Journal articles: 'Edge Histogram Descriptor'

1

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

2

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

3

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Chu, Kai, and Guang-Hai Liu. "Image Retrieval Based on a Multi-Integration Features Model." Mathematical Problems in Engineering 2020 (March 9, 2020): 1–10. http://dx.doi.org/10.1155/2020/1461459.

Full text

Abstract:

Feature integration theory can be regarded as a perception theory, but the extraction of visual features using such a theory within the CBIR framework is a challenging problem. To address this problem, we extract the color and edge features based on a multi-integration features model and use these for image retrieval. A novel and highly simple but efficient visual feature descriptor, namely, a multi-integration features histogram, is proposed for image representation and content-based image retrieval. First, a color image is converted from the RGB to the HSV color space, and the color features and color differences are extracted. Then, the color differences are calculated to extract the edge features using a set of simple integration processes. Finally, combining the color, edge, and spatial layout features allows representing the image content. Experiments show that our method produces results comparable to existing and well-known methods on three datasets that contain 25,000 natural images. The performances are significantly better than that of the BOW histogram, local binary pattern histogram, histogram of oriented gradient, and multi-texton histogram, with performances similar to the color volume histogram.

APA, Harvard, Vancouver, ISO, and other styles

12

Ma, Tao, Jie Ma, and Kun Yu. "A Local Feature Descriptor Based on Oriented Structure Maps with Guided Filtering for Multispectral Remote Sensing Image Matching." Remote Sensing 11, no. 8 (2019): 951. http://dx.doi.org/10.3390/rs11080951.

Full text

Abstract:

Multispectral image matching plays a very important role in remote sensing image processing and can be applied for registering the complementary information captured by different sensors. Due to the nonlinear intensity difference in multispectral images, many classic descriptors designed for images of the same spectrum are unable to work well. To cope with this problem, this paper proposes a new local feature descriptor termed histogram of oriented structure maps (HOSM) for multispectral image matching tasks. This proposed method consists of three steps. First, we propose a new method based on local contrast to construct the structure guidance images from the multispectral images by transferring the significant contours from source images to results, respectively. Second, we calculate oriented structure maps with guided image filtering. In details, we first construct edge maps by the progressive Sobel filters to extract the common structure characteristics from the multispectral images, and then we compute the oriented structure maps by performing the guided filtering on the edge maps with the structure guidance images constructed in the first step. Finally, we build the HOSM descriptor by calculating the histogram of oriented structure maps in a local region of each interest point and normalize the feature vector. The proposed HOSM descriptor was evaluated on three commonly used datasets and was compared with several state-of-the-art methods. The experimental results demonstrate that the HOSM descriptor can be robust to the nonlinear intensity difference in multispectral images and outperforms other methods.

APA, Harvard, Vancouver, ISO, and other styles

13

Kurchaniya, Diksha, and Punit K. Johari. "An Enhanced Approach of CBIR using Gabor Wavelet and Edge Histogram Descriptor." International Journal of Signal Processing, Image Processing and Pattern Recognition 10, no. 10 (2017): 17–28. http://dx.doi.org/10.14257/ijsip.2017.10.10.02.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Liu, Xiaomin, Jun-Bao Li, and Jeng-Shyang Pan. "Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images." Sensors 19, no. 19 (2019): 4244. http://dx.doi.org/10.3390/s19194244.

Full text

Abstract:

Infrared and visible image matching methods have been rising in popularity with the emergence of more kinds of sensors, which provide more applications in visual navigation, precision guidance, image fusion, and medical image analysis. In such applications, image matching is utilized for location, fusion, image analysis, and so on. In this paper, an infrared and visible image matching approach, based on distinct wavelength phase congruency (DWPC) and log-Gabor filters, is proposed. Furthermore, this method is modified for non-linear image matching with different physical wavelengths. Phase congruency (PC) theory is utilized to obtain PC images with intrinsic and affluent image features for images containing complex intensity changes or noise. Then, the maximum and minimum moments of the PC images are computed to obtain the corners in the matched images. In order to obtain the descriptors, log-Gabor filters are utilized and overlapping subregions are extracted in a neighborhood of certain pixels. In order to improve the accuracy of the algorithm, the moments of PCs in the original image and a Gaussian smoothed image are combined to detect the corners. Meanwhile, it is improper that the two matched images have the same PC wavelengths, due to the images having different physical wavelengths. Thus, in the experiment, the wavelength of the PC is changed for different physical wavelengths. For realistic application, BiDimRegression method is proposed to compute the similarity between two points set in infrared and visible images. The proposed approach is evaluated on four data sets with 237 pairs of visible and infrared images, and its performance is compared with state-of-the-art approaches: the edge-oriented histogram descriptor (EHD), phase congruency edge-oriented histogram descriptor (PCEHD), and log-Gabor histogram descriptor (LGHD) algorithms. The experimental results indicate that the accuracy rate of the proposed approach is 50% higher than the traditional approaches in infrared and visible images.

