A Multi-Scale Gradient Algorithm Based on Morphological Operators

Watershed transformation is a powerful morphological tool for image segmentation. However, the performance of the image segmentation methods based on watershed transformation depends largely on the algorithm for computing the gradient of the image to be segmented. In this paper, we present a multi-scale gradient algorithm based on morphological operators for watershed-based image segmentation, with effective handling of both step and blurred edges. We also present an algorithm to eliminate the local minima produced by noise and quantization errors. Experimental results indicate that watershed transformation with the algorithms proposed in this paper produces meaningful segmentations, even without a region-merging step.     

Improvement of region-based motion estimation by considering uncovered regions

In natural video sequences, object movement causes regions to be covered or uncovered. Conventional algorithms for region-based motion estimation do not take uncovered regions into full account. Uncovered regions seriously decrease the accuracy of motion estimation. This paper presents an algorithm for increasing the motion estimation accuracy. This algorithm detects uncovered regions and uses them to improve image segmentation and motion estimation. Experimental results show that the presented algorithm is effective in reducing the displaced frame difference, without introducing any extra information for coding applications.     

基于汉语分词的信息抽取技术

随着信息时代的到来，如何将大量无序的信息及时准确地提取、整理、组织成便于查询检索的形式，巳成为研究开发的焦点。尤其是随着互联网技术的高速发展，为用户提供了更加丰富的信息。将基于汉语自动分词的综合信息抽取技术应用于信息检索，具有实际应用意义和价值。     

基于边界和模糊集理论的三维图像分割

本文提出了一种基于边界和模糊集理论的三维图象分割方法，利用高斯－拉普拉斯算子检测出图象中各结构的边界后，通过区域增长获取感光趣结构的三维二值模板，由高斯－拉普拉斯图象和二值模板出发，利用模糊集理论，确定感兴趣结构的三维模糊模板，从而达到在体积图象中分割出感兴趣结构的目的。     

基于改进的CV模型的医学图像分割

基于CV模型的水平集分割是医学图像分割的一个重要的分割手段。医学图像分割要求精度高,速度快。传统的处理方式效率比较低,不能满足医学图像的分割要求。针对这一缺点,本文提出新的分割模式。首先,在分割过程中间断的对图像进行窗口化处理,减少演化过程所需计算的数据量。同时使活动轮廓的演化速度伴随窗口规模进行调整,减少演变所需的迭代次数。实验表明,改进之后分割方法能够极大的提高分割速度,同时图像细节部分的分割也有更高的精度。     

基于分块和滑动窗口的车牌定位方法

车牌定位是车牌识别系统的关键环节.提出了一种利用分块和滑动窗口快速定位车牌的新方法.首先,进行垂直边缘的检测;然后利用分块和滑动窗口的方法进行搜索,产生车牌候选区域;最后经过判别得到车牌区域,并对边界加以调整.实验结果证明这种算法具有定位准确、效率高、适应性强的特点.     

Learning of perceptual grouping for object segmentation on RGB-D data

Object segmentation of unknown objects with arbitrary shape in cluttered scenes is an ambitious goal in computer vision and became a great impulse with the introduction of cheap and powerful RGB-D sensors. We introduce a framework for segmenting RGB-D images where data is processed in a hierarchical fashion. After pre-clustering on pixel level parametric surface patches are estimated. Different relations between patch-pairs are calculated, which we derive from perceptual grouping principles, and support vector machine classification is employed to learn Perceptual Grouping. Finally, we show that object hypotheses generation with Graph-Cut finds a globally optimal solution and prevents wrong grouping. Our framework is able to segment objects, even if they are stacked or jumbled in cluttered scenes. We also tackle the problem of segmenting objects when they are partially occluded. The work is evaluated on publicly available object segmentation databases and also compared with state-of-the-art work of object segmentation.     

A New Real Time Object Segmentation and Tracking Algorithm and its Parallel Hardware Architecture

Most of the emerging content-based multimedia technologies are based on efficient methods to solve machine early vision tasks. Among other tasks, object segmentation is perhaps the most important problem in single image processing. The solution of this problem is the key technology of the development of the majority of leading-edge interactive video communication technology and telepresence systems. The aim of this paper is to present a robust framework for real-time object segmentation and tracking in video sequences taken simultaneously from different perspectives. The other contribution of the paper is to present a new dedicated parallel hardware architecture. Its composed of a mixture of Digital Signal Processing (DSP) and Field Programmable Gate Array (FPGA) technologies and uses the Content Addressable Memory (CAM) as a main processing unit. Experimental results indicate that small amount of hardware can deliver real-time performance and high accuracy. This is an improvement over previous systems, where execution time of the second-order using a greater amount of hardware has been proposed.Mahmoud Meribout (BS85, M91). Received the PhD degree in Electronic Engineering from the University of Technology of Compiegne (France), in January 3rd 1995. He worked one year as an Associate Researcher, where he has been involved in some industrial projects related to hardware board design and video processing. FromNovember 1995 to October 2000, he has beenworking in Japan, with NTT and NEC corporations respectively, where he has been involved in several projects related to hardware & software design of next generation multimedia and networking equipments. He is holding several Japanese and American patents related to this particular topic. In 1998, he has received the NTT best award for his research and development records.Maamouru Nakanishi received the B.S. and M.S. in Electronics in 1985 and 1987 respectively, all from Kyoto University. Since 1987, he has been engaged in R&D of parallel processing architectures, memory-processor integration technology at NTT Corporation (Tokyo, Japan), when he has been involved in research on Advanced Content Addressable Memory applied to video processing. In 1998, he has received the NTT best award for his research and development records.     

基于二维直方图与FCM相结合的图像快速分割方法

基于二维直方图的模糊聚类分割方法,可以有效地抑制噪声。但是FCM(模糊C均值)算法用于图像聚类时最大的缺点是运算开销太大,进而限制了该算法在图像分割中的应用。通过构造合理的二维直方图,并筛选出符合规定条件的元素作为聚类样本,再结合FCM算法进行图像分割。实验结果表明该方法具有与基于一维直方图的模糊聚类分割方法速度相近,但却比其分割精度高很多的良好特点。     

A human visual system-driven image segmentation algorithm

This paper presents a novel image segmentation algorithm driven by human visual system (HVS) properties. Segmentation quality metrics, based on perceptual properties of HVS with respect to segmentation, are integrated into an energy function. The energy function encodes the HVS properties from both region-based and boundary-based perspectives, where the just-noticeable difference (JND) model is employed when calculating the difference between the image contents. Extensive experiments are carried out to compare the performances of three variations of the presented algorithm and several representative segmentation and clustering algorithms available in the literature. The results show superior performance of our approach.