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Journal articles on the topic 'Histogram of Oriented Gradients'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 May 2022

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1

Cheon, Min-Kyu, Won-Ju Lee, Chang-Ho Hyun, and Mignon Park. "Rotation Invariant Histogram of Oriented Gradients." International Journal of Fuzzy Logic and Intelligent Systems 11, no. 4 (December 1, 2011): 293–98. http://dx.doi.org/10.5391/ijfis.2011.11.4.293.

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2

Li, Bin, Kaili Cheng, and Zhezhou Yu. "Histogram of Oriented Gradient Based Gist Feature for Building Recognition." Computational Intelligence and Neuroscience 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/6749325.

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We proposed a new method of gist feature extraction for building recognition and named the feature extracted by this method as the histogram of oriented gradient based gist (HOG-gist). The proposed method individually computes the normalized histograms of multiorientation gradients for the same image with four different scales. The traditional approach uses the Gabor filters with four angles and four different scales to extract orientation gist feature vectors from an image. Our method, in contrast, uses the normalized histogram of oriented gradient as orientation gist feature vectors of the same image. These HOG-based orientation gist vectors, combined with intensity and color gist feature vectors, are the proposed HOG-gist vectors. In general, the HOG-gist contains four multiorientation histograms (four orientation gist feature vectors), and its texture description ability is stronger than that of the traditional gist using Gabor filters with four angles. Experimental results using Sheffield Buildings Database verify the feasibility and effectiveness of the proposed HOG-gist.

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Guo, Lie, Guang Xi Zhang, Ping Shu Ge, and Lin Hui Li. "Pedestrian Tracking with HOG and Color Histogram Features." Applied Mechanics and Materials 241-244 (December 2012): 498–501. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.498.

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To improve the effectiveness of pedestrian tracking, the histograms of oriented gradients (HOG) and color histogram characteristics are adopted to track pedestrian based on particle filter. Firstly, the pedestrian is detected using the HOG features to determine the initial target position. Then the target is tracked based on particle filter utilizing color histogram, during which the HOG is used to modify particle heavy weights and particle sampling. Experimental results verify the accurateness and efficiency of the proposed method.

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4

Zhang, Li Hong, and Lin Li. "Improved Pedestrian Detection Based on Extended Histogram of Oriented Gradients." Applied Mechanics and Materials 347-350 (August 2013): 3815–20. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3815.

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In order to further improve pedestrian detection accuracy and avoid the disadvantage of original histogram of oriented gradients (HOG), differential template, overlap ratio and normalization method and so on are improved when HOG features are extracted, then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows. Considering speed, we select support vector machine (SVM) using linear function kernel as a classifier. Multi-scale detection technique and non maxima suppression method are employed for precisely locating the pedestrians in the image. Experiments show that the human detection system improves detection accuracy and still maintains a relatively satisfactory speed.

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5

K. Alilou, Vahid, and Farzin Yaghmaee. "Non-texture image inpainting using histogram of oriented gradients." Journal of Visual Communication and Image Representation 48 (October 2017): 43–53. http://dx.doi.org/10.1016/j.jvcir.2017.06.003.

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6

Gupta, Sheifali, Gurleen Kaur, Deepali Gupta, and Udit Jindal. "Brazilian Coins Recognition Using Histogram of Oriented Gradients Features." Journal of Computational and Theoretical Nanoscience 16, no. 10 (October 1, 2019): 4170–78. http://dx.doi.org/10.1166/jctn.2019.8498.

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This paper tends to the issue of coin recognition when dealing with shading and reflection variations under the same lighting conditions. In order to approach the problem, a database containing Brazilian coin images (both front and reverse side of the coin) consisting of five different denominations have been used which is provided by the kaggle-diverse and largest data community in the world. This work focuses on an automatic image classification process for Brazilian coins. The imagebased classification of coins primarily incorporates three stages where the initial step is Region of Interest (ROI) extraction; the subsequent advance is extraction of features and classification. The first step of ROI extraction is accomplished by segmenting the coin region using the proposed segmentation method. In the second step i.e., feature extraction; Histogram of Oriented Gradients (HOG) features are extracted from the image. The image is converted to a vector containing feature values. The third step is where the extracted features are mapped to the class and are known as classification. Three classification algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbour are compared for classification of five coin denominations. With the proposed segmentation methodology, the best classification accuracy of 92% is achieved in the case of ANN classifier.