APA, Harvard, Vancouver, ISO, and other styles

15

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

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

16

Lu, Yu, Sook Yoon, Shan Juan Xie, Jucheng Yang, Zhihui Wang, and Dong Sun Park. "Efficient descriptor of histogram of salient edge orientation map for finger vein recognition." Applied Optics 53, no. 20 (2014): 4585. http://dx.doi.org/10.1364/ao.53.004585.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Zhao, Dong. "Rapid Multimodal Image Registration Based on the Local Edge Histogram." Mathematical Problems in Engineering 2021 (June 2, 2021): 1–9. http://dx.doi.org/10.1155/2021/5598177.

Full text

Abstract:

Due to significant differences in imaging mechanisms between multimodal images, registration methods have difficulty in achieving the ideal effect in terms of time consumption and matching precision. Therefore, this paper puts forward a rapid and robust method for multimodal image registration by exploiting local edge information. The method is based on the framework of SURF and can simultaneously achieve real time and accuracy. Due to the unpredictability of multimodal images’ textures, the local edge descriptor is built based on the edge histogram of neighborhood around keypoints. Moreover, in order to increase the robustness of the whole algorithm and maintain the SURF’s fast characteristic, saliency assessment of keypoints and the concept of self-similar factor are presented and introduced. Experimental results show that the proposed method achieves higher precision and consumes less time than other multimodality registration methods. In addition, the robustness and stability of the method are also demonstrated in the presence of image blurring, rotation, noise, and luminance variations.

APA, Harvard, Vancouver, ISO, and other styles

18

Fu, Z., Q. Qin, C. Wu, Y. Chang, and B. Luo. "A ROBUST DESCRIPTOR BASED ON SPATIAL AND FREQUENCY STRUCTURAL INFORMATION FOR VISIBLE AND THERMAL INFRARED IMAGE MATCHING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 719–22. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-719-2017.

Full text

Abstract:

Due to the differences of imaging principles, image matching between visible and thermal infrared images still exist new challenges and difficulties. Inspired by the complementary spatial and frequency information of geometric structural features, a robust descriptor is proposed for visible and thermal infrared images matching. We first divide two different spatial regions to the region around point of interest, using the histogram of oriented magnitudes, which corresponds to the 2-D structural shape information to describe the larger region and the edge oriented histogram to describe the spatial distribution for the smaller region. Then the two vectors are normalized and combined to a higher feature vector. Finally, our proposed descriptor is obtained by applying principal component analysis (PCA) to reduce the dimension of the combined high feature vector to make our descriptor more robust. Experimental results showed that our proposed method was provided with significant improvements in correct matching numbers and obvious advantages by complementing information within spatial and frequency structural information.

APA, Harvard, Vancouver, ISO, and other styles

19

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

20

A., Pradnya, and P. P. "Content based Image Retrieval (CBIR) System using Threshold based Color Layout Descriptor (CLD) and Edge Histogram Descriptor (EHD)." International Journal of Computer Applications 179, no. 41 (2018): 39–43. http://dx.doi.org/10.5120/ijca2018916985.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Mohanraj, V., V. Vaidehi, S. Vasuhi, and Ranajit Kumar. "A Novel Approach for Face Recognition under Varying Illumination Conditions." International Journal of Intelligent Information Technologies 14, no. 2 (2018): 22–42. http://dx.doi.org/10.4018/ijiit.2018040102.

Full text

Abstract:

Face recognition systems are in great demand for domestic and commercial applications. A novel feature extraction approach is proposed based on TanTrigg Lower Edge Directional Patterns for robust face recognition. Histogram of Orientated Gradients is used to detect faces and the facial landmarks are localized using Ensemble of Regression Trees. The detected face is rotated based on facial landmarks using affine transformation followed by cropping and resizing. TanTrigg preprocessor is used to convert the aligned face region into an illumination invariant region for better feature extraction. Eight directional Kirsch compass masks are convolved with the preprocessed face image. Feature descriptor is extracted by dividing the TTLEDP image into several sub-regions and concatenating the histograms of all the sub-regions. Chi-square distance metric is used to match faces from the trained feature space. The experimental results prove that the proposed TTLEDP feature descriptor has better recognition rate than existing methods, overcoming the challenges like varying illumination and noise

APA, Harvard, Vancouver, ISO, and other styles

22

Ayeche, Farid, and Adel Alti. "Novel Descriptors for Effective Recognition of Face and Facial Expressions." Revue d'Intelligence Artificielle 34, no. 5 (2020): 521–30. http://dx.doi.org/10.18280/ria.340501.

Full text

Abstract:

In this paper, we present a face recognition approach based on extended Histogram Oriented Gradient (HOG) descriptors to extract the facial expressions features allowing classifying the faces and facial expressions. The approach is based on determining the different directional codes on the face image based on edge response values to define the feature vector from the face image. Its size is reduced to improve the performance of the SVM (Support Vector Machine) classifier. Experiments are conducted using two public datasets: JAFFE for facial expression recognition and YALE for face recognition. Experimental results show that the proposed descriptor achieves recognition rate of 92.12% and execution time ranging from 0.4s to 0.7s in all evaluated databases compared with existing works. Experiments demonstrate and confirm both the effectiveness and the efficiency of the proposed descriptor.

