An efficient implementation of GLCM algorithm in FPGA

Atitallah, Mohamed Amin Ben; Kachouri, Rostom; Kammoun, Manel; Mnif, Hassène

doi:10.1109/iintec.2018.8695275

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…The default value of the displacement vector is equal to

. GLCM have been used in many applications [ 54 , 55 , 56 , 57 , 58 ]. The exact calculation of the GLCM matrix is described in the literature [ 46 , 49 ].…”

Section: Entropy Analysismentioning

confidence: 99%

A Giga-Stable Oscillator with Hidden and Self-Excited Attractors: A Megastable Oscillator Forced by His Twin

Shaverdi

Khalaf

et al. 2019

Entropy

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In this paper, inspired by a newly proposed two-dimensional nonlinear oscillator with an infinite number of coexisting attractors, a modified nonlinear oscillator is proposed. The original system has an exciting feature of having layer–layer coexisting attractors. One of these attractors is self-excited while the rest are hidden. By forcing this system with its twin, a new four-dimensional nonlinear system is obtained which has an infinite number of coexisting torus attractors, strange attractors, and limit cycle attractors. The entropy, energy, and homogeneity of attractors’ images and their basin of attractions are calculated and reported, which showed an increase in the complexity of attractors when changing the bifurcation parameters.

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“…The default value of the displacement vector is equal to

. GLCM have been used in many applications [ 54 , 55 , 56 , 57 , 58 ]. The exact calculation of the GLCM matrix is described in the literature [ 46 , 49 ].…”

Section: Entropy Analysismentioning

confidence: 99%

A Giga-Stable Oscillator with Hidden and Self-Excited Attractors: A Megastable Oscillator Forced by His Twin

Shaverdi

Khalaf

et al. 2019

Entropy

View full text Add to dashboard Cite

show abstract

“…Amin and al [12] proposed a HW / SW implementation to calculate four GLCM matrices (0 °, 45 °, 90 ° and 135 °) in parallel. The HW implementation of GLCM was done on the Zedboard platform based on the Zynq circuit.…”

Section: Related Workmentioning

confidence: 99%

A new FPGA Accelerator Based on Circular Buffer Unit Per Orientation for a Fast and Optimised GLCM and Texture Feature Computation

Atitallah

Kachouri

Mnif

2019

2019 IEEE International Conference on Design &Amp; Test of Integrated Micro &Amp; Nano-Systems (DTS)

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This paper presents an FPGA accelerator based on circular buffer unit per orientation for a fast and optimized Gray Level Co-occurrence Matrix (GLCM) and four Texture features computation. The Four texture features namely, contrast, energy, dissimilarity and correlation are computed using Xilinx FPGA. However, the computation of GLCM and four textures features are very complex and consume a lot of execution time. In this paper, an FPGA accelerator for fast computation of GLCM and four texture features are designed and implemented. This architecture was implemented on a Xilinx Zc-702 using Vivado HLS. The obtained results are then compared against other related works. The synthesis results on FPGA prove a significant gain (about 17%) in execution time compared to the previous work.

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Automatic detection of small bowel tumors in wireless capsule endoscopy images using ensemble learning

et al. 2019

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PurposeWireless Capsule Endoscopy (WCE) is a minimally invasive diagnosis tool for lesion detection in the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the significant amount of acquired data leads to difficulties in the diagnosis by the physicians; which can be eased with computer assistance. This paper addresses a method for the automatic detection of tumors in WCE by using a two‐step based procedure: region of interest selection and classification.MethodsThe first step aims to separate abnormal from normal tissue by using automatic segmentation based on a Gaussian Mixture Model (GMM). A modified version of the Anderson method for convergence acceleration of the expectation–maximization (EM) algorithm is proposed. The proposed features for both segmentation and classification are based on the CIELab color space, as a way of bypassing lightness variations, where the L component is discarded. Tissue variability among subjects, light inhomogeneities and even intensity differences among different devices can be overcome by using simultaneously features from both regions. In the second step, an ensemble system with partition of the training data with a new training scheme is proposed. At this stage, the gating network is trained after the experts have been trained decoupling the joint maximization of both modules. The partition module is also used at the test step, leading the incoming data to the most likely expert allowing incremental adaptation by preserving data diversity.ResultsThis algorithm outperforms others based on texture features selected from Wavelets and Curvelets transforms, classified by a regular support vector machine (SVM) in more than 5%.ConclusionsThis work shows that simpler features can outperform more elaborate ones if appropriately designed. In the current case, luminance was discarded to cope with saturated tissue, facilitating the color perception. Ensemble systems remain an open research field. In the current case, changes in both topology and training strategy have led to significant performance improvements. A system with this level of performance can be used in current clinical practice.

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An efficient implementation of GLCM algorithm in FPGA

Cited by 7 publications

References 9 publications

A Giga-Stable Oscillator with Hidden and Self-Excited Attractors: A Megastable Oscillator Forced by His Twin

A Giga-Stable Oscillator with Hidden and Self-Excited Attractors: A Megastable Oscillator Forced by His Twin

A new FPGA Accelerator Based on Circular Buffer Unit Per Orientation for a Fast and Optimised GLCM and Texture Feature Computation

Automatic detection of small bowel tumors in wireless capsule endoscopy images using ensemble learning

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