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Description: 基 于 itti显 著 图 的 图 像 显 著 区 域 提 取 ,搞这方面的可以看 看
-Itti-based image map marked a significant regional extraction, to engage in this area can look at
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Size: 598016 |
Author: liang |
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Description: 基于图论的图片显著性分析算法。详细可参见NIPS08 paper: a graph-based saliency-Based on graph theory image analysis algorithm significantly. Details can be found NIPS08 paper: a graph-based saliency
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Size: 582656 |
Author: bolei |
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Description: 二维图像视觉显著性检测，效果非常好。California Institute of Technology的
Jonathan Harel写的，很经典。-Two-dimensional image saliency detection, the effect was very good. California Institute of Technology' s Jonathan Harel written, very classic.
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Size: 1052672 |
Author: jiashijie |
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Description: 显著区域提取程序，比ITTI的有所提高，很好用。-Significant regional extraction procedure, compared with ITTI'
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Size: 7481344 |
Author: songjian |
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Description: 静态图像显著性分析程序，利用matlab开发。加州理工三位教授所写。-Software] Saliency Map Algorithm : MATLAB Source Code
Below is MATLAB code which computes a salience/saliency map for an image or image sequence/video (either Graph-Based Visual Saliency (GBVS) or the standard Itti, Koch, Niebur PAMI 1998 saliency map). See the included readme file for details. I also have a newer, simpler version implementing only the Itti algorithm (see simpsal/readme.txt).
Please try it out and email me if you have any questions.
Download:
Please select one of the following to download:
[gbvs.zip]
This package includes an implementation of the full GBVS algorithm. It also lets you compute an Itti, Koch, Niebur saliency map. It includes a function for computing the ROC score between eye-movements (provided as x,y fixation points) and a saliency map, and a function for displaying a saliency map overlayed on top of an image (as in the screenshot below).
Right after you download the zip file, you must change into the gbvs/ directory and run:
>>
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Size: 8702976 |
Author: Johnny |
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Description: 显著性分析论文（英文），三个加州理工教授所写。附带有源码是实验Sample（见英文说明中的下载链接）-Saliency Map Algorithm : MATLAB Source Code
Below is MATLAB code which computes a salience/saliency map for an image or image sequence/video (either Graph-Based Visual Saliency (GBVS) or the standard Itti, Koch, Niebur PAMI 1998 saliency map). See the included readme file for details. I also have a newer, simpler version implementing only the Itti algorithm (see simpsal/readme.txt).
Please try it out and email me if you have any questions.
Download:
Please select one of the following to download:
[gbvs.zip]
This package includes an implementation of the full GBVS algorithm. It also lets you compute an Itti, Koch, Niebur saliency map. It includes a function for computing the ROC score between eye-movements (provided as x,y fixation points) and a saliency map, and a function for displaying a saliency map overlayed on top of an image (as in the screenshot below).
Right after you download the zip file, you must change into the gbvs/ directory and run:
>> gbvs_ins
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Author: Johnny |
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Description: Hi this a gbvs file. this is a matlab file
-Hi this is a gbvs file. this is a matlab file
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Size: 9143296 |
Author: Ramdas |
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Description: 实现图像的显著性，真正的原始代码，非修改，来自于外国著名实验室。-saliency map
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Size: 9255936 |
Author: 王志强 |
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Description: 求图像显著区域的GBVS算法，matlab编写，可直接运行-For image salient regions GBVS algorithm
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Size: 9146368 |
Author: yolanda |
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Description: GBVS saliency图像显著区域检测方法，可以与ITTi、谱剩余、以及全局对比方法进行比较。-The GBVS saliency image salient region detection methods, with ITTi spectrum remaining, as well as the global contrast compare.
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Size: 19378176 |
Author: 易扬 |
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Description: gbvs 提取显著区域，进行其他的程序编写。在matlab上运行，先安装，后可以直接运行-gbvs select main object
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Size: 3072 |
Author: huangyt |
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Description: 该代码是在MATLAB下运行的GBVS，用于图像特征点匹配，进一步用于图像处理，图像检索等多个领域。-GBVS the code is run in MATLAB for image feature point matching, and further used in various fields of image processing, image retri .
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Size: 9976832 |
Author: liu xuan |
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Description: Gbvs图像的显著度检测算法，用Matlab编程的，非常实用的算法适合图像处理入门学习-graph-based visual saliency
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Size: 10521600 |
Author: zhengbo |
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Description: 显著性代码，GBVS算法是一种提取人眼感兴趣区域的算法，与Itti模型不同，采用马尔科夫链，用其平稳分布来提取显著图。-Salient code, interested in the human eye area is extracted by GBVS algorithm,it is unlike Itti model. The markov chain and the stationary distribution is used to extract significant figure.
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Size: 9201664 |
Author: s |
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Description: 基于图的显著性检测方法(graph based image saliency)
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Size: 19550208 |
Author: 南瓜菠菜胡萝卜
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Description: 新的自下而上的视dmits combination with other maps. The model is simple, and biologically plausible
insofar as it is naturally parallelized. This model powerfully predicts human
xations on 749 variations of 108 natural images, achieving 98% of the ROC area
of a human-based control, whereas the classical algorithms of Itti & Koch ([2],
[3], [4]) achieve only 84%.觉显著性模型，基于视觉显著图（GBVS），提出了。(A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is
proposed. It consists of two steps: rst forming activation maps on certain feature
channels, and then normalizing them in a way which highlights conspicuity and
admits combination with other maps. The model is simple, and biologically plausible
insofar as it is naturally parallelized. This model powerfully predicts human
xations on 749 variations of 108 natural images, achieving 98% of the ROC area
of a human-based control, whereas the classical algorithms of Itti & Koch ([2],
[3], [4]) achieve only 84%.)
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Size: 9145344 |
Author: 还有叫二哈的
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Description: gbvs图像显著性检测代码（matlab）(matlab code of gbvs saliency detection.)
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Size: 9992192 |
Author: mld117
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Description: 显著性中的GBVS算法，希望可以对您提供好的帮助(This is an installation and general help file for the saliency map MATLAB code here.)
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Size: 1134592 |
Author: mir_night
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Description: GBVS model for saliency detection
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Size: 9053184 |
Author: longfr
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Description: gbvs_nips 基于图的显著性。Graph-Based Visual Saliency。是发表在2006年NIPS上的论文。(A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is proposed. It consists of two steps: first forming activation maps on certain feature channels, and then normalizing them in a way which highlights conspicuity and admits combination with other maps. The model is simple, and biologically plausible insorfar as it is naturally parallelized. This model powerfully predicts human fixations on 749 variations of 108 natural images, achieving 98% of the ROC area of a human-based control, whereas the classical algorithas of Itti & Koch ([2], [3], [4]) achieve only 84%.)
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Size: 8948736 |
Author: 佳子 |
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