基于有监督直接局部保持投影的人脸识别

提出一种用于图像识别的有监督直接局部保持投影算法,该算法结合样本类别信息,通过同时对角化的方法求解局部保持投影问题,避免矩阵的奇异性。在ORL人脸库上的测试结果表明,该算法的识别率高于PCA, PCA+LPP等方法。     

混沌敏感性在图像加密中的2种应用

混沌系统的结构参数敏感性、初值敏感性和以同一分布遍历各态的特性较好地对应密码系统的一些基本特性。分析结构参数和初始值变化对混沌序列敏感性影响,得到混沌序列敏感性效应是一个积累过程。将混沌序列按照敏感性划分为混沌敏感性前后期序列并给出界定原则。探究混沌序列敏感性在图像加密时的2种不同应用。     

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。     

三维图像中边界曲面的抽取与半透明可视化

已有算法在重构及可视化梯度值从高到低变化且包含弱边界的边界曲面时无法分辨弱边界与噪声碎片。针对上述问题,提出边界曲面半透明显示方法。采用半透明显示技术显示边界曲面及附着在其上的小碎片,利用人的智能及知识在观察可视化结果时对小碎片及弱边界进行有效的区分。结果证明,该方法有助于正确理解及完整显示三维图像中梯度值从高到低变化的边界曲面,为进一步的交互式操作去除小碎片提供依据。     

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。