2018-04-08 16:53:41 tiankongtiankong01 阅读数 287
  • 人工智能框架实战精讲:Keras项目

    Keras项目实战课程从实战的角度出发,基于真实数据集与实际业务需求,从零开始讲解如何进行数据处理,模型训练与调优,最后进行测试与结果展示分析。全程实战操作,以最接地气的方式详解每一步流程与解决方案。课程结合当下深度学习热门领域,以计算机视觉与自然语言处理为核心讲解各大网络的应用于实战方法,适合快速入门与进阶提升。 任务作业: 1.基于Keras构建VGG网络模型 2.加载与预处理细胞图像数据 3.构建完成分类模型并进行测试识别 (注意: 作业需写在CSDN博客中,请把作业链接贴在评论区,老师会定期逐个批改~~)

    1692 人正在学习 去看看 唐宇迪

最近完成一交通标志检测与识别项目,框架为ubuntu上opencv用c/c++开发。

一个项目好的算法、思想是很重要的一方面,编程实现也很重要。

而且个人实现时总会遇到许多意料不到的问题。虽然正确的程序看起来理所当然,但实际排起错来挺费时的。

下面步入正题:

        opencv使用时的常见一般又不易发现的坑

                     1. resize问题   此函数正确调用格如下

cv::resize(image,image, cv::Size(Height,Weight),INTER_LINEAR);

                     2. OpenCV Error: Assertion failed (scn == 3 || scn == 4) in cvtColor, file /build/opencv-SviWsf/opencv-2.4.9.1+dfsg/modules/imgproc/src/color.cpp, line 3737
terminate called after throwing an instance of 'cv::Exception'

  what():  /build/opencv-SviWsf/opencv-2.4.9.1+dfsg/modules/imgproc/src/color.cpp:3737: error: (-215) scn == 3 || scn == 4 in function cvtColor

   这个问题一般是由于将已经是灰度图的图片继续转为灰度图时引起的,写程序时要注意传递的Mat矩阵是不是已经是灰度阵了。

                     3. 各种内存报错

                       主要是指针操作时边界问题。在项目实际应用中,使用指针可以获取最快的速度,但高收益和高风险并存,在指针操作矩阵时一定要注意内存空间分配、边界问题。有时程序较复杂时,可以在纸上列出指针操作的实际情况,这样看起来比较直观,易于排查理解。    还有就是有些情况下程序可能没有问题,但这并步意味着万事大吉,只是可能没有遇到让程序崩溃的问题而已。在实际项目中,一定要全面考虑这些因素。考虑到可能出现的各种情况,做好情况判断和异常处理工作。保证程序在各种工况下都可以强健到运行。

排错时多输出变量的相关信息,这样能充分掌握程序运行时的各种信息,也更易于精准的发现错误之处。

2018-06-28 18:56:09 Julialove102123 阅读数 2255
  • 人工智能框架实战精讲:Keras项目

    Keras项目实战课程从实战的角度出发,基于真实数据集与实际业务需求,从零开始讲解如何进行数据处理,模型训练与调优,最后进行测试与结果展示分析。全程实战操作,以最接地气的方式详解每一步流程与解决方案。课程结合当下深度学习热门领域,以计算机视觉与自然语言处理为核心讲解各大网络的应用于实战方法,适合快速入门与进阶提升。 任务作业: 1.基于Keras构建VGG网络模型 2.加载与预处理细胞图像数据 3.构建完成分类模型并进行测试识别 (注意: 作业需写在CSDN博客中,请把作业链接贴在评论区,老师会定期逐个批改~~)

    1692 人正在学习 去看看 唐宇迪

深度学习项目图像处理领域的代码链接。

图像识别,图像生成,看图说话等等方向的代码;


图像生成

绘画风格到图片的转换:Neural Style: https://github.com/jcjohnson/neural-style

图像类比转换:image-analogies :https://github.com/awentzonline/image-analogies

根据涂鸦生成图片:Neural Doodle :https://github.com/alexjc/neural-doodle

根据涂鸦类比图片:Sketchy:https://github.com/janesjanes/sketchy

根据图片生成铅笔画:Pencil:https://github.com/fumin/pencil

把一副图像变成铅笔水粉画。

手写文字模拟:rnnlib :https://github.com/szcom/rnnlib

转换风景图片:http://transattr.cs.brown.edu

这个项目可以识别和理解图片中的风景,并且可以根据用户提出的条件,定向改变原风景画中的环境(比如more night)

图片变Emojis表情:http://engineering.curalate.com/2016/01/20/emojinet.html

增加图片分辨率:srez:https://github.com/david-gpu/srez

图片自动上色:Colornet :https://github.com/pavelgonchar/colornet

生成可爱的动漫头像:AnimeGAN :https://github.com/jayleicn/animeGAN

骡子变斑马:CycleGAN and pix2pix in PyTorch :https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix.git

强大的图像生成器:DiscoGAN in PyTorch :https://github.com/carpedm20/DiscoGAN-pytorch.git

使用RNN生成手写数字:DRAW implmentation :https://github.com/skaae/lasagne-draw

使用CNN来放大图片:waifu2x :https://github.com/nagadomi/waifu2x

根据图片生成一段描述:Show and Tell :https://github.com/tensorflow/models/tree/master/im2txt

