Topic
Sketch recognition
About: Sketch recognition is a research topic. Over the lifetime, 1611 publications have been published within this topic receiving 40284 citations.
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01 Nov 2016
TL;DR: The application and challenges of human action recognition are described, including survey on different types of actions like single person action recognition, two person or person-object interaction and multiple people action recognition.
Abstract: Human action recognition is a way of retrieving videos emerged from Content Based Video Retrieval (CBVR).It is a growing area of research in the field of computer vision nowadays. Human action recognition has gained popularity because of its wide applicability in automatic retrieval of videos of particular action using visual features. The most common stages for action recognition includes: object and human segmentation, feature extraction, activity detection and classification. This paper describes the application and challenges of human action recognition. Features and limitations of various methods for human action recognition are discussed. This paper introduces survey on different types of actions like single person action recognition, two person or person-object interaction and multiple people action recognition.
33 citations
TL;DR: A sketch auto-completion framework that addresses challenges of classifying sketched symbols before they are fully completed by learning visual appearances of partial drawings through semi-supervised clustering, followed by a supervised classification step that determines object classes.
33 citations
26 Sep 1999
TL;DR: Methods for performance improvement of gesture recognition using HMMs using KL transform to compress the input information and a recursive calculation method for the HMMs' probabilities are proposed.
Abstract: HMMs are often used for gesture recognition because of the robustness. However, the computational cost and accuracy of recognition are important for real applications such as gesture recognition, speech recognition or virtual reality. In this paper, we propose methods for performance improvement of gesture recognition using HMMs. For the computational cost, we use KL transform to compress the input information and propose a recursive calculation method for the HMMs' probabilities. For the accuracy of recognition, we use an automaton layered up on HMMs to deal with context information of gestures. We also show experimental results to make the efficiency of our methods clear.
33 citations
20 Aug 2006
TL;DR: The proposed approach combines a number of norms to evaluate the distance of the current sign, to the sign models stored in a database (a dictionary), which leads to a largely selective criterion.
Abstract: In this paper, an approach for deaf-people interfacing using computer vision is presented. The recognition of alphabetic static signs of the Spanish Sign Language is addressed. The proposed approach combines a number of norms to evaluate the distance of the current sign, to the sign models stored in a database (a dictionary). This solution leads to a largely selective criterion. The method is simple enough to provide real-time recognition, and works suitably for most letters.
33 citations
TL;DR: The field of speech recognition is presented and some of its major open problems are described from an algorithmic viewpoint to stimulate the interest of algorithm designers and experimenters to investigate the algorithmic problems of effective automatic speech recognition.
Abstract: Speech recognition is an area with a considerable literature, but there is little discussion of the topic within the computer science algorithms literature. Many computer scientists, however, are interested in the computational problems of speech recognition. This paper presents the field of speech recognition and describes some of its major open problems from an algorithmic viewpoint. Our goal is to stimulate the interest of algorithm designers and experimenters to investigate the algorithmic problems of effective automatic speech recognition.
33 citations