Segmentation algorithm via Cellular Neural/Nonlinear Network: implementation on Bio-inspired hardware platform
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Segmentation algorithm via Cellular Neural/ Nonlinear Network: implementation on Bioinspired hardware platform Fethullah Karabiber1, Pietro Vecchio2 and Giuseppe Grassi2*
Abstract The Bio-inspired (Bi-i) Cellular Vision System is a computing platform consisting of sensing, array sensingprocessing, and digital signal processing. The platform is based on the Cellular Neural/Nonlinear Network (CNN) paradigm. This article presents the implementation of a novel CNN-based segmentation algorithm onto the Bi-i system. Each part of the algorithm, along with the corresponding implementation on the hardware platform, is carefully described through the article. The experimental results, carried out for Foreman and Car-phone video sequences, highlight the feasibility of the approach, which provides a frame rate of about 26 frames/s. Comparisons with existing CNN-based methods show that the conceived approach is more accurate, thus representing a good trade-off between real-time requirements and accuracy. Keywords: Cellular Neural/Nonlinear Networks, image segmentation, Bio-inspired hardware platform
1. Introduction Due to the recent advances in communication technologies, the interest in video contents has increased significantly, and it has become more and more important to automatically analyze and understand video contents using computer vision techniques. In this regard, segmentation is essentially the first step toward many image analysis and computer vision problems [1-15]. With the recent advances in several new multimedia applications, there is the need to develop segmentation algorithms running on efficient hardware platforms [16-18]. To this purpose, in [16] an algorithm for the real-time segmentation of endoscopic images running on a special-purpose hardware architecture is described. The architecture detects the gastrointestinal lumen regions and generates binary segmented regions. In [17], a segmentation algorithm was proposed, along with the corresponding hardware architecture, mainly based on a connected component analysis of the binary difference image. In [18], a multiple-features neural-network-based segmentation algorithm and its hardware implementation have * Correspondence: [email protected] 2 Dipartimento di Ingegneria dell’Innovazione, Università del Salento, 73100 Lecce, Italy Full list of author information is available at the end of the article
been proposed. The algorithm incorporates static and dynamic features simultaneously in one scheme for segmenting a frame in an image sequence. Referring to the development of segmentation algorithms running on hardware platforms, in this article the attention is focused on the implementation of algorithms running on the Cellular Neural/Nonlinear Network (CNN) Universal Machine [5-7]. This architecture offers great computational capabilities, which are suitable for complex image-analysis operations in objectoriented approaches [8-10]. Note that so far few CNN algorithms for obtaining the segmentation of a video sequence into
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