根据图片讲故事:neural-storyteller :https://github.com/ryankiros/neural-storyteller

根据图片将故事2:NeuralTalk2:https://github.com/karpathy/neuraltalk2

识别图片中的文字:CRNN for image-based sequence recognition:https://github.com/bgshih/crnn.git

图像识别

用于物体识别的全卷积网络:PyTorch-FCN:https://github.com/wkentaro/pytorch-fcn.git

引入注意力的卷积网络:Attention Transfer:https://github.com/szagoruyko/attention-transfer.git

物体识别实例:Deep-Learning:

https://github.com/priya-dwivedi/Deep-Learning/blob/master/Object_Detection_Tensorflow_API.ipynb

物体识别API:Tensorflow Object Detection API:https://github.com/tensorflow/models/tree/master/object_detection

推理场景结构:SfMLearner :https://github.com/tinghuiz/SfMLearner

用于分辨色情图像的open_nsfw :https://github.com/yahoo/open_nsfw

人脸识别:Open Face :https://github.com/cmusatyalab/openface

易用人脸识别:Face_recognition : https://github.com/ageitgey/face_recognition#face-recognition

快速人脸识别:MobileID:https://github.com/liuziwei7/mobile-id

图像识别框架1:AlexNet & VGG Net & GoogleNet & ResNet

AlexNet

https://gist.github.com/JBed/c2fb3ce8ed299f197eff

VGG Ne

https://github.com/fchollet/keras/blob/master/keras/applications/vgg16.py

GoogleNet

https://github.com/fchollet/keras/blob/master/keras/applications/inception_v3.py

ResNet

https://github.com/fchollet/keras/blob/master/keras/applications/resnet50.py

图像识别框架2:ResNeXt & RCNN & YOLO & SqueezeNet & SegNet

ResNeXt

https://github.com/titu1994/Keras-ResNeXt

RCNN (基于区域的 CNN)

https://github.com/yhenon/keras-frcnn

YOLO (You Only Look once)

https://github.com/allanzelener/YAD2K

SqueezeNet

https://github.com/rcmalli/keras-squeezenet

SegNet

https://github.com/imlab-uiip/keras-segnet

预训练的图像识别模型:functional-zoo

https://github.com/szagoruyko/functional-zoo.git

由PyTorch和Tensorflow实现的常用图像识别模型包含预训练参数。

预定义的CNN过滤器: PyScatWave

https://github.com/edouardoyallon/pyscatwave

一套预定义的filter,用于增强图像识别的效果。

计算图片中物体的相似度:Conditional Similarity Networks (CSNs)

https://github.com/andreasveit/conditional-similarity-networks.git

《Conditional Similarity Networks》的PyTorch实现,可以根据不同的条件计算图片中物体的相似度。

量子化学中的神经信息传递(・_・;Neural Message Passing for Quantum Chemistry

https://github.com/priba/nmp_qc.git

论文《Neural Message Passing for Quantum Chemistry》的PyTorch实现,讲的是量子化学里的神经信息传递!听起来碉堡了。

图像理解

Visual Question Answering in Pytorch

https://github.com/Cadene/vqa.pytorch.git

一个PyTorch实现的优秀视觉推理问答系统,是基于论文《MUTAN: Multimodal Tucker Fusion for Visual Question Answering》实现的。项目中有详细的配置使用方法说明。

Facebook看图答题:Clevr-IEP

https://github.com/facebookresearch/clevr-iep.git

Facebook Research 论文《Inferring and Executing Programs for Visual Reasoning》的PyTorch实现,讲的是一个可以基于图片进行关系推理问答的网络。

2019-03-13 16:29:33 weixin_43839485 阅读数 176
  • 人工智能框架实战精讲:Keras项目

    Keras项目实战课程从实战的角度出发,基于真实数据集与实际业务需求,从零开始讲解如何进行数据处理,模型训练与调优,最后进行测试与结果展示分析。全程实战操作,以最接地气的方式详解每一步流程与解决方案。课程结合当下深度学习热门领域,以计算机视觉与自然语言处理为核心讲解各大网络的应用于实战方法,适合快速入门与进阶提升。 任务作业: 1.基于Keras构建VGG网络模型 2.加载与预处理细胞图像数据 3.构建完成分类模型并进行测试识别 (注意: 作业需写在CSDN博客中,请把作业链接贴在评论区,老师会定期逐个批改~~)

    1692 人正在学习 去看看 唐宇迪

数字图像处理项目记录1

3月9日:lecture1

Matlab图形处理函数

imread函数
Matlab文档解释
size函数
Matlab文档解释
imadjust函数(灰度变换函数)f1=imadjust(f,[low_in high_in],[low_out high_out],gamma)
网上说明
Matlab文档解释

imresize函数
Matlab文档解释
imshowpair函数
imshowpair函数就是指以成双成对的形式显示图片,其中一个重要的参数就是‘method’,他有4个选择
(1)‘falsecolor’ 字面意思理解就是伪彩色的意思,其实就是把两幅图像的差异用色彩来表示,这个是默认的参数。
(2)‘blend’ 这是一种混合透明处理类型,技术文档的翻译是alpha blending,大家自己理解吧。
(3)‘diff’ 这是用灰度信息来表示亮度图像之间的差异,这是对应‘falsecolor’的一种方式。
(4)参数‘montage’可以理解成‘蒙太奇’,这是一种视频剪辑的艺术手法,其实在这里我们理解成拼接的方法就可以了。
https://blog.csdn.net/ch_fei/article/details/6450372

imcomplement函数
J = imcomplement(I) computes the complement of the image I and returns the result in J.
Matlab文档解释

mat2gray函数(归一化,小于amin的化0,大于amax的化1)I = mat2gray(A,[amin amax]) I = mat2gray(A)
Matlab文档解释
网上说明

im2double函数,将[0,255]映射到[0,1]
网上说明

im2uint8函数J = im2uint8(I) J = im2uint8(I,'indexed')
把图像数据类型转换为无符号八位整型。如果输入图像是无符号八位整型的,返回的图像和
源图像相同。如果源图像不是无符号八位整型的,该函数将返回和源图像相同但数据类型为uint8的图像(必要时对图像进行调整)

