and improve the computational efficiency of the network. Moreover, the MBANet adopts a coarse-to-fine multi-scale strategy to process the input images to improve image deblurring performance. Furthermore, the global information-based fusion and reconstruction network is proposed to fuse multi-scale output maps to improve the global spatial information and recurrently refine the output deblurred image. The experiments were done on the public GoPro dataset and the realistic and dynamic scenes (REDS) dataset to evaluate the effectiveness and robustness of the proposed method. The experimental results show that the proposed method generally outperforms some traditional deburring methods and deep learning-based state-of-the-art deblurring methods such as scale-recurrent network (SRN) and denoising prior driven deep neural network (DPDNN) in terms of such quantitative indexes as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) and human vision.

A Image Segmentation Method of Improved Ant Colony Algorithm for the Manipulator Self-Recognition Target

2011 ◽  
Vol 135-136 ◽  
pp. 50-55
Author(s):  
Yuan Bin Hou ◽  
Yang Meng ◽  
Jin Bo Mao
Keyword(s):  
Image Segmentation ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Immune Algorithm ◽  
Ant Colony ◽  
Segmentation Result ◽  
Segmentation Method ◽  
Self Recognition ◽  
Local Optimal Solution ◽  
Improved Ant Colony Algorithm

According to the requirements of efficient image segmentation for the manipulator self-recognition target, a method of image segmentation based on improved ant colony algorithm is proposed in the paper. In order to avoid segmentation errors by local optimal solution and the stagnation of convergence, ant colony algorithm combined with immune algorithm are taken to traversing the whole image, which uses pheromone as standard. Further, immunization selection through vaccination optimizes the heuristic information, then it improves the efficiency of ergodic process, and shortens the time of segmentation effectively. Simulation and experimental of image segmentation result shows that this algorithm can get better effect than generic ant colony algorithm, at the same condition, segmentation time is shortened by 6.8%.

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