Using computer color effectively: an illustrated reference
Using computer color effectively: an illustrated reference
International Journal of Computer Vision
Visual information retrieval
Spatial Color Indexing and Applications
International Journal of Computer Vision
Digital Image Processing
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Real-Time Tracking Using Level Sets
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Spatiograms versus Histograms for Region-Based Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ACM Computing Surveys (CSUR)
Digital Image Processing: PIKS Scientific Inside
Digital Image Processing: PIKS Scientific Inside
Image retrieval based on energy histograms of the low frequency DCT coefficients
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Object Tracking using Color Correlogram
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
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In this paper, a novel algorithm for object tracking in image sequences using local colour histogram method (LCHM) is presented. In order to represent the object to be tracked, the proposed local colour histogram model divides the image into distinct blocks of same size and encodes the colour distribution of each local block. The histogram of each local block of the query image is compared in parallel with the corresponding local block of the test image(s) and the similarity measure is computed using a metric and compared against a threshold. Histogram matching is performed by distance measures like histogram intersection, Euclidean distance and histogram quadratic distance and their performance for detecting the presence of the object in the image is compared. Experimental results show that for retrieval of visually similar object from the image sequences, the local histogram method gives good retrieval precision with speed.