Mail: 284459143 AT qq.com

mail： ommiissyu AT gmail.com

项目外包：基于视觉的试剂条浓度定量检测仪和试管液位检测仪开发

内容：
1、基于视觉的胶体金试剂条浓度定量检测仪的轻量化、微型化和产品化改造
2、基于视觉的试管液位检测仪研制

需求：
1、胶体金检测仪主要是将交互界面由原型机的触控屏简化为液晶显示+按键控制

2、液位检测仪需要检测透明玻璃试管内试剂液位和通气管的位置，  根据液位变化控制通气管的进动

3、精通视觉测量技术、ARM/单片机电控技术、电机驱动控制技术

项目单位在北京，为方便沟通，最好是在北京求学或工作的朋友来承接。
待遇优厚，具体面议，兼职或按项目模块给钱都可以，最好两个项目都能一起做。

联系方式：qcfy1984@163.com

看到epfl的一种前景检测算法-emvisi2，感觉效果挺不错的（video）。亮点是可以处理较为剧烈的光照变化。

原始代码使用的opencv版本较早，（使用了早不被支持的CvImage）。因此花了点功夫改到opencv2.

emvisi2特点有两个：

1：建立背景模型只需单帧图像。

2：可以应对剧烈的光照变化。

可惜实测运行速度实在太慢，不能实时运行，悲剧。

代码：https://github.com/burningneutron/Emvisi2

同求可以应对剧烈光照的前景检测代码/算法。

The computer vision course is now live. The introductory lecture is posted, and that will be all for this week. This lecture includes an outline of the course.
Starting next week, there will be multiple lectures each week with embedded quizzes. The course will run for 10 weeks, concluding the last week of June.
The discussion forum for the course is alive. Please go ahead and click on the link to the left to say hello to your fellow classmates.
Welcome to the course. Computer vision is a fascinating field, with deep scientific underpinnings as well as significant practical applications, and I hope you will enjoy our exploration of the fundamentals of this subject.
Jitendra Malik

木哈哈.

April, 2012

As usual, we created 2.4 branch in our repository (http://code.opencv.org/svn/opencv/branches/2.4), where we will further stabilize the code. You can check this branch periodically, before as well as after 2.4 release.

Common changes¶

• At the age of 12, OpenCV got its own home! http://code.opencv.org is now the primary site for OpenCV development and http://opencv.org (to be launched soon) will be the official OpenCV user site.

• Some of the old functionality from the modules imgproc, video, calib3d, features2d, objdetect has been moved to legacy.

• CMake scripts have been substantially modified. Now it’s very easy to add new modules – just put the directory with include, src, doc and test sub-directories to the modules directory, create a very simple CMakeLists.txt and your module will be built as a part of OpenCV. Also, it’s possible to exclude certain modules from build (the CMake variables "BUILD_opencv_<modulename>" control that).

New functionality

• The new very base cv::Algorithm class has been introduced. It’s planned to be the base of all the "non-trivial" OpenCV functionality. All Algorithm-based classes have the following features:
  • "virtual constructor", i.e. an algorithm instance can be created by name;
  • there is a list of available algorithms;
  • one can retrieve and set algorithm parameters by name;
  • one can save algorithm parameters to XML/YAML file and then load them.

• A new ffmpeg wrapper has been created that features multi-threaded decoding, more robust video positioning etc. It’s used with ffmpeg starting with 0.7.x versions.

• features2d API has been cleaned up. There are no more numerous classes with duplicated functionality. The base classes FeatureDetector andDescriptorExtractor are now derivatives of cv::Algorithm. There is also the base Feature2D, using which you can detect keypoints and compute the descriptors in a single call. This is also more efficient.

• SIFT and SURF have been moved to a separate module named nonfree to indicate possible legal issues of using those algorithms in user applications. Also, SIFT performance has been substantially improved (by factor of 3-4x).

