带宽自适应MeanShift图像分割算法

MeanShift是目前为止特征空间分析的最好方法之一,但其分割结果受带宽参数的影响。图像粗糙度是与视觉感受相关的图像纹理特征,对图像纹理的描述能力很强。图像像素的平均偏移量也体现了图像像素的总体离散情况。通过对高斯核函数的创建以及图像粗糙度的描述,创新性地给出了MeanShift的窗口尺寸选择方法以及图像像素平均偏移的计算,仿真结果表明,该算法对不同类型的图像,均能得到令人满意的效果。     

A Generative Probabilistic Oriented Wavelet Model for Texture Segmentation

This Letter addresses image segmentation via a generative model approach. A Bayesian network (BNT) in the space of dyadic wavelet transform coefficients is introduced to model texture images. The model is similar to a Hidden Markov model (HMM), but with non-stationary transitive conditional probability distributions. It is composed of discrete hidden variables and observable Gaussian outputs for wavelet coefficients. In particular, the Gabor wavelet transform is considered. The introduced model is compared with the simplest joint Gaussian probabilistic model for Gabor wavelet coefficients for several textures from the Brodatz album [1]. The comparison is based on cross-validation and includes probabilistic model ensembles instead of single models. In addition, the robustness of the models to cope with additive Gaussian noise is investigated. We further study the feasibility of the introduced generative model for image segmentation in the novelty detection framework [2]. Two examples are considered: (i) sea surface pollution detection from intensity images and (ii) image segmentation of the still images with varying illumination across the scene.     

Perceptual grouping of segmented regions in color images

Image segmentation is often the first yet important step of an image understanding system. However, general-purpose image segmentation algorithms that do not rely on specific object models still cannot produce perceptually coherent segmentation of regions at a level comparable to humans. Over-segmentation and under-segmentation have plagued the research community in spite of many significant advances in the field. Therefore, grouping of segmented region plays a significant role in bridging image segmentation and high-level image understanding. In this paper, we focused on non-purposive grouping (NPG), which is built on general expectations of a perceptually desirable segmentation as opposed to any object specific models, such that the grouping algorithm is applicable to any image understanding application. We propose a probabilistic model for the NPG problem by defining the regions as a Markov random field (MRF). A collection of energy functions is used to characterize desired single-region properties and pair-wise region properties. The single-region properties include region area, region convexity, region compactness, and color variances in one region. The pair-wise properties include color mean differences between two regions; edge strength along the shared boundary; color variance of the cross-boundary area; and contour continuity between two regions. The grouping process is implemented by a greedy method using a highest confidence first (HCF) principle. Experiments have been performed on hundreds of color photographic images to show the effectiveness of the grouping algorithm using a set of fixed parameters.     

M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene

When occlusion is minimal, a single camera is generally sufficient to detect and track objects. However, when the density of objects is high, the resulting occlusion and lack of visibility suggests the use of multiple cameras and collaboration between them so that an object is detected using information available from all the cameras in the scene.In this paper, we present a system that is capable of segmenting, detecting and tracking multiple people in a cluttered scene using multiple synchronized surveillance cameras located far from each other. The system is fully automatic, and takes decisions about object detection and tracking using evidence collected from many pairs of cameras. Innovations that help us tackle the problem include a region-based stereo algorithm capable of finding 3D points inside an object knowing only the projections of the object (as a whole) in two views, a segmentation algorithm using bayesian classification and the use of occlusion analysis to combine evidence from different camera pairs.The system has been tested using different densities of people in the scene. This helps us determine the number of cameras required for a particular density of people. Experiments have also been conducted to verify and quantify the efficacy of the occlusion analysis scheme.     

基于字符切割拼接的字段式液晶数字识别

为了实现字段式液晶数字的自动判读,提出了一种基于字符切割和拼接的识别算法。定位字符垂直投影的波峰,在两峰之间确定切割线并对字符进行切割,利用投影特征,对切割块进行拼接,实现字符的准确分割。实验表明,该方法解决了传统算法难以准确分割字段式字符的问题,提高了识别率以及准确率。     

基于双边缘检测的车牌识别算法

随着智能交通的不断发展,车牌识别系统已经成为其中的重要组成部分。车牌识别分为车牌定位、字符分割以及字符识别三个部分。提出了一种新型车牌识别方法。在车牌定位方面,采用双边缘检测车牌定位方法;对于字符分割则提出了寻找连通域与传统投影分割相结合的方法;在字符识别上,将分类器分为三组,同时对于易混淆的字符进行了再次分类,这种做法缩短了训练时间,提高了准确率。实验结果表明,所提出的方法具有识别率高和速度快等特点。     

基于DM642的图像分割算法的设计与实现

给出了基于DSP的图像分割算法的实现方法,以高性能数字媒体处理器DM642为核心搭建系统平台。先用Matlab语言实现图像分割算法,再通过特定的命令加参数,以及转换程序,将该程序转化为CCS所支持的程序,经调试、编译、链接通过后,将程序下载到DSP中,完成算法在DSP中的快速实现。实验结果表明,在DSP平台中实现图像分割算法,大大提高了运算速度,对于提高图像处理的实时性和促进图像处理系统的产品化,都具有重大意义。     

基于空间二进制编码的阈值分割方法研究

在三维检测技术中为了得到良好的编码结果从而获取准确的三维数据,必须以清晰的阈值分割的结构光投影图案为前提.由于物体本身带有色彩和光照不均匀使得利用一般的阈值分割方法无法得到质量较好的二值化条纹图.因此根据空间二进制编码必须规范化的特点,提出了一种利用一系列投影图案而得到的基于单像素的阈值分割方法.采用新方法后,可以有效改善由于物体本身色彩和光照不均匀所引起的阈值分割结果较差的现象,得到黑白条纹清晰的二值化结构光投影图案.方法实现简单,阈值分割仅根据一系列图像中的单个像素点进行,因此不受图像中其他像素的影响,抗干扰能力强,可以很好地应用于基于空间二进制编码的形貌测量中.     

视频对象分割方法的研究

本文首先综述了基于视频对象分割方法,然后介绍了活动图像中图像分割的算法,并详述基于时间域和空间域的图像分割方法,最后对实验结果进行分析,并用MATLAB语言进行编程实现。