J = im2uint8(I) converts the grayscale, RGB, or binary image I to
uint8, rescaling or offsetting the data as necessary.

If the input image is of class uint8, then the output image is
identical. If the input image is of class logical, then im2uint8
changes true-valued elements to 255.

You optionally can perform the conversion using a GPU (requires
Parallel Computing Toolbox™). For more information, see Image
Processing on a GPU.

J = im2uint8(I,‘indexed’) converts the indexed image I to uint8,
offsetting the data if necessary.
将索引图像I转换为uint8,
必要时抵消数据。

Matlab文档解释

对数变换
对数与对比度拉伸变换是进行动态范围处理的基本工具,对数变换通过表达式:g=c*log(1+double(f))实现。

对数变换的一项主要应用是压缩动态范围,如gs=im2uint8(mat2gray(g));

2017-10-12 16:58:13 piaoxuezhong 阅读数 9096
  • 人工智能框架实战精讲:Keras项目

    Keras项目实战课程从实战的角度出发,基于真实数据集与实际业务需求,从零开始讲解如何进行数据处理,模型训练与调优,最后进行测试与结果展示分析。全程实战操作,以最接地气的方式详解每一步流程与解决方案。课程结合当下深度学习热门领域,以计算机视觉与自然语言处理为核心讲解各大网络的应用于实战方法,适合快速入门与进阶提升。 任务作业: 1.基于Keras构建VGG网络模型 2.加载与预处理细胞图像数据 3.构建完成分类模型并进行测试识别 (注意: 作业需写在CSDN博客中,请把作业链接贴在评论区,老师会定期逐个批改~~)

    1692 人正在学习 去看看 唐宇迪
       这几天在研究血管增强与分割,发现一个比较全面的图像处理方面的项目集合,里面涵盖了特征提取、图像分割、图像分类、图像匹配、图像降噪,光流法等等方面的项目和代码集合,项目是2012年之前的,但是涵盖比较基础的原理知识,用到的时候可以参考一下:

Topic

Resources

References

Feature Extraction

  1. D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints, IJCV 2004. [PDF]
  2. Y. Ke and R. Sukthankar, PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,CVPR, 2004. [PDF]
  3. J.M. Morel and G.Yu, ASIFT, A new framework for fully affine invariant image comparisonSIAM Journal on Imaging Sciences, 2009. [PDF]
  4. H. Bay, T. Tuytelaars and L. V. Gool SURF: Speeded Up Robust FeaturesECCV, 2006. [PDF]
  5. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir and L. Van Gool, A comparison of affine region detectorsIJCV, 2005. [PDF]
  6. J. Matas, O. Chum, M. Urba, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regionsBMVC, 2002. [PDF]
  7. A. C. Berg, T. L. Berg, and J. Malik. Shape matching and object recognition using low distortion correspondences. CVPR, 2005. [PDF]
  8. E. Shechtman and M. Irani. Matching local self-similarities across images and videos, CVPR, 2007. [PDF]
  9. T. Deselaers and V. Ferrari. Global and Efficient Self-Similarity for Object Classification and DetectionCVPR 2010. [PDF]
  10. N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection.CVPR 2005. [PDF]
  11. A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelopeIJCV, 2001. [PDF]
  12. S. Belongie, J. Malik and J. Puzicha. Shape matching and object recognition using shape contextsPAMI, 2002. [PDF]
  13. K. E. A. van de Sande, T. Gevers and Cees G. M. Snoek, Evaluating Color Descriptors for Object and Scene RecognitionPAMI, 2010.
  14. I. Laptev, On Space-Time Interest Points, IJCV, 2005. [PDF]
  15. J. Kim and K. Grauman, Boundary Preserving Dense Local RegionsCVPR 2011. [PDF]

Image Segmentation

 

 

  1. J. Shi and J Malik, Normalized Cuts and Image SegmentationPAMI, 2000 [PDF]
  2. X. Ren and J. Malik. Learning a classification model for segmentationICCV, 2003. [PDF]
  3. P. Felzenszwalb and D. Huttenlocher. Efficient Graph-Based Image Segmentation,IJCV 2004. [PDF]
  4. D. Comaniciu, P Meer. Mean Shift: A Robust Approach Toward Feature Space AnalysisPAMI 2002. [PDF]
  5. P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image SegmentationPAMI, 2011. [PDF]
  6. A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, TurboPixels: Fast Superpixels Using Geometric FlowsPAMI 2009. [PDF]
  7. A. Vedaldi and S. Soatto, Quick Shift and Kernel Methodsfor Mode SeekingECCV, 2008. [PDF]
  8. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, SLIC Superpixels, EPFL Technical Report, 2010. [PDF]
  9. A. Y. Yang, J. Wright, S. Shankar Sastry, Y. Ma , Unsupervised Segmentation of Natural Images via Lossy Data CompressionCVIU, 2007. [PDF]
  10. S. Maji, N. Vishnoi and J. Malik, Biased Normalized CutCVPR 2011
  11. E. Akbas and N. Ahuja, “From ramp discontinuities to segmentation tree,”  ACCV 2009. [PDF]
  12. N. Ahuja, “A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection,” PAMI 1996 [PDF]
  13. M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy Rate Superpixel Segmentation, CVPR 2011 [PDF]