• The current state-of-art textureless detection algorithm, Line-Mod by S. Hinterstoisser, has been contributed by Patrick Mihelich. See objdetect/objdetect.hpp, class Detector.

• 3 face recognition algorithms have been contributed by Philipp Wagner. Please, check opencv/contrib/contrib.hpp, FaceRecognizer class, and opencv/samples/cpp/facerec_demo.cpp.

• 2 algorithms for solving PnP problem have been added. Please, check flags parameter in solvePnP and solvePnPRansac functions.

• Enhanced LogPolar implementation (that uses Blind-Spot model) has been contributed by Fabio Solari and Manuela Chessa, see opencv/contrib/contrib.hpp, LogPolar_* classes andopencv/samples/cpp/logpolar_bsm.cpp sample.

• A stub module photo has been created to support a quickly growing "computational photography" area. Currently, it only contains inpainting algorithm, moved from imgproc, but it’s planned to add much more functionality.

• Another module videostab (beta version) has been added that solves a specific yet very important task of video stabilization. The module is under active development. Please, checkopencv/samples/cpp/videostab.cpp sample.

• findContours can now find contours on a 32-bit integer image of labels (not only on a black-and-white 8-bit image). This is a step towards more convenient connected component analysis.

• Canny edge detector can now be run on color images, which results in better edge maps

• Python bindings can now be used within python threads, so one can write multi-threaded computer vision applications in Python.

OpenCV on GPU

• Different Optical Flow algorithms have been added:
  • Brox (contributed by NVidia)
  • PyrLK – both Dense and Sparse variations
  • Farneback

• New feature detectors and descriptors:
  • GoodFeaturesToTrack
  • FAST/ORB which is patent free replacement of SURF.

• Overall GPU module enhancements:
  • The module now requires CUDA 4.1 or later;
  • Improved similarity of results between CPU and GPU;
  • Added border extrapolation support for many functions;
  • Improved performance.

• pyrUp/pyrDown implementations.

• Matrix multiplication on GPU (wrapper for the CUBLAS library). This is optional, user need to compile OpenCV with CUBLAS support.

• OpenGL back-end has been implemented for highgui module, that allows to display GpuMat directly without downloading them to CPU.

OpenCV4Android

See the Android Release Notes.

Performance

• A few OpenCV functions, like color conversion, morphology, data type conversions, brute-force feature matcher have been optimized using TBB and/or SSE intrinisics.

• Along with regression tests, now many OpenCV functions have got performance tests. Now for most modules one can build opencv_perf_<modulename> executables that run various functions from the particular module and produce a XML file. Note that if you want to run those tests, as well as the normal regression tests, you will need to get (a rather big)http://code.opencv.org/svn/opencv/trunk/opencv_extra directory and set environment variable OPENCV_TEST_DATA_PATH to "<your_copy_of_opencv_extra>/testdata".

Bug fixes

• In this version we fixed literally hundreds of bugs. Please, check http://code.opencv.org/projects/opencv/versions/1 for a list of fixed bugs.

Known issues

• When OpenCV is built statically, dynamically created classes (via Algorithm::create) can fail because linker excludes the "unused" object files. To avoid this problem, create classes explicitly, e.g

  1 Ptr<DescriptorExtractor> d = new BriefDescriptorExtractor;

http://cg.cs.tsinghua.edu.cn/people/~cmm/

@article{zcczhm_interactiveImages_sigg12,
AUTHOR = "Youyi Zheng and Xiang Chen and Ming-Ming Cheng and Kun Zhou and Shi-Min Hu and Niloy J. Mitra",
TITLE = "Interactive Images: Cuboid-based Scene Understanding for Smart Manipulation",
JOURNAL = "ACM Transactions on Graphics",
VOLUME = "31",
NUMBER = "4",
YEAR = "2012",
}

