A New Method of Histogram Computation for Efficient Implementation of the HOG Algorithm

Ilas, Mariana Eugenia; Ilas, C.

doi:10.3390/computers7010018

Cited by 10 publications

(7 citation statements)

References 30 publications

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“…The support vector machine (SVM) algorithm is often used in data classification and is prevalent in such fields as pedestrian detection, facial recognition, etc. [6][7][8][9][10]. Thus, an accelerated SVM implementation on FPGA has far-reaching significance and has attracted wide attention.…”

Section: Introductionmentioning

confidence: 99%

An Accelerator Architecture of Changeable-Dimension Matrix Computing Method for SVM

Liu

et al. 2019

Electronics

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Matrix multiplication is a critical time-consuming processing step in many machine learning applications. Due to the diversity of practical applications, the matrix dimensions are generally not fixed. However, most matrix calculation methods, based on field programmable gate array (FPGA) currently use fixed matrix dimensions, which limit the flexibility of machine learning algorithms in a FPGA. The bottleneck lies in the limited FPGA resources. Therefore, this paper proposes an accelerator architecture for matrix computing method with changeable dimensions. Multi-matrix synchronous calculation concept allows matrix data to be processed continuously, which improves the parallel computing characteristics of FPGA and optimizes the computational efficiency. This paper tests matrix multiplication using support vector machine (SVM) algorithm to verify the performance of proposed architecture on the ZYNQ platform. The experimental results show that, compared to the software processing method, the proposed architecture increases the performance by 21.18 times with 9947 dimensions. The dimension is changeable with a maximum value of 2,097,151, without changing hardware design. This method is also applicable to matrix multiplication processing with other machine learning algorithms.

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Section: Introductionmentioning

confidence: 99%

An Accelerator Architecture of Changeable-Dimension Matrix Computing Method for SVM

Liu

et al. 2019

Electronics

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“…To build our HOG, we take 9 as the number of bins and 3 as the number of HOG windows per linked box, so our HOG will be a vector composed of 81 values. Blocks (cells) and bin ideas is an efficient method to give the description the necessary stability in the face of variations in geometric conditions [34]. To this end, the first step of our proposed descriptor is to divide the object image into connected areas overlapped at 50% called cells (Figure 4).…”

Section: Histogram Of Oriented Gradientmentioning

confidence: 99%

Color Based Object Categorization Using Histograms of Oriented Hue and Saturation

Hamdini¹,

Diffellah²,

Namane³

2021

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In the last few years, there has been a lot of interest in making smart components, e.g. robots, able to simulate human capacity of object recognition and categorization. In this paper, we propose a new revolutionary approach for object categorization based on combining the HOG (Histograms of Oriented Gradients) descriptors with our two new descriptors, HOH (Histograms of Oriented Hue) and HOS (Histograms of Oriented Saturation), designed it in the HSL (Hue, Saturation and Luminance) color space and inspired by this famous HOG descriptor. By using the chrominance components, we have succeeded in making the proposed descriptor invariant to all lighting conditions changes. Moreover, the use of this oriented gradient makes our descriptor invariant to geometric condition changes including geometric and photometric transformation. Finally, the combination of color and gradient information increase the recognition rate of this descriptor and give it an exceptional performance compared to existing methods in the recognition of colored handmade objects with uniform background (98.92% for Columbia Object Image Library and 99.16% for the Amsterdam Library of Object Images). For the classification task, we propose the use of two strong and very used classifiers, SVM (Support Vector Machine) and KNN (k-nearest neighbors) classifiers.

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“…The implementation of this technique in FPGAs is one of the newest, starting in 2002 (see Figure 9). The SVM has been used in FPGAs alongside the Histogram of Oriented Gradients (HOG) for object classifications in image processing [501][502][503][504][505]. Other SVM applications include facial expression recognition [506][507][508], network traffic classification [509], melanoma detection [510][511][512], arrhythmias detection [513,514], epilepsy detection [515] and stress detection [516].…”

Section: Machine Learningmentioning

confidence: 99%

Field Programmable Gate Array Applications—A Scientometric Review

2019

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Field Programmable Gate Array (FPGA) is a general purpose programmable logic device that can be configured by a customer after manufacturing to perform from a simple logic gate operations to complex systems on chip or even artificial intelligence systems. Scientific publications related to FPGA started in 1992 and, up to now, we found more than 70,000 documents in the two leading scientific databases (Scopus and Clarivative Web of Science). These publications show the vast range of applications based on FPGAs, from the new mechanism that enables the magnetic suspension system for the kilogram redefinition, to the Mars rovers’ navigation systems. This paper reviews the top FPGAs’ applications by a scientometric analysis in ScientoPy, covering publications related to FPGAs from 1992 to 2018. Here we found the top 150 applications that we divided into the following categories: digital control, communication interfaces, networking, computer security, cryptography techniques, machine learning, digital signal processing, image and video processing, big data, computer algorithms and other applications. Also, we present an evolution and trend analysis of the related applications.

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A New Method of Histogram Computation for Efficient Implementation of the HOG Algorithm

Cited by 10 publications

References 30 publications

An Accelerator Architecture of Changeable-Dimension Matrix Computing Method for SVM

An Accelerator Architecture of Changeable-Dimension Matrix Computing Method for SVM

Color Based Object Categorization Using Histograms of Oriented Hue and Saturation

Field Programmable Gate Array Applications—A Scientometric Review

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