Sketch recognition

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

Applying Online Sketch Recognition Algorithms to a Scanned-In Sketch

Abstract: The goal of our research is to combine the power of stroke-based sketch recognition with the flexibility and ease of use of a piece of paper. In this paper we will present preliminary results of our algorithm integrated with an online sketch recognition system built with LADDER. We have also presented a comparison of our paper based interface with tablet based sketching interface.

Motion Feature Combination for Human Action Recognition in Video

Abstract: We study the human action recognition problem based on motion features directly extracted from video. In order to implement a fast human action recognition system, we select simple features that can be obtained from non-intensive computation. We propose to use the motion history image (MHI) as our fundamental representation of the motion. This is then further processed to give a histogram of the MHI and the Haar wavelet transform of the MHI. The combination of these two features is computed cheaply and has a lower dimension than the original MHI. The combined feature vector is tested in a Support Vector Machine (SVM) based human action recognition system and a significant performance improvement has been achieved. The system is efficient to be used in real-time human action classification systems.

Novel approach to human face recognition

Abstract: This paper presents a new method of human face recognition based on a novel algebraic feature extraction method. An input human face image is First transformed into a standard image; Then, the projective feature vectors of the standard image are extracted by projecting it onto the optimal discriminant projection vectors; Finally, face image recognition is completed by classifying these projective feature vectors. Experimental results showed that the present method is effective.

Discovery and learning techniques for pattern recognition

Abstract: The problem of pattern recognition is that of grasping the meaning of complex entities. A great variety of problem areas attempt to cope with Just such complexities. The importance of discovery and induction methods embedded within a pattern recognition program lies in their potential generality. The program, rather than the programmer, can be asked to discover and learn about the problem area. Pattern recognition should ultimately provide powerful techniques for use in such research areas as form perception, target recognition, language translation, theorem proving, game playing, and the testing of psychological models. Research on some of these applications is already underway. To the extent that discovery and induction can be successfully employed by the program itself, they should be used. This paper describes a program that makes a beginning attempt at such use.