Introduction to Computer Vision
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1 Introduction to Computer Vision Michael J. Black Project Ideas
2 Dates 11/13 Proposals due - 1 page write-up - summary and goals (problem/approach) - what are the key references - where will you get data and how will you evaluate your method? 12/14 Projects due
3 Eigen faces
4 Detect faces
5 Mahalanobis distance
6 1./d
7 Masked by skin detections
8 Times the image
9 If you work on face detection You must use color in some way (e.g. color eigenspace) You must search across a range of scales. You must try it on some image data that was not collected in class. Use the Moghaddam and Pentland paper. Extra: try gender recognition.
10 Space Carving Kutulakos, K. and Seitz, S Theory of Shape by Space Carving. International Journal of Computer Vision, 38(3): Images from ETH-80 database.
11 Sources of data
12 Image segmentation Normalized cuts:
13 Face gender classification? labeled faces of 5749 people found with Viola-Jones detector (Adaboost). Use names to get gender: ( baby-names.php?)
14 More face images There are many other face databases on the web. Eg:
15 Kalman Filter Tracker Real-time tracker using a PC camera.
16 Mean-shift tracking
17 Facial expressions Affine head tracking analysis of motions of facial features
18
19 EigenTracking (Black and Jepson) Combines affine motion estimation with PCA representation to allow tracking of deforming objects. Data: ~black/images.html
20
21 Pixel-wise posteriors
22 Tadpole tracker Contact black or moldovan for data.
23 Greg Nicholas 07
24 Rodent tracker Contact: Prof. Russ Church
25 Active Shape/Appearance Models Lot s of support code and data on web.
26 Stereo
27 Panoramic Mosaics
28 Dense Optical Flow or Stereo Data and ground truth flow: Same for stereo Deqing Sun
29 Pedestrian Detectors
30 Image denoising. Non-linear diffusion
31 -this turns out to be too hard and the best thing to do is a non-linear diffusion method. Image Inpainting
32 Colorization
33 Super-resolution See Michal Irani and Shmuel Peleg. Super Resolution From Image Sequences. IEEE, CS296-4 Forensic Computer Vision April 2006
34 CS296-4 Forensic Computer Vision April 2006
35 Super-resolution CS296-4 Forensic Computer Vision April 2006
36 More Project Ideas Temporal model of mouth motions (HMM) for recognition. More advanced machine learning method for mouth or person detection - AdaBoost - support vector machines Bayesian image denoising Stereo Space carving from silhouettes Grab-cut Moghaddam mixture model for face/mouth recognition.
Human Face Detection and Tracking using Skin Color Combined with Optical Flow Based Segmentation
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