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Zhao, Yong, Yongjun Zhang, Ruzhong Cheng, Daimeng Wei, and Guoliang Li. "An Enhanced Histogram of Oriented Gradients for Pedestrian Detection." IEEE Intelligent Transportation Systems Magazine 7, no. 3 (2015): 29–38. http://dx.doi.org/10.1109/mits.2015.2427366.

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8

Anggraeny, Fetty Tri, Basuki Rahmat, and Singgih Putra Pratama. "Deteksi Ikan Dengan Menggunakan Algoritma Histogram of Oriented Gradients." Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer 15, no. 2 (September 10, 2020): 114. http://dx.doi.org/10.30872/jim.v15i2.4648.

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Indonesia merupakan negara yang kaya akan sumber daya alam baik hayati maupun non-hayati. Salah satu sumber daya alam hayati yang sangat banyak jumlahnya di Indonesia adalah laut, Untuk mempermudah mengidentifikasikan ikan, dapat memanfaatkan sebuah teknologi yang dapat membantu manusia untuk dapat mengenali ikan dengan menggunakan visi komputer dan pendekatan pemrosesan gambar untuk deteksi ikan dan bukan ikan menggunakan algoritma Histogram of Oriented Gradients (HOG) dan AdaBoost-SVM. Hasil penelitian menunjukkan bahwa metode HOG dan AdaBoost-SVM dapat menghasilkan tingkat akurasi rata-rata sebesar 84.8%.

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Hafidhoh, Nisa ul, and Septian Enggar Sukmana. "Deteksi Pemain Basket Terklasifikasi Berbasis Histogram of Oriented Gradients." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 3, no. 1 (January 31, 2018): 6–11. http://dx.doi.org/10.25139/inform.v3i1.635.

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Pada olahraga basket jaman modern ini, kebutuhan analisis pergerakan pemain pada calon tim lawan olahraga basket perlu didukung oleh teknologi informasi yang mampu mengupayakan sistem yang otomatis. Analisis pergerakan pemain yang otomatis perlu didukung oleh sistem deteksi pemain yang handal dan akurat sehingga pemetaan pergerakan dapat dilakukan secara optimal. Tujuan dari penelitian ini adalah untuk mengembangkan metode Histogram of Oriented Gradients (HOG) menjadi sebuah metode deteksi yang handal untuk kasus deteksi pemain basket pada media. Tantangan pada penelitian ini adalah deteksi pemain tidak hanya pada saat berjalan dan berlari namun juga pada saat melompat. Untuk memperkuat fokus dan konsistensi terhadap objek yang terdeteksi, pemanfaatan metode klasifikasi Support Vector Machine (SVM) digunakan melalui kolaborasi terhadap HOG descriptor serta warna kostum pemain sehingga pembeda tim dari masing-masing pemain juga dapat dikenali. Tingkat akurasi dari evaluasi yang dihasilkan adalah 92% untuk true positive rate dan 40% untuk false positive rate.

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10

Zhang, Xiangyu, Fengwei An, Ikki Nakashima, Aiwen Luo, Lei Chen, Idaku Ishii, and Hans Jürgen Mattausch. "A hardware-oriented histogram of oriented gradients algorithm and its VLSI implementation." Japanese Journal of Applied Physics 56, no. 4S (January 30, 2017): 04CF01. http://dx.doi.org/10.7567/jjap.56.04cf01.

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11

Zhang, Li Hong. "Human Detection Based on SVM and Improved Histogram of Oriented Gradients." Applied Mechanics and Materials 380-384 (August 2013): 3862–65. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3862.

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Considering the fact that original histogram of oriented gradients (HOG) cannot extract the body local features in large image regions, its features are improved when extracted, then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows. Considering speed, we select support vector machine (SVM) using linear function kernel as a classifier. Combining with HOG extraction and SVM training, the process includes three steps: features extraction, training and detection. Experiments show that while maintaining a relatively satisfactory speed the human detection system improves detection accuracy.

