Histogram of Oriented Gradients Feature Extraction From Raw Bayer Pattern Images

Zhou, Wei; Gao, Shengyu; Zhang, Ling; Lou, Xin

doi:10.1109/tcsii.2020.2980557

Cited by 83 publications

(35 citation statements)

References 40 publications

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“…This study presents a Technique depend on an algorithm is called "One Millisecond Face Alignment with an Ensemble of Regression Trees" developed by two Swedish Computer Vision researchers Kazemi and Sullivan in 2014 [17,18], This detector is built in the dlib library and it detects facial landmarks very quickly and accurately and this algorithm is based on dividing the face into 68 points, Beside the pretrained facial landmark detector inside the dlib library is used to estimate the location of 68 (x, y)-coordinates that map to facial structures on the face, This occurs by calling a frontal face detector from dlib library. This is a pre-trained detector based on Histogram of Oriented Gradients (HOG) features and a Linear SVM object detector [19]. And the dataset on which the dlib facial landmark was trained in the shape predictor 68 face landmarks" dataset.…”

Section: Proposed System Architecturementioning

confidence: 99%

Wheelchair Control System based Eye Gaze

Amer¹,

Ramadan²,

Kamh³

et al. 2021

IJACSA

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The inability to control the limbs is the main reason that affects the daily activities of the disabled which causes social restrictions and isolation. More studies were performed to help disabilities for easy communication with the outside world and others. Various techniques are designed to help the disabled in carrying out daily activities easily. Among these technologies is the Smart Wheelchair. This research aims to develop a smart eye-controlled wheelchair whose movement depends on eye movement tracking. The proposed Wheelchair is simple in design and easy to use with low cost compared with previous Wheelchairs. The eye movement was detected through a camera fixed on the chair. The user's gaze direction is obtained from the captured image after some processing and analysis. The order is sent to the Arduino Uno board which controls the wheelchair movement. The Wheelchair performance was checked using different volunteers and its accuracy reached 94.4% with a very short response time compared with the other existing chairs.

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Section: Proposed System Architecturementioning

confidence: 99%

Wheelchair Control System based Eye Gaze

Amer¹,

Ramadan²,

Kamh³

et al. 2021

IJACSA

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show abstract

“…Feature description [7] is a representation of image by extracting important information. The extracted feature by HOG are computed by describing the local distribution of the edge orientations and the corresponding gradient magnitude, this is realized by defining two locally units cell (8x8 pixels) and block that contains 2x2 cells, this gives 16x16 pixels for each hog feature [8].…”

Section: Feature Descriptionmentioning

confidence: 99%

FPGA-based Hardware Acceleration for SVM Machine Learning Algorithm

2021

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“…Firstly, we extract the image feature using the histogram of oriented gradients (HOG) feature extraction method. HOG is a popular feature used in many image processing applications [36][37][38]. The HOG can be performed by dividing the image into small parts that are named cells.…”

Section: Extraction Of Image Featuresmentioning

confidence: 99%

Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

et al. 2020

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The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

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Histogram of Oriented Gradients Feature Extraction From Raw Bayer Pattern Images

Cited by 83 publications

References 40 publications

Wheelchair Control System based Eye Gaze

Wheelchair Control System based Eye Gaze

FPGA-based Hardware Acceleration for SVM Machine Learning Algorithm

Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

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