Journal articles: 'Histogram of Oriented Gradients (HOG)'

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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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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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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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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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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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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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Lebesgue spaces (Lp over Rn) 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 p-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 p-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 p-HOG algorithm shows greater efficiency in most cases.

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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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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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De Ocampo, Anton Louise Pernez, Argel Bandala, and Elmer Dadios. "Gabor-enhanced histogram of oriented gradients for human presence detection applied in aerial monitoring." International Journal of Advances in Intelligent Informatics 6, no. 3 (November 6, 2020): 223. http://dx.doi.org/10.26555/ijain.v6i3.514.

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In UAV-based human detection, the extraction and selection of the feature vector are one of the critical tasks to ensure the optimal performance of the detection system. Although UAV cameras capture high-resolution images, human figures' relative size renders persons at very low resolution and contrast. Feature descriptors that can adequately discriminate between local symmetrical patterns in a low-contrast image may improve a human figures' detection in vegetative environments. Such a descriptor is proposed and presented in this paper. Initially, the acquired images are fed to a digital processor in a ground station where the human detection algorithm is performed. Part of the human detection algorithm is the GeHOG feature extraction, where a bank of Gabor filters is used to generate textured images from the original. The local energy for each cell of the Gabor images is calculated to identify the dominant orientations. The bins of conventional HOG are enhanced based on the dominant orientation index and the accumulated local energy in Gabor images. To measure the performance of the proposed features, Gabor-enhanced HOG (GeHOG) and other two recent improvements to HOG, Histogram of Edge Oriented Gradients (HEOG) and Improved HOG (ImHOG), are used for human detection on INRIA dataset and a custom dataset of farmers working in fields captured via unmanned aerial vehicle. The proposed feature descriptor significantly improved human detection and performed better than recent improvements in conventional HOG. Using GeHOG improved the precision of human detection to 98.23% in the INRIA dataset. The proposed feature can significantly improve human detection applied in surveillance systems, especially in vegetative environments.

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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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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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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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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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Soler, J. D., H. Beuther, M. Rugel, Y. Wang, P. C. Clark, S. C. O. Glover, P. F. Goldsmith, et al. "Histogram of oriented gradients: a technique for the study of molecular cloud formation." Astronomy & Astrophysics 622 (February 2019): A166. http://dx.doi.org/10.1051/0004-6361/201834300.

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We introduce the histogram of oriented gradients (HOG), a tool developed for machine vision that we propose as a new metric for the systematic characterization of spectral line observations of atomic and molecular gas and the study of molecular cloud formation models. In essence, the HOG technique takes as input extended spectral-line observations from two tracers and provides an estimate of their spatial correlation across velocity channels. We characterized HOG using synthetic observations of HI and 13CO (J = 1 → 0) emission from numerical simulations of magnetohydrodynamic (MHD) turbulence leading to the formation of molecular gas after the collision of two atomic clouds. We found a significant spatial correlation between the two tracers in velocity channels where vHI ≈ v13CO, almost independent of the orientation of the collision with respect to the line of sight. Subsequently, we used HOG to investigate the spatial correlation of the HI, from The HI/OH/recombination line survey of the inner Milky Way (THOR), and the 13CO (J = 1 → 0) emission from the Galactic Ring Survey (GRS), toward the portion of the Galactic plane 33°.75 ≤l ≤ 35°.25 and |b| ≤ 1°.25. We found a significant spatial correlation between the two tracers in extended portions of the studied region. Although some of the regions with high spatial correlation are associated with HI self-absorption (HISA) features, suggesting that it is produced by the cold atomic gas, the correlation is not exclusive to this kind of region. The HOG results derived for the observational data indicate significant differences between individual regions: some show spatial correlation in channels around vHI ≈ v13CO while others present spatial correlations in velocity channels separated by a few kilometers per second. We associate these velocity offsets to the effect of feedback and to the presence of physical conditions that are not included in the atomic-cloud-collision simulations, such as more general magnetic field configurations, shear, and global gas infall.

