A number of content-based image and video systems are applicable to the features described in this lecture. In each case, systems are designed to interpret features from multi-modal sources such as text, audio, image and video. A feature is a descriptive parameter that is extracted from an image or video stream. Features may be used to interpret visual content, or as a measure for similarity in image and video databases. In this lecture, features are described in the following categories:
Statistical Features: Features extracted from an image or video sequence without regard to content are described as statistical features. These include parameters derived from such algorithms as image difference and camera motion.
Compressed Domain Features: A feature which is extracted from a compressed image or video stream without regard to content is described as a compressed domain feature.
Content-Based Features: A feature that is derived for the purpose of describing the actual content in an image or video stream is a content-based feature.
The presentation will describe visualization technology for browsing
and summarization, characterization and meta-data acquisition, and
user-studies to validate specific methodology. This includes a
description of traditional static presentations, such as text abstracts
and thumbnails and current research in application specific image
browsing paradigms.
A set of software tools for the automated analysis of electrophoretic gel images acquired in Bacterial Artificial Chromosome (BAC) fingerprinting experiments is described. The signal processing problem addressed is that of detection of multiple overlapping bands in a gel image, where each band indicates the presence of a DNA molecular fragment of a particular size. The required processing comprises several steps, including: 1) image pre-processing, to put the data in a form consistent with a linear model, 2) marker lane analysis, for calibration of the relationship between band location (mobility) and fragment size, 3) model parameter estimation, primarily for determining band shape as a function of mobility, and 4) data lane analysis, a 3-pass process for detecting the multiple overlapping bands while simultaneously determining the amplitude curve which describes band amplitude as a function of mobility.
The integrated suite of tools, written in MATLAB and collectively called BANDLEADER, is being used in production mode for large-scale genome mapping projects at the British Columbia Cancer Research Agency. One such project, mapping the mouse genome, is essentially complete; it involves approximately 3000 gels and 300,000 clones, representing a 20-fold redundant coverage of the genome. Participation in efforts to map the complete bovine genome is underway. We have seen that automation tools such as BANDLEADER can greatly speed up the mapping effort and reduce the number of human hours involved in these large-scale projects.
Joint work with researchers at the Washington University Genome Sequencing Center and the British Columbia Cancer Research Agency. This work was supported in part by the U.S. National Institutes of Health.
PS.: Data before 08/26/2000 has been lost.