话说一年又过去鸟，今年CVPR的组委会公布结果的时候只有paper NO.，所以到现在才整理出CVPR的结果，在这里统计了中国大陆地区（包括香港）的结果，大家积极认领吧，还是和去年一样，木有写台湾的童鞋的论文，如果需要补充也提醒下，如果有留学的童鞋，也可以补充下，手工统计，如有错误，指正。。。。

Orals:
Face Alignment by Explicit Shape Regression， MSRA. PDF
Xudong Cao; Yichen Wei; Fang Wen; Jian Sun

Understanding Collective Crowd Behaviors:Learning Mixture Model of Dynamic Pedestrian-Agents
Bolei Zhou; Xiaogang Wang

A-Optimal Non-negative Projection for Image Representation 浙大的
Zheng Yang; Haifeng Liu

Nonrigid Structure-from-Motion Factorization Made Easy
Yuchao Dai; Hongdong Li; Mingyi He

What Are We Looking For: Towards Statistical Modeling of Saccadic Eye Movements and Visual Saliency 哈工大的
Xiaoshuai Sun; Hongxun Yao; Rongrong Ji; xianming Liu; Pengfei Xu

Street-to-Shop: Cross-Scenario Clothing Retrieval via Parts Alignment and Auxiliary Set  自动化所的
Si Liu; Zheng Song; Guangcan LIU; Changsheng Xu; Hanqing Lu; Shuicheng YAN

Posters:
Towards Compact Topical Descriptors
Ling-Yu Duan; Rongrong Ji; Jie Chen; Wen Gao

Hierarchical Face Parsing via Deep Learning
Ping Luo; Xiaogang Wang; Xiaoou Tang

Computing Nearest-Neighbor Fields via Propagation-Assisted KD-Trees，MSRA的，感谢匿名童鞋
Kaiming He; Jian Sun

The Scale of Edge,, HIT, MSRA,  PDF 感谢先明童鞋的更新
Xianming Liu, Changhu Wang, Hongxun Yao, Lei Zhang

Example-Based 3D Object Reconstruction from Line Drawings
Tianfan Xue; Yanjie Li; Jianzhuang Liu

Matrix Completion by Truncated Nuclear Norm Regularization 浙大的
Debing Zhang; Yao Hu; Jieping Ye; Xuelong Li, Chinese Academy of Sciences); Xiaofei He

Semi-Coupled Dictionary Learning with Applications to Image Superresolution and Photo-Sketch Image Synthesis
Shenlong Wang; Lei Zhang; Yan Liang

A Data-driven Approach for Facial Expression Synthesis in Video
Kai Li; Feng Xu; Jue Wang; Qionghai Dai; Yebin Liu

Graph-Guided Sparse Reconstruction for Region Tagging 浙大的
Yahong Han; Fei Wu; Qi Tian; Yueting Zhuang

Detecting Texts of Arbitrary Orientations in Natural Images 华中科大的
Cong Yao; xiang Bai; Wenyu Liu; Yi Ma; Zhuowen Tu

Unsupervised Metric Fusion by Cross Diffusion Bo Wang是华中科大本科毕业，多伦多大学硕士
Bo Wang; Jiayan Jiang; Zhi-hua Zhou; Zhuowen Tu

On the Dimensionality of Video Bricks under Varying Illumination 北京理工大学的
Youdong Zhao; Xi Song; Yunde Jia

Salient Object Detection for Searched Web Images via Global Saliency  北大 MSRA  PDF 感谢Jay童鞋提供PDF地址
Peng Wang; Jingdong Wang; Gang Zeng; Jie Feng; Hongbin Zha

Learning Contour-Fragment-based Shape Model with And-Or Tree Representation 中山大学的
Liang Lin; Xiaolong Wang; Wei Yang; Jianhuang Lai

Geometry Constrained Sparse Coding for Single Image Super-resolution
Xiaoqiang Lu; Haoliang Yuan; Pingkun Yan; Yuan Yuan; Xuelong Li, Chinese Academy of Sciences)

