The rst part of this paper provides an overview of previous work on traac sign recognition. Various components are discussed, such as detection, classiication and temporal integration. The second part of this paper covers a recently developed shape-based system, based on distance transforms. This system has been quite successful in detecting and recognizing traac signs in real-timee we report s...

This chapter covers the key aspects of Sign Language Recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gest...

Traffic sign recognition through artificial intelligence tools is an attractive topic in the computer vision community for its clear applications in the automotive industry. This problem is a subset of the larger problem of improving safe driving through intelligent technologies that can recognize objects on the road and help drivers avoid hazardous situations. In this work we evaluated a numbe...

We have developed the Hand Motion Understanding (HMU) system that understands static and dynamic signs of the Australian Sign Language (Auslan). The HMU system uses a visual 3D hand tracker for motion sensing, and an adaptive fuzzy expert system for classification of the signs. This paper presents the hand tracker that extracts 3D hand configuration data with 21 degrees-of-freedom (DOFs) from a...

hand gesture recognition is very important to communicate in sign language. in this paper, an effective object tracking and hand gesture recognition method is proposed. this method is combination of two well-known approaches, the mean shift and the motion detection algorithm. the mean shift algorithm can track objects based on the color, then when hand passes the face occlusion happens. several...

A common problem encountered in recognizing signs in realscene images is the perspective deformation. In this paper, we employ a descriptor named Cross Ratio Spectrum for recognizing real scene signs. Particularly, this method will be applied in two different ways: recognizing a multi-component sign as an whole entity or recognizing individual components separately. For the second strategy, a g...

Our goal here is to recognize a sign language measured from wearable sensor gloves. A sign language is expressed as a sequence of gestural patterns to convey a meaning. Hidden Markov models (HMMs) have been shown to be successful in temporal pattern recognition, such as speech, handwriting, and gesture recognition [4]. In this project, we investigate how well HMMs can perform when applied to si...

Traffic sign recognition is a difficult task if aim is at detecting and recognizing signs in images captured from unfavorable environments. Complex background, weather, shadow, and other lighting-related problems may make it difficult to detect and recognize signs in the rural as well as the urban areas. Two major problems exist in the whole detection process. Road signs are frequently occluded...

Computer-based sign language recognition from video is a challenging problem because of the spatiotemporal complexities inherent in sign production and the variations within and across signers. However, linguistic information can help constrain sign recognition to make it a more feasible classification problem. We have previously explored recognition of linguistically significant 3D hand config...