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TR, Athira, and Abraham Varghese. "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 (March 1, 2017): 8. http://dx.doi.org/10.11591/ijai.v6.i1.pp8-17.

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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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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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Ouanan, Hamid, Mohammed Ouanan, and Brahim Aksasse. "Gabor-HOG Features based Face Recognition Scheme." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (August 1, 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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Thakur, Surendra, Emmanuel Adetiba, Oludayo O. Olugbara, and Richard Millham. "Experimentation Using Short-Term Spectral Features for Secure Mobile Internet Voting Authentication." Mathematical Problems in Engineering 2015 (2015): 1–21. http://dx.doi.org/10.1155/2015/564904.

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We propose a secure mobile Internet voting architecture based on the Sensus reference architecture and report the experiments carried out using short-term spectral features for realizing the voice biometric based authentication module of the architecture being proposed. The short-term spectral features investigated are Mel-Frequency Cepstral Coefficients (MFCCs), Mel-Frequency Discrete Wavelet Coefficients (MFDWC), Linear Predictive Cepstral Coefficients (LPCC), and Spectral Histogram of Oriented Gradients (SHOGs). The MFCC, MFDWC, and LPCC usually have higher dimensions that oftentimes lead to high computational complexity of the pattern matching algorithms in automatic speaker recognition systems. In this study, higher dimensions of each of the short-term features were reduced to an 81-element feature vector per Speaker using Histogram of Oriented Gradients (HOG) algorithm while neural network ensemble was utilized as the pattern matching algorithm. Out of the four short-term spectral features investigated, the LPCC-HOG gave the best statistical results withRstatistic of 0.9127 and mean square error of 0.0407. These compact LPCC-HOG features are highly promising for implementing the authentication module of the secure mobile Internet voting architecture we are proposing in this paper.

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Wibowo, Suryo Adhi, Hansoo Lee, Eun Kyeong Kim, and Sungshin Kim. "Convolutional Shallow Features for Performance Improvement of Histogram of Oriented Gradients in Visual Object Tracking." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6329864.

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Histogram of oriented gradients (HOG) is a feature descriptor typically used for object detection. For object tracking, this feature has certain drawbacks when the target object is influenced by a change in motion or size. In this paper, the use of convolutional shallow features is proposed to improve the performance of HOG feature-based object tracking. Because the proposed method works based on a correlation filter, the response maps for each feature are summed in order to obtain the final response map. The location of the target object is then predicted based on the maximum value of the optimized final response map. Further, a model update is used to overcome the change in appearance of the target object during tracking. A performance evaluation of the proposed method is obtained by using Visual Object Tracking 2015 (VOT2015) benchmark dataset and its protocols. The results are then provided based on their accuracy-robustness (AR) rank. Furthermore, through a comparison with several state-of-the-art tracking algorithms, the proposed method was shown to achieve the highest rank in terms of accuracy and a third rank for robustness. In addition, the proposed method significantly improves the robustness of HOG-based features.

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Janardanan, Rameswari Poornima, and Rajasvaran Logeswaran. "Dental Radiograph Segmentation and Classification—A Comparative Study of Hu’s Moments and Histogram of Oriented Gradients." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3612–16. http://dx.doi.org/10.1166/jctn.2019.8334.

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This paper proposes a method to compare two feature descriptors to classify dental X-rays, using Hu’s Moments (HM) and the Histogram of Oriented Gradients (HOG). The dental radiographs are preprocessed, and the shape features of teeth are derived using HM and HOG. Support Vector Machine (SVM) is then used for tooth classification and recognition. Comparison of the results of using the two approaches as feature descriptors revealed that regardless of its orientation, size and position, moment invariant functions are very useful for object classification. The classification of images into molar and premolar has been done on manually cropped images. This method was validated on periapical radiographs. Results obtained show that using both HM and HOG to classify and recognize teeth shape description accuracy as better than, or at least comparable, to the state-of-the-art approaches. This work aids to improve the computer-assisted diagnosis and decision in dentistry. The forensic odonatological applications of this approach are wide and of immense benefits in both forensic and biometric identification.

