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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.

Connected component analysis is one of the most fundamental steps used in several image processing systems. This technique allows for distinguishing and detecting different objects in images by assigning a unique label to all pixels that refer to the same object. Most of the previous published algorithms have been designed for implementation by software. However, due to the large number of memory accesses and compare, lookup, and control operations when executed on a general-purpose processor, they do not satisfy the speed performance required by the next generation high performance computer vision systems. In this paper, we present the design of a new Connected Component Labeling hardware architecture suitable for high performance heterogeneous image processing of embedded designs. When implemented on a Zynq All Programmable-System on Chip (AP-SOC) 7045 chip, the proposed design allows a throughput rate higher of 220 Mpixels/s to be reached using less than 18,000 LUTs and 5000 FFs, dissipating about 620 µJ.