Deep learning & CNN

关于深度学习和卷积神经网络

1. ImageNet Large Scale Visual Recognition Competition (ILSVRC)
  ImageNet数据库：链接
2. 关于核函数的理论讨论，见链接[4]
3. 关于神经网络的感性探讨，见链接[5]
4. 链接[8-11]讨论了卷积神经网络的原理及细节
5. 链接[7]详细描述了卷积神经网络的意义、卷积的用法，及图像滤波器（核）的意义。
6. 链接[8]描述了一个深度卷积神经网络的计算过程

read topic
[1] http://www.zhihu.com/topic/19607065
[2] http://www.zhihu.com/question/24904450
[3] http://zhuanlan.zhihu.com/cvprnet/19821292
[4] http://www.zhihu.com/question/24627666
[5] http://www.zhihu.com/question/22553761
[6] https://colah.github.io/posts/2014-07-Conv-Nets-Modular/
[7] https://colah.github.io/posts/2014-07-Understanding-Convolutions/
[8] http://blog.csdn.net/lu597203933/article/details/46575779
[9] http://blog.csdn.net/whiteinblue/article/details/25281459
[10] http://blog.csdn.net/zouxy09/article/details/9993743
[11] http://blog.csdn.net/zouxy09/article/details/9993371
[12] http://blog.csdn.net/happynear/article/details/46822109

Yi-Ting Chen, Multi-instance Object Segmentation with Occlusion Handling

Multi-instance Object Segmentation with Occlusion Handling

Yi-Ting Chen1 Xiaokai Liu1;2 Ming-Hsuan Yang1

University of California at Merced1 Dalian University of Technology2

  文档链接

  来源CVPR2015

  名词：intersection-over-union (IoU)

    两个图像的重合部分面积与合并面积之比，反映两幅图像的形状重合度

    参考

  

  

  本文使用SDS算法[1]。

  SDS算法主要分为4步：

    （1）通过MCG[2]方法生成基于像素的分割。

    （2）通过R-CNN[3]进行图像形状的分割和预测

    （3）通过Box CNN结合Region CNN进行分类

    (4)性能优化

  参考文献：

  [1] B. Hariharan, P. Arbel´aez, R. Girshick, and J. Malik. Simultaneous detection and segmentation. In ECCV, 2014.

  [2] P. Arbel´aez, J. Pont-Tuset, J. T. Barron, F. Marques, and J. Malik.  ultiscale combinatorial grouping. In CVPR, 2014.

  [3] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.

CVPR 2015

1. RGB Segmentation 与 Depth Segmentation融合进行图像划分。
  文档链接
Banica D, Sminchisescu C. Second-Order Constrained Parametric Proposals and Sequential Search-Based Structured Prediction for Semantic Segmentation in RGB-D Images[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 3517-3526.

2.   CNN识别图片中的材质（Texture）
     文档链接
Cimpoi M, Maji S, Vedaldi A. Deep filter banks for texture recognition and segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 3828-3836.

3. CNN划分并识别图像区域
    文档链接
Dai J, He K, Sun J. Convolutional feature masking for joint object and stuff segmentation[J]. arXiv preprint arXiv:1412.1283, 2014.

4.CVPR 2015总结
    链接：http://www.computervisionblog.com/2015/06/deep-down-rabbit-hole-cvpr-2015-and.html

Shervin Ardeshir. Geo-semantic Segmentation

Geo-semantic Segmentation

Shervin Ardeshir1 Kofi Malcolm Collins-Sibley2 Mubarak Shah1

1 Center for Research in Computer Vision, University of Central Florida

2 Northeastern University

  文档链接

  来源 CVPR2015

  利用GIS中的3D模型信息“GIS database containing the building outlines”，和照片中的相机参数（EXIF参数），GPS等信息。将上述二者进行迭代fitting。最终正确分割图片。

  

  GIS Dataset available at: http://crcv.ucf.edu/projects/Geosemantic/

  相关参考文献：

  [1] M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa. Entropy rate superpixel segmentation. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 2097–2104. IEEE, 2011.

    Segmenting the image into an initial set of superpixels.