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12

Jiao, Jichao, and Zhongliang Deng. "Deep combining of local phase quantization and histogram of oriented gradients for indoor positioning based on smartphone camera." International Journal of Distributed Sensor Networks 13, no. 1 (January 2017): 155014771668697. http://dx.doi.org/10.1177/1550147716686978.

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To achieve high accuracy in indoor positioning using a smartphone, there are two limitations: (1) limited computational and memory resources of the smartphone and (2) the human walking in large buildings. To address these issues, we propose a new feature descriptor by deeply combining histogram of oriented gradients and local phase quantization. This feature is a local phase quantization of a salient histogram of oriented gradient visualizing image, which is robust in indoor scenarios. Moreover, we introduce a base station–based indoor positioning system for assisting to reduce the image matching at runtime. The experimental results show that accurate and efficient indoor location positioning is achieved.

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13

Lei, Zhen. "Histogram of oriented gradient detector with color-invariant gradients in Gaussian color space." Optical Engineering 49, no. 10 (October 1, 2010): 109701. http://dx.doi.org/10.1117/1.3503944.

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14

HuiMing Huang, HeSheng Liu, and GuoPing Liu. "Face Recognition Using Pyramid Histogram of Oriented Gradients and SVM." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 18 (October 31, 2012): 1–8. http://dx.doi.org/10.4156/aiss.vol4.issue18.1.

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15

K.Velmurugan, Author, and S. Santhosh Baboo. "Image Retrieval using Harris Corners and Histogram of Oriented Gradients." International Journal of Computer Applications 24, no. 7 (June 30, 2011): 6–10. http://dx.doi.org/10.5120/2968-3968.

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16

Sharma, Riti, and Andreas Savakis. "Lean histogram of oriented gradients features for effective eye detection." Journal of Electronic Imaging 24, no. 6 (November 23, 2015): 063007. http://dx.doi.org/10.1117/1.jei.24.6.063007.

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17

Farhan, Athraa H., and Mohammed Y. Kamil. "Texture Analysis of Mammogram Using Histogram of Oriented Gradients Method." IOP Conference Series: Materials Science and Engineering 881 (August 11, 2020): 012149. http://dx.doi.org/10.1088/1757-899x/881/1/012149.

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18

Sulistyaningrum, D. R., T. Ummah, B. Setiyono, D. B. Utomo, Soetrisno, and B. A. Sanjoyo. "Vehicle detection using histogram of oriented gradients and real adaboost." Journal of Physics: Conference Series 1490 (March 2020): 012001. http://dx.doi.org/10.1088/1742-6596/1490/1/012001.

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19

Jumani, Sahar Zafar, Fayyaz Ali, Subhash Guriro, Irfan Ali Kandhro, Asif Khan, and Adnan Zaidi. "Facial Expression Recognition with Histogram of Oriented Gradients using CNN." Indian Journal of Science and Technology 12, no. 24 (June 1, 2019): 1–8. http://dx.doi.org/10.17485/ijst/2019/v12i24/145093.

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20

Christanti Mawardi, Viny, Yoferen Yoferen, and Stéphane Bressan. "Sketch-Based Image Retrieval with Histogram of Oriented Gradients and Hierarchical Centroid Methods." E3S Web of Conferences 188 (2020): 00026. http://dx.doi.org/10.1051/e3sconf/202018800026.

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Searching images from digital image dataset can be done using sketch-based image retrieval that performs retrieval based on the similarity between dataset images and sketch image input. Preprocessing is done by using Canny Edge Detection to detect edges of dataset images. Feature extraction will be done using Histogram of Oriented Gradients and Hierarchical Centroid on the sketch image and all the preprocessed dataset images. The features distance between sketch image and all dataset images is calculated by Euclidean Distance. Dataset images used in the test consist of 10 classes. The test results show Histogram of Oriented Gradients, Hierarchical Centroid, and combination of both methods with low and high threshold of 0.05 and 0.5 have average precision and recall values of 90.8 % and 13.45 %, 70 % and 10.64 %, 91.4 % and 13.58 %. The average precision and recall values with low and high threshold of 0.01 and 0.1, 0.3 and 0.7 are 87.2 % and 13.19 %, 86.7 % and 12.57 %. Combination of the Histogram of Oriented Gradients and Hierarchical Centroid methods with low and high threshold of 0.05 and 0.5 produce better retrieval results than using the method individually or using other low and high threshold.

