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Journal articles on the topic 'Gradient magnitude histogram'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

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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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 abun

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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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Zeng, Hui, Rui Zhang, Mingming Huang, and Xiuqing Wang. "Compact Local Directional Texture Pattern for Local Image Description." Advances in Multimedia 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/360186.

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This paper presents an effective local image feature region descriptor, called CLDTP descriptor (Compact Local Directional Texture Pattern), and its application in image matching and object recognition. The CLDTP descriptor encodes the directional and contrast information in a local region, so it contains the gradient orientation information and the gradient magnitude information. As the dimension of the CLDTP histogram is much lower than the dimension of the LDTP histogram, the CLDTP descriptor has higher computational efficiency and it is suitable for image matching. Extensive experiments ha

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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 (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 computatio

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Nguyen-Quoc, Huy, and Vinh Truong Hoang. "A Revisit Histogram of Oriented Descriptor for Facial Color Image Classification Based on Fusion of Color Information." Journal of Sensors 2021 (November 30, 2021): 1–12. http://dx.doi.org/10.1155/2021/6296505.

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Histogram of Oriented Gradient (HOG) is a robust descriptor which is widely used in many real-life applications, including human detection, face recognition, object counting, and video surveillance. In order to extract HOG descriptor from color images whose information is three times more than the grayscale images, researchers currently apply the maximum magnitude selection method. This method makes the information of the resulted image is reduced by selecting the maximum magnitudes. However, after we extract HOG using the unselected magnitudes of the maximum magnitude selection method, we obs

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Ilham Firman Ashari, Mohamad Idri, and M. Anas Nasrulah. "Analysis of Combination of Parking System with Face Recognition and QR Code using Histogram of Oriented Gradient Method." IT Journal Research and Development 7, no. 1 (2022): 94–110. http://dx.doi.org/10.25299/itjrd.2022.9958.

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Security is very important everywhere, including in the campus environment. To provide security and comfort for those who park their vehicles, a parking application is needed that can provide vehicle security while undergoing academic activities on campus. QR code (Quick Response Code) is a technology for converting written data into a two-dimensional code, which is printed on a more compact medium capable of storing various types of data. The most common individual part used to identify a person is the face because it has the unique characteristics of everyone. Histogram of Oriented Gradient

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Zhang, Tianjin, Zongrui Yi, Jinta Zheng, et al. "A Clustering-Based Automatic Transfer Function Design for Volume Visualization." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/4547138.

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The two-dimensional transfer functions (TFs) designed based on intensity-gradient magnitude (IGM) histogram are effective tools for the visualization and exploration of 3D volume data. However, traditional design methods usually depend on multiple times of trial-and-error. We propose a novel method for the automatic generation of transfer functions by performing the affinity propagation (AP) clustering algorithm on the IGM histogram. Compared with previous clustering algorithms that were employed in volume visualization, the AP clustering algorithm has much faster convergence speed and can ach

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Lu, Ming, and Shaozhang Niu. "Detection of Image Seam Carving Using a Novel Pattern." Computational Intelligence and Neuroscience 2019 (November 11, 2019): 1–15. http://dx.doi.org/10.1155/2019/9492358.

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Seam carving is an excellent content-aware image resizing technology widely used, and it is also a means of image tampering. Once an image is seam carved, the distribution of magnitude levels for the pixel intensity differences in the local neighborhood will be changed, which can be considered as a clue for detection of seam carving for forensic purposes. In order to accurately describe the distribution of magnitude levels for the pixel intensity differences in the local neighborhood, local neighborhood magnitude occurrence pattern (LNMOP) is proposed in this paper. The LNMOP pattern describes

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A. Aaisha Nazleem, Et al. "Advancement in Denoising MRI Images via 3D-GAN Model with Direction Coupled Magnitude Histogram Consistency Loss." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11 (2023): 105–17. http://dx.doi.org/10.17762/ijritcc.v11i11.9112.

