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CVPR
2004
IEEE
16 years 8 months ago
Incremental Density Approximation and Kernel-Based Bayesian Filtering for Object Tracking
Statistical density estimation techniques are used in many computer vision applications such as object tracking, background subtraction, motion estimation and segmentation. The pa...
Bohyung Han, Dorin Comaniciu, Ying Zhu, Larry S. D...
ICVGIP
2008
15 years 8 months ago
Automated Flower Classification over a Large Number of Classes
We investigate to what extent combinations of features can improve classification performance on a large dataset of similar classes. To this end we introduce a 103 class flower da...
Maria-Elena Nilsback, Andrew Zisserman
ICIP
2001
IEEE
16 years 8 months ago
Adaptive motion search with elastic diamond for MPEG-4 video coding
Video coding is a complex process, comprising a combination of spatial, temporal and statistical data reduction techniques. Of these techniques, motion estimation taking advantage...
Weiguo Zheng, Ishfaq Ahmad, Ming Lei Liou
EMMCVPR
2003
Springer
15 years 12 months ago
Asymptotic Characterization of Log-Likelihood Maximization Based Algorithms and Applications
The asymptotic distribution of estimates that are based on a sub-optimal search for the maximum of the log-likelihood function is considered. In particular, estimation schemes that...
Doron Blatt, Alfred O. Hero
CVPR
2005
IEEE
16 years 8 months ago
Multiple Object Tracking with Kernel Particle Filter
A new particle filter, Kernel Particle Filter (KPF), is proposed for visual tracking for multiple objects in image sequences. The KPF invokes kernels to form a continuous estimate...
Cheng Chang, Rashid Ansari, Ashfaq A. Khokhar