Sketch recognition

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

LADDER, a sketching language for user interface developers

Abstract: Sketch recognition systems are currently being developed for many domains, but can be time consuming to build if they are to handle the intricacies of each domain. In order to aid sketch-based user interface developers, we have developed tools to simplify the development of a new sketch recognition interface. We created LADDER, a language to describe how sketched diagrams in a domain are drawn, displayed, and edited. We then automatically transform LADDER structural descriptions into domain specific shape recognizers, editing recognizers, and shape exhibitors for use in conjunction with a domain independent sketch recognition system, creating a sketch recognition system for that domain. We have tested our framework by writing several domain descriptions and automatically generating a domain specific sketch recognition system from each description.

Face Detection and Gesture Recognition for Human-Computer Interaction

Abstract: With the ubiquity of new information technology and media, more effective and friendly methods for human computer interaction (HCI) are being developed which do not rely on traditional devices such as keyboards, mice and displays. The first step for any intelligent HCI system is face detection, and one of most friendly HCI systems is hand gesture. Face Detection and Gesture Recognition for Human-Computer Interaction introduces the frontiers of vision-based interfaces for intelligent human computer interaction with focus on two main issues: face detection and gesture recognition. The first part of the book reviews and discusses existing face detection methods, followed by a discussion on future research. Performance evaluation issues on the face detection methods are also addressed. The second part discusses an interesting hand gesture recognition method based on a generic motion segmentation algorithm. The system has been tested with gestures from American Sign Language with promising results. We conclude this book with comments on future work in face detection and hand gesture recognition. Face Detection and Gesture Recognition for Human-Computer Interaction will interest those working in vision-based interfaces for intelligent human computer interaction. It also contains a comprehensive survey on existing face detection methods, which will serve as the entry point for new researchers embarking on such topics. Furthermore, this book also covers in-depth discussion on motion segmentation algorithms and applications, which will benefit more seasoned graduate students or researchers interested in motion pattern recognition.

iCanDraw: using sketch recognition and corrective feedback to assist a user in drawing human faces

Abstract: When asked to draw, many people are hesitant because they consider themselves unable to draw well. This paper describes the first system for a computer to provide direction and feedback for assisting a user to draw a human face as accurately as possible from an image. Face recognition is first used to model the features of a human face in an image, which the user wishes to replicate. Novel sketch recognition algorithms were developed to use the information provided by the face recognition to evaluate the hand-drawn face. Two design iterations and user studies led to nine design principles for providing such instruction, presenting reference media, giving corrective feedback, and receiving actions from the user. The result is a proof-of-concept application that can guide a person through step-by-step instruction and generated feedback toward producing his/her own sketch of a human face in a reference image.

Memetically Optimized MCWLD for Matching Sketches With Digital Face Images

Abstract: One of the important cues in solving crimes and apprehending criminals is matching sketches with digital face images. This paper presents an automated algorithm to extract discriminating information from local regions of both sketches and digital face images. Structural information along with minute details present in local facial regions are encoded using multiscale circular Weber's local descriptor. Further, an evolutionary memetic optimization algorithm is proposed to assign optimal weight to every local facial region to boost the identification performance. Since forensic sketches or digital face images can be of poor quality, a preprocessing technique is used to enhance the quality of images and improve the identification performance. Comprehensive experimental evaluation on different sketch databases show that the proposed algorithm yields better identification performance compared to existing face recognition algorithms and two commercial face recognition systems.