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Journal articles on the topic 'Histogram oriented'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Essa, Almabrok, and Vijayan K. Asari. "Histogram of Oriented Directional Features for Robust Face Recognition." International Journal of Monitoring and Surveillance Technologies Research 4, no. 3 (2016): 35–51. http://dx.doi.org/10.4018/ijmstr.2016070103.

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This paper presents an illumination invariant face recognition system that uses directional features and modular histogram. The proposed Histogram of Oriented Directional Features (HODF) produces multi-region histograms for each face image, then concatenates these histograms to form the final feature vector. This feature vector is used to recognize the face image by the help of k nearest neighbors classifier (KNN). The edge responses and the relationship among pixels are very important and play the main role for improving the face recognition accuracy. Therefore, this work presents the effectiveness of using different directional masks for detecting the edge responses on face recognition accuracy, such as Prewitt kernels, Kirsch masks, Sobel kernels, and Gaussian derivative masks. The performance evaluation of the proposed HODF algorithm is conducted on several publicly available databases and observed promising recognition rates.

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Gadetska, Svitlana, Valeriy Dubnitskiy, Alexander Khodyrev, and Yuri Kushneruk. "Excel-oriented calculator for calculating results of entropy analysis of data distributed by categories." Advanced Information Systems 7, no. 2 (2023): 28–40. http://dx.doi.org/10.20998/2522-9052.2023.2.05.

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The goal of the work. Development of EXCEL-oriented calculator for calculating the results of entropy analysis of data, which are distributed by categories. The subject of research is histograms of arbitrary distribution laws and conjugation tables 2×2. Research methods: Entropy and information analysis of histograms of arbitrary distribution laws and conjugation tables. The obtained results. It is proposed to use methods of entropy analysis for the analysis of data distributed by categories; information on the structure of the EXCEL-oriented calculator designed for this purpose is given. The calculator makes it possible to calculate entropy characteristics of histograms, namely: histogram entropy, histogram dispersion, histogram confidence intervals, diversity information index. The calculator performs a pairwise comparison of entropies of histograms using the Hutcheson method, determines Hellinger and Kullback-Leibler distances between histograms of arbitrary distribution laws and thus complements the chi-square criterion, determines the informational correlation coefficient. The correspondence between the Pearson correlation coefficient and the information correlation coefficient is established by the method of statistical modeling. For 2×2 conjugation tables, the calculator makes it possible to estimate the significance of the interaction between the row factor and the column factor. The calculator determines the values of conditional entropies for 2×2 conjugation tables. The proposed calculator fills the gaps in existing software products and can be used to process data distributed by categories using entropy analysis methods. It is shown that entropy methods of analysis are appropriate to use in cases where histograms determine arbitrary distribution laws.

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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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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 (2011): 293–98. http://dx.doi.org/10.5391/ijfis.2011.11.4.293.

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Jia, Wei, Rong-Xiang Hu, Ying-Ke Lei, Yang Zhao, and Jie Gui. "Histogram of Oriented Lines for Palmprint Recognition." IEEE Transactions on Systems, Man, and Cybernetics: Systems 44, no. 3 (2014): 385–95. http://dx.doi.org/10.1109/tsmc.2013.2258010.

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Overbeek, Marlinda Vasty. "HISTOGRAM OF ORIENTED GRADIENT UNTUK DETEKSI EKSPRESI WAJAH MANUSIA." High Education of Organization Archive Quality: Jurnal Teknologi Informasi 10, no. 2 (2018): 81–86. http://dx.doi.org/10.52972/hoaq.vol10no2.p81-86.

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This research focuses on the detection of human facial expressions using the Histogram of Oriented Gradient algorithm. Whereas for the classification algorithm, Convolutional Neural Network is used. Image data used in the form of seven different expressions of humans with the extraction of 48x48 pixels. The use of Histogram of Oriented Gradient as a feature extracting algorithm, because Histogram of Oriented Gradient is good to be used in detecting moving objects. Whereas Convolutional Neural Network is used because it is an improvement of the Multi Layer Perceptron algorithm. Of the three epoches done, it produced the best accuracy of 77% re-introduction of human facial expressions. These results are quite convincing because it only uses three epochs.