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Singh, Geetika, and Indu Chhabra. "Effective and Fast Face Recognition System Using Complementary OC-LBP and HOG Feature Descriptors With SVM Classifier." Journal of Information Technology Research 11, no. 1 (January 2018): 91–110. http://dx.doi.org/10.4018/jitr.2018010106.

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Selection and implementation of a face descriptor that is both discriminative and computationally efficient is crucial. Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) have been proven effective for face recognition. LBPs are fast to compute and are easy to extract the texture features. OC-LBP descriptors have been proposed to reduce the dimensionality of LBP while increasing the discrimination power. HOG features capture the edge features that are invariant to rotation and light. Owing to the fact that both texture and edge information is important for face representation, this article proposes a framework to combine OC-LBP and HOG. First, OC-LBP and HOG features are extracted, normalized and fused together. Next, classification is achieved using a histogram-based chi-square, square-chord and extended-canberra metrics and SVM with a normalized chi-square kernel. Experiments on three benchmark databases: ORL, Yale and FERET show that the proposed method is fast to compute and outperforms other similar state-of-the-art methods.

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Mutia, Cut, Fitri Arnia, and Rusdha Muharar. "Improving the Performance of CBIR on Islamic Women Apparels Using Normalized PHOG." Bulletin of Electrical Engineering and Informatics 6, no. 3 (September 1, 2017): 271–80. http://dx.doi.org/10.11591/eei.v6i3.657.

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The designs of Islamic women apparels is dynamically changing, which can be shown by emerging of online shops selling clothing with fast updates of newest models. Traditionally, buying the clothes online can be done by querying the keywords to the retrieval system. The approach has a drawback that the keywords cannot describe the clothes designs precisely. Therefore, a searching based on content–known as content-based image retrieval (CBIR)–is required. One of the features used in CBIR is the shape. This article presents a new normalization approach to the Pyramid Histogram of Oriented Gradients (PHOG) as a mean for shape feature extraction of women Islamic clothing in a retrieval system. We refer to the proposed approach as normalized PHOG (NPHOG). The Euclidean distance measured the similarity of the clothing. The performance of the system was evaluated by using 340 clothing images, comprised of four clothing categories, 85 images for each category: blouse-pants, long dress, outerwear, and tunic. The recall and precision parameters measured the retrieval performance; the Histogram of Oriented Gradients (HOG) and PHOG were the methods for comparison. The experiments showed that NPHOG improved the HOG and PHOG performance in three clothing categories.

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Ramadhani, Kurniawan Nur, Ade Saepul Mugni, and Mohamad Syahrul Mubarok. "Deteksi dan Tracking Pemain Sepakbola menggunakan Histogram of Oriented Gradients (HOG) dan Kalman Filter." Indonesian Journal on Computing (Indo-JC) 3, no. 1 (May 23, 2018): 33. http://dx.doi.org/10.21108/indojc.2018.3.1.211.

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Dalam penelitian ini, dibangun sebuah sistem untuk melakukan tracking pemain sepakbola pada data video. Penelitian ini menggunakan ekstraksi ciri Histogram of Oriented Gradient (HOG) yang cocok untuk digunakan pada kondisi intensitas pencahayaan tidak stabil. Selain melakukan deteksi pemain bola, dalam penelitian ini dilakukan pengklasifikasian tim menggunakan clustering pada vektor ciri color moment. Untuk menjaga performansi deteksi, dilakukan evaluasi tracking menggunakan Kalman Filter. Berdasarkan hasil penelitian, sistem tracking yang dibangun memberikan performansi F1-score tertinggi mencapai 0.87 (skala 0-1) dengan berbagai kondisi pencahayaan video.

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Luo, Jian, and Chang Lin. "Pure FPGA Implementation of an HOG Based Real-Time Pedestrian Detection System." Sensors 18, no. 4 (April 12, 2018): 1174. http://dx.doi.org/10.3390/s18041174.

