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Academic literature on the topic 'Histograms intersection'

Author: Grafiati

Published: 3 June 2025

Last updated: 7 July 2025

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Journal articles on the topic "Histograms intersection"

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Sun, Shengnan, Lindu Zhao, and Shicai Yang. "Gabor Weber Local Descriptor for Bovine Iris Recognition." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/920597.

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Iris recognition is a robust biometric technology. This paper proposes a novel local descriptor for bovine iris recognition, named Gabor Weber local descriptor (GWLD). We first compute the Gabor magnitude maps for the input bovine iris image, and then calculate the differential excitation and orientation for each pixel over each Gabor magnitude map. After that, we use these differential excitations and orientations to construct the GWLD histogram representation. Finally, histogram intersection is adopted to measure the similarity between different GWLD histograms. The experimental results on the SEU bovine iris database verify the representation power of our proposed local descriptor.

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Ragumadhavan, R., K. R. Aravind Britto, and R. Vimala. "Melanoma Skin Cancer Detection Using Wavelet Transform and Local Ternary Pattern." Journal of Medical Imaging and Health Informatics 12, no. 1 (2022): 15–19. http://dx.doi.org/10.1166/jmihi.2022.3856.

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Melanoma is the most serious form of skin cancer that affects millions of people globally. Through image analytics, early identification of skin cancer is enabled, resulting in more effective treatment and a lower mortality rate. The ph2 and human against machine datasets were used to collect images. After preprocessing the image with a weighted median filter, segmentation is investigated using a number of common techniques, with the best result generated by combining watershed transform and maximum similarity region merging. U-net architecture is explored for segmentation. Segmentation efficiency is calculated by dice loss and Jaccard coefficient. Segmentation architecture outperform the conventional method. Additionally, a novel wavelet transform-based approach is used to extract features, followed by local ternary pattern analysis. The intersection of the histograms, the Bhattacharya distance, the Chi-square distance, and the Pearson correlation coefficients are all computed. This inquiry makes use of only the Histogram intersection and Chi-square distance characteristics. Additional categorization is examined through the use of a range of machine learning algorithms, including the k-nearest neighbour approach, Bayesian classification, decision trees, and Support Vector Machines (SVM). When a Radial Basis Function (RBF) kernel based SVM is applied, the classification accuracy is maximised. This work is entirely devoted to binary categorization. As evidenced by the data, they outperform other state-of-the-art approaches reported in the literature. SVM classifies data with an accuracy of 98.6 percent. Weighted median filter, Watershed transform, Merging regions with the highest degree of similarity, Wavelet transform, Local Ternary Pattern, Histogram intersection Pearson correlation coefficient, chi-square distance Distance between Bhattacharya and support vector machine.

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Wei, Guodong, Ying Tian, Shun’ichi Kaneko, and Zhengang Jiang. "Robust Template Matching Using Multiple-Layered Absent Color Indexing." Sensors 22, no. 17 (2022): 6661. http://dx.doi.org/10.3390/s22176661.

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Color is an essential feature in histogram-based matching. This can be extracted as statistical data during the comparison process. Although the applicability of color features in histogram-based techniques has been proven, position information is lacking during the matching process. We present a conceptually simple and effective method called multiple-layered absent color indexing (ABC-ML) for template matching. Apparent and absent color histograms are obtained from the original color histogram, where the absent colors belong to low-frequency or vacant bins. To determine the color range of compared images, we propose a total color space (TCS) that can determine the operating range of the histogram bins. Furthermore, we invert the absent colors to obtain the properties of these colors using threshold hT. Then, we compute the similarity using the intersection. A multiple-layered structure is proposed against the shift issue in histogram-based approaches. Each layer is constructed using the isotonic principle. Thus, absent color indexing and multiple-layered structure are combined to solve the precision problem. Our experiments on real-world images and open data demonstrated that they have produced state-of-the-art results. Moreover, they retained the histogram merits of robustness in cases of deformation and scaling.

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Okatev, Roman S., and Peter G. Frick. "Recurrence time statistics for chaos analysis in dynamical systems." Вестник Пермского университета. Физика, no. 3 (2024): 19–27. http://dx.doi.org/10.17072/1994-3598-2024-3-19-27.

