Abstract
Connected Component Analysis (CCA) plays an important role in several image analysis and pattern recognition algorithms. Being one of the most time-consuming tasks in such applications, specific hardware accelerator for the CCA are highly desirable. As its main characteristic, the design of such an accelerator must be able to complete a run-time process of the input image frame without suspending the input streaming data-flow, by using a reasonable amount of hardware resources. This paper presents a new approach that allows virtually any feature of interest to be extracted in a single-pass from the input image frames. The proposed method has been validated by a proper system hardware implemented in a complete heterogeneous design, within a Xilinx Zynq-7000 Field Programmable Gate Array (FPGA) System on Chip (SoC) device. For processing 640 × 480 input image resolution, only 760 LUTs and 787 FFs were required. Moreover, a frame-rate of ~325 fps and a throughput of 95.37 Mp/s were achieved. When compared to several recent competitors, the proposed design exhibits the most favorable performance-resources trade-off.
Highlights
In recent years, advances in embedded vision systems led to the implementation of compact smart cameras
Smart cameras are machine vision systems that, in addition to sensors that capture images, provide the capability of extracting application-specific information from captured images. These embedded systems equipped with camera sensors represent efficient on-board solutions for several commercial applications, ranging from Advanced Driver-Assistance Systems (ADAS) to automated surveillance systems [1,2]
The Connected Component Analysis (CCA) is one of the above-mentioned tasks, and it is very frequently used in several image analysis and pattern recognition algorithms [3]
Summary
Advances in embedded vision systems led to the implementation of compact smart cameras. Smart cameras are machine vision systems that, in addition to sensors that capture images, provide the capability of extracting application-specific information from captured images. These embedded systems equipped with camera sensors represent efficient on-board solutions for several commercial applications, ranging from Advanced Driver-Assistance Systems (ADAS) to automated surveillance systems [1,2]. CCA is the combination of two subsequent computations: Connected Component Labeling (CCL) and Features Computation (FC). In order to assign a unique label topixels all pixels and a features extraction (FC).
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