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In this study, we propose a real-time pedestrian detection system using a FPGA with a digital image sensor. Comparing with some prior works, the proposed implementation realizes both the histogram of oriented gradients (HOG) and the trained support vector machine (SVM) classification on a FPGA. Moreover, the implementation does not use any external memory or processors to assist the implementation. Although the implementation implements both the HOG algorithm and the SVM classification in hardware without using any external memory modules and processors, the proposed implementation’s resource utilization of the FPGA is lower than most of the prior art. The main reasons resulting in the lower resource usage are: (1) simplification in the Getting Bin sub-module; (2) distributed writing and two shift registers in the Cell Histogram Generation sub-module; (3) reuse of each sum of the cell histogram in the Block Histogram Normalization sub-module; and (4) regarding a window of the SVM classification as 105 blocks of the SVM classification. Moreover, compared to Dalal and Triggs’s pure software HOG implementation, the proposed implementation‘s average detection rate is just about 4.05% less, but can achieve a much higher frame rate.

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Zeng, Junying, Yao Chen, Yikui Zhai, Junying Gan, Wulin Feng, and Fan Wang. "A Novel Finger-Vein Recognition Based on Quality Assessment and Multi-Scale Histogram of Oriented Gradients Feature." International Journal of Enterprise Information Systems 15, no. 1 (January 2019): 100–115. http://dx.doi.org/10.4018/ijeis.2019010106.

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Inferior finger vein images would seriously alter the completion of recognition systems. A modern finger-vein recognition technique combined with image quality assessment is developed to overcome those drawbacks. By the quality assessment, this article can discard the inferior images and retain the superior images which are then transferred to the recognition system. Different from previous methods, this article assesses the quality features of the image for the purpose of distinguishing whether the image contains rich and stable vein characteristics. In light of this purpose, the quality assessment is implemented: first, the finger vein image is automatically annotated; second, the finger vein image is cut into image blocks to expand the training set; third, the average quality score of multiple image blocks from an image is the final quality score of the image in the course of testing. Next, the Histogram of Oriented Gradients (HOG) features are extracted from the four transformed high-quality sub-images, whose features are cascaded into the multi-scale HOG feature of an image. Finally, two modules, the quality assessment module using Convolutional Neural Networks (CNN) and finger vein recognition module which make full use of multi-scale HOG, are perfectly combined in this article. The test results have demonstrated that light-CNN can identifies inferior and superior images accurately and the multi-scale HOG is feasible and effective. What's more, this article can see the robustness of this combined method in this article.

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Prabowo, Yudhi, and Kenlo Nishida Nasahara. "DETECTING AND COUNTING COCONUT TREES IN PLEIADES SATELLITE IMAGERY USING HISTOGRAM OF ORIENTED GRADIENTS AND SUPPORT VECTOR MACHINE." International Journal of Remote Sensing and Earth Sciences (IJReSES) 16, no. 1 (November 5, 2019): 87. http://dx.doi.org/10.30536/j.ijreses.2019.v16.a3089.

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This paper describes the detection of coconut trees using very-high-resolution optical satellite imagery. The satellite imagery used in this study was a panchromatic band of Pleiades imagery with a spatial resolution of 0.5 metres. The authors proposed the use of a histogram of oriented gradients (HOG) algorithm as the feature extractor and a support vector machine (SVM) as the classifier for this detection. The main objective of this study is to find out the parameter combination for the HOG algorithm that could provide the best performance for coconut-tree detection. The study shows that the best parameter combination for the HOG algorithm is a configuration of 3 x 3 blocks, 9 orientation bins, and L2-norm block normalization. These parameters provide overall accuracy, precision and recall of approximately 80%, 73% and 87%, respectively.

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Al Rivan, Muhammad Ezar, Hafiz Irsyad, Kevin Kevin, and Arta Tri Narta. "Implementasi LDA pada fitur HOG untuk Klasifikasi ASL Menggunakan K-NN." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 7, no. 2 (August 15, 2020): 214–25. http://dx.doi.org/10.35957/jatisi.v7i2.286.