Object Detection

  • A simple object detector with boosting [Project]

  • INRIA Object Detection and Localization Toolkit [1] [Project]

  • Discriminatively Trained Deformable Part Models [2] [Project]

  • Cascade Object Detection with Deformable Part Models [3] [Project]

  • Poselet [4] [Project]

  • Implicit Shape Model [5] [Project]

  • Viola and Jones's Face Detection [6] [Project]
  1. N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection.CVPR 2005. [PDF]
  2. P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan.
    Object Detection with Discriminatively Trained Part Based ModelsPAMI, 2010 [PDF]
  3. P. Felzenszwalb, R. Girshick, D. McAllester. Cascade Object Detection with Deformable Part ModelsCVPR 2010 [PDF]
  4. L. Bourdev, J. Malik, Poselets: Body Part Detectors Trained Using 3D Human Pose AnnotationsICCV 2009 [PDF]
  5. B. Leibe, A. Leonardis, B. Schiele. Robust Object Detection with Interleaved Categorization and SegmentationIJCV, 2008. [PDF]
  6. P. Viola and M. Jones, Rapid Object Detection Using a Boosted Cascade of Simple FeaturesCVPR 2001. [PDF]

Saliency Detection

  • Itti, Koch, and Niebur' saliency detection [1] [Matlab code]

  • Frequency-tuned salient region detection [2] [Project]

  • Saliency detection using maximum symmetric surround [3] [Project]

  • Attention via Information Maximization [4] [Matlab code]

  • Context-aware saliency detection [5] [Matlab code]

  • Graph-based visual saliency [6] [Matlab code]

  • Saliency detection: A spectral residual approach. [7] [Matlab code]

  • Segmenting salient objects from images and videos. [8] [Matlab code]

  • Saliency Using Natural statistics. [9] [Matlab code]

  • Discriminant Saliency for Visual Recognition from Cluttered Scenes. [10] [Code]

  • Learning to Predict Where Humans Look [11] [Project]

  • Global Contrast based Salient Region Detection [12] [Project]
  1. L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysisPAMI, 1998. [PDF]
  2. R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009. [PDF]
  3. R. Achanta and S. Susstrunk. Saliency detection using maximum symmetric surround. In ICIP, 2010. [PDF]
  4. N. Bruce and J. Tsotsos. Saliency based on information maximization. In NIPS, 2005. [PDF]
  5. S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. InCVPR, 2010. [PDF]
  6. J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. NIPS, 2007. [PDF]
  7. X. Hou and L. Zhang. Saliency detection: A spectral residual approachCVPR, 2007. [PDF]
  8. E. Rahtu, J. Kannala, M. Salo, and J. Heikkila. Segmenting salient objects from images and videosCVPR, 2010. [PDF]
  9. L. Zhang, M. Tong, T. Marks, H. Shan, and G. Cottrell. Sun: A bayesian framework for saliency using natural statisticsJournal of Vision, 2008. [PDF]
  10. D. Gao and N. Vasconcelos, Discriminant Saliency for Visual Recognition from Cluttered ScenesNIPS, 2004. [PDF]
  11. T. Judd and K. Ehinger and F. Durand and A. Torralba, Learning to Predict Where Humans LookICCV, 2009. [PDF]
  12. M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, S.-M. Hu. Global Contrast based Salient Region DetectionCVPR 2011.

Image Classification

  1. K. Grauman and T. Darrell, The Pyramid Match Kernel: Discriminative Classification with Sets of Image FeaturesICCV 2005. [PDF]
  2. S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene CategoriesCVPR 2006 [PDF]
  3. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong. Locality-constrained Linear Coding for Image ClassificationCVPR, 2010 [PDF]
  4. J. Yang, K. Yu, Y. Gong, T. Huang, Linear Spatial Pyramid Matching using Sparse Coding for Image ClassificationCVPR, 2009 [PDF]
  5. M. Varma and A. Zisserman, A statistical approach to texture classification from single images, IJCV2005. [PDF]
  6. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, Multiple Kernels for Object DetectionICCV, 2009. [PDF]
  7. P. Gehler and S. Nowozin, On Feature Combination for Multiclass Object Detection, ICCV, 2009. [PDF]
  8. J. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image
    Parsing with Superpixels
    , ECCV 2010. [PDF]

Category-Independent Object Proposal

  • Objectness measure [1] [Code]

  • Parametric min-cut [2] [Project]

  • Object proposal [3] [Project]

  1. B. Alexe, T. Deselaers, V. Ferrari, What is an Object?CVPR 2010 [PDF]
  2. J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object SegmentationCVPR 2010. [PDF]
  3. I. Endres and D. Hoiem. Category Independent Object Proposals, ECCV 2010. [PDF]

