FPGA-based System for Real-Time Video Texture Analysis

Maroulis, Dimitris; Iakovidis, Dimitris K.; Bariamis, Dimitris

doi:10.1007/s11265-008-0246-y

Cited by 11 publications

(2 citation statements)

References 36 publications

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“…However, the redundancy led to high memory capacity requirements and redundant transfers of data over the PCI bus. They have proposed an FPGA implementation for real-time extraction of GLCM texture features from video frames in [18]. Sieler et al [17] implemented the GLCMs and Haralick texture features on the FPGA architecture for image size of 128 × 128.…”

Section: Fpgas Acceleratorsmentioning

confidence: 99%

Parallel implementation of Gray Level Co-occurrence Matrices and Haralick texture features on cell architecture

2011

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Texture features extraction algorithms are key functions in various image processing applications such as medical images, remote sensing, and content-based image retrieval. The most common way to extract texture features is the use of Gray Level Co-occurrence Matrices (GLCMs). The GLCM contains the second-order statistical information of spatial relationship of the pixels of an image. Haralick texture features are extracted using these GLCMs. However, the GLCMs and Haralick texture features extraction algorithms are computationally intensive. In this paper, we apply different parallel techniques such as task-and data-level parallelism to exploit available parallelism of those applications on the Cell multi-core processor. Experimental results have shown that our parallel implementations using 16 Synergistic Processor Elements significantly reduce the computational times of the GLCMs and texture features extraction algorithms by a factor of 10× over non-parallel optimized implementations for different image sizes from 128 × 128 to 1024 × 1024.

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Section: Fpgas Acceleratorsmentioning

confidence: 99%

Parallel implementation of Gray Level Co-occurrence Matrices and Haralick texture features on cell architecture

2011

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“…Besides, the calculation of the texture features is realized based fixed-point operation which can generate an inaccurate result. Maroulis et al [11] proposed a HW design to calculate sixteen 64 × 64 GLCMs in parallel and four texture features which are the entropy using a single core, the inverse difference moment, the correlation and the angular second moment. The inconvenient of this design that it uses the fixed-point operations to calculate the texture features instead of the floating-point operations.…”

Section: Introductionmentioning

confidence: 99%

An Efficient HW/SW Design for Text Extraction from Complex Color Image

Atitallah¹,

Kachouri²,

Atitallah³

et al. 2022

Computers, Materials &Amp; Continua

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In the context of constructing an embedded system to help visually impaired people to interpret text, in this paper, an efficient High-level synthesis (HLS) Hardware/Software (HW/SW) design for text extraction using the Gamma Correction Method (GCM) is proposed. Indeed, the GCM is a common method used to extract text from a complex color image and video. The purpose of this work is to study the complexity of the GCM method on Xilinx ZCU102 FPGA board and to propose a HW implementation as Intellectual Property (IP) block of the critical blocks in this method using HLS flow with taking account the quality of the text extraction. This IP is integrated and connected to the ARM Cortex-A53 as coprocessor in HW/SW codesign context. The experimental results show that the HLS HW/SW implementation of the GCM method on ZCU102 FPGA board allows a reduction in processing time by about 89% compared to the SW implementation. This result is given for the same potency and strength of SW implementation for the text extraction.

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An FPGA Implementation for Texture Analysis Considering the Real-Time Requirements of Vision-Based Systems

Ibarra-Manzano

Almanza-Ojeda

2011

Lecture Notes in Computer Science

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FPGA-based System for Real-Time Video Texture Analysis

Cited by 11 publications

References 36 publications

Parallel implementation of Gray Level Co-occurrence Matrices and Haralick texture features on cell architecture

Parallel implementation of Gray Level Co-occurrence Matrices and Haralick texture features on cell architecture

An Efficient HW/SW Design for Text Extraction from Complex Color Image

An FPGA Implementation for Texture Analysis Considering the Real-Time Requirements of Vision-Based Systems

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