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Sign language merupakan suatu cara alternatif yang bisa digunakan untuk berkomunikasi dengan menggunakan isyarat, salah satu jenisnya yaitu American Sign Language (ASL). Dataset sign language yang digunakan yaitu dalam bentuk dataset citra yang diproses menggunakan ekstraksi fitur Histogram of Oriented Gradients (HOG) dan selanjutnya direduksi menggunakan Linear Discriminant Analsysis (LDA). Selanjutnya hasil reduksi digunakan untuk klasifikasi K-Nearest Neighbors (k-NN). Tiga jenis distance yang digunakan yaitu euclidean, manhattan dan chebyshev. Hasil terbaik diperoleh menggunakan manhattan distance dengan nilai K = 3 dengan presisi sebesar 72,42 %.

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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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Pandey, Ramesh Chand, Sanjay Kumar Singh, and K. K. Shukla. "Passive Copy- Move Forgery Detection Using Speed-Up Robust Features, Histogram Oriented Gradients and Scale Invariant Feature Transform." International Journal of System Dynamics Applications 4, no. 3 (July 2015): 70–89. http://dx.doi.org/10.4018/ijsda.2015070104.

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Copy-Move is one of the most common technique for digital image tampering or forgery. Copy-Move in an image might be done to duplicate something or to hide an undesirable region. In some cases where these images are used for important purposes such as evidence in court of law, it is important to verify their authenticity. In this paper the authors propose a novel method to detect single region Copy-Move Forgery Detection (CMFD) using Speed-Up Robust Features (SURF), Histogram Oriented Gradient (HOG), Scale Invariant Features Transform (SIFT), and hybrid features such as SURF-HOG and SIFT-HOG. SIFT and SURF image features are immune to various transformations like rotation, scaling, translation, so SIFT and SURF image features help in detecting Copy-Move regions more accurately in compared to other image features. Further the authors have detected multiple regions COPY-MOVE forgery using SURF and SIFT image features. Experimental results demonstrate commendable performance of proposed methods.

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Huang, Juanjuan, Ihtisham Ul Haq, Chaolan Dai, Sulaiman Khan, Shah Nazir, and Muhammad Imtiaz. "Isolated Handwritten Pashto Character Recognition Using a K-NN Classification Tool based on Zoning and HOG Feature Extraction Techniques." Complexity 2021 (March 24, 2021): 1–8. http://dx.doi.org/10.1155/2021/5558373.

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Handwritten text recognition is considered as the most challenging task for the research community due to slight change in different characters’ shape in handwritten documents. The unavailability of a standard dataset makes it vaguer in nature for the researchers to work on. To address these problems, this paper presents an optical character recognition system for the recognition of offline Pashto characters. The problem of the unavailability of a standard handwritten Pashto characters database is addressed by developing a medium-sized database of offline Pashto characters. This database consists of 11352 character images (258 samples for each 44 characters in a Pashto script). Enriched feature extraction techniques of histogram of oriented gradients and zoning-based density features are used for feature extraction of carved Pashto characters. K-nearest neighbors is considered as a classification tool for the proposed algorithm based on the proposed feature sets. A resultant accuracy of 80.34% is calculated for the histogram of oriented gradients, while for zoning-based density features, 76.42% is achieved using 10-fold cross validation.

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A. Dhulekar, P., and S. T. Gandhe. "Action recognition based on histogram of oriented gradients and spatio-temporal interest points." International Journal of Engineering & Technology 7, no. 4 (September 16, 2018): 2153. http://dx.doi.org/10.14419/ijet.v7i4.17274.

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In modern years large extent of the work has been carried out to recognize human actions perhaps because of its wide range of applications in the field of surveillance, human-machine interaction and video analysis. Several methods were proposed by researchers to resolve action recognition challenges such as variations in viewpoints, occlusion, cluttered backgrounds and camera motion. To address these challenges, we propose a novel method comprise of features extraction using histogram of oriented gradients (HOG), and their classification using k-nearest neighbor (k-NN) and support vector machine (SVM). Six different experimentations were carried out on the basis of hybrid combinations of feature extractors and classifiers. Two gold standard datasets; KTH and Weizmann were used for training and testing purpose. The quantitative parameters such as recognition accuracy, training time and prediction speed were used for evaluation. To validate the applicability of proposed algorithm, its performance has been compared with spatio-temporal interest points (STIP) technique which was proposed as state of art method in the domain.

