Relevant bibliographies by topics / Histogram of Gradient Feature descriptor

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Histogram of Gradient Feature descriptor'

Author: Grafiati
Published: 3 June 2025
Last updated: 23 June 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Histogram of Gradient Feature descriptor.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Histogram of Gradient Feature descriptor"

1

YU, SHENGSHENG, CHAOBING HUANG, and JINGLI ZHOU. "COLOR IMAGE RETRIEVAL BASED ON COLOR-TEXTURE-EDGE FEATURE HISTOGRAMS." International Journal of Image and Graphics 06, no. 04 (2006): 583–98. http://dx.doi.org/10.1142/s0219467806002392.

Full text
Abstract:
In this paper, two novel texture descriptors and two novel edge descriptors are proposed, which are low-dimension, effective, and are obtained by a relative simple approach. The two texture descriptors are the directional difference unit and the gradient unit histogram, which are rotation invariant. The two edge descriptors are the quantized max-min difference histogram and the quantized fuzzy entropy histogram, the former is more suitable for describing the images with relatively regular texture characteristic, the latter is more suitable for describing the images with relatively regular structure characteristic or no regular characteristic. Combing color descriptor, texture descriptor and edge descriptor to form hybrid visual feature index to retrieve natural color images, this method is insensitive to image rotation and translation. Experimental results show that the method achieves better performance than other recent relevant methods.
APA, Harvard, Vancouver, ISO, and other styles
2

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 (2021): 2628. http://dx.doi.org/10.3390/rs13132628.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
Abstract:
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 have validated the effectiveness of the designed CLDTP descriptor.
APA, Harvard, Vancouver, ISO, and other styles
4

Yang, Jianguo, Dian Sheng, Weiqi Jin, and Li Li. "Histogram of Polarization Gradient for Target Tracking in Infrared DoFP Polarization Thermal Imaging." Remote Sensing 17, no. 5 (2025): 907. https://doi.org/10.3390/rs17050907.

Full text
Abstract:
Division-of-focal-plane (DoFP) polarization imaging systems have demonstrated considerable promise in target detection and tracking in complex backgrounds. However, existing methods face challenges, including dependence on complex image preprocessing procedures and limited real-time performance. To address these issues, this study presents a novel histogram of polarization gradient (HPG) feature descriptor that enables efficient feature representation of polarization mosaic images. First, a polarization distance calculation model based on normalized cross-correlation (NCC) and local variance is constructed, which enhances the robustness of gradient feature extraction through dynamic weight adjustment. Second, a sparse Laplacian filter is introduced to achieve refined gradient feature representation. Subsequently, adaptive polarization channel correlation weights and the second-order gradient are utilized to reconstruct the degree of linear polarization (DoLP). Finally, the gradient and DoLP sign information are ingeniously integrated to enhance the capability of directional expression, thus providing a new theoretical perspective for polarization mosaic image structure analysis. The experimental results obtained using a self-developed long-wave infrared DoFP polarization thermal imaging system demonstrate that, within the same FBACF tracking framework, the proposed HPG feature descriptor significantly outperforms traditional grayscale {8.22%, 2.93%}, histogram of oriented gradient (HOG) {5.86%, 2.41%}, and mosaic gradient histogram (MGH) {27.19%, 18.11%} feature descriptors in terms of precision and success rate. The processing speed of approximately 20 fps meets the requirements for real-time tracking applications, providing a novel technical solution for polarization imaging applications.
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
6

Alreshidi, Eissa, Rabie A. Ramadan, Md Haidar Sharif, Omer Faruk Ince, and Ibrahim Furkan Ince. "A Comparative Study of Image Descriptors in Recognizing Human Faces Supported by Distributed Platforms." Electronics 10, no. 8 (2021): 915. http://dx.doi.org/10.3390/electronics10080915.