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21

SIDDIK, MUHAMMAD ARSYAD, LEDYA NOVAMIZANTI, and I. NYOMAN APRAZ RAMATRYANA. "Deteksi Level Kolesterol melalui Citra Mata Berbasis HOG dan ANN." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 7, no. 2 (May 24, 2019): 284. http://dx.doi.org/10.26760/elkomika.v7i2.284.

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ABSTRAKKolesterol merupakan lemak yang berada di dalam darah yang dibutuhkan untuk pembentukan hormon dan sel baru. Kadar kolesterol normal harus kurang dari 200 mg/dL, namun jika di atas 240 mg/dL akan berisiko tinggi terkena penyakit stroke dan jantung koroner. Penelitian ini menghasilkan suatu sistem yang dapat mendeteksi kadar kolesterol seseorang melalui citra mata menggunakan metode iridologi dan image processing. Citra mata diperoleh dari pasien laboratorium klinik sebanyak 120 citra mata. Proses sistem diawali dengan mengolah citra mata dengan metode cropping, resize, dan segmentasi. Metode ekstaksi ciri menggunakan Histogram of Oriented Gradients (HOG), dan klasifikasi menggunakan Artificial Neural Network (ANN). Sistem dapat mendeteksi kadar kolesterol dengan tiga level klasifikasi, yaitu normal, berisiko kolesterol tinggi, dan kolesterol tinggi dengan tingkat akurasi sebesar 93% dan waktu komputasi 0,0862 detik.Kata kunci: citra mata, kadar kolesterol, Histogram of Oriented Gradients, Artificial Neural Network ABSTRACTCholesterol is fat in the blood that is needed for the formation of hormones and new cells. Normal cholesterol levels should be less than 200 mg / dL, but if above 240 mg / dL will be at high risk of stroke and coronary heart disease. This study produced a system that can detect a person's cholesterol levels through eye images using iridology and image processing methods. Eye images obtained from clinical laboratory patients were 120 eye images. The system process begins with processing eye images using the method of cropping, resizing, and segmentation. Feature extraction method uses Histogram of Oriented Gradients (HOG), and classification using Artificial Neural Network (ANN). The system can detect cholesterol levels with three levels of classification, namely normal, at high risk of cholesterol, and high cholesterol with an accuracy rate of 93% and computing time of 0.0862 seconds.Keywords: eye image, cholesterol level, Histogram of Oriented Gradients, Artificial Neural Network

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22

HUA, Chunsheng, Yasushi MAKIHARA, and Yasushi YAGI. "Pedestrian Detection by Using a Spatio-Temporal Histogram of Oriented Gradients." IEICE Transactions on Information and Systems E96.D, no. 6 (2013): 1376–86. http://dx.doi.org/10.1587/transinf.e96.d.1376.

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23

Salfikar, Inzar, Indra Adji Sulistijono, and Achmad Basuki. "Automatic Samples Selection Using Histogram of Oriented Gradients (HOG) Feature Distance." EMITTER International Journal of Engineering Technology 5, no. 2 (January 13, 2018): 234–54. http://dx.doi.org/10.24003/emitter.v5i2.182.

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Finding victims at a disaster site is the primary goal of Search-and-Rescue (SAR) operations. Many technologies created from research for searching disaster victims through aerial imaging. but, most of them are difficult to detect victims at tsunami disaster sites with victims and backgrounds which are look similar. This research collects post-tsunami aerial imaging data from the internet to builds dataset and model for detecting tsunami disaster victims. Datasets are built based on distance differences from features every sample using Histogram-of-Oriented-Gradient (HOG) method. We use the longest distance to collect samples from photo to generate victim and non-victim samples. We claim steps to collect samples by measuring HOG feature distance from all samples. the longest distance between samples will take as a candidate to build the dataset, then classify victim (positives) and non-victim (negatives) samples manually. The dataset of tsunami disaster victims was re-analyzed using cross-validation Leave-One-Out (LOO) with Support-Vector-Machine (SVM) method. The experimental results show the performance of two test photos with 61.70% precision, 77.60% accuracy, 74.36% recall and f-measure 67.44% to distinguish victim (positives) and non-victim (negatives).