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The diagnostics of medical pictures are essential for recognizing and comprehending a wide range of medical problems. This work introduces the Direction Coupled Magnitude Histogram (DCMH) as a novel structure picture descriptor to improve diagnostic accuracy. One of DCMH's unique selling points is its ability to include the edge oriented information that are oriented in any way inside a frame, enabling the expression of delicate nuances using various gradient features. The proposed method applies cartoon texture based textural loss and DCMH based structural loss to identify and analyse structu

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Jayasree, M., K. Narayanan N, V. Kabeer, and C. R. Arun. "An Enhanced Block Based Edge Detection Technique Using Hysteresis Thresholding." Signal & Image Processing : An International Journal (SIPIJ) 9, no. 2 (2019): 15–26. https://doi.org/10.5281/zenodo.3248683.

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Edge detection is a crucial step in various image processing systems like computer vision , pattern recognition and feature extraction. The Canny edge detection algorithm even though exhibits high accuracy, is computationally more complex compared to other edge detection techniques. A block based distributed edge detection technique is presented in this paper, which adaptively finds the thresholds for edge detection depending on block type and the distribution of gradients in each block. A novel method of computation of high threshold has been proposed in this paper. Block-based hysteresis thr

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Yang, Yi, Shuo Liu, Haitao Zhang, Dacheng Li, and Ling Ma. "Multi-Modal Remote Sensing Image Registration Method Combining Scale-Invariant Feature Transform with Co-Occurrence Filter and Histogram of Oriented Gradients Features." Remote Sensing 17, no. 13 (2025): 2246. https://doi.org/10.3390/rs17132246.

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Multi-modal remote sensing images often exhibit complex and nonlinear radiation differences which significantly hinder the performance of traditional feature-based image registration methods such as Scale-Invariant Feature Transform (SIFT). In contrast, structural features—such as edges and contours—remain relatively consistent across modalities. To address this challenge, we propose a novel multi-modal image registration method, Cof-SIFT, which integrates a co-occurrence filter with SIFT. By replacing the traditional Gaussian filter with a co-occurrence filter, Cof-SIFT effectively suppresses

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Zhang, Jianlong, Yifan Liu, Qiao Li, Ci He, Bin Wang, and Tianhong Wang. "Object Relocation Visual Tracking Based on Histogram Filter and Siamese Network in Intelligent Transportation." Sensors 22, no. 22 (2022): 8591. http://dx.doi.org/10.3390/s22228591.

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Target detection and tracking algorithms are one of the key technologies in the field of autonomous driving in intelligent transportation, providing important sensing capabilities for vehicle localization and path planning. Siamese network-based trackers formulate the visual tracking mission as an image-matching process by regression and classification branches, which simplifies the network structure and improves the tracking accuracy. However, there remain many problems, as described below. (1) The lightweight neural networks decrease the feature representation ability. It is easy for the tra

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Taher, Sarab M., Mustafa Ghanim, and Chen Soong Der. "Applied Improved Canny Edge Detection for Diagnosis Medical Images of Human Brain Tumors." Al-Mustansiriyah Journal of Science 34, no. 4 (2023): 66–74. http://dx.doi.org/10.23851/mjs.v34i4.1392.

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Medical image processing has become one of the crucial elements of the diagnostic process because of the increased usage of medical imaging recently, and clinicians' dependence on such computer-processed medical images in diagnosing patients. As the traditional Canny edge detection algorithm is sensitive to noise, it is easy to lose weak edge information when filtering out the noise, and its fixed parameters show poor adaptability. The suggested algorithm introduced the concept of image block intensity operator to replace image gradient. In addition, the computing speed of the suggested algori

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S. Sathiya, Devi. "Texture classification with modified rotation invariant local binary pattern and gradient boosting." International Journal of Knowledge-based and Intelligent Engineering Systems 26, no. 2 (2022): 125–36. http://dx.doi.org/10.3233/kes220012.

