基于视频对象的区域分割及其应用

为适应MPEG-4基于对象的应用,提出一种基于视频对象的区域分割算法。在视频对象的指导下,利用均值漂移进行分割,性能比传统均值漂移算法在计算时间、占用内存等方面均有提高,适用于运算速度要求高和处理器资源有限的情况。将分割结果应用于区域指导的图像插值,能保证较快的处理速度和兴趣区域较好的图像质量。实验结果证明了该方法的有效性。     

基于CPSO的二维Otsu图像分割法

二维Otsu方法同时考虑了图像的灰度信息和像素问的空间邻域信息,图像分割效果好但算法计算量较大。针对上述情况,提出一种基于混沌粒子群优化算法（CPSO）的策略,将其用于二维Otsu方法中,并与标准粒子群优化算法（SPSO）进行仿真实验对比。实验结果表明,该方法可以提高分割速度,克服SPSO的缺点,图像分割结果较理想。     

Self-consistent gradient flow for shape optimization

We present a model for image segmentation and describe a gradient-descent method for level-set based shape optimization. It is commonly known that gradient-descent methods converge slowly due to zig–zag movement. This can also be observed for our problem, especially when sharp edges are present in the image. We interpret this in our specific context to gain a better understanding of the involved difficulties. One way to overcome slow convergence is the use of second-order methods. For our situation, they require derivatives of the potentially noisy image data and are thus undesirable. Hence, we propose a new method that can be interpreted as a self-consistent gradient flow and does not need any derivatives of the image data. It works very well in practice and leads to a far more efficient optimization algorithm. A related idea can also be used to describe the mean-curvature flow of a mean-convex surface. For this, we formulate a mean-curvature Eikonal equation, which allows a numerical propagation of the mean-curvature flow of a surface without explicit time stepping.     

A New Medical Image Enhancement Based on Human Visual Characteristics

Study of image enhancement shows that the quality of image heavily relies on human visual system. In this paper, we apply this fact effectively to design a new image enhancement method for medical images that improves the detail regions. First, the eye region of interest （ROI） is segmented; then the Un-sharp Masking （USM） is used to enhance the detail regions. Experiments show that the proposed method can effectively improve the accuracy of medical image enhancement and has a significant effect.     

Similarity-based image organization and browsing using multi-resolution self-organizing map

An algorithm is presented in this paper to facilitate the exploration of large image collections based on visual similarities. Starting with an unordered and unannotated set of images, the algorithm first extracts the salient details into feature vectors using both color and gradient information. The feature vectors are then used to train a self-organizing map which maps high-dimensional feature vectors onto a 2D canvas so that images with similar feature vectors are grouped together. When users browse the image collection, an image collage is generated that selects and displays the most pertinent set of images based on which portion of the 2D canvas is currently in view. Flowing from an overview to details is a seamless operation controlled simply by pan and zoom, with representative images selected in a consistent and predictable way. To make organizing larger image collections practical in interactive time, the organization algorithm is designed to run in parallel on graphics processing units. Overall this paper presents an end-to-end solution that facilitates the surfing of image collections in a fresh way.     

用于半自动视频对象提取的自适应网格图像分割

随着MPEG－4标准的发展和基于内容的视频处理研究,视频对象平面（VOP）的有效产生成为一个关键问题。本文提出一种基于区域的自适应网格彩色图像分割方法,可用于获得半自动视频对象跟踪和提取所需的初始VOP。该方法利用CIE L＊a*b＊色彩空间的特征量,对视频序列的第一帧进行三角形网格的分裂与合并。对MPEG－4标准测试序列的分割实验取得了较好的结果。     

Comparison of image reconstruction by using near‐field and far‐field data for an imperfect conductor

Image reconstruction by using near‐field and far‐field data for an imperfectly conducting cylinder is investigated. A conducting cylinder of unknown shape and conductivity scatters the incident wave in free space and the scattered near and far fields are measured. By using measured fields, the imaging problem is reformulated into an optimization problem and solved by the genetic algorithm. Numerical results show that the convergence speed and final reconstructed results by using near‐field data are better than those obtained by using far‐field data. This work provides both comparative and quantitative information. © 2001 John Wiley & Sons, Inc. Int J RF and Microwave CAE 11: 69–73, 2001.     

An Analysis of Edge Detection by Using the Jensen-Shannon Divergence

This work constitutes a theoretical study of the edge-detection method by means of the Jensen-Shannon divergence, as proposed by the authors. The overall aim is to establish formally the suitability of the procedure of edge detection in digital images, as a step prior to segmentation. In specific, an analysis is made not only of the properties of the divergence used, but also of the method's sensitivity to the spatial variation, as well as the detection-error risk associated with the operating conditions due to the randomness of the spatial configuration of the pixels. Although the paper deals with the procedure based on the Jensen-Shannon divergence, some problems are also related to other methods based on local detection with a sliding window, and part of the study is focused to noisy and textured images.     

医院超声图象管理系统的设计与实现

介绍了医院超声图象管理系统的功能和特点,并阐述了其关键技术。