APA, Harvard, Vancouver, ISO, and other styles

23

Kiranyaz, S., M. Ferreira, and M. Gabbouj. "A Generic Shape/Texture Descriptor Over Multiscale Edge Field: 2-D Walking Ant Histogram." IEEE Transactions on Image Processing 17, no. 3 (2008): 377–91. http://dx.doi.org/10.1109/tip.2007.915562.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Hua, Ji-Zhao, Guang-Hai Liu, and Shu-Xiang Song. "Content-Based Image Retrieval Using Color Volume Histograms." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 11 (2019): 1940010. http://dx.doi.org/10.1142/s021800141940010x.

Full text

Abstract:

Human visual perception has a close relationship with the HSV color space, which can be represented as a cylinder. The question of how visual features are extracted using such an attribute is important. In this paper, a new feature descriptor; namely, a color volume histogram, is proposed for image representation and content-based image retrieval. It converts a color image from RGB color space to HSV color space and then uniformly quantizes it into 72 bins of color cues and 32 bins of edge cues. Finally, color volumes are used to represent the image content. The proposed algorithm is extensively tested on two Corel datasets containing 15[Formula: see text]000 natural images. These image retrieval experiments show that the color volume histogram has the power to describe color, texture, shape and spatial features and performs significantly better than the local binary pattern histogram and multi-texton histogram approaches.

APA, Harvard, Vancouver, ISO, and other styles

25

Liu, Guang-Hai, and Zhao Wei. "Image Retrieval Using the Fused Perceptual Color Histogram." Computational Intelligence and Neuroscience 2020 (November 24, 2020): 1–10. http://dx.doi.org/10.1155/2020/8876480.

Full text

Abstract:

Extracting visual features for image retrieval by mimicking human cognition remains a challenge. Opponent color and HSV color spaces can mimic human visual perception well. In this paper, we improve and extend the CDH method using a multi-stage model to extract and represent an image in a way that mimics human perception. Our main contributions are as follows: (1) a visual feature descriptor is proposed to represent an image. It has the advantages of a histogram-based method and is consistent with visual perception factors such as spatial layout, intensity, edge orientation, and the opponent colors. (2) We improve the distance formula of CDHs; it can effectively adjust the similarity between images according to two parameters. The proposed method provides efficient performance in similar image retrieval rather than instance retrieval. Experiments with four benchmark datasets demonstrate that the proposed method can describe color, texture, and spatial features and performs significantly better than the color volume histogram, color difference histogram, local binary pattern histogram, and multi-texton histogram, and some SURF-based approaches.

APA, Harvard, Vancouver, ISO, and other styles

26

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

27

Zhang, Zheng-Ben, and Yu-Fen Wang. "Object Contour Tracking Algorithm of Infrared Image Under Complex Background." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 2 (2019): 351–55. http://dx.doi.org/10.20965/jaciii.2019.p0351.

Full text

Abstract:

Traditional image contour tracking algorithm has low tracking accuracy. To solve this problem, an object contour tracking algorithm based on local significant edge features in complex background is proposed. In the algorithm, the projective invariant is firstly introduced, to construct the geometric information descriptor between the edge positions of the infrared image, and set up the histogram of the feature number of each target contour. The geometric similarity between the features is measured by the pasteurized coefficient, the edge features of the neighbourhood around the object contour are established, and the object contour with significant features in the edge of the image is searched. Combining Shape-context operator with edge feature, the feature description vector can be formed, and Euclidean distance is defined to track measurement function. Using this function, the selected object contour is tracked preliminarily. The random consistency checking algorithm is used to eliminate the false tracking feature points and obtain the best tracking value of the object contour, thus the infrared image’s object contour tracking is carried out in the complex background. Experimental simulation shows that the proposed algorithm has high tracking accuracy and effectively improves the quality of infrared image analysis.

APA, Harvard, Vancouver, ISO, and other styles

28

Han, Xian-Hua, and Yen-Wei Chen. "Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms." International Journal of Biomedical Imaging 2011 (2011): 1–7. http://dx.doi.org/10.1155/2011/241396.

Full text

Abstract:

We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.

APA, Harvard, Vancouver, ISO, and other styles

29

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

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

30

Wang, Lina, Mingchao Sun, Jinghong Liu, Lihua Cao, and Guoqing Ma. "A Robust Algorithm Based on Phase Congruency for Optical and SAR Image Registration in Suburban Areas." Remote Sensing 12, no. 20 (2020): 3339. http://dx.doi.org/10.3390/rs12203339.