Full text
Abstract:
Face recognition is one of the emergent technologies that has been used in many applications. It is a process of labeling pictures, especially those with human faces. One of the critical applications of face recognition is security monitoring, where captured images are compared to thousands, or even millions, of stored images. The problem occurs when different types of noise manipulate the captured images. This paper contributes to the body of knowledge by proposing an innovative framework for face recognition based on various descriptors, including the following: Color and Edge Directivity Descriptor (CEDD), Fuzzy Color and Texture Histogram Descriptor (FCTH), Color Histogram, Color Layout, Edge Histogram, Gabor, Hashing CEDD, Joint Composite Descriptor (JCD), Joint Histogram, Luminance Layout, Opponent Histogram, Pyramid of Gradient Histograms Descriptor (PHOG), Tamura. The proposed framework considers image set indexing and retrieval phases with multi-feature descriptors. The examined dataset contains 23,707 images of different genders and ages, ranging from 1 to 116 years old. The framework is extensively examined with different image filters such as random noise, rotation, cropping, glow, inversion, and grayscale. The indexer’s performance is measured based on a distributed environment based on sample size and multiprocessors as well as multithreads. Moreover, image retrieval performance is measured using three criteria: rank, score, and accuracy. The implemented framework was able to recognize the manipulated images using different descriptors with a high accuracy rate. The proposed innovative framework proves that image descriptors could be efficient in face recognition even with noise added to the images based on the outcomes. The concluded results are as follows: (a) the Edge Histogram could be best used with glow, gray, and inverted images; (b) the FCTH, Color Histogram, Color Layout, and Joint Histogram could be best used with cropped images; and (c) the CEDD could be best used with random noise and rotated images.
APA, Harvard, Vancouver, ISO, and other styles
7

Patel, Chirag I., Dileep Labana, Sharnil Pandya, Kirit Modi, Hemant Ghayvat, and Muhammad Awais. "Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences." Sensors 20, no. 24 (2020): 7299. http://dx.doi.org/10.3390/s20247299.

Full text
Abstract:
Human Action Recognition (HAR) is the classification of an action performed by a human. The goal of this study was to recognize human actions in action video sequences. We present a novel feature descriptor for HAR that involves multiple features and combining them using fusion technique. The major focus of the feature descriptor is to exploits the action dissimilarities. The key contribution of the proposed approach is to built robust features descriptor that can work for underlying video sequences and various classification models. To achieve the objective of the proposed work, HAR has been performed in the following manner. First, moving object detection and segmentation are performed from the background. The features are calculated using the histogram of oriented gradient (HOG) from a segmented moving object. To reduce the feature descriptor size, we take an averaging of the HOG features across non-overlapping video frames. For the frequency domain information we have calculated regional features from the Fourier hog. Moreover, we have also included the velocity and displacement of moving object. Finally, we use fusion technique to combine these features in the proposed work. After a feature descriptor is prepared, it is provided to the classifier. Here, we have used well-known classifiers such as artificial neural networks (ANNs), support vector machine (SVM), multiple kernel learning (MKL), Meta-cognitive Neural Network (McNN), and the late fusion methods. The main objective of the proposed approach is to prepare a robust feature descriptor and to show the diversity of our feature descriptor. Though we are using five different classifiers, our feature descriptor performs relatively well across the various classifiers. The proposed approach is performed and compared with the state-of-the-art methods for action recognition on two publicly available benchmark datasets (KTH and Weizmann) and for cross-validation on the UCF11 dataset, HMDB51 dataset, and UCF101 dataset. Results of the control experiments, such as a change in the SVM classifier and the effects of the second hidden layer in ANN, are also reported. The results demonstrate that the proposed method performs reasonably compared with the majority of existing state-of-the-art methods, including the convolutional neural network-based feature extractors.
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
9

Tong, Ying, Liang Bao Jiao, and Xue Hong Cao. "A Novel HOG Descriptor with Spatial Multi-Scale Feature for FER." Applied Mechanics and Materials 596 (July 2014): 322–27. http://dx.doi.org/10.4028/www.scientific.net/amm.596.322.

