i-manager Publications

An Improved Hardware-Oriented Level Set (Hols) for Complex Image Segmentation

Souleymane Balla-Arabe*

State key Laboratory of Integrated Services Networks, Xi’an, China.

Periodicity:January - March'2020
DOI : https://doi.org/10.26634/jip.7.1.17087

Abstract

Level Set Methods (LSMs) offer many advantages comparing with traditional snake models, among others, it has the ability to automatically handle topological changes. Nevertheless, they are computationally expensive. Today's solution to this problem is the implementation using massively parallel devices such as Graphics Processing Unit (GPU), while the development of the AMD's Heterogeneous System Architecture (HSA) systems will definitively provide future effective solutions. However, for the LSM to be suitable to parallel architectures, the curve evolution should be local and almost all the LSMs use regional statistics in order to be effective. Those whouse fully local statistics suffer from a lack of accuracy in detecting boundaries without well-defined edges. In this work, we design a novel hardware-oriented LSM, adequate to future HSA systems and today's hybrid platforms. The non-local step is done by the CPU, while the fully local curve evolution is executed in the GPU. Compared with state-of-the-art methods, this method presents better results both in terms of effectiveness and efficiency. Furthermore, it allows the detection of multiclass boundaries by using only one Level Set Function (LSF) whatever the number of classes n , while the most effective multiphase LSMs would use log n LSF. 2 Intensive experiments demonstrate the high performance of the proposed framework.

Keywords

Hardware Architectures, Image Segmentation, Level Set Method, Parallel Computing.

How to Cite this Article?

Balla-Arabé, S. (2020). An Improved Hardware-Oriented Level Set (HOLS) for Complex Image Segmentation. i-manager's Journal on Image Processing , 7(1), 1-14. https://doi.org/10.26634/jip.7.1.17087

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An Improved Hardware-Oriented Level Set (Hols) for Complex Image Segmentation

Abstract

Keywords

How to Cite this Article?

References

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