Full text

Abstract:

Automatic registration of optical and synthetic aperture radar (SAR) images is a challenging task due to the influence of SAR speckle noise and nonlinear radiometric differences. This study proposes a robust algorithm based on phase congruency to register optical and SAR images (ROS-PC). It consists of a uniform Harris feature detection method based on multi-moment of the phase congruency map (UMPC-Harris) and a local feature descriptor based on the histogram of phase congruency orientation on multi-scale max amplitude index maps (HOSMI). The UMPC-Harris detects corners and edge points based on a voting strategy, the multi-moment of phase congruency maps, and an overlapping block strategy, which is used to detect stable and uniformly distributed keypoints. Subsequently, HOSMI is derived for a keypoint by utilizing the histogram of phase congruency orientation on multi-scale max amplitude index maps, which effectively increases the discriminability and robustness of the final descriptor. Finally, experimental results obtained using simulated images show that the UMPC-Harris detector has a superior repeatability rate. The image registration results obtained on test images show that the ROS-PC is robust against SAR speckle noise and nonlinear radiometric differences. The ROS-PC can tolerate some rotational and scale changes.

APA, Harvard, Vancouver, ISO, and other styles

31

Vilar, Cristian, Silvia Krug, and Benny Thörnberg. "Processing chain for 3D histogram of gradients based real-time object recognition." International Journal of Advanced Robotic Systems 18, no. 1 (2021): 172988142097836. http://dx.doi.org/10.1177/1729881420978363.

Full text

Abstract:

3D object recognition has been a cutting-edge research topic since the popularization of depth cameras. These cameras enhance the perception of the environment and so are particularly suitable for autonomous robot navigation applications. Advanced deep learning approaches for 3D object recognition are based on complex algorithms and demand powerful hardware resources. However, autonomous robots and powered wheelchairs have limited resources, which affects the implementation of these algorithms for real-time performance. We propose to use instead a 3D voxel-based extension of the 2D histogram of oriented gradients (3DVHOG) as a handcrafted object descriptor for 3D object recognition in combination with a pose normalization method for rotational invariance and a supervised object classifier. The experimental goal is to reduce the overall complexity and the system hardware requirements, and thus enable a feasible real-time hardware implementation. This article compares the 3DVHOG object recognition rates with those of other 3D recognition approaches, using the ModelNet10 object data set as a reference. We analyze the recognition accuracy for 3DVHOG using a variety of voxel grid selections, different numbers of neurons ( Nh) in the single hidden layer feedforward neural network, and feature dimensionality reduction using principal component analysis. The experimental results show that the 3DVHOG descriptor achieves a recognition accuracy of 84.91% with a total processing time of 21.4 ms. Despite the lower recognition accuracy, this is close to the current state-of-the-art approaches for deep learning while enabling real-time performance.

APA, Harvard, Vancouver, ISO, and other styles

32

Su, Ching Hung, Huang Sen Chiu, Mohd Helmy A. Wahab, Tsai Ming Hsiehb, You Chiuan Li, and Jhao Hong Lin. "Images Retrieval Based on Integrated Features." Applied Mechanics and Materials 543-547 (March 2014): 2292–95. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2292.

Full text

Abstract:

We propose a practical image retrieval scheme to retrieve images efficiently. The proposed scheme transfers each image to a color sequence using straightforward 8 rules. Subsequently, using the color sequences to compare the images, namely color sequences comparison. We succeed in transferring the image retrieval problem to sequences comparison and subsequently using the color sequences comparison along with the texture feature of Edge Histogram Descriptor to compare the images of database. We succeed in transferring the image retrieval problem to quantized code comparison. Thus the computational complexity is decreased obviously. Our results illustrate it has virtues both of the content based image retrieval system and a text based image retrieval system.

APA, Harvard, Vancouver, ISO, and other styles

33

Kalsum, Tehmina, Zahid Mehmood, Farzana Kulsoom, et al. "Localization and classification of human facial emotions using local intensity order pattern and shape-based texture features." Journal of Intelligent & Fuzzy Systems 40, no. 5 (2021): 9311–31. http://dx.doi.org/10.3233/jifs-201799.

Full text

Abstract:

Facial emotion recognition system (FERS) recognize the person’s emotions based on various image processing stages including feature extraction as one of the major processing steps. In this study, we presented a hybrid approach for recognizing facial expressions by performing the feature level fusion of a local and a global feature descriptor that is classified by a support vector machine (SVM) classifier. Histogram of oriented gradients (HoG) is selected for the extraction of global facial features and local intensity order pattern (LIOP) to extract the local features. As HoG is a shape-based descriptor, with the help of edge information, it can extract the deformations caused in facial muscles due to changing emotions. On the contrary, LIOP works based on the information of pixels intensity order and is invariant to change in image viewpoint, illumination conditions, JPEG compression, and image blurring as well. Thus both the descriptors proved useful to recognize the emotions effectively in the images captured in both constrained and realistic scenarios. The performance of the proposed model is evaluated based on the lab-constrained datasets including CK+, TFEID, JAFFE as well as on realistic datasets including SFEW, RaF, and FER-2013 dataset. The optimal recognition accuracy of 99.8%, 98.2%, 93.5%, 78.1%, 63.0%, 56.0% achieved respectively for CK+, JAFFE, TFEID, RaF, FER-2013 and SFEW datasets respectively.

