A Cellular Active Contours Algorithm Based on Region Evolution

2008 IEEE International Symposium on Circuits and Systems (ISCAS)

Cabello

2008

Self Cite

Abstract-In this paper a new technique for segmenting and tracking moving objects in a user-defined control area is presented. It is based on an active contours technique called Pixel-Level Snakes (PLS) whose capabilities to manage changes of contour topology and to introduce additional constraints in the contour evolution are used to define a control area as well as to segment and track moving objects. Furthermore, PLS can reach a very high speed of response when they are implemented on a pixel-parallel hardware platform. To illustrate the validity of the proposal some examples and results regarding the computation time achieved in the implementation of the proposed algorithm on a cellular processor array (SCAMP-3 vision chip) have been included.

Section: Moving Object Segmentation Approachmentioning

confidence: 99%

“…PLS have been implemented on hardware architectures with pixel-parallel processing capabilities, such as ACE4K [6,7] (based on cellular neural network paradigm) and SCAMP-3 [8,9] (based on SIMD processor array). As a consequence a high speed of processing is achieved.…”

Section: Pixel Level Snakesmentioning

confidence: 99%

Focal-plane moving object segmentation for realtime video surveillance

Vilariño

2008 IEEE International Symposium on Circuits and Systems (ISCAS)

Cabello

2008

Self Cite

“…In this paper, the PLS region-based approach proposed in [11] has been implemented, where the contours are implicity represented as the boundaries of active regions.…”

Section: Pixel Level Snakes (Pls)mentioning

confidence: 99%

“…The external potential guides the PLS evolution towards the vessel edges, whereas the internal potential controls the smoothing effect. Due to the specific PLS implementation in the SCAMP system [11], a fixed number of cycles is defined to stop the evolution. Taking into account all these considerations, this stage has been divided into several steps (see Fig.…”

Section: Stage 4 Pls Evolutionmentioning

confidence: 99%

“…The initialisation stage is also clearly easier, especially in an automatic process, since there is a larger proportion of background pixels. PLS have been implemented on hardware architectures with capabilities of single instruction multiple data (SIMD) processing, like the CNN-based chips [9] as well as the focal plane cellular processor array SCAMP-3 vision system [10], [11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On chip implementation of a pixel-parallel approach for retinal vessel tree extraction

Alonso-Montes

2007 18th European Conference on Circuit Theory and Design

Vilarifio

et al. 2007

Abstract-Retinal vessel tree extraction from angiography images plays an important role not only in the medical domain, but also in biometric identification applications. From the image processing point of view, many algorithms and strategies have been developed to deal with this topic. Although reliable results have been obtained, the main disadvantage in most of these proposals is still the high computation effort required.In this paper, a methodology to extract the retinal vessel tree has been developed, specially defined in terms of fine grain SIMD processing with the purpose of improving the computation time. The proposal has been implemented on a cellular processor array VLSI chip. The execution times for the main modules of the proposed algorithm have been included to show its capability.

Fast retinal vessel tree extraction: A pixel parallel approach

Alonso-Montes

Vilariño

et al. 2008

Circuit Theory & Apps

SUMMARYEarly ocular disease diagnosis is an important field in medical research. From the image processing point of view, many strategies and algorithms have been developed to deal with the extraction of the retinal vessel tree. Although reliable and accurate results have been obtained, the main disadvantage in most of these proposals is the high computation effort required. In this paper, a methodology to extract the retinal vessel tree has been developed and tested in a fine-grain pixel-parallel processor array. An analysis of the execution time has been made to demonstrate its capabilities regarding the computation speed. Moreover, an analysis of the accuracy using a publicly available database has been made to validate the algorithm performance.