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We propose a method of delimiting the existence domains of periodic, quasi-periodic and chaotic solutions of dynamical systems in the parameter space. The method is based on an analysis of the sampling of the times of passing a phase point from the previous intersection of the Poincaré plane to the next one (or from one local maximum to the next). An algorithm for generating a sample of recurrence times with subsequent analysis of the histogram of the obtained sample is constructed. A simple measure of histogram filling allows us to separate periodic and chaotic modes, as well as to estimate the degree of chaoticity of intermediate modes. On simple model signals it is shown that the distribution of recurrence times gives information not contained in the spectral densities of the signal. Then, on the example of the classical Lorentz system, it is shown how a simple measure of filling the histogram of recurrence times allows us to obtain a visual map of modes. The paper presents the results of a comparative analysis of power spectral density and histograms of recurrence times for different modes realized in the Lorentz system at different values of the control parameter (Rayleigh number).

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Sinha, Abhijeet Kumar, and K. K. Shukla. "A Study of Distance Metrics in Histogram Based Image Retrieval." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 3 (2013): 821–30. http://dx.doi.org/10.24297/ijct.v4i3.4205.

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There has been a profound expansion of digital data both in terms of quality and heterogeneity. Trivial searching techniques of images by using metadata, keywords or tags are not sufficient. Efficient Content-based Image Retrieval (CBIR) is certainly the only solution to this problem. Difference between colors of two images can be an important metric to measure their similarity or dissimilarity. Content-based Image Retrieval is all about generating signatures of images in database and comparing the signature of the query image with these stored signatures. Color histogram can be used as signature of an image and used to compare two images based on certain distance metric.In this study, COREL Database is used for an exhaustive study of various distance metrics on different color spaces. Euclidean distance, Manhattan distance, Histogram Intersection and Vector Cosine Angle distances are used to compare histograms in both RGB and HSV color spaces. So, a total of 8 distance metrics for comparison of images for the sake of CBIR are discussed in this work.

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Yu, Peng Fei, Hao Zhou, and Hai Yan Li. "Personal Identification Using Finger-Knuckle-Print Based on Local Binary Pattern." Applied Mechanics and Materials 441 (December 2013): 703–6. http://dx.doi.org/10.4028/www.scientific.net/amm.441.703.

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Over the last ten years, considerable progress has been made on the new hand-based biometric recognition, such as palmprint and hand vein. During this period, it has been proved that Finger-Knuckle-Print (FKP) can be used as a biometric identifier. In this paper, we present an effective FKP identification method based on Local Binary Pattern (LBP), whose idea is to divide the region of interest (ROI) of FKP into a set of sub-image blocks, which can be applied to extract the local features of the FKP. After that, LBP histograms of image blocks in a FKP ROI image are connected together to build the feature vector of the FKP ROI image. In the match stage, histogram intersection distance is applied as the similarity measurement between sample and template. Experimental results conducted on a database of 165 persons (4 fingers per person) show that the proposed method is effective.

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Kusakunniran, Worapan, Anuwat Wiratsudakul, Udom Chuachan, et al. "Biometric for Cattle Identification using Muzzle Patterns." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (2020): 2056007. http://dx.doi.org/10.1142/s0218001420560078.

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Similar to human biometrics such as faces and fingerprints, animals also have biometrics for individual identifiers. This research paper works on biometrics of cattle using images of muzzle patterns. The proposed approach begins with a training process to construct a cattle face localization model using a Haar feature-based cascade classifier. Then, the watershed technique is applied to segment a region of interest (RoI) of a muzzle area in the detected region of the cattle face. This muzzle ROI is further enhanced to make ridge lines more outstanding. The next step, using two approaches, is to extract a main feature descriptor based on a bag of histograms of oriented gradients (BoHoG) and a histogram of local binary patterns (LBP). Then, the support vector machine (SVM) is applied with the histogram intersection kernel for a final cattle identifier. The proposed method is evaluated using five different datasets including one existing cattle dataset used in previous research works, one newly collected dataset of swamp buffalo captured in a controlled environment, and three newly collected datasets of swamp buffalo captured in an outdoor field environment. This outdoor field environment includes challenges of freely moving cattle and differences in daylight. It could achieve a promising accuracy of 95% for a large dataset of 431 subjects.

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Molnár, László, József Domokos, Isabella Ferando та István Módy. "Bimodal RMS distributions for the objective detection of theta (θ) and gamma (γ) brain oscillations during long-term continuous LFP recordings in mice". MACRo 2015 2, № 1 (2017): 31–37. http://dx.doi.org/10.1515/macro-2017-0004.

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AbstractIn the rodent’s brain the theta (5-12 Hz) and gamma (30-120 Hz) oscillations can be readily detected in local field potential (LFP) recordings, but there is no universal consensus about an objective threshold for their detection.We FIR-filtered the long-term local field potential (LFP) recordings for theta and gamma oscillations. The RMS (root mean square) values of 8 s epochs in 0.5-4 s steps (using corresponding overlaps) were obtained from the filtered recordings. For both theta and gamma oscillations, the histograms showed a bimodal distribution well fitted by two Gaussians. The point of intersection between the two distributions proved to be the most reliable for separating the RMS values belonging to the two Gaussians.