APA, Harvard, Vancouver, ISO, and other styles

34

Mohammed, Mamoun Jassim, Suphian Mohammed Tariq, and Hayder Ayad. "Isolated Arabic handwritten words recognition using EHD and HOG methods." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 801. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp801-808.

Full text

Abstract:

<span>Handwriting recognition is a growing field of study in computer vision, artificial intelligence and pattern recognition technology aimed to recognizing texts and handwritings of hefty amount of produced official documents and paper works by institutes or governments. Using computer to distinguish and make these documents accessible and approachable is the goal of these efforts. Moreover, recognition of text has accomplished practically a major progress in many domains such as security sector and e-government structure and more. A system for recognition text’s handwriting was presented here relied on edge histogram descriptor (EHD), histogram of orientated gradients (HOG) features extraction and support vector machine (SVM) as a classifier is proposed in this paper. HOG and EHD give an optimal features of the Arabic hand-written text by extracting the directional properties of the text. Besides that, SVM is a most common machine learning classifier that obtaining an essential classification results within various kernel functions. The experimental evaluation is carried out for Arabic handwritten images from IESK-ArDB database using HOG, EHD features and proposed work provides 85% recognition rate.</span>

APA, Harvard, Vancouver, ISO, and other styles

35

WEN, JING, BIN FANG, Y. Y. TANG, PATRICK S. P. WANG, MIAO CHENG, and TAIPING ZHANG. "COMBINING EODH AND DIRECTIONAL GRADIENT DENSITY FOR OFFLINE SIGNATURE VERIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 06 (2009): 1161–77. http://dx.doi.org/10.1142/s0218001409007491.

Full text

Abstract:

The main problem to identify skilled forgeries for offline signature verification lies in the fact that it is difficult to formalize distinguished feature representation of the signature patterns and design appropriate fusion scheme for various types of feature vectors. To tackle these problems, in this paper, we propose an approach to extract robust Edge Orientation Distance Histogram (EODH) descriptor which effectively reflects signature structure variations. In addition, directional gradient density features are employed for skilled forgery verification attempt. To exploit the full capacity of two sets of features, we designed the multilevel weighted fuzzy classifier and fuse match scores by way of selection priority. Experiments were conducted on a subcorpus of open MCYT signature database which is widely used for performance evaluation. It shows that the proposed method was able to improve verification accuracy.

APA, Harvard, Vancouver, ISO, and other styles

36

Raikwar, Suresh Chandra, Charul Bhatnagar, and Anand Singh Jalal. "A Novel Framework for Efficient Extraction of Meaningful Key Frames from Surveillance Video." International Journal of System Dynamics Applications 4, no. 2 (2015): 56–73. http://dx.doi.org/10.4018/ijsda.2015040104.

Full text

Abstract:

The key frame extraction, aimed at reducing the amount of information from a surveillance video for analysis by human. The key frame is an important frame of a video to provide an overview of the video. Extraction of key frames from surveillance video is of great interest in effective monitoring and later analysis of video. The computational cost of the existing methods of key frame extraction is very high. The proposed method is a framework for Key frame extraction from a long surveillance video with significantly reduced computational cost. The proposed framework incorporates human intelligence in the process of key frame extraction. The results of proposed framework are compared with the results of IMARS (IBM multimedia analysis and retrieval system), results of the key frame extraction methods based on entropy difference method, spatial color distribution method and edge histogram descriptor method. The proposed framework has been objectively evaluated by fidelity. The experimental results demonstrate evidence of the effectiveness of the proposed approach.

APA, Harvard, Vancouver, ISO, and other styles

37

Kugunavar, Sneha, and Prabhakar C. J. "Content-Based Medical Image Retrieval Using Delaunay Triangulation Segmentation Technique." Journal of Information Technology Research 14, no. 2 (2021): 48–66. http://dx.doi.org/10.4018/jitr.2021040103.

Full text

Abstract:

This article presents a novel technique for retrieval of lung images from the collection of medical CT images. The proposed content-based medical image retrieval (CBMIR) technique uses an automated image segmentation technique called Delaunay triangulation (DT) in order to segment lung organ (region of interest) from the original medical image. The proposed method extracts novel and discriminant features from the segmented lung region instead of extracting novel features from the whole original image. For the extraction of shape features, the authors employ edge histogram descriptor (EHD) and geometric moments (GM), and for the extraction of texture features, the authors use gray-level co-occurrence matrix (GLCM) technique. The shape and texture features are combined to form the hybrid feature which is used for retrieval of similar lung images. The proposed method is evaluated using two benchmark datasets of lung CT images. The simulation results prove that the proposed CBMIR framework shows improved performance in terms of retrieval accuracy and retrieval time.

