Sketch recognition

About: Sketch recognition is a research topic. Over the lifetime, 1611 publications have been published within this topic receiving 40284 citations.

Structure-Based and Template-Based Automatic Speech Recognition: Comparing Parametric and Non-Parametric Approaches

Abstract: This paper provides an introductory tutorial for the Interspeech07 special session on “Structure-Based and Template-Based Automatic Speech Recognition”. The purpose of the special session is to bring together researchers who have special interest in novel techniques that are aimed at overcoming weaknesses of HMMs for acoustic modeling in speech recognition. Numerous such approaches have been taken over the past dozen years, which can be broadly classified into structured-based (parametric) and templatebased (non-parametric) ones. In this paper, we will provide an overview of both approaches, focusing on the incorporation of long-range temporal dependencies of the speech features and phonetic detail in speech recognition algorithms. We will provide a high-level survey on major existing work and systems using these two types of “beyond-HMM” frameworks. The contributed papers in this special session will elaborate further on the related topics.

Gesture Recognition for Human-Computer Interaction (HCI)

Abstract: Considerable effort has been put towards developing intelligent and natural interfaces between users and computer systems. This is done by means of a variety of modes of information (visual, audio, pen, etc.) either used individually or in combination. The use of gestures as means to convey information is an important part of human communication. The automatic recognition of gestures enriches Human–Computer Interaction by offering a natural and intuitive method of data input. This paper presents a new technique for static hand gesture recognition, for the Human-Computer Interaction (HCI) based on shape analysis. The objective of this effort was to explore the utility of a neural network-based approach to the recognition of the hand gestures. The proposed system used the hand contour as a geometry feature. A unique multi-layer perceptron neural network is build for the classification by using back-propagation learning algorithm. The overall model is designed to be a simple and robust gestural interface prototype for various PC applications.

From members to teams to committee-a robust approach to gestural and multimodal recognition

Abstract: When building a complex pattern recognizer with high-dimensional input features, a number of selection uncertainties arise. Traditional approaches to resolving these uncertainties typically rely either on the researcher's intuition or performance evaluation on validation data, both of which result in poor generalization and robustness on test data. This paper describes a novel recognition technique called members to teams to committee (MTC), which is designed to reduce modeling uncertainty. In particular, the MTC posterior estimator is based on a coordinated set of divide-and-conquer estimators that derive from a three-tiered architectural structure corresponding to individual members, teams, and the overall committee. Basically, the MTC recognition decision is determined by the whole empirical posterior distribution, rather than a single estimate. This paper describes the application of the MTC technique to handwritten gesture recognition and multimodal system integration and presents a comprehensive analysis of the characteristics and advantages of the MTC approach.