Full text
Abstract:
HOG Feature is an efficient edge information descriptor, but it ignores the spatial arrangement of local FER features. In this respect, this paper puts forward a spatial multi-scale model based on an improved HOG algorithm which uses canny operator instead of traditional gradient operator. After the image is divided into a series of sub-regions layer by layer, the histogram of orient gradients for each sub-region is calculated and connected in sequence to obtain the spatial multi-scale HOG feature of whole image. Compared with traditional HOG and the improved PHOG, the proposed SMS_HOG algorithm acquires 5% recognition rate improvement and 50% processing time reduction.
APA, Harvard, Vancouver, ISO, and other styles
10

Yu, Yang, Yong Ma, Xiaoguang Mei, Fan Fan, Jun Huang, and Jiayi Ma. "A Spatial-Spectral Feature Descriptor for Hyperspectral Image Matching." Remote Sensing 13, no. 23 (2021): 4912. http://dx.doi.org/10.3390/rs13234912.

Full text
Abstract:
Hyperspectral Images (HSIs) have been utilized in many fields which contain spatial and spectral features of objects simultaneously. Hyperspectral image matching is a fundamental and critical problem in a wide range of HSI applications. Feature descriptors for grayscale image matching are well studied, but few descriptors are elaborately designed for HSI matching. HSI descriptors, which should have made good use of the spectral feature, are essential in HSI matching tasks. Therefore, this paper presents a descriptor for HSI matching, called HOSG-SIFT, which ensembles spectral features with spatial features of objects. First, we obtain the grayscale image by dimensional reduction from HSI and apply it to extract keypoints and descriptors of spatial features. Second, the descriptors of spectral features are designed based on the histogram of the spectral gradient (HOSG), which effectively preserves the physical significance of the spectral profile. Third, we concatenate the spatial descriptors and spectral descriptors with the same weights into a new descriptor and apply it for HSI matching. Experimental results demonstrate that the proposed HOSG-SIFT performs superior against traditional feature descriptors.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Histogram of Gradient Feature descriptor"

1

Sailer, Zbyněk. "Vyhledání podobných obrázků pomocí popisu barevným histogramem." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236514.

Full text
Abstract:
This thesis deals with description of existing methods of image retrieval. It contains set of methods for image description, coding of global and local descriptor (SIFT, etc.) and describes method of effective searching in multidimensional space (LSH). It continues with proposal and testing of three global descriptors using color histograms, histogram of gradients and the combination of both. The last part deals with similar image retrieval using proposed descriptors and the indexing method LSH and compares the results with the existing method. Product of this work is an experimental application which demonstrates the proposed solution.
APA, Harvard, Vancouver, ISO, and other styles
2

Leoputra, Wilson Suryajaya. "Video foreground extraction for mobile camera platforms." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/1384.

Full text
Abstract:
Foreground object detection is a fundamental task in computer vision with many applications in areas such as object tracking, event identification, and behavior analysis. Most conventional foreground object detection methods work only in a stable illumination environments using fixed cameras. In real-world applications, however, it is often the case that the algorithm needs to operate under the following challenging conditions: drastic lighting changes, object shape complexity, moving cameras, low frame capture rates, and low resolution images. This thesis presents four novel approaches for foreground object detection on real-world datasets using cameras deployed on moving vehicles.The first problem addresses passenger detection and tracking tasks for public transport buses investigating the problem of changing illumination conditions and low frame capture rates. Our approach integrates a stable SIFT (Scale Invariant Feature Transform) background seat modelling method with a human shape model into a weighted Bayesian framework to detect passengers. To deal with the problem of tracking multiple targets, we employ the Reversible Jump Monte Carlo Markov Chain tracking algorithm. Using the SVM classifier, the appearance transformation models capture changes in the appearance of the foreground objects across two consecutives frames under low frame rate conditions. In the second problem, we present a system for pedestrian detection involving scenes captured by a mobile bus surveillance system. It integrates scene localization, foreground-background separation, and pedestrian detection modules into a unified detection framework. The scene localization module performs a two stage clustering of the video data.In the first stage, SIFT Homography is applied to cluster frames in terms of their structural similarity, and the second stage further clusters these aligned frames according to consistency in illumination. This produces clusters of images that are differential in viewpoint and lighting. A kernel density estimation (KDE) technique for colour and gradient is then used to construct background models for each image cluster, which is further used to detect candidate foreground pixels. Finally, using a hierarchical template matching approach, pedestrians can be detected.In addition to the second problem, we present three direct pedestrian detection methods that extend the HOG (Histogram of Oriented Gradient) techniques (Dalal and Triggs, 2005) and provide a comparative evaluation of these approaches. The three approaches include: a) a new histogram feature, that is formed by the weighted sum of both the gradient magnitude and the filter responses from a set of elongated Gaussian filters (Leung and Malik, 2001) corresponding to the quantised orientation, which we refer to as the Histogram of Oriented Gradient Banks (HOGB) approach; b) the codebook based HOG feature with branch-and-bound (efficient subwindow search) algorithm (Lampert et al., 2008) and; c) the codebook based HOGB approach.In the third problem, a unified framework that combines 3D and 2D background modelling is proposed to detect scene changes using a camera mounted on a moving vehicle. The 3D scene is first reconstructed from a set of videos taken at different times. The 3D background modelling identifies inconsistent scene structures as foreground objects. For the 2D approach, foreground objects are detected using the spatio-temporal MRF algorithm. Finally, the 3D and 2D results are combined using morphological operations.The significance of these research is that it provides basic frameworks for automatic large-scale mobile surveillance applications and facilitates many higher-level applications such as object tracking and behaviour analysis.
APA, Harvard, Vancouver, ISO, and other styles
3

