scholarly journals A Rate-Distortion-Based Merging Algorithm for Compressed Image Segmentation

2012 ◽  
Vol 2012 ◽  
pp. 1-7
Author(s):  
Ying-Shen Juang ◽  
Hsi-Chin Hsin ◽  
Tze-Yun Sung ◽  
Yaw-Shih Shieh ◽  
Carlo Cattani
Keyword(s):  
Image Segmentation ◽  
Rate Distortion ◽  
Image Database ◽  
Compressed Domain ◽  
Segmentation Result ◽  
Arithmetic Code ◽  
Boundary Displacement ◽  
Running Time ◽  
Binary Arithmetic ◽  
Code Table

Original images are often compressed for the communication applications. In order to avoid the burden of decompressing computations, it is thus desirable to segment images in the compressed domain directly. This paper presents a simple rate-distortion-based scheme to segment images in the JPEG2000 domain. It is based on a binary arithmetic code table used in the JPEG2000 standard, which is available at both encoder and decoder; thus, there is no need to transmit the segmentation result. Experimental results on the Berkeley image database show that the proposed algorithm is preferable in terms of the running time and the quantitative measures: probabilistic Rand index (PRI) and boundary displacement error (BDE).

scholarly journals Adaptive Binary Arithmetic Coder-Based Image Feature and Segmentation in the Compressed Domain

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Hsi-Chin Hsin ◽  
Tze-Yun Sung ◽  
Yaw-Shih Shieh ◽  
Carlo Cattani
Keyword(s):  
Computational Cost ◽  
Rate Distortion ◽  
Image Feature ◽  
Compressed Domain ◽  
Boundary Displacement ◽  
Binary Arithmetic ◽  
Efficient Scheme ◽  
Band Limited ◽  
Arithmetic Coder ◽  
Efficient Transmission

Image compression is necessary in various applications, especially for efficient transmission over a band-limited channel. It is thus desirable to be able to segment an image in the compressed domain directly such that the burden of decompressing computation can be avoided. Motivated by the adaptive binary arithmetic coder (MQ coder) of JPEG2000, we propose an efficient scheme to segment the feature vectors that are extracted from the code stream of an image. We modify the Compression-based Texture Merging (CTM) algorithm to alleviate the influence of overmerging problem by making use of the rate distortion information. Experimental results show that the MQ coder-based image segmentation is preferable in terms of the boundary displacement error (BDE) measure. It has the advantage of saving computational cost as the segmentation results even at low rates of bits per pixel (bpp) are satisfactory.

scholarly journals Fast Algorithm for Selective Image Segmentation Model

2018 ◽  
Vol 7 (4.33) ◽  
pp. 41
Author(s):  
Abdul K Jumaat ◽  
Ke Chen
Keyword(s):  
Image Segmentation ◽  
Fast Algorithm ◽  
Visual Quality ◽  
Projection Algorithm ◽  
Computational Time ◽  
Segmentation Result ◽  
Numerical Tests ◽  
Specific Object ◽  
The Common

Selective image segmentation model aims to separate a specific object from its surroundings. To solve the model, the common practice to deal with its non-differentiable term is to approximate the original functional. While this approach yields to successful segmentation result, however the segmentation process can be slow. In this paper, we showed how to solve the model without approximation using Chambolle’s projection algorithm. Numerical tests show that good visual quality of segmentation is obtained in a fast-computational time.  

Probability quantiser for multiplication free binary arithmetic code

1997 ◽  
Vol 33 (12) ◽  
pp. 1030
Author(s):  
Jonglak Kim ◽  
Taejeong Kim
Keyword(s):  
Arithmetic Code ◽  
Binary Arithmetic

A Novel Image Segmentation Method for Cardiac MRI Using Support Vector Machine Algorithm Based on Particle Swarm Optimization

2021 ◽  
Vol 11 (12) ◽  
pp. 3174-3180
Author(s):  
Guanghui Wang ◽  
Lihong Ma
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Heart Disease ◽  
Cardiac Mri ◽  
Support Vector ◽  
Swarm Optimization ◽  
Running Time ◽  
Segmentation Accuracy

At present, heart disease not only has a significant impact on the quality of human life but also poses a greater impact on people’s health. Therefore, it is very important to be able to diagnose heart disease as early as possible and give corresponding treatment. Heart image segmentation is the primary operation of intelligent heart disease diagnosis. The quality of segmentation directly determines the effect of intelligent diagnosis. Because the running time of image segmentation is often longer, coupled with the characteristics of cardiac MR imaging technology and the structural characteristics of the cardiac target itself, the rapid segmentation of cardiac MRI images still has challenges. Aiming at the long running time of traditional methods and low segmentation accuracy, a medical image segmentation (MIS) method based on particle swarm optimization (PSO) optimized support vector machine (SVM) is proposed, referred to as PSO-SVM. First, the current iteration number and population number in PSO are added to the control strategy of inertial weight λ to improve the performance of PSO inertial weight λ. Find the optimal penalty coefficient C and γ in the gaussian kernel function by PSO. Then use the SVM method to establish the best classification model and test the data. Compared with traditional methods, this method not only shortens the running time, but also improves the segmentation accuracy. At the same time, comparing the influence of traditional inertial weights on segmentation results, the improved method reduces the average convergence algebra and shortens the optimization time.

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%.

scholarly journals Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Huiyan Jiang ◽  
Baochun He ◽  
Zhiyuan Ma ◽  
Mao Zong ◽  
Xiangrong Zhou ◽  
...  
Keyword(s):  
Ct Images ◽  
Experimental Results ◽  
Initial Contour ◽  
Segmentation Result ◽  
Ct Image ◽  
Liver Segmentation ◽  
Abdominal Ct ◽  
Running Time ◽  
Novel Method ◽  
Liver Region

A novel method based on Snakes Model and GrowCut algorithm is proposed to segment liver region in abdominal CT images. First, according to the traditional GrowCut method, a pretreatment process using K-means algorithm is conducted to reduce the running time. Then, the segmentation result of our improved GrowCut approach is used as an initial contour for the future precise segmentation based on Snakes model. At last, several experiments are carried out to demonstrate the performance of our proposed approach and some comparisons are conducted between the traditional GrowCut algorithm. Experimental results show that the improved approach not only has a better robustness and precision but also is more efficient than the traditional GrowCut method.

Research on Aluminum-Plastic Blister Drug Image Segmentation Method Based on Improved Otsu Theory

2014 ◽  
Vol 962-965 ◽  
pp. 2797-2800
Author(s):  
Hui Jun Yu ◽  
Wu Wan ◽  
Chen Yun ◽  
Cai Biao Chen
Keyword(s):  
Image Segmentation ◽  
Defect Detection ◽  
Segmentation Result ◽  
Two Dimensional ◽  
Segmentation Method ◽  
Computing Complexity ◽  
Integral Effect ◽  
Result Show ◽  
Computing Speed ◽  
Improved Algorithm

In the digital image processing, Otsu algorithm uses the criterion of maximum between-cluster to make image segmentation. In this paper, combined with drug defect detection requirements, the new threshold output functions is put forward which studies on the existing two-dimensional Otsu algorithm in a deep way from the computing complexity and integral effect. The improved algorithm improves the computing speed of the algorithm and optimizes the segmentation effect which is a good segmentation algorithm. The effectiveness of the proposed algorithm has been proved by relevant experiments, and the medicine image segmentation result show that the improved algorithm has a good application prospect in drug defect detection.

Sign in / Sign up

Export Citation Format

Share Document