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S. Gornale, Shivanand, Pooja U. Patravali, Kiran S. Marathe, and Prakash S. Hiremath. "Determination of Osteoarthritis Using Histogram of Oriented Gradients and Multiclass SVM." International Journal of Image, Graphics and Signal Processing 9, no. 12 (December 8, 2017): 41–49. http://dx.doi.org/10.5815/ijigsp.2017.12.05.

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25

M., Hany, Abeer S., Asmaa Mohammed, and Rachid Jennane. "Histogram of Oriented Gradients and Texture Features for Bone Texture Characterization." International Journal of Computer Applications 165, no. 3 (May 17, 2017): 23–28. http://dx.doi.org/10.5120/ijca2017913820.

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26

Shidlovskiy, S. V., A. S. Bondarchuk, S. Poslavsky, and M. V. Shikhman. "Reducing dimensions of the histogram of oriented gradients (HOG) feature vector." Journal of Physics: Conference Series 1611 (August 2020): 012072. http://dx.doi.org/10.1088/1742-6596/1611/1/012072.

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27

Wójcikowski, Marek. "Histogram of Oriented Gradients with Cell Average Brightness for Human Detection." Metrology and Measurement Systems 23, no. 1 (March 1, 2016): 27–36. http://dx.doi.org/10.1515/mms-2016-0012.

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Abstract A modification of the descriptor in a human detector using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) is presented. The proposed modification requires inserting the values of average cell brightness resulting in the increase of the descriptor length from 3780 to 3908 values, but it is easy to compute and instantly gives ≈ 25% improvement of the miss rate at 10‒4 False Positives Per Window (FPPW). The modification has been tested on two versions of HOG-based descriptors: the classic Dalal-Triggs and the modified one, where, instead of spatial Gaussian masks for blocks, an additional central cell has been used. The proposed modification is suitable for hardware implementations of HOG-based detectors, enabling an increase of the detection accuracy or resignation from the use of some hardware-unfriendly operations, such as a spatial Gaussian mask. The results of testing its influence on the brightness changes of test images are also presented. The descriptor may be used in sensor networks equipped with hardware acceleration of image processing to detect humans in the images.

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Zhou, Wei, Shengyu Gao, Ling Zhang, and Xin Lou. "Histogram of Oriented Gradients Feature Extraction From Raw Bayer Pattern Images." IEEE Transactions on Circuits and Systems II: Express Briefs 67, no. 5 (May 2020): 946–50. http://dx.doi.org/10.1109/tcsii.2020.2980557.

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Ghaffari, Sina, Parastoo Soleimani, Kin Fun Li, and David W. Capson. "Analysis and Comparison of FPGA-Based Histogram of Oriented Gradients Implementations." IEEE Access 8 (2020): 79920–34. http://dx.doi.org/10.1109/access.2020.2989267.

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30

El-Beheiry, S. S., A. E. El-Fiqi, A. El-Mahlaway, Gh El-Banby, Fathi E. Abd EL-Samie, and S. El-Rabaie. "Proposed Cancelable Face Recognition System Based on Histogram of Oriented Gradients." Menoufia Journal of Electronic Engineering Research 28, no. 1 (December 1, 2019): 138–44. http://dx.doi.org/10.21608/mjeer.2019.76987.

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Nazir, Muhammad, Zahoor Jan, and Muhammad Sajjad. "Facial expression recognition using histogram of oriented gradients based transformed features." Cluster Computing 21, no. 1 (May 30, 2017): 539–48. http://dx.doi.org/10.1007/s10586-017-0921-5.

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32

Hamada, Nuha H., and Faten F. Kharbat. "p-norms of histogram of oriented gradients for X-ray images." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (October 1, 2021): 4423. http://dx.doi.org/10.11591/ijece.v11i5.pp4423-4430.