Su, Cing-De, and 蘇慶德. "Vehicle Detection Algorithm Based on Modified Gradient Oriented Histogram Feature." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/4329xy.

Full text
Abstract:
碩士<br>國立雲林科技大學<br>電子工程系<br>104<br>In recent years, the public safety and home security are more and more important. The surveillance system will be becoming a hot industry. Therefore, this thesis proposed a modified gradient oriented histogram feature to identify vehicle for effective traffic control. This proposed method is divided into two parts. The first part is vehicle algorithm which use positive and negative samples to be input images in the training. The principal direction and the direction histogram are used for classification characteristics. Each pixel in the oriented image is represented by an angle bin, and 8*8 pixels for a cell histogram calculated is the presented by a 6*6 cell direction histogram. According the direction histogram, the maximal number of direction is the principal direction. The modified histogram orientation gradient (MHOG) feature is obtained by overlapping two cell in the cell direction histogram. The training parameters are obtained by inputting the MHOG features to SVM. When the principal direction of input image is same with the principal direction of training image, and the decision function of SVM is 1. Then, the window image will be a vehicle image. Experimental results show that the vehicle detect algorithm to achieve 98% which is better than SVM by HOG Feature detection. And average executing velocity of our method increase 40% in computer.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Histogram of Gradient Feature descriptor"

1

Won, Chee Sun. "Feature Extraction and Evaluation Using Edge Histogram Descriptor in MPEG-7." In Advances in Multimedia Information Processing - PCM 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30543-9_73.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Tsai, Wen-Kai, Sheng-Kai Lo, Ching-De Su, and Ming-Hwa Sheu. "Vehicle Detection Algorithm Based on Modified Gradient Oriented Histogram Feature." In Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50212-0_16.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Zhang, Huanhuan, and Lin Li. "Facial Expression Recognition Using Histogram Sequence of Local Gabor Gradient Code-Horizontal Diagonal and Oriented Gradient Descriptor." In Lecture Notes in Electrical Engineering. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6499-9_24.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Feng, Sibo, Shijia Li, Ping Guo, and Qian Yin. "Image Recognition with Histogram of Oriented Gradient Feature and Pseudoinverse Learning AutoEncoders." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70136-3_78.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Sirisha, B., B. Sandhya, and J. Prasanna Kumar. "Improved Multi-modal Image Registration Using Geometric Edge-Oriented Histogram Feature Descriptor: G-EOH." In Smart Intelligent Computing and Applications, Volume 1. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9669-5_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Liu, Mingkang, Qi Li, Zhenan Sun, and Qiyao Deng. "Face Clustering Utilizing Scalable Sparse Subspace Clustering and the Image Gradient Feature Descriptor." In Biometric Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97909-0_34.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Lai, Chi Qin, and Soo Siang Teoh. "Efficiency Improvement in the Extraction of Histogram Oriented Gradient Feature for Human Detection Using Selective Histogram Bins and PCA." In 9th International Conference on Robotic, Vision, Signal Processing and Power Applications. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1721-6_29.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Malik, Shaveta, Archana Mire, Amit Kumar Tyagi, and Vasudha Arora. "A Novel Feature Extractor Based on the Modified Approach of Histogram of Oriented Gradient." In Computational Science and Its Applications – ICCSA 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58817-5_54.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Sheriff, M., S. Jalaja, Kumar T. R. Dinesh, J. Pavithra, Yerramachetty Puja, and M. Sowmiya. "Face Emotion Recognition Using Histogram of Oriented Gradient (HOG) Feature Extraction and Neural Networks." In Recent Trends in Computational Intelligence and Its Application. CRC Press, 2023. http://dx.doi.org/10.1201/9781003388913-15.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Bondugula, Rohit Kumar, and Siba K. Udgata. "Modified Local Gradient Coding Pattern (MLGCP): A Handcrafted Feature Descriptor for Classification of Infectious Diseases." In Proceedings of Data Analytics and Management. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6553-3_36.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Histogram of Gradient Feature descriptor"