MRF

  1. Y. Boykov, O. Veksler and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, PAMI 2001 [PDF]

Shadow Detection

  • Shadow Detection using Paired Region [Project]

  • Ground shadow detection [Project]


  1. R. Guo, Q. Dai and D. Hoiem, Single-Image Shadow Detection and Removal using Paired Regions, CVPR 2011 [PDF]
  2. J.-F. Lalonde, A. A. Efros, S. G. Narasimhan, Detecting Ground Shadowsin Outdoor Consumer PhotographsECCV 2010 [PDF]

Optical Flow

  1. B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo VisionIJCAI 1981. [PDF]
  2. J. Shi, C. Tomasi, Good Feature to TrackCVPR 1994. [PDF]
  3. C. Liu. Beyond Pixels: Exploring New Representations and Applications for Motion Analysis. Doctoral ThesisMIT 2009. [PDF]
  4. B.K.P. Horn and B.G. Schunck, Determining Optical FlowArtificial Intelligence1981. [PDF]
  5. M. J. Black and P. Anandan, A framework for the robust estimation of optical flow, ICCV 93. [PDF]
  6. D. Sun, S. Roth, and M. J. Black, Secrets of optical flow estimation and their principlesCVPR 2010. [PDF]
  7. T. Brox, J. Malik, Large displacement optical flow: descriptor matching in variational motion estimationPAMI, 2010 [PDF]
  8. T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warpingECCV 2004 [PDF]

Object Tracking

  • Particle filter object tracking [1] [Project]

  • KLT Tracker [2-3] [Project]

  • MILTrack [4] [Code]

  • Incremental Learning for Robust Visual Tracking [5] [Project]

  • Online Boosting Trackers [6-7] [Project]

  • L1 Tracking [8] [Matlab code]

  1. P. Perez, C. Hue, J. Vermaak, and M. Gangnet. Color-Based Probabilistic Tracking ECCV, 2002. [PDF]
  2. B.D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo VisionIJCAI 1981. [PDF]
  3. J. Shi, C. Tomasi, Good Feature to TrackCVPR 1994. [PDF]
  4. B. Babenko, M. H. Yang, S. Belongie, Robust Object Tracking with Online Multiple Instance LearningPAMI 2011 [PDF]
  5. D. Ross, J. Lim, R.-S. Lin, M.-H. Yang, Incremental Learning for Robust Visual TrackingIJCV 2007 [PDF]
  6. H. Grabner, and H. Bischof, On-line Boosting and Vision, CVPR 2006 [PDF]
  7. H. Grabner, C. Leistner, and H. Bischof, Semi-supervised On-line Boosting for Robust TrackingECCV 2008 [PDF]
  8. X. Mei and H. Ling, Robust Visual Tracking using L1 Minimization, ICCV, 2009. [PDF]

Image Matting

  • Closed Form Matting [Code]

  • Spectral Matting [Project]

  • Learning-based Matting [Code]

  1. A. Levin D. Lischinski and Y. WeissA Closed Form Solution to Natural Image MattingPAMI 2008 [PDF]
  2. A. Levin, A. Rav-Acha, D. Lischinski. Spectral MattingPAMI 2008. [PDF]
  3. Y. Zheng and C. Kambhamettu, Learning Based Digital MattingICCV 2009 [PDF]

Bilateral Filtering

  • Fast Bilateral Filter [Project]

  • Real-time O(1) Bilateral Filtering [Code]

  • SVM for Edge-Preserving Filtering [Code]

  1. Q. Yang, K.-H. Tan and N. Ahuja,  Real-time O(1) Bilateral Filtering
    CVPR 2009. [PDF]
  2. Q. Yang, S. Wang, and N. Ahuja, SVM for Edge-Preserving Filtering
    CVPR 2010. [PDF]

Image Denoising

 

Image Super-Resolution

  • MRF for image super-resolution [Project]

  • Multi-frame image super-resolution [Project]

  • UCSC Super-resolution [Project]

  • Sprarse coding super-resolution [Code]

 

Image Deblurring

  • Eficient Marginal Likelihood Optimization in Blind Deconvolution [Code]

  • Analyzing spatially varying blur [Project]

  • Radon Transform [Code]

 

Image Quality Assessment

  1. L. Zhang, L. Zhang, X. Mou and D. Zhang, FSIM: A Feature Similarity Index for Image Quality AssessmentTIP 2011. [PDF]
  2. N. Damera-Venkata, and T. D. Kite, W. S. Geisler, B. L. Evans, and A. C. Bovik,Image Quality Assessment Based on a Degradation ModelTIP 2000. [PDF]
  3. Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, Image quality assessment: from error visibility to structural similarity, TIP 2004. [PDF]
  4. B. Ghanem, E. Resendiz, and N. Ahuja, Segmentation-Based Perceptual Image Quality Assessment (SPIQA)ICIP 2008. [PDF]

Density Estimation

  • Kernel Density Estimation Toolbox [Project]
 

Dimension Reduction

 

Sparse Coding

   

Low-Rank Matrix Completion

   

Nearest Neighbors matching

 

Steoreo

  1. D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithmsIJCV 2002 [PDF]

Structure from motion

 

  1. N. Snavely, S. M. Seitz, R. Szeliski. Photo Tourism: Exploring image collections in 3DSIGGRAPH, 2006. [PDF]

Distance Transformation

  • Distance Transforms of Sampled Functions [1] [Project]
  1. P. F. Felzenszwalb and D. P. Huttenlocher. Distance transforms of sampled functionsTechnical report, Cornell University, 2004. [PDF]