APA, Harvard, Vancouver, ISO, and other styles

38

Kavitha, H., and M. V. Sudhamani. "Content-Based Image Retrieval Using Edge and Gradient Orientation Features of an Object in an Image From Database." Journal of Intelligent Systems 25, no. 3 (2016): 441–54. http://dx.doi.org/10.1515/jisys-2014-0088.

Full text

Abstract:

AbstractIn this work, we present a combination of edge feature and distribution of the gradient orientation of an object technique for content-based image retrieval (CBIR). First, the bidimensional empirical mode decomposition (BEMD) technique is employed to get the edge features of an image. Later, the information about the gradient orientation is obtained by the histogram of oriented gradient (HOG) descriptor. These two features are extracted from the images and stored in the database for further usage. When the user submits the query image, the features are extracted in same way and compared with the features of the data set images. Based on the similarity, the relevant images have been selected as a resultant set. These images are ranked from higher similarity to lower similarity and displayed on the user interface. The experiments are carried out using the Columbia Object Image Library (COIL-100) dataset. The COIL-100 database is a collection of 7200 color images belonging to 100 various objects, each with 72 different orientations. Our proposed method results are high with precision and recall values of 93.00 and 77.70, respectively. Taken individually, the precision and recall values for BEMD are 82.25 and 68.54 and for HOG are 85.00, 71.10, respectively. The observation from the experimental result is that the combined method performs better than the individual methods. Experiments are conducted in the presence of noise, and the robustness of the method is verified.

APA, Harvard, Vancouver, ISO, and other styles

39

Sharma, Prabhat, Bambam Kumar, and Dharmendra Singh. "Development of Adaptive Threshold and Data Smoothening Algorithm for GPR Imaging." Defence Science Journal 68, no. 3 (2018): 316. http://dx.doi.org/10.14429/dsj.68.12354.

Full text

Abstract:

There are many approaches available to separate the background and foreground in image processing applications. Currently, researchers are focusing on wavelet De-noising, curvelet threshold, Edge Histogram Descriptor threshold, Otsu thresholding, recursive thresholding and adaptive progressive thresholding. In fixed and predictable background conditions, above techniques separate background and foreground efficiently. In a common scenario, background reference is blind due to soil surface moisture content and its non-linearity. There are many methodologies proposed from time to time by researchers to solve this blind reference background separation. But challenges still now remain, because there are two major problems in ground penetrating radar imaging such as targets like ground enhances the false alarm and non-metallic target detection, where the threshold decision is a critical task. In this paper, a novel real time blind adaptive threshold algorithm is proposed for ground penetrating radar image processing. The blind threshold was decided to use normal random variable variance and image data variance. Further, the image was smoothened by random variance ratio to image data variance. Experimental results showed satisfactory results for the background separation and smoothening the targeted image data with the proposed algorithm.

APA, Harvard, Vancouver, ISO, and other styles

40

Gong, Jinqi, Xiangyun Hu, Shiyan Pang, and Kun Li. "Patch Matching and Dense CRF-Based Co-Refinement for Building Change Detection from Bi-Temporal Aerial Images." Sensors 19, no. 7 (2019): 1557. http://dx.doi.org/10.3390/s19071557.

Full text

Abstract:

The identification and monitoring of buildings from remotely sensed imagery are of considerable value for urbanization monitoring. Two outstanding issues in the detection of changes in buildings with composite structures and relief displacements are heterogeneous appearances and positional inconsistencies. In this paper, a novel patch-based matching approach is developed using densely connected conditional random field (CRF) optimization to detect building changes from bi-temporal aerial images. First, the bi-temporal aerial images are combined to obtain change information using an object-oriented technique, and then semantic segmentation based on a deep convolutional neural network is used to extract building areas. With the change information and extracted buildings, a graph-cuts-based segmentation algorithm is applied to generate the bi-temporal changed building proposals. Next, in the bi-temporal changed building proposals, corner and edge information are integrated for feature detection through a phase congruency (PC) model, and the structural feature descriptor, called the histogram of orientated PC, is used to perform patch-based roof matching. We determined the final change in buildings by gathering matched roof and bi-temporal changed building proposals using co-refinement based on CRF, which were further classified as “newly built,” “demolished”, or “changed”. Experiments were conducted with two typical datasets covering complex urban scenes with diverse building types. The results confirm the effectiveness and generality of the proposed algorithm, with more than 85% and 90% in overall accuracy and completeness, respectively.

APA, Harvard, Vancouver, ISO, and other styles

41

Maver, Jasna, and Danijel Skočaj. "EL: Local Image Descriptor Based on Extreme Responses to Partial Derivatives of 2D Gaussian Function." Mathematical Problems in Engineering 2019 (September 17, 2019): 1–10. http://dx.doi.org/10.1155/2019/1247925.

