Reconfigurable Morphological Image Processing Accelerator for Video Object Segmentation

Abstract

Video object segmentation is an important pre-processing task for many video analysis systems. To achieve the requirement of real-time video analysis, hardware acceleration is required. In this paper, after analyzing existing video object segmentation algorithms, it is found that most of the core operations can be implemented with simple morphology operations. Therefore, with the concepts of morphological image processing element array and stream processing, a reconfigurable morphological image processing accelerator is proposed, where by the proposed instruction set, the operation of each processing element can be controlled, and the interconnection between processing elements can also be reconfigured. Simulation results show that most of the core operations of video object segmentation can be supported by the accelerator by only changing the instructions. A prototype chip is designed to support real-time change-detection-andbackground-registration based video object segmentation algorithm. This chip incorporates eight macro processing elements and can support a processing capacity of 6,200 9-bit morphological operations per second on a SIF image. Furthermore, with the proposed tiling and pipelined-parallel techniques, a realtime watershed transform can be achieved using 32 macro processing elements.

Image Recognition Using MATLAB Simulink Blockset

“…In the 1970's, object recognition was treated as a 2D pattern recognition problem [2]. The distances between important points where used to recognize known object, however, with technological drive object 2D and 3D objects recognition methods are fully automated.…”

“…A considerable amount of literature has also been published on digital image processing and intelligent target recognition. In [2], an approach on image/video segmentation was taken in which existing video object segmentation algorithm and morphological operation was used to analyse core operations. Hence, morphology image processing element array and stream processing was used.…”

Automated Traffic Counting and Control.

“…Chien and L. Chen [5] stated that the canny edge detector is used for edge detection and localization in image processing. Edge detectors based on morphological gradient performs well in edge detection.…”

Design and validation of a low resource-cost video data processing method for embedded implementation of optical flow extraction

“…On the other hand, it is known that for many artificial vision algorithms, parallelized implementations with pipelined FPGA circuits can deliver significantly better performance results [4]. While there is a need for high computational performance solutions, the relative difficulty and required expertise have resulted in a small number of such implementations reported in scientific works [13], [14].…”

“…Because this kind of image processing by software implementations is often not effective enough, the solution applied in some published studies is the use of different hardware accelerators [4], [5].…”