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Since texture is prominent low level feature of an image, most of the image processing and computer vision applications rely on this feature for efficient extraction, retrieval, visualization and classification of the images. Hence, the texture analysis method mainly concentrates on efficient feature extraction and representation of the image. The images captured and analyzed in many of the applications are not in same (or) similar scale, orientation and illumination and also texture has regular, stochastic, periodic, homogeneous (or) inhomogeneous and directional in nature. To address these i

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Liu, Boyu, Jun Chen, Jiage Chen, and Weiwei Zhang. "Land Cover Change Detection Using Multiple Shape Parameters of Spectral and NDVI Curves." Remote Sensing 10, no. 8 (2018): 1251. http://dx.doi.org/10.3390/rs10081251.

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Spectral and NDVI values have been used to calculate the change magnitudes of land cover, but may result in many pseudo-changes because of inter-class variance. Recently, the shape information of spectral or NDVI curves such as direction, angle, gradient, or other mathematical indicators have been used to improve the accuracy of land cover change detection. However, these measurements, in terms of the single shape features, can hardly capture the complete trends of curves affected by the unsynchronized phenology. Therefore, the calculated change magnitudes are indistinct such that changes and

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Müller, Martin, Gerd Stanke, Ulrich Sonntag, Dominik Britz, and Frank Mücklich. "Segmentation of Lath-Like Structures via Localized Identification of Directionality in a Complex-Phase Steel." Metallography, Microstructure, and Analysis 9, no. 5 (2020): 709–20. http://dx.doi.org/10.1007/s13632-020-00676-9.

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AbstractIn this work, a segmentation approach based on analyzing local orientations and directions in an image, in order to distinguish lath-like from granular structures, is presented. It is based on common image processing operations. A window of appropriate size slides over the image, and the gradient direction and its magnitude inside this window are determined for each pixel. The histogram of all possible directions yields the main direction and its directionality. These two parameters enable the extraction of window positions which represent lath-like structures, and procedures to join t

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Lobachev, Mykhaylo V., and Sergiy V. Purish. "Machine learning models and methods for human gait recognition." Herald of Advanced Information Technology 6, no. 3 (2023): 263–77. http://dx.doi.org/10.15276/hait.06.2023.18.

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The paper explores the challenge of human identification through gait recognition within biometric identification systems. Itoutlines the essential criteria for human biometric features, discusses primary biometric characteristics, and their application in biometric identification systems. The paper also examines the feasibility of utilizing gait as a biometric identifier, emphasizing its advantages, such as not requiring the upfront provision of personal biometric information and specialized equipment.The authors conduct an analysis of existing scientific literature in the field of gait recog

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Ms, Nisha. "Classification of celiac disease using novel approach of three-level DWT decomposition and linear support vector machine." Journal of Medical pharmaceutical and allied sciences 13, no. 1 (2024): 6408–16. http://dx.doi.org/10.55522/jmpas.v13i1.6039.

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In the medical field, the requirement for automated medical diagnosis software has risen as the application uses machine learning to facilitate health analysis from different data points from a patient comparing it with massive amounts of medical data to diagnose and prevent disease. The automated system can run multiple tests simultaneously and thus resulting to faster turnaround time and enables timely patient care along with higher accuracy and reliability. The irregularities in the small intestine villi’s structure cause various autoimmune disorders. So, this work aims to find a novel tech

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Chan, Kelvin, Raymond Chan, and Mila Nikolova. "A Convex Model for Edge-Histogram Specification with Applications to Edge-Preserving Smoothing." Axioms 7, no. 3 (2018): 53. http://dx.doi.org/10.3390/axioms7030053.

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The goal of edge-histogram specification is to find an image whose edge image has a histogram that matches a given edge-histogram as much as possible. Mignotte has proposed a non-convex model for the problem in 2012. In his work, edge magnitudes of an input image are first modified by histogram specification to match the given edge-histogram. Then, a non-convex model is minimized to find an output image whose edge-histogram matches the modified edge-histogram. The non-convexity of the model hinders the computations and the inclusion of useful constraints such as the dynamic range constraint. I

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Karaali, S., S. Bilir, and R. Buser. "Comprehensive Analysis of RGU Photometry in the Direction to M5." Publications of the Astronomical Society of Australia 21, no. 3 (2004): 275–83. http://dx.doi.org/10.1071/as03056.