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<span>Lebesgue spaces (</span><em><span>L<sup>p</sup></span></em><span> over </span><em><span>R<sup>n</sup></span></em><span>) play a significant role in mathematical analysis. They are widely used in machine learning and artificial intelligence to maximize performance or minimize error. The well-known histogram of oriented gradients (HOG) algorithm applies the 2-norm (Euclidean distance) to detect features in images. In this paper, we apply different </span><em><span>p</span></em><span>-norm values to identify the impact that changing these norms has on the original algorithm. The aim of this modification is to achieve better performance in classifying X-ray medical images related to of COVID-19 patients. The efficiency of the </span><em><span>p</span></em><span>-HOG algorithm is compared with the original HOG descriptor using a support vector machine implemented in Python. The results of the comparisons are promising, and the </span><em><span>p</span></em><span>-HOG algorithm shows greater efficiency in most cases.</span>

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Ghaffari, Sina, Parastoo Soleimani, Kin Fun Li, and David W. Capson. "A Novel Hardware–Software Co-Design and Implementation of the HOG Algorithm." Sensors 20, no. 19 (October 2, 2020): 5655. http://dx.doi.org/10.3390/s20195655.

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The histogram of oriented gradients is a commonly used feature extraction algorithm in many applications. Hardware acceleration can boost the speed of this algorithm due to its large number of computations. We propose a hardware–software co-design of the histogram of oriented gradients and the subsequent support vector machine classifier, which can be used to process data from digital image sensors. Our main focus is to minimize the resource usage of the algorithm while maintaining its accuracy and speed. This design and implementation make four contributions. First, we allocate the computationally expensive steps of the algorithm, including gradient calculation, magnitude computation, bin assignment, normalization and classification, to hardware, and the less complex windowing step to software. Second, we introduce a logarithm-based bin assignment. Third, we use parallel computation and a time-sharing protocol to create a histogram in order to achieve the processing of one pixel per clock cycle after the initialization (setup time) of the pipeline, and produce valid results at each clock cycle afterwards. Finally, we use a simplified block normalization logic to reduce hardware resource usage while maintaining accuracy. Our design attains a frame rate of 115 frames per second on a Xilinx® Kintex® Ultrascale™ FPGA while using less hardware resources, and only losing accuracy marginally, in comparison with other existing work.

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Moldovanu, Simona, Lenuta Pană Toporaș, Anjan Biswas, and Luminita Moraru. "Combining Sparse and Dense Features to Improve Multi-Modal Registration for Brain DTI Images." Entropy 22, no. 11 (November 14, 2020): 1299. http://dx.doi.org/10.3390/e22111299.

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A new solution to overcome the constraints of multimodality medical intra-subject image registration is proposed, using the mutual information (MI) of image histogram-oriented gradients as a new matching criterion. We present a rigid, multi-modal image registration algorithm based on linear transformation and oriented gradients for the alignment of T2-weighted (T2w) images (as a fixed reference) and diffusion tensor imaging (DTI) (b-values of 500 and 1250 s/mm2) as floating images of three patients to compensate for the motion during the acquisition process. Diffusion MRI is very sensitive to motion, especially when the intensity and duration of the gradient pulses (characterized by the b-value) increases. The proposed method relies on the whole brain surface and addresses the variability of anatomical features into an image stack. The sparse features refer to corners detected using the Harris corner detector operator, while dense features use all image pixels through the image histogram of oriented gradients (HOG) as a measure of the degree of statistical dependence between a pair of registered images. HOG as a dense feature is focused on the structure and extracts the oriented gradient image in the x and y directions. MI is used as an objective function for the optimization process. The entropy functions and joint entropy function are determined using the HOGs data. To determine the best image transformation, the fiducial registration error (FRE) measure is used. We compare the results against the MI-based intensities results computed using a statistical intensity relationship between corresponding pixels in source and target images. Our approach, which is devoted to the whole brain, shows improved registration accuracy, robustness, and computational cost compared with the registration algorithms, which use anatomical features or regions of interest areas with specific neuroanatomy. Despite the supplementary HOG computation task, the computation time is comparable for MI-based intensities and MI-based HOG methods.