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Akimoto, Shohei, Tomokazu Takahashi, Masato Suzuki, Yasuhiko Arai, and Seiji Aoyagi. "Human Detection by Fourier Descriptors and Fuzzy Color Histograms with Fuzzyc-Means Method." Journal of Robotics and Mechatronics 28, no. 4 (August 19, 2016): 491–99. http://dx.doi.org/10.20965/jrm.2016.p0491.

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[abstFig src='/00280004/07.jpg' width='300' text='Result of specific person detection in Tsukuba Challenge' ] It is difficult to use histograms of oriented gradients (HOG) or other gradient-based features to detect persons in outdoor environments given that the background or scale undergoes considerable changes. This study involved the segmentation of depth images. Additionally, P-type Fourier descriptors were extracted as shape features from two-dimensional coordinates of a contour in the segmentation domains. With respect to the P-type Fourier descriptors, a person detector was created with the fuzzyc-means method (for general person detection). Furthermore, a fuzzy color histogram was extracted in terms of color features from the RGB values of the domain surface. With respect to the fuzzy color histogram, a detector of a person wearing specific clothes was created with the fuzzyc-means method (specific person detection). The study includes the following characteristics: 1) The general person detection requires less number of images used for learning and is robust against a change in the scale when compared to that in cases in which HOG or other methods are used. 2) The specific person detection gives results close to those obtained by human color vision when compared to the color indices such as RGB or CIEDE. This method was applied for a person search application at the Tsukuba Challenge, and the obtained results confirmed the effectiveness of the proposed method.

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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 (May 26, 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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Ibrahim, Shafaf. "Histogram of Oriented Gradient (HOG) for Off-Line Handwritten Signature Authentication." International Journal of Emerging Trends in Engineering Research 8, no. 1.1 (September 15, 2020): 102–07. http://dx.doi.org/10.30534/ijeter/2020/1681.12020.

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Jamshed, Muhammed, Shahnaj Parvin, and Subrina Akter. "Significant HOG-Histogram of Oriented Gradient Feature Selection for Human Detection." International Journal of Computer Applications 132, no. 17 (December 17, 2015): 20–24. http://dx.doi.org/10.5120/ijca2015907704.

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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 (February 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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Win, Khin Yadanar, Noppadol Maneerat, Kazuhiko Hamamoto, and Syna Sreng. "Hybrid Learning of Hand-Crafted and Deep-Activated Features Using Particle Swarm Optimization and Optimized Support Vector Machine for Tuberculosis Screening." Applied Sciences 10, no. 17 (August 20, 2020): 5749. http://dx.doi.org/10.3390/app10175749.

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Tuberculosis (TB) is a leading infectious killer, especially for people with Human Immunodeficiency Virus (HIV) and Acquired Immunodeficiency Syndrome (AIDS). Early diagnosis of TB is crucial for disease treatment and control. Radiology is a fundamental diagnostic tool used to screen or triage TB. Automated chest x-rays analysis can facilitate and expedite TB screening with fast and accurate reports of radiological findings and can rapidly screen large populations and alleviate a shortage of skilled experts in remote areas. We describe a hybrid feature-learning algorithm for automatic screening of TB in chest x-rays: it first segmented the lung regions using the DeepLabv3+ model. Then, six sets of hand-crafted features from statistical textures, local binary pattern, GIST, histogram of oriented gradients (HOG), pyramid histogram of oriented gradients and bags of visual words (BoVW), and nine sets of deep-activated features from AlexNet, GoogLeNet, InceptionV3, XceptionNet, ResNet-50, SqueezeNet, ShuffleNet, MobileNet, and DenseNet, were extracted. The dominant features of each feature set were selected using particle swarm optimization, and then separately input to an optimized support vector machine classifier to label ‘normal’ and ‘TB’ x-rays. GIST, HOG, BoVW from hand-crafted features, and MobileNet and DenseNet from deep-activated features performed better than the others. Finally, we combined these five best-performing feature sets to build a hybrid-learning algorithm. Using the Montgomery County (MC) and Shenzen datasets, we found that the hybrid features of GIST, HOG, BoVW, MobileNet and DenseNet, performed best, achieving an accuracy of 92.5% for the MC dataset and 95.5% for the Shenzen dataset.

