Relevant bibliographies by topics / Gradient magnitude histogram

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Gradient magnitude histogram'

Author: Grafiati
Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "Gradient magnitude histogram"

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

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

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In this paper, we propose a gray-scale texture descriptor, name the global and local oriented edge magnitude patterns (GLOEMP), for texture classification. GLOEMP is a framework, which is able to effectively combine local texture, global structure information and contrast of texture images. In GLOEMP, the principal orientation is determined by Histogram of Gradient (HOG) feature, then each direction is respectively shown in detail by a local binary patterns (LBP) occurrence histogram. Due to the fact that GLOEMP characterizes image information across different directions, it contains very abundant information. The global-level rotation compensation method is proposed, which shifts the principal orientation of the HOG to the first position, thus allowing GLOEMP to be robust to rotations. In addition, gradient magnitudes are used as weights to add to the histogram, making GLOEMP robust to lighting variances as well, and it also possesses a strong ability to express edge information. The experimental results obtained from the representative databases demonstrate that the proposed GLOEMP framework is capable of achieving significant improvement, in some cases reaching classification accuracy of 10% higher than over the traditional rotation invariant LBP method.
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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 have validated the effectiveness of the designed CLDTP descriptor.
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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 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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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 observe that the performance is better than using the maximum magnitudes in several cases. Therefore, in this paper, we propose a novel approach for extracting HOG from color images such as Color Component Selection and Color Component Fusion. We also propose the extended kernels in order to improve the performance of HOG. With our new approaches in the color component analysis, the experimental results of several facial benchmark datasets are enhanced with the increment from 3 to 10% of accuracy. Specifically, a 95.92% of precision is achieved on the Face AR database and 75% on the Georgia Face database. The results are better more than 10 times compared with the original HOG approach.
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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 (HOG) is a feature extraction used for face identification based on histogram of gradient orientation and gradient magnitude. This application is implemented using the Dlib library for facial recognition. The implementation of this method is expected to improve parking security and provide a record of parked vehicles. The results of testing the implementation of facial recognition methods into android applications show very satisfactory results. With the results of testing the QR code scanning accuracy of 100% and an accuracy of 90% for a 7% damage rate and an accuracy of 85% for a 15% damage rate, and the results of facial recognition testing of 90% on face photos wearing helmets and an accuracy of 92% on photo of face without helmet.
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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 achieve more accurate clustering results. In order to obtain meaningful clustering results, we introduce two similarity measurements: IGM similarity and spatial similarity. These two similarity measurements can effectively bring the voxels of the same tissue together and differentiate the voxels of different tissues so that the generated TFs can assign different optical properties to different tissues. Before performing the clustering algorithm on the IGM histogram, we propose to remove noisy voxels based on the spatial information of voxels. Our method does not require users to input the number of clusters, and the classification and visualization process is automatic and efficient. Experiments on various datasets demonstrate the effectiveness of the proposed method.
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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 the distribution of intensity difference by counting up the number of magnitude level occurrences in the local neighborhood. Based on this, a forensic approach for image seam carving is proposed in this paper. Firstly, the histogram features of LNMOP and HOG (histogram of oriented gradient) are extracted from the images for seam carving forgery detection. Then, the final features for the classifier are selected from the extracted LNMOP features. The LNMOP feature selection method based on HOG feature hierarchical matching is proposed, which determines the LNMOP features to be selected by the HOG feature level. Finally, support vector machine (SVM) is utilized as a classifier to train and test by the above selected features to distinguish tampered images from normal images. In order to create training sets and test sets, images are extracted from the UCID image database. The experimental results of a large number of test images show that the proposed approach can achieve an overall better performance than the state-of-the-art approaches.
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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 structural and textural information during the denoising time. A major contribution that improves the interpretability of images by emphasizing structural aspects that is inherent to the image. The proposed DCMH_3D_GANaverage results show exceptional performance, with an SSIM of 0.972995 and PSNR of 48.74, highlighting the effectiveness of the DCMH-based method in enhancing medical picture diagnosis. The capacity of Structured Loss to improve picture interpretability and lead to a more precise diagnosis is unquestionably advantageous. The newly developed DCMH-based approach, which includes texture loss and structured components, is a promising development in healthcare image processing that will enable better patient care through enhanced diagnostic abilities.
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Dissertations / Theses on the topic "Gradient magnitude histogram"

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Nallaivarothayan, Hajananth. "Video based detection of normal and anomalous behaviour of individuals." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/106947/1/Hajananth_Nallaivarothayan_Thesis.pdf.

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This PhD research has proposed novel computer vision and machine learning algorithms for the problem of video based anomalous event detection of individuals. Varieties of Hidden Markov Models were designed to model the temporal and spatial causalities of crowd behaviour. A Markov Random Field on top of a Gaussian Mixture Model is proposed to incorporate spatial context information during classification. Discriminative conditional random field methods are also proposed. Novel features are proposed to extract motion and appearance information. Most of the proposed approaches comprehensively outperform other techniques on publicly available datasets during the time of publications originating from the results.
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Book chapters on the topic "Gradient magnitude histogram"

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Guzmán-Ramírez, Enrique, Ayax García, Esteban Guerrero-Ramírez, Antonio Orantes Molina, Oscar Ramírez, and Ignacio Arroyo. "Multi-object Recognition Using a Feature Descriptor and Neural Classifier." In Vision Sensors - Recent Advances [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.106754.

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In the field of object recognition, feature descriptors have proven to be able to provide accurate representations of objects facilitating the recognition task. In this sense, Histograms of Oriented Gradients (HOG), a descriptor that uses this approach, together with Support Vector Machines (SVM) have proven to be successful human detection methods. In this paper, we propose a scheme consisting of improved HOG and a classifier with a neural approach to producing a robust system for object recognition. The main contributions of this work are: First, we propose an improved gradient calculation that allows for better discrimination for the classifier system, which consists of performing a threshold over both the magnitude and direction of the gradients. This improvement reduces the rate of false positives. Second, although HOG is particularly suited for human detection, we demonstrate that it can be used to represent different objects accurately, and even perform well in multi-class applications. Third, we show that a classifier that uses a neuronal approach is an excellent complement to a HOG-based feature extractor. Finally, experimental results on the well-known Caltech 101 dataset illustrate the benefits of the proposed scheme.
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Ragb, Hussin K., and Vijayan K. Asari. "Local Phase Features in Chromatic Domain for Human Detection." In Human Performance Technology. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8356-1.ch033.

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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 maximum phase congruency values are selected from the corresponding color channels. Histograms of the phase congruency values of the local regions in the image are computed with respect to its orientation. These histograms are concatenated to construct the proposed descriptor. Results of the experiments performed on the proposed descriptor show that it has better detection performance and lower error rates than a set of the state of the art feature extraction methodologies.
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Conference papers on the topic "Gradient magnitude histogram"

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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.

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