Tuesday, August 09, 2005
Human Vision Models for Perceptually Optimized Image Processing - A Review
Marcus J. Nadenau, Stefan Winkler, David Alleysson, Murat Kunt
Human Visual System, Color Perception, Image Compression
Person Identification Using Multiple Cues
Roberto Brunelli, Daniele Falavigna
IEEE Transactions on Pattern Analysis and Machine Intell. V17 No. 10 oct 95
Face Recognition. Not very useful for my project.
On Illumination Invariance In Color Object Recognition
M Drew, J. Wei, Ze-Nian Li
Linear Color Algorithm
Mobile Robot Localization Under Varying Illumination
M Jogan, H Wildenauer H Bischof
Eigenspace EigenImage
-each learned image is point in low-dim eigenspace. Project scene on eigenspace and search for closest learned point. Uses PCA to reduce the dimentionality of an image to plot as a point. Method is robust with occulusions and illumination artifacts but global Illumination must be delt with by filters.
Robustly Estimating Changes in Image Appearance
Black, Fleet, Yacoob
appearance change, optical flow. Review of multitude problems of image change.
Illumination Invariant and Occlusion Robust Vehicle Tracking by Spatio-Temporal MRF Model
Kamijo, Sakauchi
Spatio-Temporal Markoff Random Field.
An Illumination Invariant Change Detection Algorithm
Lou Yang Hu Tan
Seems useful but difficult to understand. Color. Divides image into stationary background and moving foreground. homomorphic filtering for Ill, invar. Internet sites on this method say it is very slow.
Illumination-Invariant Image Retrieval and Video Segmentation
Drew, Wei, Li.
A lot of emph on Color.
Adaptive Background Estimation: Computing a pixel-wise learning rate from local confidence and global correlation values
PIC, Berthouze, Kurita 2004
Very interesting. Results in robust moving object segmentation.
A Novel Background Initialization Method in Visual Surveillance
Bevilacqua
Bayes Theorm. looks at each pixel as it changes through time. Picks most usual intensity and calls it background, under the assumption that moving foreground objects pass by quickly and background intensities are dominant in time. Uses this to build a background model and subtracts from image to get moving objects. Doesn't deal with background illumination changes in time but says method robust for all illumination conditions (assuming they are constant in time)
Effective Object Segmentation in a Traffic Monitoring Application
Bevilacqua
3x3 morph method for segmenting video objects.
Image Segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm.
Tao, Tian, Liu
probability partition. Extends work of Zhoa et al 2001, IEEE Trans. Fuzzy Systems 9 (3), 469-479
Local Entropy-based region extraction and thresholding
Yan, Sang, Zhang
Local Entropy, Transition Region, Gradient, Segmentation.
Phd: Real-Time Occupant Detection in High Dynamic Range Environments - Carsten Koch
Journal Review
Optical Dynamic Range - Yamada: effectivness of video camera dynamic range expansion for lane mark detection, IEEE Int. Conf on Intell. Vehicles, 1998 -> CCD cameras fail 10% of time to detect lane marks in daylight
Usuall Dynamic Range of Cameras is 60-70dB. High Dynamic Range cameras for use in extreme lighting envoron use CMOS and give 80dB
Background Segmentation. When an object moves it joins the foreground but fades back to the background shortly after stopping.
Whole object should be moved to foreground - not just it's moving components.
---------------------------------------------------
Watkinson "The MPEG HandBook"
-motion compensation - optical axis in 3d (2d space, 1 time) space. optical axis at angle to time axis if object moving. object changes little as it moves along axis. (MPEG-4)
Stauffer, Grimson, "Learning Patterns of Activity Using Real-Time Tracking"
realtime visual tracking, adaptive background estimation, gaussian, surveillance.
# posted by Seabhcan @ 9:57 AM 