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Widodo, Agus Wahyu, and Agus Harjoko. "Sistem Verifikasi Tanda Tangan Off-Line Berdasar Ciri Histogram Of Oriented Gradient (HOG) Dan Histogram Of Curvature (HoC)." Jurnal Teknologi Informasi dan Ilmu Komputer 2, no. 1 (August 19, 2015): 1. http://dx.doi.org/10.25126/jtiik.201521121.

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Bakheet, Samy, and Ayoub Al-Hamadi. "A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification." Brain Sciences 11, no. 2 (February 14, 2021): 240. http://dx.doi.org/10.3390/brainsci11020240.

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Due to their high distinctiveness, robustness to illumination and simple computation, Histogram of Oriented Gradient (HOG) features have attracted much attention and achieved remarkable success in many computer vision tasks. In this paper, an innovative framework for driver drowsiness detection is proposed, where an adaptive descriptor that possesses the virtue of distinctiveness, robustness and compactness is formed from an improved version of HOG features based on binarized histograms of shifted orientations. The final HOG descriptor generated from binarized HOG features is fed to the trained Naïve Bayes (NB) classifier to make the final driver drowsiness determination. Experimental results on the publicly available NTHU-DDD dataset verify that the proposed framework has the potential to be a strong contender for several state-of-the-art baselines, by achieving a competitive detection accuracy of 85.62%, without loss of efficiency or stability.

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Rahmad, C., R. A. Asmara, D. R. H. Putra, I. Dharma, H. Darmono, and I. Muhiqqin. "Comparison of Viola-Jones Haar Cascade Classifier and Histogram of Oriented Gradients (HOG) for face detection." IOP Conference Series: Materials Science and Engineering 732 (January 27, 2020): 012038. http://dx.doi.org/10.1088/1757-899x/732/1/012038.

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Venkateswara Rao, N., G. Anil Kumar, and B. Harish. "HOG based object detection and classification." International Journal of Engineering & Technology 7, no. 3.3 (June 21, 2018): 151. http://dx.doi.org/10.14419/ijet.v7i3.3.15585.

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The intension of the project is to classify objects in real world and to tracks them throughout their life spans. Object detection algorithms use feature extraction and learning algorithms to classification of an object category. Our algorithm uses a combination of “histogram of oriented gradient” (HOG) and “support vector machine” (SVM) classifier to classify of objects. Results have shown this to be a robust method in both classifying the objects along with tracking them in real time world.

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Jacob, I. Jeena, Betty Paulraj, P. Ebby Darney, Hoang Viet Long, Tran Manh Tuan, Harold Robinson Yesudhas, Vimal Shanmuganathan, and Golden Julie Eanoch. "Image Retrieval Using Intensity Gradients and Texture Chromatic Pattern." International Journal of Data Warehousing and Mining 17, no. 1 (January 2021): 57–73. http://dx.doi.org/10.4018/ijdwm.2021010104.

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Methods to retrieve images involve retrieving images from the database by using features of it. They are colour, shape, and texture. These features are used to find the similarity for the query image with that of images in the database. The images are sorted in the order with this similarity. The article uses intra- and inter-texture chrominance and its intensity. Here inter-chromatic texture feature is extracted by LOCTP (local oppugnant colored texture pattern). Local binary pattern (LBP) gives the intra-texture information. Histogram of oriented gradient (HoG) is used to get the shape information from the satellite images. The performance analysis is land-cover remote sensing database, NWPU-VHR-10 dataset, and satellite optical land cover database gives better results than the previous works.

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Li, Zeyi, Haitao Zhang, and Yihang Huang. "A Rotation-Invariant Optical and SAR Image Registration Algorithm Based on Deep and Gaussian Features." Remote Sensing 13, no. 13 (July 4, 2021): 2628. http://dx.doi.org/10.3390/rs13132628.