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AbstractThe RGU-photographic investigation of an intermediate latitude field in the direction to the Galactic centre is presented. 164 extra-galactic objects, identified by comparison of Minnesota and Basel charts, are excluded from the program. Also, a region with size 0.104 square-degrees, contaminated by cluster (M5) stars and affected by background light of the bright star HD 136202 is omitted. Contrary to previous investigations, a reddening of E(B – V) = 0.046, corresponding to E(G – R) = 0.07 mag is adopted. The separation of dwarfs and evolved stars is carried out by an empirical metho

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Penumadu, Dayakar. "Evaluating Clay Microfabric Using Scanning Electron Microscopy and Digital Information Processing." Transportation Research Record: Journal of the Transportation Research Board 1526, no. 1 (1996): 112–20. http://dx.doi.org/10.1177/0361198196152600114.

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Several aspects related to the qualitative and quantitative evaluation of clay microfabric are presented. A digital image processing technique called “intensity gradient analysis” is used in evaluating clay microfabric from scanning electron micrographs. Details are given of three computer programs (histogram.c, average.c and ellipse.c) that provide for the quantitative analysis of the orientation of clay particles. The important aspects related to sample and surface preparation, digital image acquisition, and analysis are explained. Two natural clays, Pennsylvanian fire clay and Pleistocene v

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Hast, Anders. "Magnitude of Semicircle Tiles in Fourier-space - A Handcrafted Feature Descriptor for Word Recognition using Embedded Prototype Subspace Classifiers." Journal of WSCG 30, no. 1-2 (2022): 82–90. http://dx.doi.org/10.24132/jwscg.2022.10.

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The purpose of this paper is to in detail describe and analyse a Fourier based handcrafted descriptor for word recognition. Especially, it is discussed how the Variability in the results can be analysed and visualised. This efficiency of the descriptor is evaluated for the use with embedded prototype subspace classifiers for handwritten word recognition. Nonetheless, it can be used with any classifier for any purpose. An hierarchical composition of discrete semicircles in the Fourier-space is proposed and it will will be show how this compares to Gabor filters, which can be used to extract edg

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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 o

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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 o

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WANG, CHI-CHEN RAXLE, JIN-YI WU, and JENN-JIER JAMES LIEN. "PEDESTRIAN DETECTION SYSTEM USING CASCADED BOOSTING WITH INVARIANCE OF ORIENTED GRADIENTS." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 04 (2009): 801–23. http://dx.doi.org/10.1142/s0218001409007363.

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This study presents a novel learning-based pedestrian detection system capable of automatically detecting individuals of different sizes and orientations against a wide variety of backgrounds, including crowds, even when the individual is partially occluded. To render the detection performance robust toward the effects of geometric and rotational variations in the original image, the feature extraction process is performed using both rectangular- and circular-type blocks of various sizes and aspect ratios. The extracted blocks are rotated in accordance with their dominant orientation(s) such t

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Giri, Rajkumar. S.M, S. Hariprasath., D. Sivabalan, K. Sakthiganesh., K. Varun., and S. Thangasabari. "Design Analyze and Implementation of Hand written Digit Recognition System Using HOG Feature." International Journal of Multidisciplinary Research Transactions 5, no. 7 (2023): 40–48. https://doi.org/10.5281/zenodo.7916199.

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A feature descriptor method called HOG (Histogram of Oriented Gradients) is frequently employed in tasks involving the detection and identification of objects. HOG may be used to extract characteristics from pictures of handwritten digits in order to categories them into the appropriate classes for digit classification. We may utilize HOG for digit categorization by doing the following: Obtain a collection of images of handwritten digits. The training set and testing set of this dataset should be separated. The photos should be preprocessed to make sure they are uniform in size and orientation

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Nine, Julkar, and Aarti Kishor Anapunje. "Dataset Evaluation for Multi Vehicle Detection using Vision Based Techniques." Embedded Selforganising Systems 8, no. 2 (2021): 8–14. http://dx.doi.org/10.14464/ess.v8i2.492.