1

Chandrasekhar, V., G. Takacs, D. Chen, S. Tsai, R. Grzeszczuk, and B. Girod. "CHoG: Compressed histogram of gradients A low bit-rate feature descriptor." In 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2009. http://dx.doi.org/10.1109/cvprw.2009.5206733.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Chandrasekhar, Vijay, Gabriel Takacs, David Chen, Sam Tsai, Radek Grzeszczuk, and Bernd Girod. "CHoG: Compressed histogram of gradients A low bit-rate feature descriptor." In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2009. http://dx.doi.org/10.1109/cvpr.2009.5206733.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Hayashi, Ryosuke, Shuichi Enokida, and Toshiaki Ejima. "Local Feature Descriptor based on High-Order Co-Occurrence of Gradient Orientation Histograms." In Signal and Image Processing. ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.786-073.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Bao, Jia-qi, and Xing-peng Mao. "A novel approach for image feature description based on dual gradient orientation histogram." In Ninth International Conference on Digital Image Processing (ICDIP 2017), edited by Charles M. Falco and Xudong Jiang. SPIE, 2017. http://dx.doi.org/10.1117/12.2281669.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Zhang, Xinman, Dongxu Cheng, and Xuebin Xu. "Weighted Multimodal Biometric Recognition Algorithm Based on Histogram of Contourlet Oriented Gradient Feature Description." In 2019 Federated Conference on Computer Science and Information Systems. IEEE, 2019. http://dx.doi.org/10.15439/2019f178.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Sharma, Monika, and Hiranmay Ghosh. "Histogram of gradient magnitudes: A rotation invariant texture-descriptor." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351681.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Reddy, Dandu Amarnatha, Java Prakash Sahoo, and Samit Ari. "Hand Gesture Recognition Using Local Histogram Feature Descriptor." In 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2018. http://dx.doi.org/10.1109/icoei.2018.8553849.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Asha, M. Madlin, and J. Jennifer Ranjani. "Secure image retrieval using pyramid histogram of oriented gradient descriptor." In 2013 International Conference on Advanced Computing & Communication Systems (ICACCS). IEEE, 2013. http://dx.doi.org/10.1109/icaccs.2013.6938712.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Saeed, Fahman, Muhammad Hussain, and Hatim A. Aboalsamh. "Classification of Live Scanned Fingerprints using Histogram of Gradient Descriptor." In 2018 21st Saudi Computer Society National Computer Conference (NCC). IEEE, 2018. http://dx.doi.org/10.1109/ncg.2018.8592949.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Wang, GuanHao, Ning Li, and Shaoyuan Li. "Enhanced histogram feature descriptor for automated point cloud registration." In 2016 35th Chinese Control Conference (CCC). IEEE, 2016. http://dx.doi.org/10.1109/chicc.2016.7554466.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Histogram of Gradient Feature descriptor' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography