Description: 视觉注意力模型，寻找感兴趣区域，模仿人眼找出最感兴趣的区域，再找出第二感兴趣的区域，以此类推-Visual attention model to find the region of interest, to imitate the human eye to identify the most interesting region, and then find the second region of interest, and so on Platform: |
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Author:panda |
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Description: 多媒体技术的发展和视觉信息的飞速膨胀迫切需要对视觉信息资源的有效管理和检索。由此，基于内容的图像和视频检索技术得到了越来越多的重视，成为了多媒体信息检索和图像处理领域中的重要研究方向。CBIR技术将对大规模图像信息的管理和访问提供有力支持。
本文这种介绍了内容图像检索的灰度特征实现方法，具有理论意义和实际应用价值。针对基于内容图像检索技术进行了研究，介绍了其研究现状和关键技术，讨论了其技术瓶颈和发展趋势。共生矩阵法，是对图像的所有像元进行统计调查，以便描述其灰度分布的一种方法。分析了这种基于广义图像灰度共生矩阵的图像检索方法，该方法首先将原图像作平滑处理得到平滑图像，然后将原图像和平滑图像组合得到广义图像灰度共生矩阵，提取该矩阵的统计特征量，最后将该统计量组成归一化向量用以检索。
实验结果表明，本方法的效果要优于单纯的灰度共生矩阵法。在对相似图像检索时，该算法与基于共生矩阵的经典算法相当，在面临同一图像大小显著改变或发生旋转的情况下，该算法明显优于经典算法。相比较而言，该算法提高了检索的有效性。
-The development of multimedia technology and the expansion of visual information，which cries for the effective management and retrieval methods of visual information. Thus, the image and video searching technology based on content is gaining more and more attention to become a multimedia information retrieval and image processing in the field of important research direction. The technology of CBIR provides effective supports for the management and access of extensive image information.
This paper introduced the content of the image retrieval method characteristics of gray, with theoretical and practical value. This paper does an extensive and in-depth study of technology based on content image retrieval to, presenting their research status and key technologies, discussing its development trends and technological bottlenecks. Co-occurrence matrix of all the pixel image survey has been conducted in order to describe a method of distribution of gray. This paper analyses this broad-based Platform: |
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Author:qichao |
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Description: 利用视觉注意机制生成显著图（包括matlab程序、exe程序和相应的文章）-Use of visual attention mechanisms generate significant plan (including the matlab program, exe and the related article) Platform: |
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Author:陈龙 |
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Description: 计算视觉注意系统及其认知基础调查，matlab英文论文-Computational Visual Attention Systems and Their Cognitive Foundations A Survey Platform: |
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Author:Rock |
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Description: 此代码用于动态视觉检测。用matlab实现静态显著性！-The code is used for Dynamic Visual Attention.MATLAB implementation available for static saliency! Platform: |
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Author:呵呵 |
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Description: Matlab编写的Itti视觉注意模型的经典算法，有需要的可以下载，内容较多，网上有类似的英文原文文献。-Matlab prepared by the classical algorithm of the Itti visual attention model, there is a need can download more content, similar to the original English text literature online. Platform: |
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Author:wj |
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Description: 基于matlab利用视觉注意机制模型和图像分割技术提取感兴趣区域-The use of visual attention mechanism based on matlab image segmentation technique to extract the model and region of interest Platform: |
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Author:陈林 |
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Description: 这是我自己根据Visual Attention Detection in Video Sequences Using Spatiotemporal Cues这篇文章写的matlab程序，比较好用，但是效果还有待完善，贴出来大家看看，有好的改进办法欢迎留言。压缩文件里面包含论文以及LC算法的代码。-This is based on my own Using Visual Attention Detection in Video Sequences Spatiotemporal Cues This article was written matlab program, relatively easy to use, but the results have yet to be improved, stickers we look, there is a good way to improve the welcome message. Zip file which contains the papers, and LC algorithm code. Platform: |
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Author:slaim |
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Description: 时空上下文视觉跟踪（STC）算法的解读与代码复现
该论文提出一种简单却非常有效的视觉跟踪方法。更迷人的一点是，它速度很快，原作者实现的Matlab代码在i7的电脑上达到350fps。
该论文的关键点是对时空上下文（Spatio-Temporal Context）信息的利用。主要思想是通过贝叶斯框架对要跟踪的目标和它的局部上下文区域的时空关系进行建模，得到目标和其周围区域低级特征的统计相关性。然后综合这一时空关系和生物视觉系统上的focus of attention特性来评估新的一帧中目标出现位置的置信图，置信最大的位置就是我们得到的新的一帧的目标位置。另外，时空模型的学习和目标的检测都是通过FFT（傅里叶变换）来实现，所以学习和检测的速度都比较快。-Space-time visual tracking context (STC) algorithm for interpretation and code reuse the existing paper proposes a simple but very effective visual tracking method. More attractive is that it is fast, Matlab codes to achieve the original author reaches 350fps on i7 computer. The key point of the paper is a space-time context (Spatio-Temporal Context) access to information. The main idea is to be tracked through a Bayesian framework of goals and temporal relationship between its local context area is modeled to obtain objective and its surrounding area statistical correlation between low-level features. Then focus on the relationship between the biological and the integrated vision system to uate the spatial and temporal characteristics of attention of a new target position occurs confidence map, is the new position of maximum confidence of an objective position we get. In addition, the study and detection of target space-time model through FFT (Fourier transform) to achieve, so learni Platform: |
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Author:老王 |
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