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Vehicle detection is one of the primal challenges of modern driver-assistance systems owing to the numerous factors, for instance, complicated surroundings, diverse types of vehicles with varied appearance and magnitude, low-resolution videos, fast-moving vehicles. It is utilized for multitudinous applications including traffic surveillance and collision prevention. This paper suggests a Vehicle Detection algorithm developed on Image Processing and Machine Learning. The presented algorithm is predicated on a Support Vector Machine(SVM) Classifier which employs feature vectors extracted via His

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Ragb, Hussin K., and Vijayan K. Asari. "Local Phase Features in Chromatic Domain for Human Detection." International Journal of Monitoring and Surveillance Technologies Research 4, no. 3 (2016): 52–72. http://dx.doi.org/10.4018/ijmstr.2016070104.

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In this paper, a new descriptor based on phase congruency concept and LUV color space features is presented. Since the phase of the signal conveys more information regarding signal structure than the magnitude and the indispensable quality of the color in describing the world around us, the proposed descriptor can precisely identify and localize image features over the gradient based techniques, especially in the regions affected by illumination changes. The proposed features can be formed by extracting the phase congruency information for each pixel in the three-color image channels. The maxi

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Sladojevic, Srdjan, Andras Anderla, Dubravko Culibrk, Darko Stefanovic, and Bojan Lalic. "Integer arithmetic approximation of the hog algorithm used for pedestrian detection." Computer Science and Information Systems 14, no. 2 (2017): 329–46. http://dx.doi.org/10.2298/csis160229011s.

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This paper presents the results of a study of the effects of integer (fixed-point) arithmetic implementation on classification accuracy of a popular open-source people detection system based on Histogram of Oriented Gradients. It is investigated how the system performance deviates from the reference algorithm performance as integer arithmetic is introduced with different bit-width in several critical parts of the system. In performed experiments, the effects of different bit-width integer arithmetic implementation for four key operations were separately considered: HoG descriptor magnitude cal

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Wang, Ende, Jinlei Jiao, Jingchao Yang, Dongyi Liang, and Jiandong Tian. "Tri-SIFT: A Triangulation-Based Detection and Matching Algorithm for Fish-Eye Images." Information 9, no. 12 (2018): 299. http://dx.doi.org/10.3390/info9120299.

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Keypoint matching is of fundamental importance in computer vision applications. Fish-eye lenses are convenient in such applications that involve a very wide angle of view. However, their use has been limited by the lack of an effective matching algorithm. The Scale Invariant Feature Transform (SIFT) algorithm is an important technique in computer vision to detect and describe local features in images. Thus, we present a Tri-SIFT algorithm, which has a set of modifications to the SIFT algorithm that improve the descriptor accuracy and matching performance for fish-eye images, while preserving i

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Fawad, Muhammad Jamil Khan, MuhibUr Rahman, Yasar Amin, and Hannu Tenhunen. "Low-Rank Multi-Channel Features for Robust Visual Object Tracking." Symmetry 11, no. 9 (2019): 1155. http://dx.doi.org/10.3390/sym11091155.

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Kernel correlation filters (KCF) demonstrate significant potential in visual object tracking by employing robust descriptors. Proper selection of color and texture features can provide robustness against appearance variations. However, the use of multiple descriptors would lead to a considerable feature dimension. In this paper, we propose a novel low-rank descriptor, that provides better precision and success rate in comparison to state-of-the-art trackers. We accomplished this by concatenating the magnitude component of the Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP), R

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Fawad, Muhammad Jamil Khan, and MuhibUr Rahman. "Person Re-Identification by Discriminative Local Features of Overlapping Stripes." Symmetry 12, no. 4 (2020): 647. http://dx.doi.org/10.3390/sym12040647.

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The human visual system can recognize a person based on his physical appearance, even if extreme spatio-temporal variations exist. However, the surveillance system deployed so far fails to re-identify the individual when it travels through the non-overlapping camera’s field-of-view. Person re-identification (Re-ID) is the task of associating individuals across disjoint camera views. In this paper, we propose a robust feature extraction model named Discriminative Local Features of Overlapping Stripes (DLFOS) that can associate corresponding actual individuals in the disjoint visual surveillance

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Keen, Douglas A., Li-Wei Chou, Michael A. Nordstrom, and Andrew J. Fuglevand. "Short-term synchrony in diverse motor nuclei presumed to receive different extents of direct cortical input." Journal of Neurophysiology 108, no. 12 (2012): 3264–75. http://dx.doi.org/10.1152/jn.01154.2011.