Chamfer Matching

  • Fast Directional Chamfer Matching [Code]
  1. M.-Y. Liu, O. Tuzel, A. Veeraraghavan, and R. Chellappa, Fast Directional Chamfer MatchingCVPR 2010 [PDF]

Clustering

 

Classification

 

Regression

  • SVM

  • RVM

  • GPR

 

Multiple Kernel Learning (MKL)

  1. S. Sonnenburg, G. R?tsch, C. Sch?fer, B. Sch?lkopf . Large scale multiple kernel learningJMLR, 2006. [PDF]
  2. F. Orabona and L. Jie. Ultra-fast optimization algorithm for sparse multi kernel learning. ICML, 2011. [PDF]
  3. F. Orabona, L. Jie, and B. Caputo. Online-batch strongly convex multi kernel learningCVPR, 2010. [PDF]
  4. A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. SimplemklJMRL, 2008. [PDF]

Multiple Instance Learning (MIL)

  1. C. Leistner, A. Saffari, and H. Bischof, MIForests: Multiple-Instance Learning with Randomized TreesECCV 2010. [PDF]
  2. Z. Fu, A. Robles-Kelly, and J. Zhou, MILIS: Multiple instance learning with instance selectionPAMI 2010. [PDF]
  3. Y. Chen, J. Bi and J. Z. Wang, MILES: Multiple-Instance Learning via Embedded Instance SelectionPAMI 2006 [PDF]
  4. Yixin Chen and James Z. Wang, Image Categorization by Learning and Reasoning with RegionsJMLR 2004. [PDF]

Other Utilities

  • Code for downloading Flickr images, by James Hays [Code]

  • The Lightspeed Matlab Toolbox by Tom Minka [Code]

  • MATLAB Functions for Multiple View Geometry [Code]

  • Peter's Functions for Computer Vision [Code]

  • Statistical Pattern Recognition Toolbox [Code]
 

 

Useful Links (dataset, lectures, and other softwares)

Conference Information

Papers

Datasets

Lectures

Source Codes


一、特征提取Feature Extraction:


二、图像分割Image Segmentation:

  • Normalized Cut [1] [Matlab code]

  • Gerg Mori’ Superpixel code [2] [Matlab code]

  • Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper]

  • Mean-Shift Image Segmentation [4] [EDISON C++ code] [Matlab wrapper]

  • OWT-UCM Hierarchical Segmentation [5] [Resources]

  • Turbepixels [6] [Matlab code 32bit] [Matlab code 64bit] [Updated code]

  • Quick-Shift [7] [VLFeat]

  • SLIC Superpixels [8] [Project]

  • Segmentation by Minimum Code Length [9] [Project]

  • Biased Normalized Cut [10] [Project]

  • Segmentation Tree [11-12] [Project]

  • Entropy Rate Superpixel Segmentation [13] [Code]

  • Fast Approximate Energy Minimization via Graph Cuts[Paper][Code]

  • Efficient Planar Graph Cuts with Applications in Computer Vision[Paper][Code]

  • Isoperimetric Graph Partitioning for Image Segmentation[Paper][Code]

  • Random Walks for Image Segmentation[Paper][Code]

  • Blossom V: A new implementation of a minimum cost perfect matching algorithm[Code]

  • An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision[Paper][Code]

  • Geodesic Star Convexity for Interactive Image Segmentation[Project]

  • Contour Detection and Image Segmentation Resources[Project][Code]

  • Biased Normalized Cuts[Project]

  • Max-flow/min-cut[Project]

  • Chan-Vese Segmentation using Level Set[Project]

  • A Toolbox of Level Set Methods[Project]

  • Re-initialization Free Level Set Evolution via Reaction Diffusion[Project]

  • Improved C-V active contour model[Paper][Code]

  • A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction[Paper][Code]

  • Level Set Method Research by Chunming Li[Project]

  • ClassCut for Unsupervised Class Segmentation[code]

  • SEEDS: Superpixels Extracted via Energy-Driven Sampling [Project][other]


三、目标检测Object Detection:

  • A simple object detector with boosting [Project]

  • INRIA Object Detection and Localization Toolkit [1] [Project]

  • Discriminatively Trained Deformable Part Models [2] [Project]

  • Cascade Object Detection with Deformable Part Models [3] [Project]

  • Poselet [4] [Project]

  • Implicit Shape Model [5] [Project]

  • Viola and Jones’s Face Detection [6] [Project]

  • Bayesian Modelling of Dyanmic Scenes for Object Detection[Paper][Code]

  • Hand detection using multiple proposals[Project]

  • Color Constancy, Intrinsic Images, and Shape Estimation[Paper][Code]

  • Discriminatively trained deformable part models[Project]

  • Gradient Response Maps for Real-Time Detection of Texture-Less Objects: LineMOD [Project]

  • Image Processing On Line[Project]

  • Robust Optical Flow Estimation[Project]

  • Where's Waldo: Matching People in Images of Crowds[Project]

  • Scalable Multi-class Object Detection[Project]

  • Class-Specific Hough Forests for Object Detection[Project]

  • Deformed Lattice Detection In Real-World Images[Project]

  • Discriminatively trained deformable part models[Project]


四、显著性检测Saliency Detection:

  • Itti, Koch, and Niebur’ saliency detection [1] [Matlab code]

  • Frequency-tuned salient region detection [2] [Project]

  • Saliency detection using maximum symmetric surround [3] [Project]

  • Attention via Information Maximization [4] [Matlab code]

  • Context-aware saliency detection [5] [Matlab code]

  • Graph-based visual saliency [6] [Matlab code]

  • Saliency detection: A spectral residual approach. [7] [Matlab code]

  • Segmenting salient objects from images and videos. [8] [Matlab code]

  • Saliency Using Natural statistics. [9] [Matlab code]

  • Discriminant Saliency for Visual Recognition from Cluttered Scenes. [10] [Code]

  • Learning to Predict Where Humans Look [11] [Project]

  • Global Contrast based Salient Region Detection [12] [Project]

  • Bayesian Saliency via Low and Mid Level Cues[Project]

  • Top-Down Visual Saliency via Joint CRF and Dictionary Learning[Paper][Code]

  • Saliency Detection: A Spectral Residual Approach[Code]


五、图像分类、聚类Image Classification, Clustering

  • Pyramid Match [1] [Project]

  • Spatial Pyramid Matching [2] [Code]

  • Locality-constrained Linear Coding [3] [Project] [Matlab code]

  • Sparse Coding [4] [Project] [Matlab code]

  • Texture Classification [5] [Project]

  • Multiple Kernels for Image Classification [6] [Project]

  • Feature Combination [7] [Project]

  • SuperParsing [Code]

  • Large Scale Correlation Clustering Optimization[Matlab code]

  • Detecting and Sketching the Common[Project]

  • Self-Tuning Spectral Clustering[Project][Code]

  • User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior[Paper][Code]

  • Filters for Texture Classification[Project]

  • Multiple Kernel Learning for Image Classification[Project]

  • SLIC Superpixels[Project]


六、抠图Image Matting

  • A Closed Form Solution to Natural Image Matting [Code]

  • Spectral Matting [Project]

  • Learning-based Matting [Code]


七、目标跟踪Object Tracking:

  • A Forest of Sensors - Tracking Adaptive Background Mixture Models [Project]

  • Object Tracking via Partial Least Squares Analysis[Paper][Code]

  • Robust Object Tracking with Online Multiple Instance Learning[Paper][Code]

  • Online Visual Tracking with Histograms and Articulating Blocks[Project]

  • Incremental Learning for Robust Visual Tracking[Project]

  • Real-time Compressive Tracking[Project]

  • Robust Object Tracking via Sparsity-based Collaborative Model[Project]

  • Visual Tracking via Adaptive Structural Local Sparse Appearance Model[Project]

  • Online Discriminative Object Tracking with Local Sparse Representation[Paper][Code]

  • Superpixel Tracking[Project]

  • Learning Hierarchical Image Representation with Sparsity, Saliency and Locality[Paper][Code]

  • Online Multiple Support Instance Tracking [Paper][Code]

  • Visual Tracking with Online Multiple Instance Learning[Project]

  • Object detection and recognition[Project]

  • Compressive Sensing Resources[Project]

  • Robust Real-Time Visual Tracking using Pixel-Wise Posteriors[Project]

  • Tracking-Learning-Detection[Project][OpenTLD/C++ Code]

  • the HandVu:vision-based hand gesture interface[Project]

  • Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities[Project]


八、Kinect:


九、3D相关:

  • 3D Reconstruction of a Moving Object[Paper] [Code]

  • Shape From Shading Using Linear Approximation[Code]

  • Combining Shape from Shading and Stereo Depth Maps[Project][Code]

  • Shape from Shading: A Survey[Paper][Code]

  • A Spatio-Temporal Descriptor based on 3D Gradients (HOG3D)[Project][Code]

  • Multi-camera Scene Reconstruction via Graph Cuts[Paper][Code]

  • A Fast Marching Formulation of Perspective Shape from Shading under Frontal Illumination[Paper][Code]

  • Reconstruction:3D Shape, Illumination, Shading, Reflectance, Texture[Project]

  • Monocular Tracking of 3D Human Motion with a Coordinated Mixture of Factor Analyzers[Code]

  • Learning 3-D Scene Structure from a Single Still Image[Project]


十、机器学习算法:

  • Matlab class for computing Approximate Nearest Nieghbor (ANN) [Matlab class providing interface toANN library]

  • Random Sampling[code]

  • Probabilistic Latent Semantic Analysis (pLSA)[Code]

  • FASTANN and FASTCLUSTER for approximate k-means (AKM)[Project]

  • Fast Intersection / Additive Kernel SVMs[Project]

  • SVM[Code]

  • Ensemble learning[Project]

  • Deep Learning[Net]

  • Deep Learning Methods for Vision[Project]

  • Neural Network for Recognition of Handwritten Digits[Project]

  • Training a deep autoencoder or a classifier on MNIST digits[Project]

  • THE MNIST DATABASE of handwritten digits[Project]

  • Ersatz:deep neural networks in the cloud[Project]

  • Deep Learning [Project]

  • sparseLM : Sparse Levenberg-Marquardt nonlinear least squares in C/C++[Project]

  • Weka 3: Data Mining Software in Java[Project]

  • Invited talk "A Tutorial on Deep Learning" by Dr. Kai Yu (余凯)[Video]

  • CNN - Convolutional neural network class[Matlab Tool]

