@inproceedings {291, title = {Video pause detection using wavelets}, booktitle = {6th Canadian Conference on Computer and Robot Vision}, year = {2009}, month = {05/2009}, address = {Kelowna, British Columbia, Canada}, abstract = {

As the volume of digital video captured and stored continues to increase, research efforts have focused on content management systems for video indexing and retrieval applications. A first step in generic video processing is shot boundary detection. This paper addresses a novel algorithm for abrupt shot (cut/pause) detection-especially on frames with similar statistics-based on the wavelet transform and content entropy. The algorithm has been successfully tested on some video categories including sport and live videos. Its quantitative performance has been compared to other known methods including pixel, histogram, frequency domain and statistics difference. In each test, the proposed wavelet method outperforms the others.

}, keywords = {content entropy, content management, content management system, digital video capture, entropy, indexing, shot boundary detection, statistical analysis, statistics-based wavelet transform, video indexing, Video Recording, video retrieval, wavelet transforms}, doi = {http://dx.doi.org/10.1109/CRV.2009.20}, author = {S Zaboli and D A. Clausi} } @inproceedings {331, title = {Shape-guided active contour based segmentation and tracking of lumbar vertebrae in video fluoroscopy using complex wavelets}, booktitle = {30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2008}, month = {08/2008}, address = {Vancouver, British Columbia, Canada}, abstract = {

This paper presents a novel shape-guided active contour based approach for segmenting and tracking lumbar vertebrae in video fluoroscopy using complex-valued wavelets. representations. Due to low radiation exposure levels, fluoroscopic images are characterized by low signal-to-noise ratios, low contrast resolution, and illumination non-homogeneities both spatially and temporally, making current methods ill-suited for segmenting and tracking lumbar vertebrae based on existing energy functionals. Furthermore, current methods perform poorly in situations characterized by high curvature as found in the structure of lumbar spine vertebrae. In this paper, a novel iterative estimation approach is used to determine an external energy functional based on complex wavelets. A shaped-guided algorithm is used to evolve the contour around a lumbar spine vertebra based on the complex wavelet energy. The high curvature exhibited by the lumbar spine vertebra is addressed through a novel importance sampling scheme. Experimental results show that the proposed algorithm achieves significantly better segmentation and tracking performance for lumbar spine vertebrae in fluoroscopic images when compared to existing techniques.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Computer-Assisted, Female, Fluoroscopy, Humans, Lumbar Vertebrae, Male, Pattern Recognition, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Sensitivity and Specificity, Video Recording, Young Adult}, doi = {http://dx.doi.org/10.1109/IEMBS.2008.4649290}, author = {A Wong and A Mishra and P Fieguth and D A. Clausi and N M. Dunk and J Callaghan} } @article {418, title = {Statistical processing of large image sequences}, journal = {IEEE Transactions on Image Processing}, volume = {14}, year = {2005}, pages = {80 - 93}, abstract = {

The dynamic estimation of large-scale stochastic image sequences, as frequently encountered in remote sensing, is important in a variety of scientific applications. However, the size of such images makes conventional dynamic estimation methods, for example, the Kalman and related filters, impractical. We present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a 512 times; 512 image sequence of ocean surface temperature. The static estimation step, the primary contribution here, uses a mixture of stationary models to accurately mimic the effect of a nonstationary prior, simplifying both computational complexity and modeling. Our approach provides an efficient, stable, positive-definite model which is consistent with the given correlation structure. Thus, the methods of this paper may find application in modeling and single-frame estimation.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Biological, Cluster Analysis, computational complexity, Computer Graphics, Computer Simulation, Computer-Assisted, correlation methods, correlation structure, dynamic estimation method, image enhancement, image interpretation, image sequences, Imaging, Information Storage and Retrieval, Kalman filter, Kalman filters, large-scale stochastic image sequence estimation, Models, Movement, Numerical Analysis, ocean surface temperature, Pattern Recognition, Remote Sensing, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, single-frame estimation, static estimation step, Statistical, statistical analysis, statistical image processing, stochastic processes, Subtraction Technique, Three-Dimensional, Video Recording}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/TIP.2004.838703}, author = {F M. Khellah and P Fieguth and M J. Murray and M R. Allen} }