搜索资源列表
Cosaliency_tip2013-master
- 图像协同显著性检测，用于寻找突出目标，以便于进一步分割或者检索等。-cosaliency detection for image to search for salient object in order to segment or retrieve images.
2015CVPR_Cellular-Automata
- 显著性检测，能够清晰的分离目标和背景，对于图像处理很很大帮助-Significant test, can clear separation of target and background, is of great help for image processing
CmCode-master
- 根据图像的全局对比度进行视觉的显著性区域检测，该算法较经典算法更快更准确。-According significant global contrast of the image area is detected visually, the algorithm faster and more accurate than the classical algorithm.
Lu_BFS_15
- 基于背景和前景种子选择的显著性检测，文章和代码-Saliency detection via background and foreground seed selection
TIP_14_SPL
- 基于多尺度超像素的显著性检测代码和文章-Saliency Detection with Multi-Scale Superpixels
one-way-ANOVA
- 单因素方差分析求解及作图，包括分析均值、方差以及显著性等-ANOVA solving and mapping, including an analysis of the mean, variance and significance, etc.
normal
- 在给定的显著性水平下，判断数据是否是正态分布的MATLAB实现-At a given level of significance, it is determined whether the data are normally distributed MATLAB realization
code-BL
- 大连理工大学卢湖川教授团队写的显著性检测的方法-Salient Object Detection via Bootstrap Learning
code
- 大连理工大学卢湖川教授团队完成的显著性检测方法2-CVPR-Cellular Automata_Chinese
BFS_Codes
- 大连理工大学卢湖川教授团队显著性检测方法3-Saliency Detection via Background and Foreground Seed Selection
Code_v1
- 大连理工大学卢湖川教授团队显著性检测方法4-Visual Tracking via Probability Continuous Outlier Model
spt_v2.4
- 大连理工大学卢湖川教授团队显著性检测方法5-Robust Superpixel Tracking, IEEE Transaction on Image Processing
saliency123
- 一个很好的关于图像显著性提取的代码，很值得学习！-A good image about significant extraction code, it is worth learning!
IttiMATLAB工具箱
- itti的MATLAB工具箱，经本文亲测可用，这是一个经典的显著性检测算法，希望对你有用。
HFT-Code
- 显著性目标检测HFT，基于频域的显著性目标检测方法，cvpr会议上发表的-saliency detect
LabelMeToolbox-master
- c此代码实现显著性检测，很好的实现，对学习显著性监测的图像帮助很大-This code implements significant testing, well implemented, significant learning of great help monitor image
SUN
- 显著性sun方法代码，此方法是显著性检测的重要方法-Significant sun method code, this method is an important method of detection of significant
Bayesian-Saliency
- 2013年IEEE TRANSACTIONS ON IMAGE PROCESSING发表的一篇利用中低水平线索进行贝叶斯显著性检测的佳作。-2013 IEEE TRANSACTIONS ON IMAGE PROCESSING published a low utilization clues Bayesian significance testing works.
CompressedDomain
- 2012年IEEE TRANSACTIONS ON IMAGE PROCESSING发表的一篇在压缩域进行显著性检测的论文，效果极佳。-2012 IEEE TRANSACTIONS ON IMAGE PROCESSING published an article in the compressed domain significant test papers, with excellent results.
bottom-up-visual-attention-model
- 基于显著性的视觉注意模型用于快速场景分析，效果不错。-Based on significant visual attention model for fast scene analysis, good results.