  • Yann LeCun's Publications[Wedsite]

  • LeNet-5, convolutional neural networks[Project]

  • Training a deep autoencoder or a classifier on MNIST digits[Project]

  • Deep Learning 大牛Geoffrey E. Hinton's HomePage[Website]

  • Multiple Instance Logistic Discriminant-based Metric Learning (MildML) and Logistic Discriminant-based Metric Learning (LDML)[Code]

  • Sparse coding simulation software[Project]

  • Visual Recognition and Machine Learning Summer School[Software]


十一、目标、行为识别Object, Action Recognition:

  • Action Recognition by Dense Trajectories[Project][Code]

  • Action Recognition Using a Distributed Representation of Pose and Appearance[Project]

  • Recognition Using Regions[Paper][Code]

  • 2D Articulated Human Pose Estimation[Project]

  • Fast Human Pose Estimation Using Appearance and Motion via Multi-Dimensional Boosting Regression[Paper][Code]

  • Estimating Human Pose from Occluded Images[Paper][Code]

  • Quasi-dense wide baseline matching[Project]

  • ChaLearn Gesture Challenge: Principal motion: PCA-based reconstruction of motion histograms[Project]

  • Real Time Head Pose Estimation with Random Regression Forests[Project]

  • 2D Action Recognition Serves 3D Human Pose Estimation[

  • A Hough Transform-Based Voting Framework for Action Recognition[

  • Motion Interchange Patterns for Action Recognition in Unconstrained Videos[

  • 2D articulated human pose estimation software[Project]

  • Learning and detecting shape models [code]

  • Progressive Search Space Reduction for Human Pose Estimation[Project]

  • Learning Non-Rigid 3D Shape from 2D Motion[Project]


十二、图像处理:

  • Distance Transforms of Sampled Functions[Project]

  • The Computer Vision Homepage[Project]

  • Efficient appearance distances between windows[code]

  • Image Exploration algorithm[code]

  • Motion Magnification 运动放大 [Project]

  • Bilateral Filtering for Gray and Color Images 双边滤波器 [Project]

  • A Fast Approximation of the Bilateral Filter using a Signal Processing Approach [


十三、一些实用工具:

  • EGT: a Toolbox for Multiple View Geometry and Visual Servoing[Project] [Code]

  • a development kit of matlab mex functions for OpenCV library[Project]

  • Fast Artificial Neural Network Library[Project]


十四、人手及指尖检测与识别:

  • finger-detection-and-gesture-recognition [Code]

  • Hand and Finger Detection using JavaCV[Project]

  • Hand and fingers detection[Code]


十五、场景解释:

  • Nonparametric Scene Parsing via Label Transfer [Project]


十六、光流Optical flow:

  • High accuracy optical flow using a theory for warping [Project]

  • Dense Trajectories Video Description [Project]

  • SIFT Flow: Dense Correspondence across Scenes and its Applications[Project]

  • KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker [Project]

  • Tracking Cars Using Optical Flow[Project]

  • Secrets of optical flow estimation and their principles[Project]

  • implmentation of the Black and Anandan dense optical flow method[Project]

  • Optical Flow Computation[Project]

  • Beyond Pixels: Exploring New Representations and Applications for Motion Analysis[Project]

  • A Database and Evaluation Methodology for Optical Flow[Project]

  • optical flow relative[Project]

  • Robust Optical Flow Estimation [Project]

  • optical flow[Project]


十七、图像检索Image Retrieval:

  • Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval [Paper][code]


十八、马尔科夫随机场Markov Random Fields:

  • Markov Random Fields for Super-Resolution [Project]

  • A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors [Project]


十九、运动检测Motion detection:

以上是从下面网址中汇总来的:

http://www.360doc.com/content/12/0201/11/8703626_183332994.shtml

https://www.cnblogs.com/findumars/p/5009003.html

另外,在http://blog.csdn.net/zouxy09/article/details/8550952里也给出了一些项目链接汇总。


2017-08-23 20:38:32 Terrenceyuu 阅读数 3418
  • 人工智能框架实战精讲:Keras项目

    Keras项目实战课程从实战的角度出发,基于真实数据集与实际业务需求,从零开始讲解如何进行数据处理,模型训练与调优,最后进行测试与结果展示分析。全程实战操作,以最接地气的方式详解每一步流程与解决方案。课程结合当下深度学习热门领域,以计算机视觉与自然语言处理为核心讲解各大网络的应用于实战方法,适合快速入门与进阶提升。 任务作业: 1.基于Keras构建VGG网络模型 2.加载与预处理细胞图像数据 3.构建完成分类模型并进行测试识别 (注意: 作业需写在CSDN博客中,请把作业链接贴在评论区,老师会定期逐个批改~~)

    1692 人正在学习 去看看 唐宇迪

图像处理项目-车标识别

概述

  • 我们的目标是提供一张原始车标图像作为基准。然后,提供一个实际车辆图片的车标部分的截图。然后实现两者之间的匹配。

实现

  • 车标识别显然其特征是与图像尺度及旋转无关的,所以SIFT特征是一个不错的选择。

  • 匹配算法的选择:FLNN


  • 相同车标的匹配效果:

这里写图片描述

  • 不同车标的匹配效果:

这里写图片描述

这里写图片描述

图像处理学习之路

阅读数 15640

图片处理开源项目

阅读数 3532

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