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Traditional feature matching methods of optical and synthetic aperture radar (SAR) used gradient are sensitive to non-linear radiation distortions (NRD) and the rotation between two images. To address this problem, this study presents a novel approach to solving the rigid body rotation problem by a two-step process. The first step proposes a deep learning neural network named RotNET to predict the rotation relationship between two images. The second step uses a local feature descriptor based on the Gaussian pyramid named Gaussian pyramid features of oriented gradients (GPOG) to match two images. The RotNET uses a neural network to analyze the gradient histogram of the two images to derive the rotation relationship between optical and SAR images. Subsequently, GPOG is depicted a keypoint by using the histogram of Gaussian pyramid to make one-cell block structure which is simpler and more stable than HOG structure-based descriptors. Finally, this paper designs experiments to prove that the gradient histogram of the optical and SAR images can reflect the rotation relationship and the RotNET can correctly predict them. The similarity map test and the image registration results obtained on experiments show that GPOG descriptor is robust to SAR speckle noise and NRD.

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Ge, Ping Shu, Guo Kai Xu, Xiu Chun Zhao, Peng Song, and Lie Guo. "Pedestrian Detection Based on Histograms of Oriented Gradients in ROI." Advanced Materials Research 542-543 (June 2012): 937–40. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.937.

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To locate pedestrian faster and more accurately, a pedestrian detection method based on histograms of oriented gradients (HOG) in region of interest (ROI) is introduced. The features are extracted in the ROI where the pedestrian's legs may exist, which is helpful to decrease the dimension of feature vector and simplify the calculation. Then the vertical edge symmetry of pedestrian's legs is fused to confirm the detection. Experimental results indicate that this method can achieve an ideal accuracy with lower process time compared to traditional method.

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Adetiba, Emmanuel, and Oludayo O. Olugbara. "Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features." Scientific World Journal 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/786013.

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This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their “nonensemble” variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations.

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Gupta, Tania, and Dr Amandeep Verma. "Performance evaluation of the relevance feedback and Histogram of Oriented Gradients (HOG) based CBIR techniques – A Review." International Journal of Computer Trends and Technology 23, no. 2 (May 25, 2015): 49–52. http://dx.doi.org/10.14445/22312803/ijctt-v23p111.

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Tian, Qing, Wen Hua Zhao, Long Zhang, and Yun Wei. "Vehicle and Pedestrian Detection and Tracking." Applied Mechanics and Materials 401-403 (September 2013): 1432–35. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1432.

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Vehicle and pedestrian detection plays a critical role in the intelligent transportation system. The paper proposes an algorithm which can solve the problem effectively by Histograms of Oriented Gradients (HOG) features extraction and Support Vector Machine (SVM). This detection system is based on Histograms of Oriented Gradients features combined with Support Vector Machine for the recognition stage which is insensitive to lightings and noises. We use Kalman filter to track the objects. As shown in experiments, the method has high detection rate and can also satisfy the real-time intelligent transportation system.

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Abdul Haris, Muhamad Amin Husni, and Sin Liang Lim. "Neural Network Facial Authentication for Public Electric Vehicle Charging Station." Journal of Engineering Technology and Applied Physics 3, no. 1 (June 15, 2021): 17–21. http://dx.doi.org/10.33093/jetap.2021.3.1.4.

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This study is to investigate and compare the facial recognition accuracy performance of Dlib ResNet against a K-Nearest Neighbour (KNN) classifier. Particularly when used against a dataset from an Asian ethnicity as Dlib ResNet was reported to have an accuracy deficiency when it comes to Asian faces. The comparisons are both implemented on the facial vectors extracted using the Histogram of Oriented Gradients (HOG) method and use the same dataset for a fair comparison. Authentication of a user by facial recognition in an electric vehicle (EV) charging station demonstrates a practical use case for such an authentication system.

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Journal articles on the topic 'Histogram of Oriented Gradients (HOG)'

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