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Motor units within human muscles usually exhibit a significant degree of short-term synchronization. Such coincident spiking typically has been attributed to last-order projections that provide common synaptic input across motor neurons. The extent of branched input arising directly from cortical neurons has often been suggested as a critical factor determining the magnitude of short-term synchrony. The purpose of this study, therefore, was to quantify motor unit synchrony in a variety of human muscles differing in the presumed extent of cortical input to their respective motor nuclei. Cross-c

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Tofte, Marshal, and Alan Carroll. "Spatiotemporal quantification of organic matter accumulation in the Eocene Green River Formation, Bridger Basin, Wyoming." Geosites 50 (September 1, 2022): 1–20. http://dx.doi.org/10.31711/ugap.v50i.117.

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It has long been recognized that lakes can bury large amounts of organic carbon (CORG) in their sediment, with important consequences for conventional and unconventional petroleum resources and potentially for the global carbon cycle. The detailed distribution of lacustrine organic carbon through space and time is important to understanding its commercial and climatic implications, but has seldom been documented in detail. The Green River Formation offers a unique opportunity to improve this understanding, due to extensive Fischer assay analyses of its oil generative potential and to recently

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Babu, D. Ravindra, R. C. Verma, Navneet Kumar Agrawal, and Isha Suwalk. "Comparison of Adapted and Improved Feature Extraction Techniques of Different Potatoes Types using Image Processing." International Journal of Environment, Agriculture and Biotechnology 9 (2024): 161–70. http://dx.doi.org/10.22161/ijeab.94.22.

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The characteristics of crack, rotten, sprout, skin peel and good potatoes non destructively with gray level co-occurrence matrix properties (GLCMP), radon, gabor, local binary patterns (LBP) and histogram of oriented gradients (HOG) with default parameters and values i.e. adapted method were compared with improved method. Gabor feature length (16) of improved method was lower compared to adapted method and improved method and it requires less time to plot gabor magnitude and spatial kernels for all potato classes. Radon feature row vector size is same for both adapted and improved methods for

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Ghaffarian, S., and N. Kerle. "TOWARDS POST-DISASTER DEBRIS IDENTIFICATION FOR PRECISE DAMAGE AND RECOVERY ASSESSMENTS FROM UAV AND SATELLITE IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 297–302. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-297-2019.

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<p><strong>Abstract.</strong> Often disasters cause structural damages and produce rubble and debris, depending on their magnitude and type. The initial disaster response activity is evaluation of the damages, i.e. creation of a detailed damage estimation for different object types throughout the affected area. First responders and government stakeholders require the damage information to plan rescue operations and later on to guide the recovery process. Remote sensing, due to its agile data acquisition capability, synoptic coverage and low cost, has long been used as a vital

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Silva, Gustavo, and Paul Rodriguez. "Fine-tuning adaptive stochastic optimizers: determining the optimal hyperparameter $$\epsilon$$ via gradient magnitude histogram analysis." Neural Computing and Applications, September 18, 2024. http://dx.doi.org/10.1007/s00521-024-10302-2.

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Jayasree, M., K. Narayanan N, V. Kabeer, and C. R. Arun. "AN ENHANCED BLOCK BASED EDGE DETECTION TECHNIQUE USING HYSTERESIS THRESHOLDING." April 30, 2018. https://doi.org/10.5121/sipij.2018.9202.

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Edge detection is a crucial step in various image processing systems like computer vision , pattern recognition and feature extraction. The Canny edge detection algorithm even though exhibits high accuracy, is computationally more complex compared to other edge detection techniques. A block based distributed edge detection technique is presented in this paper, which adaptively finds the thresholds for edge detection depending on block type and the distribution of gradients in each block. A novel method of computation of high threshold has been proposed in this paper. Block-based hysteresis thr

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Zhang, Yanhu, and Lijuan Yan. "A fast face recognition based on image gradient compensation for feature description." Multimedia Tools and Applications, March 25, 2022. http://dx.doi.org/10.1007/s11042-022-12804-4.