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Chen, Ji, Kaiping Zhan, Qingzhou Li, Zhiyang Tang, Chenwei Zhu, Ke Liu, and Xiangyou Li. "Spectral clustering based on histogram of oriented gradient (HOG) of coal using laser-induced breakdown spectroscopy." Journal of Analytical Atomic Spectrometry 36, no. 6 (2021): 1297–305. http://dx.doi.org/10.1039/d1ja00104c.

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Histogram of oriented gradients (HOG) was introduced in the unsupervised spectral clustering in LIBS. After clustering, the spectra of different matrices were clearly distinguished, and the accuracy of quantitative analysis of coal was improved.

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36

Ismail, Mohamed Maher Ben, Arwa AlGabas, and Ouiem Bchir. "Fall Detection Using the Histogram of Oriented Gradients and Decision-Based Fusion." Journal of Computer Science 16, no. 2 (February 1, 2020): 257–65. http://dx.doi.org/10.3844/jcssp.2020.257.265.

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37

Nguyen, Trung Quy, Soo Hyung Kim, and In Seop Na. "Fast Pedestrian Detection Using Histogram of Oriented Gradients and Principal Components Analysis." International Journal of Contents 9, no. 3 (September 28, 2013): 1–9. http://dx.doi.org/10.5392/ijoc.2013.9.3.001.

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38

Ebrahimzadeh, Reza, and Mahdi Jampour. "Efficient Handwritten Digit Recognition based on Histogram of Oriented Gradients and SVM." International Journal of Computer Applications 104, no. 9 (October 18, 2014): 10–13. http://dx.doi.org/10.5120/18229-9167.

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Islam, Kh, Sudanthi Wijewickrema, Ram Raj, and Stephen O’Leary. "Street Sign Recognition Using Histogram of Oriented Gradients and Artificial Neural Networks." Journal of Imaging 5, no. 4 (April 3, 2019): 44. http://dx.doi.org/10.3390/jimaging5040044.

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Street sign identification is an important problem in applications such as autonomous vehicle navigation and aids for individuals with vision impairments. It can be especially useful in instances where navigation techniques such as global positioning system (GPS) are not available. In this paper, we present a method of detection and interpretation of Malaysian street signs using image processing and machine learning techniques. First, we eliminate the background from an image to segment the region of interest (i.e., the street sign). Then, we extract the text from the segmented image and classify it. Finally, we present the identified text to the user as a voice notification. We also show through experimental results that the system performs well in real-time with a high level of accuracy. To this end, we use a database of Malaysian street sign images captured through an on-board camera.

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Puyda, Volodymyr, and S. Shurhot. "On application of the histogram of oriented gradients method to vehicles identification." Computer systems and network 1, no. 1 (December 3, 2019): 69–75. http://dx.doi.org/10.23939/csn2019.01.069.

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R.F, Prates, Cámara-Chávez G, William R. Schwartz, and Menotti D. "Brazilian License Plate Detection Using Histogram of Oriented Gradients and Sliding Windows." International Journal of Computer Science and Information Technology 5, no. 6 (December 31, 2013): 39–52. http://dx.doi.org/10.5121/ijcsit.2013.5603.

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Tian, Shangxuan, Ujjwal Bhattacharya, Shijian Lu, Bolan Su, Qingqing Wang, Xiaohua Wei, Yue Lu, and Chew Lim Tan. "Multilingual scene character recognition with co-occurrence of histogram of oriented gradients." Pattern Recognition 51 (March 2016): 125–34. http://dx.doi.org/10.1016/j.patcog.2015.07.009.

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Nigam, Swati, Rajiv Singh, and A. K. Misra. "Efficient facial expression recognition using histogram of oriented gradients in wavelet domain." Multimedia Tools and Applications 77, no. 21 (May 4, 2018): 28725–47. http://dx.doi.org/10.1007/s11042-018-6040-3.

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Vo, Tan, Dat Tran, and Wanli Ma. "Tensor decomposition and application in image classification with histogram of oriented gradients." Neurocomputing 165 (October 2015): 38–45. http://dx.doi.org/10.1016/j.neucom.2014.06.093.

