Now showing items 1-6 of 6
Boosting Image Database Retrieval
We present an approach for image database retrieval using a very large number of highly-selective features and simple on-line learning. Our approach is predicated on the assumption that each image is generated by a ...
Combining Variable Selection with Dimensionality Reduction
This paper bridges the gap between variable selection methods (e.g., Pearson coefficients, KS test) and dimensionality reductionalgorithms (e.g., PCA, LDA). Variable selection algorithms encounter difficulties dealing with ...
Model-Based Matching by Linear Combinations of Prototypes
We describe a method for modeling object classes (such as faces) using 2D example images and an algorithm for matching a model to a novel image. The object class models are "learned'' from example images that we call ...
Automatically Recovering Geometry and Texture from Large Sets of Calibrated Images
Three-dimensional models which contain both geometry and texture have numerous applications such as urban planning, physical simulation, and virtual environments. A major focus of computer vision (and recently graphics) ...
Learning and Example Selection for Object and Pattern Detection
This thesis presents a learning based approach for detecting classes of objects and patterns with variable image appearance but highly predictable image boundaries. It consists of two parts. In part one, we introduce our ...
The Role of Fixation and Visual Attention in Object Recognition
This research project is a study of the role of fixation and visual attention in object recognition. In this project, we build an active vision system which can recognize a target object in a cluttered scene efficiently ...