Representation (systemics)

About: Representation (systemics) is a research topic. Over the lifetime, 33821 publications have been published within this topic receiving 475461 citations.

Large hierarchical object recognition using libraries of parameterized model sub-parts

Abstract: A description is given of the development of a model-based vision system that utilizes hierarchies of both object structure and object scale. The focus of the research is to use these hierarchies to achieve robust recognition based on effective organization and indexing schemes for model libraries. The goal of the system is to recognize parameterized instances of nonrigid model objects contained in a large knowledge base, despite the presence of noise and occlusion. The approach presented is to develop an object shape representation that incorporates a component subpart hierarchy, to allow for efficient and correct indexing into an automatically generated model library as well as for relative parametrization among subparts, and a scale hierarchy, to allow for a general to specific recognition procedure. The implemented system uses a representation based on significant contour curvature changes and recognition engine based on geometric constraints of feature properties. Examples of the system's performance are given, followed by an analysis of the results. >

Methods and apparatuses for automatic speech recognition

Abstract: Exemplary embodiments of methods and apparatuses for automatic speech recognition are described. First model parameters associated with a first representation of an input signal are generated. The first representation of the input signal is a discrete parameter representation. Second model parameters associated with a second representation of the input signal are generated. The second representation of the input signal includes a continuous parameter representation of residuals of the input signal. The first representation of the input signal includes discrete parameters representing first portions of the input signal. The second representation includes discrete parameters representing second portions of the input signal that are smaller than the first portions. Third model parameters are generated to couple the first representation of the input signal with the second representation of the input signal. The first representation and the second representation of the input signal are mapped into a vector space.

Learning to Detect Objects in Images via a Sparse, Part-Based Representation

Abstract: We study the problem of detecting objects in still, gray-scale images. Our primary focus is the development of a learning- based approach to the problem that makes use of a sparse, part-based representation. A vocabulary of distinctive object parts is automatically constructed from a set of sample images of the object class of interest; images are then represented using parts from this vocabulary, together with spatial relations observed among the parts. Based on this representation, a learning algorithm is used to automatically learn to detect instances of the object class in new images. The approach can be applied to any object with distinguishable parts in a relatively fixed spatial configuration; it is evaluated here on difficult sets of real-world images containing side views of cars, and is seen to successfully detect objects in varying conditions amidst background clutter and mild occlusion. In evaluating object detection approaches, several important methodological issues arise that have not been satisfactorily addressed in previous work. A secondary focus of this paper is to highlight these issues and to develop rigorous evaluation standards for the object detection problem. A critical evaluation of our approach under the proposed standards is presented.