Abstract: In this paper we proposed a novel method to acquire
the ROI (Region of interest) of unsupervised and touch-less palmprint
captured from a web camera in real-time. We use Viola-Jones
approach and skin model to get the target area in real time. Then an
innovative course-to-fine approach to detect the key points on the hand
is described. A new algorithm is used to find the candidate key points
coarsely and quickly. In finely stage, we verify the hand key points
with the shape context descriptor. To make the user much comfortable,
it can process the hand image with different poses, even the hand is
closed. Experiments show promising result by using the proposed
method in various conditions.
Abstract: Using vision based solution in intelligent vehicle application often needs large memory to handle video stream and image process which increase complexity of hardware and software. In this paper, we present a FPGA implement of a vision based lane departure warning system. By taking frame of videos, the line gradient of line is estimated and the lane marks are found. By analysis the position of lane mark, departure of vehicle will be detected in time. This idea has been implemented in Xilinx Spartan6 FPGA. The lane departure warning system used 39% logic resources and no memory of the device. The average availability is 92.5%. The frame rate is more than 30 frames per second (fps).