Masaru Ohki

Description and recognition methods for sign language based on gesture components

Sign language gestures are inflected in accordance with the context. To recognize such sign language properly, the structure of sign kmgoage must be made clear. It is well known that the structure of sign Iangoage is represented as a combination of basic components of gestures. Sign language can be recognized by using such components. In this paper, a format to describe sign language gestures and a method to recognize the meaning of the gesture based on the components of gestures are discussed.

Pattern Recognition and Synthesis for a Sign Language Translation System

Abstract Sign language is one means of communication for hearing-impaired people. Words and sentences in sign language are mainly represented by hand gestures. In this report, we describe a sign language translation system which we are developing. The system translates Japanese Sign Language into Japanese text and vice versa . In this system, hand shapes and positions are provided by hand-based input in the form of time series. Then, hand gestures are recognized and translated into Japanese sentences using continuous DP (dynamic programming) matching. Japanese text is translated into sign language by generating three-dimensional computer-graphic animation of sign language gestures.

Development of a high-performance general purpose neuro-computer composed of 512 digital neurons

A high-performance, general-purpose neuro-computer composed of 512 digital neurons is developed. Each neuron has an execution unit which is optimized for traditional neural functions, but the use of a micro-programming architecture makes it general enough to implement any neural function. Horizontal micro-instruction formats and massively parallel-pipelined computation allows high-speed on-chip learning. The theoretical maximum learning speed for the backpropagation algorithm is 1.25 GCUPS (giga connection updates per second). Eight digital neurons are integrated on each neuron chip by using 1.0-/spl mu/m CMOS technology, and 64 neuron chips are packaged in this hardware. This hardware can be connected to a host workstation by a SCSI network. We applied this neuro-computer to handwritten numerals recognition. The learning speed by using the neuro-computer is over 1000 times faster than by using the workstation.

Methods to describe and recognize sign language based on gesture components represented by symbols and numerical values

Sign-language gestures inflect according to the context. To recognize such sign language properly, the structure of sign language must be made clear. It is well known that the structure of sign language is represented as a combination of gesture components. In this paper, methods for the description and recognition of sign-language gestures based on the gesture components are discussed. In these methods, which we have developed, a sign-language gesture is recognized by integrating the recognized gesture components according to the structure of the gesture. The results of an experiment on recognizing sign-language gestures are also examined and it is shown that the developed methods are effective.

Weighted parallel problem solving by optimization networks

Abstract Based on the analysis of the connections of optimization networks, we present the weighted intrazonal connection (WIZC) method to solve the quadratic assignment problem (QAP). This method improves the dynamics of the network by weighting and controlling the convergence speed of parallel problem solving, and works most effectively with up-to-date techniques such as subspace projection and matrix graduated non-convexity. Then more detailed formulation of the WICZ is given for the traveling salesman problem (TSP) which is an example of the QAP. The results obtained for TSPs show that the proposed method is effective on average, even though its performance depends on the problem itself.

Design support to determine the range of design parameters by qualitative reasoning

We present a new application of qualitative reasoning to design: suggesting valid ranges for design parameters after the structure has been determined. This design step is implemented by using an envisioning mechanism that uses qualitative reasoning to determine all possible behaviors of a system. Our method finds all possible behaviors by envisioning with design parameters whose values are initially indeterminate and with whatever specifications the designer has. If several behaviors are found, the designer selects the ones he prefers. The design-support system Desq (design support system based on qualitative reasoning) is based on an earlier qualitative reasoning system Qupras (qualitative physical reasoning system) with three improvements: envisioning, propagating new constraints on constant parameters, and solving constraint in parallel. Like Qupras, the Desq system can deal with quantities qualitatively and quantitatively. Therefore, if the parameters can be expressed quantitatively, we may be able to determine the quantitative ranges, which are often more useful than qualitative values. >