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Morsy, S., A. Shaker, and A. El-Rabbany. "CLUSTERING OF MULTISPECTRAL AIRBORNE LASER SCANNING DATA USING GAUSSIAN DECOMPOSITION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 269–76. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-269-2017.

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With the evolution of the LiDAR technology, multispectral airborne laser scanning systems are currently available. The first operational multispectral airborne LiDAR sensor, the Optech Titan, acquires LiDAR point clouds at three different wavelengths (1.550, 1.064, 0.532&amp;thinsp;μm), allowing the acquisition of different spectral information of land surface. Consequently, the recent studies are devoted to use the radiometric information (i.e., intensity) of the LiDAR data along with the geometric information (e.g., height) for classification purposes. In this study, a data clustering method, based on Gaussian decomposition, is presented. First, a ground filtering mechanism is applied to separate non-ground from ground points. Then, three normalized difference vegetation indices (NDVIs) are computed for both non-ground and ground points, followed by histograms construction from each NDVI. The Gaussian function model is used to decompose the histograms into a number of Gaussian components. The maximum likelihood estimate of the Gaussian components is then optimized using Expectation – Maximization algorithm. The intersection points of the adjacent Gaussian components are subsequently used as threshold values, whereas different classes can be clustered. This method is used to classify the terrain of an urban area in Oshawa, Ontario, Canada, into four main classes, namely roofs, trees, asphalt and grass. It is shown that the proposed method has achieved an overall accuracy up to 95.1&amp;thinsp;% using different NDVIs.

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Lemenkova, Polina. "Distance-based vegetation indices computed by SAGA GIS: A comparison of the perpendicular and transformed soil adjusted approaches for the LANDSAT TM image." Poljoprivredna tehnika 46, no. 3 (2021): 49–60. http://dx.doi.org/10.5937/poljteh2103049l.

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Landsat-TM of 2001 covering Iceland (15.5°W-21°W, 64.5°N-67°N) was processed using SAGA GIS for testing distance-based Vegetation Indices (VIs): four approaches of Perpendicular Vegetation Index (PVI) and two approaches of Transformed Soil Adjusted Vegetation Index TSAVI. The PVI of vegetation from the soil background line indicated healthiness as a leaf area index (LAI). The results showed that the reflectance for vegetation has a linear relation with soil background line. Four PVI models and two TSAVI shown coefficients of determination with LAI. The dataset demonstrate variations in the calculated coefficients. The mode in the histograms of the PVI based on four different algorithms show the difference:-7.1,-8.36, 2.78 and 7.0. The dataset for the two approaches of TSAVI: first case ranges in 4.4.-80.6 with a bell-shape mode of a histogram (8.09 to 23.29) for the first algorithm and an irregular shape for the second algorithm with several modes starting from 0.11 to 0.2 and decreasing to 0.26. SAGA GIS permits the calculation of PVI and TSAVI by computed NDVI based on the intersection of vegetation and soil background. Masking the NIR and R, a linear regression of grids was performed using an equation embedded in SAGA GIS. The advantages of the distance-based PVI and TSAVI consists in the adjusted position of pixels on the soil brightness line which refines it comparing to the slope-based VIs. The paper demonstrates SAGA GIS application in agricultural studies.

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Book chapters on the topic "Histograms intersection"

1

Cheng, Jian, Siegbert Drüe, and Georg Hartmann. "Graph Based Histogram Intersection for Efficient Location of Color Objects." In Informatik aktuell. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59802-9_38.

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Freytag, Alexander, Erik Rodner, Paul Bodesheim, and Joachim Denzler. "Rapid Uncertainty Computation with Gaussian Processes and Histogram Intersection Kernels." In Computer Vision – ACCV 2012. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37444-9_40.

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Zheng, Yong, Mayur Agnani, and Mili Singh. "Identification of Grey Sheep Users by Histogram Intersection in Recommender Systems." In Advanced Data Mining and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69179-4_11.

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Müller, Wolfgang, and Andreas Henrich. "Faster Exact Histogram Intersection on Large Data Collections Using Inverted VA-Files." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27814-6_54.

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Mei, Lin, Gerd Brunner, Lokesh Setia, and Hans Burkhardt. "Kernel Biased Discriminant Analysis Using Histogram Intersection Kernel for Content-Based Image Retrieval." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11508069_9.