Multiple Clustered Instance Learning for Histopathology Cancer Image Recognition，MSRA
Yan Xu; Junyan Zhu; Eric Chang; Zhuowen Tu

Learning Ordinal Discriminative Features for Age Estimation 自动化所的
Changsheng Li; Qingshan Liu; jing Liu; Hanqing Lu

Automatic Mitral Leaflet Tracking in Echocardiography by Outlier Detection in the Low-rank Representation
Xiaowei Zhou; Weichuan Yu

Unsupervised Object Class Discovery via Bottom-up Multiple Class Learning，MSRA&清华，感谢匿名童鞋
Junyan Zhu; Jiajun Wu; Yichen Wei; Eric Chang; Zhuowen Tu

Image search results refinement via outlier detection using deep contexts
Junyang Lu; Jiazhen Zhou; Jingdong Wang

Scalable $k$-NN graph construction for visual descriptors
Jing Wang; Jingdong Wang; Gang Zeng; Zhuowen Tu; Shipeng Li

Sparse Representation for Face Recognition based on Discriminative Low-Rank Dictionary Learning 自动化所的
Long Ma; Chunheng Wang; Baihua Xiao; Wen Zhou

Exemplar-Based Human Action Pose Correction 华中科大的
Wei Shen; Zhuowen Tu; Ke Deng; Xiang Bai; Tommer Leyvand; Baining Guo

Affinity Learning via Self-diffusion for Image Segmentation and Clustering Bo Wang是华中科大本科毕业，多伦多大学硕士，感谢123童鞋
Bo Wang; Zhuowen Tu

Fast approximate k-means via cluster closures
Jing Wang; Jingdong Wang; Qifa Ke; Gang Zeng; Shipeng Li

Non-Negative Low Rank and Sparse Graph for Semi-Supervised Learning
Liansheng Zhuang; Haoyuan Gao; Zhouchen Lin; Yi Ma; Nenghai Yu

Robust Maximum Likelihood Estimation by Sparse Bundle Adjustment using the $L_1$ Norm
Zhijun Dai; Fengjun Zhang; Hongan Wang

Sparse Representation for Blind Image Quality Assessment 西安电子科大的
Lihuo He

Robust Plane-Based Structure From Motion
Zihan Zhou; Hailin Jin; Yi Ma

3D Visual Phrases for Landmark Recognition
Qiang Hao; Rui Cai; Zhiwei Li; Lei Zhang; Yanwei Pang ; Feng Wu

MAP-MRF Inference Based on Extended Junction Tree Representation
Zheng Yun; Chen Pei; Cao Jiangzhong

A Discriminative Deep Model for Pedestrian Detection with Occlusion Handling 中大的
Wanli Ouyang; Xiaogang Wang

Image Sets Alignment for Video-based Face Recognition 中科院计算所
Zhen Cui; Shiguang Shan; Haihong Zhang; Shihong Lao; Xilin Chen

Video Stabilization with a Depth Camera，MSRA & NUS，感谢匿名童鞋
Shuaicheng Liu; Yingting Wang; Lu Yuan; Ping Tan; Jian Sun

Transferring a Generic Pedestrian Detector Towards Specific Scenes
Meng Wang; Wei Li; Xiaogang Wang

 Metric Learning with Two-Dimensional Smoothness for Visual Analysis  浙大的，感谢匿名童鞋补充
Xinlei Chen; Zifei Tong; Deng Cai

Fan Shape Model for Object Detection 华中科大的,感谢xiaoming2007童鞋补充
Xinggang Wang; Tianyang Ma; Xiang Bai; Wenyu Liu; Longin Jan Latecki

Order Determination and Sparsity-Regularized Metric Learning for Adaptive Visual Tracking 华中科大的,感谢xiaoming2007童鞋补充
Nan Jiang; Wenyu Liu; Ying Wu