Full text

Abstract:

We propose a two-part local image descriptor EL (Edges and Lines), based on the strongest image responses to the first- and second-order partial derivatives of the two-dimensional Gaussian function. Using the steering theorems, the proposed method finds the filter orientations giving the strongest image responses. The orientations are quantized, and the magnitudes of the image responses are histogrammed. Iterative adaptive thresholding of histogram values is then applied to normalize the histogram, thereby making the descriptor robust to nonlinear illumination changes. The two-part descriptor is empirically evaluated on the HPatches benchmark for three different tasks, namely, patch verification, image matching, and patch retrieval. The proposed EL descriptor outperforms the traditional descriptors such as SIFT and RootSIFT on all three evaluation tasks and the deep-learning-based descriptors DeepCompare, DeepDesc, and TFeat on the tasks of image matching and patch retrieval.

APA, Harvard, Vancouver, ISO, and other styles

42

Zhao, Rongchang, Min Wu, Xiyao Liu, Beiji Zou, and Fangfang Li. "Orientation Histogram-Based Center-Surround Interaction: An Integration Approach for Contour Detection." Neural Computation 29, no. 1 (2017): 171–93. http://dx.doi.org/10.1162/neco_a_00911.

Full text

Abstract:

Contour is a critical feature for image description and object recognition in many computer vision tasks. However, detection of object contour remains a challenging problem because of disturbances from texture edges. This letter proposes a scheme to handle texture edges by implementing contour integration. The proposed scheme integrates structural segments into contours while inhibiting texture edges with the help of the orientation histogram-based center-surround interaction model. In the model, local edges within surroundings exert a modulatory effect on central contour cues based on the co-occurrence statistics of local edges described by the divergence of orientation histograms in the local region. We evaluate the proposed scheme on two well-known challenging boundary detection data sets (RuG and BSDS500). The experiments demonstrate that our scheme achieves a high [Formula: see text]-measure of up to 0.74. Results show that our scheme achieves integrating accurate contour while eliminating most of texture edges, a novel approach to long-range feature analysis.

APA, Harvard, Vancouver, ISO, and other styles

43

Yasmin, Mussarat, Muhammad Sharif, lsma lrum, and Sajjad Mohsin. "Powerful Descriptor for Image Retrieval Based on Angle Edge and Histograms." Journal of Applied Research and Technology 11, no. 5 (2013): 727–32. http://dx.doi.org/10.1016/s1665-6423(13)71581-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Fan, Deng Ping, Juan Wang, and Xue Mei Liang. "Improving Image Retrieval Using the Context-Aware Saliency Areas." Applied Mechanics and Materials 734 (February 2015): 596–99. http://dx.doi.org/10.4028/www.scientific.net/amm.734.596.

Full text

Abstract:

The Context-Aware Saliency (CA) model—is a new model used for saliency detection—has strong limitations: It is very time consuming. This paper improved the shortcoming of this model namely Fast-CA and proposed a novel framework for image retrieval and image representation. The proposed framework derives from Fast-CA and multi-texton histogram. And the mechanisms of visual attention are simulated and used to detect saliency areas of an image. Furthermore, a very simple threshold method is adopted to detect the dominant saliency areas. Color, texture and edge features are further extracted to describe image content within the dominant saliency areas, and then those features are integrated into one entity as image representation, where image representation is so called the dominant saliency areas histogram (DSAH) and used for image retrieval. Experimental results indicate that our algorithm outperform multi-texton histogram (MTH) and edge histogram descriptors (EHD) on Corel dataset with 10000 natural images.

APA, Harvard, Vancouver, ISO, and other styles

45

ZHANG, BAILING, and YIFAN ZHOU. "VEHICLE TYPE AND MAKE RECOGNITION BY COMBINED FEATURES AND ROTATION FOREST ENSEMBLE." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 03 (2012): 1250004. http://dx.doi.org/10.1142/s0218001412500048.

Full text

Abstract:

Vehicle type/make recognition based on images captured by surveillance cameras is a challenging task in intelligent transportation system and automatic surveillance. In this paper, we comparatively studied two feature extraction methods for image description, i.e. a new multiresolution analysis tool called Fast Discrete Curvelet Transform and the pyramid histogram of oriented gradients (PHOG). Curvelet Transform has better directional and edge representation abilities than widely used wavelet transform, which is particularly appropriate for the description of images rich with edges. PHOG represents the local shape by a histogram of edge orientations computed for each image sub-region, quantized into a number of bins, thus has the ascendency in its description of more discriminating information. A composite feature description from PHOG and Curvelet can further increase the accuracy of classification by taking their complementary information. We also investigated the applicability of the Rotation Forest (RF) ensemble method for vehicle classification based on the combined features. The RF ensemble contains a set of base multilayer perceptrons which are trained using principal component analysis to rotate the original axes of combined features of vehicle images. The class label is assigned by the ensemble via majority voting. Experimental results using more than 600 images from 21 makes of cars/vans show the effectiveness of the proposed approach. The composite feature is better than any single feature in the classification accuracy and the ensemble model produces better performance compared to any of the individual neural network base classifier. With a moderate ensemble size of 20, the Rotation Forest ensembles offers a classification rate close to 96.5%, exhibiting promising potentials for real-life applications.

APA, Harvard, Vancouver, ISO, and other styles

46

Grycuk, Rafał, Adam Wojciechowski, Wei Wei, and Agnieszka Siwocha. "Detecting Visual Objects by Edge Crawling." Journal of Artificial Intelligence and Soft Computing Research 10, no. 3 (2020): 223–37. http://dx.doi.org/10.2478/jaiscr-2020-0015.

Full text

Abstract:

AbstractContent-based image retrieval methods develop rapidly with a growing scale of image repositories. They are usually based on comparing and indexing some image features. We developed a new algorithm for finding objects in images by traversing their edges. Moreover, we describe the objects by histograms of local features and angles. We use such a description to retrieve similar images fast. We performed extensive experiments on three established image datasets proving the effectiveness of the proposed method.

APA, Harvard, Vancouver, ISO, and other styles

47

Panakarn, Piyanan, Suphakant Phimoltares, and Chidchanok Lursinsap. "Identifying Sport Types and Postures with Complex Background by Fusion of Local Descriptors." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 02 (2015): 1550008. http://dx.doi.org/10.1142/s0218001415500081.

Full text

Abstract:

Sport type classification and posture identification based on visual meaning of posture semantic in still images are challenging tasks. The difficulty of these tasks comes from the complex image content consisting of a player's posture, the color and texture of a player's clothes as well as complexity of the background. Player detection is one of the most important tasks in posture identification. For sport type classification without object segmentation, the new set of features, based on 64-bins color histogram, DCT coefficients, and Cb and Cr components, is introduced. To achieve high accuracy, an appropriate feature extraction technique should be also realized. For posture identification, three algorithms, concerning player region detection and suitable features for posture identification, are proposed namely blurred background elimination, irrelevant region elimination, and trimming players region. The DFT coefficients, based on image resizing and slicing techniques, are used as significant features in posture identification. Our proposed features were compared with Edge Histogram and Region-based Shape (EH and RS), two of MPEG-7 descriptors. The experimental results showed that our proposed features yielded better performance with 85.76% of accuracy in sport classification and 86.66% of accuracy in posture identification.

APA, Harvard, Vancouver, ISO, and other styles

48

Chaki, Jyotismita, Nilanjan Dey, Luminiţa Moraru, and Fuqian Shi. "Fragmented plant leaf recognition: Bag-of-features, fuzzy-color and edge-texture histogram descriptors with multi-layer perceptron." Optik 181 (March 2019): 639–50. http://dx.doi.org/10.1016/j.ijleo.2018.12.107.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Fu, Y., Y. Ye, G. Liu, B. Zhang, and R. Zhang. "ROBUST MULTIMODAL IMAGE MATCHING BASED ON MAIN STRUCTURE FEATURE REPRESENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 583–89. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-583-2020.

Full text

Abstract:

Abstract. Image matching is a crucial procedure for multimodal remote sensing image processing. However, the performance of conventional methods is often degraded in matching multimodal images due to significant nonlinear intensity differences. To address this problem, this letter proposes a novel image feature representation named Main Structure with Histogram of Orientated Phase Congruency (M-HOPC). M-HOPC is able to precisely capture similar structure properties between multimodal images by reinforcing the main structure information for the construction of the phase congruency feature description. Specifically, each pixel of an image is assigned an independent weight for feature descriptor according to the main structure such as large contours and edges. Then M-HOPC is integrated as the similarity measure for correspondence detection by a template matching scheme. Three pairs of multimodal images including optical, LiDAR, and SAR data have been used to evaluate the proposed method. The results show that M-HOPC is robust to nonlinear intensity differences and achieves the superior matching performance compared with other state-of-the-art methods.

APA, Harvard, Vancouver, ISO, and other styles

50

Zhou, Guodong, Huailiang Zhang, and Raquel Martínez Lucas. "Compressed sensing image restoration algorithm based on improved SURF operator." Open Physics 16, no. 1 (2018): 1033–45. http://dx.doi.org/10.1515/phys-2018-0124.

Full text

Abstract:

Abstract Aiming at the excellent descriptive ability of SURF operator for local features of images, except for the shortcoming of global feature description ability, a compressed sensing image restoration algorithm based on improved SURF operator is proposed. The SURF feature vector set of the image is extracted, and the vector set data is reduced into a single high-dimensional feature vector by using a histogram algorithm, and then the image HSV color histogram is extracted.MSA image decomposition algorithm is used to obtain sparse representation of image feature vectors. Total variation curvature diffusion method and Bayesian weighting method perform image restoration for data smoothing feature and local similarity feature of texture part respectively. A compressed sensing image restoration model is obtained by using Schatten-p norm, and image color supplement is performed on the model. The compressed sensing image is iteratively solved by alternating optimization method, and the compressed sensing image is restored. The experimental results show that the proposed algorithm has good restoration performance, and the restored image has finer edge and texture structure and better visual effect.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Edge Histogram Descriptor'

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