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Bhattacharjee, Tanusree, Biplab Banerjee, and Nirmalya Chowdhury. "An Interactive Content Based Image Retrieval Method Integrating Intersection Kernel Based Support Vector Machine and Histogram Intersection Based Similarity Measure for Nearest Neighbor Ranking." In Information and Communication Technologies. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15766-0_74.

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Banerjee, Biplab, Tanusree Bhattacharjee, and Nirmalya Chowdhury. "Image Object Classification Using Scale Invariant Feature Transform Descriptor with Support Vector Machine Classifier with Histogram Intersection Kernel." In Information and Communication Technologies. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15766-0_71.

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Sasaki Shiori, Itabashi Yoshiko, Kiyoki Yasushi, and Chen Xing. "An Image-Query Creation Method for Representing Impression by Color-based Combination of Multiple Images." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2009. https://doi.org/10.3233/978-1-58603-957-8-105.

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This paper presents a dynamic image-query creation and metadata extraction method with semantic correlation computation between color-combinations and impressions of multiple image data. The main features of our method are (1) to create an image-query which reflects user's intention dynamically according to the color-based combinations of images with common features selected by a user as context, (2) to extract appropriate impression by each image collection which cannot be easily extracted from a single image, (3) to provide users an image retrieval environment reflecting historical and cultural semantics and impression of color especially for cultural properties, and (4) to enable an image retrieval environment for the collection of images by time, culture, author e.t.c.. The queries are created by the combination of multiple image sets and operations, which are intersection, accumulation, average, difference of color elements of sample images. First, a set of multiple images with common features is set as sample data for a query creation. Second, color histograms are extracted from the image sets for creating feature vector of a query. Third, the correlations between an image-query vector and target image vectors are calculated on a space which represents the relationship between color and the impression according to historical and cultural semantics of color. This image-query creation method representing impression of color makes it possible to expand the range of image retrieval for a large number of image data of cultural property in digital archives, such as electronic library and electronic museum, automatically.

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Conference papers on the topic "Histograms intersection"

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Sivri, E., and S. Kalkan. "A novel shape descriptor: Intersection Consistency Histograms." In 2013 21st Signal Processing and Communications Applications Conference (SIU). IEEE, 2013. http://dx.doi.org/10.1109/siu.2013.6531579.

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Cheng, Erkang, Nianhua Xie, Haibin Ling, Predrag R. Bakic, Andrew D. A. Maidment, and Vasileios Megalooikonomou. "Mammographic image classification using histogram intersection." In 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2010. http://dx.doi.org/10.1109/isbi.2010.5490381.

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Boughorbel, S., J. P. Tarel, and N. Boujemaa. "Generalized histogram intersection kernel for image recognition." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530353.

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Gao, Xing, and Zhenjiang Miao. "Generalized Histogram Intersection kernel for image classification." In 2014 12th International Conference on Signal Processing (ICSP 2014). IEEE, 2014. http://dx.doi.org/10.1109/icosp.2014.7015127.

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Jiang Qiang-rong and Gao Yuan. "Face recognition based on Detail Histogram Intersection kernel." In 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2009). IEEE, 2009. http://dx.doi.org/10.1109/icicisys.2009.5357743.

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Wenjing Jia, Huaifeng Zhang, Xiangjian He, and Qiang Wu. "Gaussian Weighted Histogram Intersection for License Plate Classification." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.596.

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Barbu, Tudor, Mihaela Costin, and Adrian Ciobanu. "Histogram intersection based image retrieval technique using relevance feedback." In 2009 3rd International Workshop on Soft Computing Applications (SOFA). IEEE, 2009. http://dx.doi.org/10.1109/sofa.2009.5254878.

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Inoue, Kohei, Kenji Hara, and Kiichi Urahama. "Image and video clipping by weighted histogram intersection minimization." In 2010 IEEE Region 10 Conference (TENCON 2010). IEEE, 2010. http://dx.doi.org/10.1109/tencon.2010.5686033.

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Liu, Shu, Shao-Zi Li, Xian-Ming Liu, and Hong-Bo Zhang. "Entropy-based action features selection using histogram intersection kernel." In 2010 2nd International Conference on Signal Processing Systems (ICSPS). IEEE, 2010. http://dx.doi.org/10.1109/icsps.2010.5555433.

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Cheng, Y., S. Z. Li, and S. Z. Su. "Combine histogram intersection kernel with linear kernel for pedestrian classification." In IET International Conference on Information Science and Control Engineering 2012 (ICISCE 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/cp.2012.2480.

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