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Fernandes, Fernando, Lucas Weigel, Claudio Jung, Philippe Navaux, Luigi Carro, and Paolo Rech. "Evaluation of Histogram of Oriented Gradients Soft Errors Criticality for Automotive Applications." ACM Transactions on Architecture and Code Optimization 13, no. 4 (December 28, 2016): 1–25. http://dx.doi.org/10.1145/2998573.

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El-Sayed, Rania Salah, and Mohamed Nour El-Sayed. "Classification of vehicles’ types using histogram oriented gradients: comparative study and modification." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (December 1, 2020): 700. http://dx.doi.org/10.11591/ijai.v9.i4.pp700-712.

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This paper proposes an efficient model for recognizing and classifying a vehicle type. The model localizes each object in the image then identifies the vehicle type. The features of an image are extracted using the histogram oriented gradients (HOG) and ant colony optimization (ACO). A vehicle type is determined using different classifiers namely: the k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and Softmax classifiers. The model is implemented and operated on two datasets of vehicles' images as test-beds. From the comparative study, the SVM outperforms the other adopted classifiers and is also better using HOG than that using ACO. A modification is done on HOG by adding the Laplacian filter to select the most significant image features. The accuracy of the SVM classifier using modified HOG outperforms that one using the traditional HOG. The proposed model is analyzed and discussed regardless the local geometric and photometric transformations like illumination variations.

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Zhang, Bailing. "Off‐line signature verification and identification by pyramid histogram of oriented gradients." International Journal of Intelligent Computing and Cybernetics 3, no. 4 (November 23, 2010): 611–30. http://dx.doi.org/10.1108/17563781011094197.

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Nasr, Abdurrahman A., and Mohamed Z. Abdulmageed. "An Efficient Reverse Engineering Hardware Trojan Detector Using Histogram of Oriented Gradients." Journal of Electronic Testing 33, no. 1 (December 22, 2016): 93–105. http://dx.doi.org/10.1007/s10836-016-5631-z.

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Vatsaraj, Meenal Suryakant, Rajan Vishnu Parab, and Prof D. S. Bade. "ANOMALY DETECTION OF EVENTS IN CROWDED ENVIRONMENT AND STUDY OF VARIOUS BACKGROUND SUBTRACTION METHODS." International Journal of Students' Research in Technology & Management 5, no. 1 (May 6, 2017): 32. http://dx.doi.org/10.18510/ijsrtm.2017.517.

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Anomalous behavior detection and localization in videos of the crowded area that is specific from a dominant pattern are obtained. Appearance and motion information are taken into account to robustly identify different kinds of an anomaly considering a wide range of scenes. Our concept based on histogram of oriented gradients and markov random field easily captures varying dynamic of the crowded environment. Histogram of oriented gradients along with well known markov random field will effectively recognize and characterizes each frame of each scene. Anomaly detection using artificial neural network consist both appearance and motion features which extract within spatio temporal domain of moving pixels that ensures robustness to local noise and thus increases accuracy in detection of a local anomaly with low computational cost. To extract a region of interest we have to subtract background. Background subtraction is done by various methods like Weighted moving mean, Gaussian mixture model, Kernel density estimation.

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Bulbul, Mohammad Farhad, Yunsheng Jiang, and Jinwen Ma. "DMMs-Based Multiple Features Fusion for Human Action Recognition." International Journal of Multimedia Data Engineering and Management 6, no. 4 (October 2015): 23–39. http://dx.doi.org/10.4018/ijmdem.2015100102.

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The emerging cost-effective depth sensors have facilitated the action recognition task significantly. In this paper, the authors address the action recognition problem using depth video sequences combining three discriminative features. More specifically, the authors generate three Depth Motion Maps (DMMs) over the entire video sequence corresponding to the front, side, and top projection views. Contourlet-based Histogram of Oriented Gradients (CT-HOG), Local Binary Patterns (LBP), and Edge Oriented Histograms (EOH) are then computed from the DMMs. To merge these features, the authors consider decision-level fusion, where a soft decision-fusion rule, Logarithmic Opinion Pool (LOGP), is used to combine the classification outcomes from multiple classifiers each with an individual set of features. Experimental results on two datasets reveal that the fusion scheme achieves superior action recognition performance over the situations when using each feature individually.

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