Novel fast and scalable parallel union-find ASIC implementation for real-time digital image segmentation

This paper presents a new fast and scalable Parallel Union-Find algorithm for image segmentation and its System-on-Chip (SoC) implementation using 65nm CMOS technology following the Application-Specific Integrated Circuit (ASIC) design flow. The algorithm is capable of labeling all foreground and background pixels, using the least possible pixels scanning. This contrasts the classical labeling algorithms that label only foreground (or background) pixels in a single run. The new algorithm utilizes only two memory blocks. In one memory block, it labels image segments using their seeds as the label and, simultaneously, the segments sizes are used as the other label in second memory block. By this parallel labeling, monitoring the image segments is very fast and efficient. With 350 MHz operating frequency, the processing rate estimated to be 2100 frames/sec, the total chip area of 15950.5 μm2 (off-chip memory) and very low-power of 0.3 mW, the SoC tends to be an excellent candidate for mobile devices and real-time applications.

[1]  Kenji Suzuki,et al.  A Run-Based One-and-a-Half-Scan Connected-Component Labeling Algorithm , 2010, Int. J. Pattern Recognit. Artif. Intell..

[2]  Peter Pirsch,et al.  A parallel hardware architecture for connected component labeling based on fast label merging , 2008, 2008 International Conference on Application-Specific Systems, Architectures and Processors.

[3]  Rita Cucchiara,et al.  Fast block based connected components labeling , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[4]  Narayanan Vijaykrishnan,et al.  A Scalable Bandwidth Aware Architecture for Connected Component Labeling , 2010, 2010 IEEE Computer Society Annual Symposium on VLSI.

[5]  Kenji Suzuki,et al.  A Run-Based Two-Scan Labeling Algorithm , 2008, IEEE Transactions on Image Processing.

[6]  Kenji Suzuki,et al.  Two Efficient Label-Equivalence-Based Connected-Component Labeling Algorithms for 3-D Binary Images , 2011, IEEE Transactions on Image Processing.

[7]  Rita Cucchiara,et al.  Optimized Block-Based Connected Components Labeling With Decision Trees , 2010, IEEE Transactions on Image Processing.

[8]  Andrew Hunter,et al.  A run-length based connected component algorithm for FPGA implementation , 2008, 2008 International Conference on Field-Programmable Technology.

[9]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[10]  Kenji Suzuki,et al.  Linear-time connected-component labeling based on sequential local operations , 2003, Comput. Vis. Image Underst..

[11]  Andrzej Sluzek,et al.  A maximally stable extremal regions system-on-chip for real-time visual surveillance , 2015, IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society.

[12]  Fachao Qin,et al.  Superpixel Segmentation for Polarimetric SAR Imagery Using Local Iterative Clustering , 2015, IEEE Geoscience and Remote Sensing Letters.

[13]  Donald G. Bailey,et al.  Optimised single pass connected components analysis , 2008, 2008 International Conference on Field-Programmable Technology.

[14]  George D. C. Cavalcanti,et al.  Fast block-based algorithms for connected components labeling , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.