Modelling the stereovision-correspondence-analysis task by lateral inhibition in accumulative computation problem-solving method.

Recently, the Algorithmic Lateral Inhibition (ALI) method and the Accumulative Computation (AC) method have proven to be efficient in modelling at the knowledge level for general-motion-detection tasks in video sequences. More precisely, the task of persistent motion detection has been widely expressed by means of the AC method, whereas the ALI method has been used with the objective of moving objects detection, labelling and further tracking. This paper exploits the current knowledge of our research team on the mentioned problem-solving methods to model the Stereovision-Correspondence-Analysis (SCA) task. For this purpose, ALI and AC methods are combined into the Lateral Inhibition in Accumulative Computation (LIAC) method. The four basic subtasks, namely ?LIAC 2D Charge-Memory Calculation?, ?LIAC 2D Charge-Disparity Analysis? and ?LIAC 3D Charge-Memory Calculation? in our proposal of SCA are described in detail by inferential CommonKADS schemes. It is shown that the LIAC method may perfectly be used to solve a complex task based on motion information inherent to binocular video sequences

Measurement of range of motion of human finger joints, using a computer vision system

Assessment of finger range of motion (ROM) is often required for monitoring the effectiveness of rehabilitative treatments and for evaluating patients' functional impairment. There are several devices which are used to measure this motion, such as wire tracing, tracing onto paper and mechanical and electronic goniometry. These devices are quite cheap, excluding electronic goniometry; however the drawbacks of these devices are their lack of accuracy and the time- consuming nature of the measurement process. The work described in this thesis considers the design, implementation and validation of a new medical measurement system utilized in the evaluation of the range of motion of the human finger joints instead of the current measurement tools. The proposed system is a non-contact measurement device based on computer vision technology and has many advantages over the existing measurement devices. In terms of accuracy, better results are achieved by this system, it can be operated by semi-skilled person, and is time saving for the evaluator. The computer vision system in this study consists of CCD cameras to capture the images, a frame-grabber to change the analogue signal from the cameras to digital signals which can be manipulated by a computer, Ultra Violet light (UV) to illuminate the measurement space, software to process the images and perform the required computation, a darkened enclosure to accommodate the cameras and UV light and to shield the working area from any undesirable ambient light. Two calibration techniques were used to calibrate the cameras, Direct Linear Transformation and Tsai. A calibration piece that suits this application was designed and manufactured. A steel hand model was used to measure the fingers joint angles. The average error from measuring the finger angles using this system was around 1 degree compared with 5 degrees for the existing used techniques