Academic literature on the topic 'Edge Histogram Descriptor'

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Journal articles on the topic "Edge Histogram Descriptor"

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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.

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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.
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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.

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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.
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Park, Sung Hee, Soo Jun Park, and Seon Hee Park. "A Protein Structure Retrieval System Using 3D Edge Histogram." Key Engineering Materials 277-279 (January 2005): 324–30. http://dx.doi.org/10.4028/www.scientific.net/kem.277-279.324.

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This paper proposes a novel protein structure descriptor (or representation) and its application for structure comparison. Since the functions of protein may come from its structure, the method of measuring structural similarities between two proteins can infer their functional closeness. In this paper, we have developed a novel descriptor (3D edge histogram) to compare the structures of proteins. The 3D edge histogram is a local distribution of bonds between the atoms in a protein. We have designed and implemented a protein structure retrieval system based on the 3D edge histogram to demonstrate that it could be effective in protein structure comparison. In this system, principal component analysis for aligning, voxelization for volume generation, quantization, 3D edge extraction, and comparison of 3D edge histogram are performed. The protein structure retrieval system using the 3D edge histogram shows fast retrieval with relatively precise results. It can be used for pre-screening purposes with a huge database.
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Won, Chee Sun Won, Dong Kwon Park Park, and Soo-Jun Park Park. "Efficient Use of MPEG-7 Edge Histogram Descriptor." ETRI Journal 24, no. 1 (2002): 23–30. http://dx.doi.org/10.4218/etrij.02.0102.0103.

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Imran, Muhammad, Rathiah Hashim, Aun Irtaz, Azhar Mahmood, and Umair Abdullah. "Class wise image retrieval through scalable color descriptor and edge histogram descriptor." International Journal of ADVANCED AND APPLIED SCIENCES 3, no. 12 (2016): 32–36. http://dx.doi.org/10.21833/ijaas.2016.12.005.

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EOM, M., and Y. CHOE. "Edge Histogram Descriptor in Wavelet Domain Based on JPEG2000." IEICE Transactions on Communications E90-B, no. 12 (2007): 3745–47. http://dx.doi.org/10.1093/ietcom/e90-b.12.3745.

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WU, PENG, YANGLIM CHOI, YONG MAN RO, and CHEE SUN WON. "MPEG-7 TEXTURE DESCRIPTORS." International Journal of Image and Graphics 01, no. 03 (2001): 547–63. http://dx.doi.org/10.1142/s0219467801000311.

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A texture description is useful in many applications including similarity based image search and browsing. We present here three texture descriptors that are being considered for the final committee draft of the ISO/MPEG-7 standard. A comprehensive overview of the syntax and semantics of these texture descriptors is provided. The Homogeneous Texture Descriptor (HTD) and the Edge Histogram Descriptor (EHD) are useful in similarity search. The HTD characterizes homogeneous texture regions and is also useful in texture classification and recognition. The EHD is applicable when the underlying texture is not homogeneous and can also be used in sketch based retrieval. In addition, a compact descriptor that facilitates browsing applications is also defined. These descriptors are selected after a highly competitive test and evaluation phase within the MPEG group and we briefly summarize the evaluation criteria, the datasets used and the experimental results.
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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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Hong, Yameng, Chengcai Leng, Xinyue Zhang, Zhao Pei, Irene Cheng, and Anup Basu. "HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor." Remote Sensing 13, no. 12 (2021): 2328. http://dx.doi.org/10.3390/rs13122328.

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Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images.
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Won, Chee Sun. "Image Fidelity Assessment Using the Edge Histogram Descriptor of MPEG-7." ETRI Journal 29, no. 5 (2007): 703–5. http://dx.doi.org/10.4218/etrij.07.0207.0071.

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