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Liu, Yun-Fu, Jing-Ming Guo, and Jie-Cyun Yu. "Contrast Enhancement Using Stratified Parametric-Oriented Histogram Equalization." IEEE Transactions on Circuits and Systems for Video Technology 27, no. 6 (2017): 1171–81. http://dx.doi.org/10.1109/tcsvt.2016.2527338.

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Shu, Chang, Xiaoqing Ding, and Chi Fang. "Histogram of the oriented gradient for face recognition." Tsinghua Science and Technology 16, no. 2 (2011): 216–24. http://dx.doi.org/10.1016/s1007-0214(11)70032-3.

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10

Umar, Siyudi Shafi’I, Zaharaddeen S. Iro, Abubakar Y. Zandam, and Saifulllahi Sadi Shitu. "Accelerated Histogram of Oriented Gradients for Human Detection." Dutse Journal of Pure and Applied Sciences 9, no. 1a (2023): 44–56. http://dx.doi.org/10.4314/dujopas.v9i1a.5.

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Histogram of Oriented Gradients (HOG) is an object detection algorithm used to detect people from an image. It involves features extraction called ‘HOG descriptor’ which are used to identify a person in the image. Several operations are involved in the feature extraction process. Hence performing numerous computations in order to obtain HOG descriptors takes some considerable amount of time. This slow computation speed limits HOG’s application in real-time systems. This paper investigates HOG with a view to improve its speed, modify the feature computation process to develop a faster version of HOG and finally evaluate against existing HOG. The technique of asymptotic notation in particular Big-O notation was applied to each stage of HOG and the complexity for the binning stage was modified. This results in a HOG version with a reduced complexity from n4 to n2 thereby having an improved speed as compared to the original HOG.

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Zhang, Xiangyu, Fengwei An, Ikki Nakashima, et al. "A hardware-oriented histogram of oriented gradients algorithm and its VLSI implementation." Japanese Journal of Applied Physics 56, no. 4S (2017): 04CF01. http://dx.doi.org/10.7567/jjap.56.04cf01.

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Ouanan, Hamid, Mohammed Ouanan, and Brahim Aksasse. "Gabor-HOG Features based Face Recognition Scheme." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (2015): 331. http://dx.doi.org/10.11591/tijee.v15i2.1546.

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Extraction of invariant features is the core of Face RecognitionSystems (FRS). This work proposes a novel feature extractor-fusion scheme using two powerful feature descriptor known as Gabor Filters (GFs) and Histogram of Oriented Gradient (HOG), which the face image is filtered with the multiscale multiresolution Gabor filter bank to generate multiple Gabor magnitude images (GMIs), then the down-sampled GMIs and apply Histogram of Oriented Gradient to form the features. The experimental results on the FERET face database show the effectiveness of our methods.

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Arunkumar, M., and S. Valarmathy. "Palm Print Identification Using Improved Histogram of Oriented Lines." Circuits and Systems 07, no. 08 (2016): 1665–76. http://dx.doi.org/10.4236/cs.2016.78144.

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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 (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 (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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Das, Dipankar. "Activity Recognition Using Histogram of Oriented Gradient Pattern History." International Journal of Computer Science, Engineering and Information Technology 4, no. 4 (2014): 23–31. http://dx.doi.org/10.5121/ijcseit.2014.4403.

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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 (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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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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Boubou, Somar, and Einoshin Suzuki. "Classifying actions based on histogram of oriented velocity vectors." Journal of Intelligent Information Systems 44, no. 1 (2014): 49–65. http://dx.doi.org/10.1007/s10844-014-0329-0.

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Dong, Jun, Xue Yuan, and Fanlun Xiong. "Global and Local Oriented Edge Magnitude Patterns for Texture Classification." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 03 (2017): 1750007. http://dx.doi.org/10.1142/s0218001417500070.

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In this paper, we propose a gray-scale texture descriptor, name the global and local oriented edge magnitude patterns (GLOEMP), for texture classification. GLOEMP is a framework, which is able to effectively combine local texture, global structure information and contrast of texture images. In GLOEMP, the principal orientation is determined by Histogram of Gradient (HOG) feature, then each direction is respectively shown in detail by a local binary patterns (LBP) occurrence histogram. Due to the fact that GLOEMP characterizes image information across different directions, it contains very abundant information. The global-level rotation compensation method is proposed, which shifts the principal orientation of the HOG to the first position, thus allowing GLOEMP to be robust to rotations. In addition, gradient magnitudes are used as weights to add to the histogram, making GLOEMP robust to lighting variances as well, and it also possesses a strong ability to express edge information. The experimental results obtained from the representative databases demonstrate that the proposed GLOEMP framework is capable of achieving significant improvement, in some cases reaching classification accuracy of 10% higher than over the traditional rotation invariant LBP method.

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Lina, Lina, Fundroo Orlando, Gloria Valerie Lao, Billy Marcelino, and Jerry Ruslim. "Glass Packaged Mineral Water Recognition System Based On Logo Using The Histogram Of Oriented Gradients Method." Journal of Computer Networks, Architecture and High Performance Computing 6, no. 1 (2023): 14–24. http://dx.doi.org/10.47709/cnahpc.v6i1.3199.

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Application of Glass Packaged Mineral Water Recognition System Based on Logo Using Method Histogram of Oriented Gradients is an application program used to introduce brands or logos of glass bottled mineral water. This application was designed on the Windows operating system and uses Python and Open CV software. The methods used in this design are Histogram of Oriented Gradients as a method for feature extraction, Method Euclidean Distance as a method for measuring similarity distance, and k-Nearest Neighbor as a method of recognizing a logo or brand. The average accuracy of the test is 13.575%.

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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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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 (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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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 (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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Khare, Vijeta, Palaiahnakote Shivakumara, and Paramesran Raveendran. "A new Histogram Oriented Moments descriptor for multi-oriented moving text detection in video." Expert Systems with Applications 42, no. 21 (2015): 7627–40. http://dx.doi.org/10.1016/j.eswa.2015.06.002.

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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 (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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Dubovečak, Mario, Emil Dumić, and Andrija Bernik. "Face Detection and Recognition Using Raspberry PI Computer." Tehnički glasnik 17, no. 3 (2023): 346–52. http://dx.doi.org/10.31803/tg-20220321232047.

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This paper presents a face detection and recognition system utilizing a Raspberry Pi computer that is built on a predefined framework. The theoretical section of this article shows several techniques that can be used for face detection, including Haar cascades, Histograms of Oriented Gradients, Support Vector Machine and Deep Learning Methods. The paper also provides examples of some commonly used face recognition techniques, including Fisherfaces, Eigenfaces, Histogram of Local Binary Patterns, SIFT and SURF descriptor-based methods and Deep Learning Methods. The practical aspect of this paper demonstrates use of a Raspberry Pi computer, along with supplementary tools and software, to detect and recognize faces using a pre-defined dataset.

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Alzahrani, Abdullah. "Efficient Descriptor of Histogram of Ridges Orientation Delineate for Fingernail." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 3 (2023): 34–42. http://dx.doi.org/10.35940/ijrte.a7577.0912323.

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Fingernails structure are rich in orientation, ridges and edge features. Inspired by Edge Histogram Descriptor (EHD), this paper presents an efficient orientation-based local descriptor, named histogram of ridges orientation delineate (HROD). HROD is based on the fact that human vision is sensitive to edge features for image perception. For a given image, HROD algorithm first execute and perform a pre-process i.e., re-sizing, filtering, enhancement, segmentation, edge detection and feature extraction. Then, finds oriented edge maps according to predefined orientations using a well-known edge operator mask (2×2 sub block) and obtains a ridges orientation delineate map by choosing an orientation with the maximum edge magnitude for each pixel. In the experiment on this research, five oriented edge maps were used to generate and detect the maximum edge orientation construction of each block, namely vertical, horizontal, diagonal 45°, diagonal 135° and isotropic (non-orientation specific) orientation. Experimental results on fingernail images show that the performance of HROD comparable with the state-of-the-art orientation-based methods (e.g., Gabor filter, histogram of oriented gradients, and local directional code). Furthermore, the proposed HROD algorithm has advantages of low feature dimensionality and fast implementation for a real-time fingernails orientation recognition system.

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Abdullah, Alzahrani. "Efficient Descriptor of Histogram of Ridges Orientation Delineate for Fingernail." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 3 (2023): 34–42. https://doi.org/10.35940/ijrte.A7577.0912323.

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Fingernails structure are rich in orientation, ridges and edge features. Inspired by Edge Histogram Descriptor (EHD), this paper presents an efficient orientation-based local descriptor, named histogram of ridges orientation delineate (HROD). HROD is based on the fact that human vision is sensitive to edge features for image perception. For a given image, HROD algorithm first execute and perform a pre-process i.e., re-sizing, filtering, enhancement, segmentation, edge detection and feature extraction. Then, finds oriented edge maps according to predefined orientations using a well-known edge operator mask (2×2 sub block) and obtains a ridges orientation delineate map by choosing an orientation with the maximum edge magnitude for each pixel. In the experiment on this research, five oriented edge maps were used to generate and detect the maximum edge orientation construction of each block, namely vertical, horizontal, diagonal 45°, diagonal 135° and isotropic (non-orientation specific) orientation. Experimental results on fingernail images show that the performance of HROD comparable with the state-of-the-art orientation-based methods (e.g., Gabor filter, histogram of oriented gradients, and local directional code). Furthermore, the proposed HROD algorithm has advantages of low feature dimensionality and fast implementation for a real-time fingernails orientation recognition system.

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Yu, Guorong, and Shuangming Zhao. "A New Feature Descriptor for Multimodal Image Registration Using Phase Congruency." Sensors 20, no. 18 (2020): 5105. http://dx.doi.org/10.3390/s20185105.

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Images captured by different sensors with different spectral bands cause non-linear intensity changes between image pairs. Classic feature descriptors cannot handle this problem and are prone to yielding unsatisfactory results. Inspired by the illumination and contrast invariant properties of phase congruency, here, we propose a new descriptor to tackle this problem. The proposed descriptor generation mainly involves three steps. (1) Images are convolved with a bank of log-Gabor filters with different scales and orientations. (2) A window of fixed size is selected and divided into several blocks for each keypoint, and an oriented magnitude histogram and the orientation of the minimum moment of a phase congruency-based histogram are calculated in each block. (3) These two histograms are normalized respectively and concatenated to form the proposed descriptor. Performance evaluation experiments on three datasets were carried out to validate the superiority of the proposed method. Experimental results indicated that the proposed descriptor outperformed most of the classic and state-of-art descriptors in terms of precision and recall within an acceptable computational time.

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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 (2012): 1–8. http://dx.doi.org/10.4156/aiss.vol4.issue18.1.

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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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Xu, Liangpeng, Yong Li, Chunxiao Fan, Hongbin Jin, and Xiang shi. "Incorporating Gradient Magnitude in Computation of Edge Oriented Histogram Descriptor." Electronic Imaging 2016, no. 2 (2016): 1–7. http://dx.doi.org/10.2352/issn.2470-1173.2016.2.vipc-241.

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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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Rahmani, Hossein, Arif Mahmood, Du Huynh, and Ajmal Mian. "Histogram of Oriented Principal Components for Cross-View Action Recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 12 (2016): 2430–43. http://dx.doi.org/10.1109/tpami.2016.2533389.

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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 (2019): 1–8. http://dx.doi.org/10.17485/ijst/2019/v12i24/145093.

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Kong Yueping, 孔月萍, 刘霞 Liu Xia, 谢心谦 Xie Xinqian, and 李凤洁 Li Fengjie. "Face Liveness Detection Method Based on Histogram of Oriented Gradient." Laser & Optoelectronics Progress 55, no. 3 (2018): 031009. http://dx.doi.org/10.3788/lop55.031009.

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Hmood, Ali K., Ching Y. Suen, and Louisa Lam. "An Enhanced Histogram of Oriented Gradient Descriptor for Numismatic Applications." Pattern Recognition and Image Analysis 28, no. 4 (2018): 569–87. http://dx.doi.org/10.1134/s1054661818040028.

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

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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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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 (2011): 6–10. http://dx.doi.org/10.5120/2968-3968.

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Maher Kh. Hussien and Fawziya Mahmood Ramo. "Facial expression using Histogram of Oriented Gradients and Ensemble Classifier." Tikrit Journal of Pure Science 27, no. 2 (2022): 52–56. http://dx.doi.org/10.25130/tjps.v27i2.67.

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In this research, two methods were proposed to design a new system to recognize facial expressions. The first method relies on extracting features from the face area, and the second method relies on the process of extracting features on the parts of the face (eyes, nose, and mouth) where the histogram of oriented gradients (HOG) algorithm was used in the feature extraction process in addition to the principal component analysis algorithm to reduce feature dimensions in both methods. We have proposed a group classifier consisting of three basic classifiers: support vector machines, knn-algorithm closest to neighbors, and Naive Bayes in the classification stage. Our proposed algorithm was tested on japanese female facial expression (JAFFE) Dataset and Cohn-Kanade (CK) dataset. It was found that higher overall accuracy is achieved for F1-Score when using the second method of 93.82 % and 94.12% for CK and JAFFA, respectively.

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Chaudhari, Jitendra P., Hiren Mewada, Amit V. Patel, and Keyur Mahant. "Automated bacteria genera classification using histogram-oriented optimized capsule network." Engineering Science and Technology, an International Journal 46 (October 2023): 101500. http://dx.doi.org/10.1016/j.jestch.2023.101500.

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Ashok, T. Gaikwad. "PALMPRINT IDENTIFICATION SYSTEM BASED ON HISTOGRAM OF ORIENTED GRADIENT (HOG)." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 7 (2018): 482–90. https://doi.org/10.5281/zenodo.1323534.

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In this paper, an efficient palmprint identification system based on Histogram of Oriented Gradient (HOG) technique, the pieces of the work focused on many steps and the goal of these steps to make palmprint images very clear and extract The Region Of Interest (ROI). ROI was the most important in palmprint identification system to extract the area which has a lot of information useful for identifying the human by biometric data. In this work, the ROI method was used by Competitive Hand valley Detection (CHVD) while the feature extraction extracted by using HOG and distance measure used as the matching technique by using Euclidean distance (ED), finally the decision was done with help of threshold values (T)to decide the accepted or rejected. All these steps were performed on CASIA dataset to test our algorithm for palmprint identification system. The results show that HOG technique was achieved the good result with minimum EER of 1.966089 % and maximum GAR reach to 98.03391%.

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Haroske, G., V. Dimmer, W. Meyer, and K. D. Kunze. "DNA Histogram Interpretation Based on Statistical Approaches." Analytical Cellular Pathology 15, no. 3 (1997): 157–73. http://dx.doi.org/10.1155/1997/935728.

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Image cytometric DNA measurements provide data which are most often interpreted as equivalent to the chromosomal ploidy although the chromosomal and the DNA ploidy are not identical. The common link between them is the cell cycle. Therefore, if destined for DNA ploidy interpretations, the DNA cytometry should be performed on a population‐oriented stochastic basis. Using stochastic sampling the data can be interpreted by applying the rules of stochastic processes. A set of statistical methods is given that enables a DNA histogram to be interpreted objectively and without human interaction. These statistics analyse the precision and accuracy of the entire measurement process. They give in error probabilities for accepting a measurement as reliable, for recognition of stemlines, stemline aneuploidy, and for evaluating so‐called rare events. Nearly 300 image cytometric DNA measurements from breast cancers and rat liver imprints examples have been selected to demonstrate the efficiency of the statistics in each step of interpreting DNA histograms.

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Adhinata, Faisal Dharma, Muhammad Ikhsan, and Wahyono Wahyono. "People counter on CCTV video using histogram of oriented gradient and Kalman filter methods." Jurnal Teknologi dan Sistem Komputer 8, no. 3 (2020): 222–27. http://dx.doi.org/10.14710/jtsiskom.2020.13660.

