Real-Time Object Segmentation of the Disparity Map Using Projection-Based Region Merging

Abstract

Robots have been mostly used in industrial environment, but modern developments of household robot-cleaner suggest the necessity of household robots as becoming in reality. Most industrial robots have been used for factory automation that perform simple and iterative tasks at high speed, whereas household robots need various interfaces with a man while moving in indoor environment like a household robot-cleaner does. Robots activate in indoor environment using various sensors such as vision, laser, ultrasonic sensor, or voice sensor to detect indoor circumstance. Especially robot’s routing plan and collision avoidance need three-dimensional information of robot’s surrounding environment. This can be obtained by using a stereo vision camera which provides a general and huge amount of 3-D information. But this computation is too big to solve in real-time with the existing microprocessor when using a stereo vision camera for capturing 3-D image information. High-level computer vision tasks, such as robot navigation and collision avoidance, require 3-D depth information of the surrounding environment at video rate. Current generalpurpose microprocessors are too slow to perform stereo vision at video rate. For example, it takes several seconds to execute a medium-sized stereo vision algorithm for a single pair of images using one 1 GHz general-purpose microprocessor. To overcome this limitation, designers in the last decade have built reprogrammable chips called FPGA(Field-Programmable Gate Arrays) hardware systems to accelerate the performance of the vision systems. These devices consist of programmable logic gates and routing which can be re-configured to implement practically any hardware function. Hardware implementations allow one to apply the parallelism that is common in image processing and vision algorithms, and to build systems to perform specific calculations quickly compared to software implementations. A number of methods of finding depth information in video-rate have been reported. Among others, multi-baseline stereo theory is developed and the video-rate stereo machine has the capability of generating a dense depth map of 256x240 pixels at the frame rate of 30 frames/sec in [1-2]. An algorithm proposed from parallel relaxation algorithm for disparity computation [3] results reduction of error rate and enhancement of computational complexity