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AbstractTo improve the efficiency of traditional face recognition techniques, this paper proposes a novel face recognition algorithm called Image Gradient Feature Compensation (IGFC). Based on the gradients along four directions in an image, a fusion algorithm and a compensation method are implemented to obtain features of the original image. In this study, gradient magnitude maps of a face image are calculated along four directions. Fusion gradients and differential fusion gradients are produced by fusing the four gradient magnitude maps of a face image in multiple ways, and they are used as

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Kuo, Hsiang-Chi, U. Mahmood, Assen S. Kirov, et al. "An automated technique for global noise level measurement in CT image with a conjunction of image gradient." Physics in Medicine & Biology, March 27, 2024. http://dx.doi.org/10.1088/1361-6560/ad3883.

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Abstract Automated assessment of noise level in clinical CT images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta. Initially, a noise map is generated by sliding a 10x10 pixel for calculating HU standard deviation (SD) and the noise map is further combined with the gradien

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S, Rashmi, Srinath S, Prashanth S. Murthy, and Seema Deshmukh. "Landmark annotation through feature combinations: a comparative study on cephalometric images with in-depth analysis of model’s explainability." Dentomaxillofacial Radiology, December 13, 2023. http://dx.doi.org/10.1093/dmfr/twad011.

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Abstract Objectives The objectives of this study are to explore and evaluate the automation of anatomical landmark localization in cephalometric images using machine learning techniques, with a focus on feature extraction and combinations, contextual analysis, and model interpretability through Shapley Additive exPlanations (SHAP) values. Methods We conducted extensive experimentation on a private dataset of 300 lateral cephalograms to thoroughly study the annotation results obtained using pixel feature descriptors including raw pixel, gradient magnitude, gradient direction, and histogram-orie

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Vincent, Olivier, Pierre Bergeron, and Patrick Dufour. "Synthetic Spectroscopy for White Dwarf Classification: Addressing Label Uncertainty and Class Imbalance." Monthly Notices of the Royal Astronomical Society, March 18, 2025. https://doi.org/10.1093/mnras/staf448.

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Abstract With the imminent data releases from next-generation spectroscopic surveys, hundreds of thousands of white dwarf spectra are expected to become available within the next few years, increasing the data volume by an order of magnitude. This surge in data has created a pressing need for automated tools to efficiently analyze and classify these spectra. Although machine learning algorithms have recently been applied to classify large spectroscopic datasets, they remain constrained by the limited availability of training data. The Sloan Digital Sky Survey (SDSS) serves as the current stand

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Divya, C. D., H. L. Gururaj, R. Rohan, et al. "An efficient machine learning approach to nephrology through iris recognition." Discover Artificial Intelligence 1, no. 1 (2021). http://dx.doi.org/10.1007/s44163-021-00010-4.

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AbstractIridology is a technique in science used to analyze color, patterns, and various other properties of the iris to assess an individual's general health. Few regions in the iris are connected by nerves coming from different organs of body, this shows some special unique qualities which is advantageous along with which assist in psychological condition, particular organ conditions and construction of the body. The structural and designed patterns present on specific part of iris represent the level of intensity of disorder caused by the organs. This method of approach can be employed as r

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Prof. Umesh B. Pawar, Pandore Saiprasad, More Pallavi, Surashe Sharda, and Unwane Priyanka. "Wild Animal Intrusion Detection." International Journal of Advanced Research in Science, Communication and Technology, December 22, 2023, 44–48. http://dx.doi.org/10.48175/ijarsct-14303.