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CCTV cameras have an important function in the field of public service, especially for convenience. The objects recorded through CCTV cameras are processed into information to support service satisfaction in the community. This study uses the function of CCTV for people counting from objects recorded by a camera. Currently, the process of detecting and tracking people takes a long time to detect all frames. In this study, the frame selection into keyframes uses the mutual information entropy method. The keyframes processing uses the Histogram of Oriented Gradient (HOG) and Kalman filter methods. The proposed method results F1 value of 0.85, recall of 76 %, and precision of 97 % with winStride parameter (12,12), scale 1.05, and the distance of the human object to CCTV 4 meters.

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Athira, TR, and Varghese Abraham. "CBIR of Brain MR Images Using Histogram of Fuzzy Oriented Gradients and Fuzzy Local Binary Patterns." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 1 (2017): 8–17. https://doi.org/10.5281/zenodo.4108200.

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Retrieval of similar images from large dataset of brain images across patients would help the experts in the decision diagnosis process of diseases. Generally used feature extraction methods are color, texture and shape. In medical images texture and shape features are most efficient. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are good descriptor for brain MR image retrieval. But there are many challenges facing in medical application. An empirical study of the impact of increasing bins number in the HOG descriptor concluded that larger the number is more accurate the descriptor is. In fact this is due to the reduction of orientations range that each bin covers. Despite the efficiency of augmenting the bins number, this technique has limited spatial support as the augmentation of the number of bins used leads to increase the histogram dimension. So here proposed a method called Histogram of Fuzzy Oriented Gradients (HFOG), in which a pixel can belong several bins with different degrees. The Local Binary Patterns feature extraction method is widely used for texture analysis; however, the original LBP is based on hard thresholding the neighborhood of each pixel. Therefore, texture representation with LBP is very sensitive to noise and cannot distinguish between a strong and a weak pattern. In this study, Fuzzy Local Binary Patterns was introduced to improve the original LBP.

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Zhang, Li, Weiyue Xu, Cong Shen, and Yingping Huang. "Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features." Sensors 24, no. 5 (2024): 1590. http://dx.doi.org/10.3390/s24051590.

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The lack of discernible vehicle contour features in low-light conditions poses a formidable challenge for nighttime vehicle detection under hardware cost constraints. Addressing this issue, an enhanced histogram of oriented gradients (HOGs) approach is introduced to extract relevant vehicle features. Initially, vehicle lights are extracted using a combination of background illumination removal and a saliency model. Subsequently, these lights are integrated with a template-based approach to delineate regions containing potential vehicles. In the next step, the fusion of superpixel and HOG (S-HOG) features within these regions is performed, and the support vector machine (SVM) is employed for classification. A non-maximum suppression (NMS) method is applied to eliminate overlapping areas, incorporating the fusion of vertical histograms of symmetrical features of oriented gradients (V-HOGs). Finally, the Kalman filter is utilized for tracking candidate vehicles over time. Experimental results demonstrate a significant improvement in the accuracy of vehicle recognition in nighttime scenarios with the proposed method.

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GUZMAN CABRERA, RAFAEL, DEBORAH MARTINEZ, MIGUEL TORRES CISNEROS, DANIEL MAY ARRIOJA, and MARY CARMEN PEÑA GOMAR. "AUTOMATIC RECOGNITION OF AUTOMOBILES USING MACHINE LEARNING." DYNA DYNA-ACELERADO (July 7, 2023): [ 6 pp. ]. http://dx.doi.org/10.6036/10673.

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In this work, we perform the automatic classification of 1,000 images of five different models of automobiles. To obtain the highest precision, we have used two different classification scenarios, three algorithms, and five metrics. Also, we assume that the results can be improved by extracting the image characteristics using descriptors and using them as input. Then, we used two descriptors: a histogram of oriented gradient and a convolutional neural network ResNet-50. Our results show that the descriptors improve the classification results and obtain the highest value for the accuracy metric of 88.01 % using the ResNet-50 as a descriptor, the Training and Test Set as a scenario, and Vector Support Machine as the classification algorithm. Keywords: Convolutional Neural Networks, Gradient Oriented Histogram, Machine Learning, Fine Grain Classification, Car Images.

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