Visual Tracking via Adaptive Structural Local Sparse Appearance Model 大连理工的，感谢nasi 童鞋补充，最开始看到的，然后最后的老板在ucmerced的，所以就。。。。
Xu Jia; Huchuan Lu; Ming-Hsuan Yang

Robust Object Tracking via Sparsity-based Collaborative Model 大连理工的，感谢nasi 童鞋补充
Wei Zhong; Huchuan Lu; Ming-Hsuan Yang

KNN Matting 港科大,本科生 ，感谢blur童鞋补充
Qifeng Chen; Dingzeyu Li; Chi-keung Tang

Learning Sparse Covariance Patterns for Natural Scenes  MSRA&CUHK 感谢匿名童鞋补充
Liwei Wang; Yin Li（happyharry童鞋）; Jiaya Jia; Jian Sun; David Wipf; James Rehg

Context Aware Topic Model for Scene Recognition 西安电子科技大学的
Zhenxing Niu; Gang Hua; Xinbo Gao; Qi Tian

 Multi-scale Dictionary for Single Image Super-resolution  西安电子科技大学的，感谢仙人童鞋补充
Kaibing Zhang

 Multi-feature Similarity Learning with Knowledge Transfer among Semantics and Social Tagging. 中科院计算所的，感谢匿名童鞋补充
Shuhui Wang; Shuqiang Jiang; Qingming Huang; Qi Tian

Visual Stem Mapping and Geometric Tense Coding for Augmented Visual Vocabulary.  中科院计算所的，感谢匿名童鞋补充
Ke Gao; Yong Zhang; Lin Shouxun; Zhang Wei

Online Content-aware Video Condensation 自动化所NLPR的 我错了，原来Stan.Z.Li是李子青教授，还以为是新加坡人呢，木有放上来，感谢咆哮的狼同学补充
Shikun Feng, Zhen Lei, Stan.Z. Li

Multi-Pedestrian Detection in Crowded Scenes: A Global View 自动化所NLPR的 感谢咆哮的狼同学补充
Junjie Yan, Zhen Lei, Dong Yi, Stan.Z. Li

Discriminant Image Filter Learning for Face Recognition with Local Binary Pattern Like Representation 自动化所NLPR的 感谢咆哮的狼同学补充
Zhen Lei, Stan Li

 Robust Tracking via Weakly Supervised Ranking SVM 自动化所的 感谢咆哮的狼同学补充
Ming Tang; Ming Tang

Sparse Representation for Face Recognition based on Discriminative Low-Rank Dictionary Learning 自动化所的 感谢咆哮的狼同学补充
Long Ma; Chunheng Wang; Baihua Xiao; Wen Zhou

l21 Regularized Correntropy for Robust Feature Selection自动化所的 感谢咆哮的狼同学补充
Ran He, Tieniu Tan, Liang Wang, Wei-Shi Zheng

Bag of Textons for Image Segmentation via Soft Clustering and Convex Shift
Zhiding Yu (一方通行 同学), Ang Li, Oscar C. Au, Chunjing Xu 香港科技大学，CMU，SIAT

Fixed-Rank Representation for Unsupervised Visual Learning
Risheng Liu, Zhouchen Lin, Fernando De La Torre, and Zhixun Su CMU, MSRA, 大连理工 感谢之鼎同学

今年的华人是异常的多呀。。。。国内对这个领域异常的有兴趣呀。。。。国内的主力还是MSRA、浙大、清华、中科院。。。

今年亮了的是Marr奖得主 Zhuowen Tu老师，署名的有7篇，额，您是要超过kristen grauman, 超过kristen grauman，还是要超过kristen grauman呢？

今年做saliency attention的人特别特别的多。。。。以前都是2篇左右的小领域，今年挤进去了13篇。。。汗。。。

再多废话一句，论文还是要读的，如果不介意的话，认领的童鞋可以将link扔上来，能先看到这些新想法肯定会很有意思的。。。。

很炫啊