FMSLIC: Fast Memory-Efficient Structure for Implementation of SLIC on FPGA

Mighani, Mojtaba; Khakpour, Ali

doi:10.1007/s00034-023-02353-y

Cited by 1 publication

(2 citation statements)

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“…The proposed SLIC algorithm achieves a frame rate of 24 fps for a camera with a resolution of 300x400 pixels. Mighani et al [13] introduces a structure that replaces the cluster-based search operation with the pixel-based process in the assignment step. By eliminating the data dependency between the assignment and update steps, simultaneous execution is enabled.…”

Section: Acceleration Of Slicmentioning

confidence: 99%

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Grid Sample Based Temporal Iteration for Fully Pipelined 1-ms SLIC Superpixel Segmentation System

LI,

HU,

FUCHIKAMI

et al. 2024

IEICE Trans. Inf. & Syst.

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A 1 millisecond (1-ms) vision system, which processes videos at 1000 frames per second (FPS) within 1 ms/frame delay, plays an increasingly important role in fields such as robotics and factory automation. Superpixel as one of the most extensively employed image oversegmentation methods is a crucial pre-processing step for reducing computations in various computer vision applications. Among the different superpixel methods, simple linear iterative clustering (SLIC) has gained widespread adoption due to its simplicity, effectiveness, and computational efficiency. However, the iterative assignment and update steps in SLIC make it challenging to achieve high processing speed. To address this limitation and develop a SLIC superpixel segmentation system with a 1 ms delay, this paper proposes grid sample based temporal iteration. By leveraging the high frame rate of the input video, the proposed method distributes the iterations into the temporal domain, ensuring that the system's delay keeps within one frame. Additionally, grid sample information is added as initialization information to the obtained superpixel centers for enhancing the stability of superpixels. Furthermore, a selective label propagation based pipeline architecture is proposed for parallel computation of all the possibilities of label propagation. This eliminates data dependency between adjacent pixels and enables a fully pipelined system. The evaluation results demonstrate that the proposed superpixel segmentation system achieves boundary recall and under-segmentation error comparable to the original SLIC algorithm. When considering label consistency, the proposed system surpasses the performance of state-of-the-art superpixel segmentation methods. Moreover, in terms of hardware performance, the proposed system processes 1000 FPS images with 0.985 ms/frame delay.

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Section: Acceleration Of Slicmentioning

confidence: 99%

“…gSLIC [12] implements SLIC in GPU and achieves a six-fold speed improvement when compared to CPU implementations. FMSLIC [13] introduces a pixel-based search operation in the assignment step to remove data dependency. FMSLIC achieves a processing speed of 143 frames per second for input images.…”

Section: Introductionmentioning

confidence: 99%