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Animal Vehicle Collision, commonly called as roadkill, is an emerging threat to humans and wild animals with increasing fatalities every year. Amid Vehicular crashes, animal actions (i.e. deer) are unpredictable and erratic on roadways. This paper unveils a newer dimension for wild animals’ auto-detection during active nocturnal hours using thermal image processing over camera car mount in the vehicle. To implement effective hot spot and moving object detection, obtained radiometric images are transformed and processed by an intelligent system. This intelligent system extracts the features of

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Kushwaha, Arati, Ashish Khare, and Manish Khare. "Human Activity Recognition Algorithm in Video Sequences Based on Integration of Magnitude and Orientation Information of Optical Flow." International Journal of Image and Graphics, April 22, 2021, 2250009. http://dx.doi.org/10.1142/s0219467822500097.

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Human activity recognition from video sequences has emerged recently as pivotal research area due to its importance in a large number of applications such as real-time surveillance monitoring, healthcare, smart homes, security, behavior analysis, and many more. However, lots of challenges also exist such as intra-class variations, object occlusion, varying illumination condition, complex background, camera motion, etc. In this work, we introduce a novel feature descriptor based on the integration of magnitude and orientation information of optical flow and histogram of oriented gradients which

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Jha, Dhirendra Kumar, and Jitendra K. Mishra. "XGBoost-enabled low-complexity approach for signal integrity assessment in coherent communication systems." Physica Scripta, December 10, 2024. https://doi.org/10.1088/1402-4896/ad9d01.

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Abstract Error vector magnitude (EVM) provides critical information for assessing signal integrity and system performance in optical communication systems. In this research, a regression approach using boosting algorithms is developed to retrieve EVM information from complex signal constellation structures applicable to various modulation formats. Amplitude histograms are created and collected at different OSNR levels, launch powers, and transmission distances using an offline preprocessing approach. The impact of various evaluation techniques, including mean absolute error, coefficient of det

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Reddy, G. Nagarjuna, and Nagireddy K. "A Robust Machine Learning Approach for Multiclass Alzheimer’s Disease Detection using 3D Brain Magnetic Resonance Images." Journal of Engineering Research, October 13, 2021. http://dx.doi.org/10.36909/jer.10511.

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Alzheimer’s disease (AD), a progressive dementia is the neurodegenerative disorder that worsens memory and mental capabilities mostly in aged people. Currently, clinical and psychometric assessments are being used to diagnose the disease in patients. In clinical procedures, 3D Magnetic Resonance Image qualitative parameters are analyzed to identify the abnormality in brain shape, volume, texture, and cortical thickness. This paper presents a robust approach for categorizing 3D MR images into multiple stages of AD using hybrid features viz., Gray Level Co-occurrence Matrix (GLCM), 3D Scale and

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Reddy, G. Nagarjuna, and Nagireddy K. "A Robust Machine Learning Approach for Multiclass Alzheimer’s Disease Detection using 3D Brain Magnetic Resonance Images." Journal of Engineering Research, October 13, 2021. http://dx.doi.org/10.36909/jer.10511.

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Alzheimer’s disease (AD), a progressive dementia is the neurodegenerative disorder that worsens memory and mental capabilities mostly in aged people. Currently, clinical and psychometric assessments are being used to diagnose the disease in patients. In clinical procedures, 3D Magnetic Resonance Image qualitative parameters are analyzed to identify the abnormality in brain shape, volume, texture, and cortical thickness. This paper presents a robust approach for categorizing 3D MR images into multiple stages of AD using hybrid features viz., Gray Level Co-occurrence Matrix (GLCM), 3D Scale and

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L. Rocha, Rafael, Cleison D. Silva, Ana C. S. Gomes, et al. "CNN-DFT Based Approach Applied to Image Inspection of Railcar Component: A Comparison with Machine Learning Methods." Journal of Information and Data Management 11, no. 1 (2021). http://dx.doi.org/10.5753/jidm.2020.2027.

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The railcar component inspection is one of the most critical tasks in railway maintenance. The use of image processing, coupled with machine learning has emerged as a solution for replacing current standard methodologies. The spectral analysis gives the frequency representation of a signal and has been largely used in signal processing tasks. In this sense, this work proposes the evaluation of the use of the discrete Fourier transform (DFT) in addition to the spatial representation image of railcar component for an automatic detector of defective parts performed by convolutional neural network

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