Relevant bibliographies by topics / Color SIFT

Contents

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

Academic literature on the topic 'Color SIFT'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Color SIFT"

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Zhang, Jie Yu, Hai Yong Wu, Shu Chen, and De Shen Xia. "The Target Tracking Method Based on Camshift Algorithm Combined with SIFT." Advanced Materials Research 186 (January 2011): 281–86. http://dx.doi.org/10.4028/www.scientific.net/amr.186.281.

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Since Camshift algorithm leads to failed tracking results when the color information of the target region is similar with the background or is not precise enough, to solve this problem a tracking method based on Camshift and SIFT was proposed in this paper. In this method, SIFT feature points, which were used to construct the color histogram and the color probability distribution, were extracted from the target region first. Then SIFT points were also extracted from the search region and these two sets of SIFT points were matched. Since the proposed method used the matched SIFT points to prope
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Trujillo-Romero, Felipe, Santiago Omar Caballero Morales, Karen-Lizbeth Flores-Rodriguez, Carlos Garcia-Capulin, and Raúl Sanchez-Yanez. "A Reduced Spherical Model for Optimization of Image Recognition through 3D Color Histograms." International Journal of Combinatorial Optimization Problems and Informatics 13, no. 3 (2022): 43–55. https://doi.org/10.61467/2007.1558.2022.v13i3.308.

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A 3D color histogram is an image processing technique used to visualize the distribution of colors (Red-Blue-Green) in a picture. Because color distribution does not significantly change if a pictured object is translated or rotated, a 3D color histogram can be used as descriptor for automatic object recognition. However, this task requires cubes with high dimensionality. Within this context, the present work contributes with an approach to reduce the high dimensionality of the 3D color histogram and improve it as descriptor for object recognition. Tests performed with three databases (COIL-10
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HAN, Yechen, and HIRAI Shinichi. "2A1-J03 Color Filter in SIFT Matching(Robot Vision (1))." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2013 (2013): _2A1—J03_1—_2A1—J03_4. http://dx.doi.org/10.1299/jsmermd.2013._2a1-j03_1.

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Yuan, Wenan, Sai Raghavendra Prasad Poosa, and Rutger Francisco Dirks. "Comparative Analysis of Color Space and Channel, Detector, and Descriptor for Feature-Based Image Registration." Journal of Imaging 10, no. 5 (2024): 105. http://dx.doi.org/10.3390/jimaging10050105.

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The current study aimed to quantify the value of color spaces and channels as a potential superior replacement for standard grayscale images, as well as the relative performance of open-source detectors and descriptors for general feature-based image registration purposes, based on a large benchmark dataset. The public dataset UDIS-D, with 1106 diverse image pairs, was selected. In total, 21 color spaces or channels including RGB, XYZ, Y′CrCb, HLS, L*a*b* and their corresponding channels in addition to grayscale, nine feature detectors including AKAZE, BRISK, CSE, FAST, HL, KAZE, ORB, SIFT, an
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Zhang, Jun, Lin Zhang, An Quan Sun, Shu Bing Li, Xiao Bing Tang, and Hong Wang. "SIFT Feature Extraction Based on Color Energy Region." Applied Mechanics and Materials 411-414 (September 2013): 1201–4. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1201.

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In content-based image feature extraction research areas, SIFT feature occupies a very important position. In 2004 it was first proposed, widely used in object recognition, video tracking, scene recognition, image retrieval and other issues, and achieved great success. But the extraction of image SIFT features needs a huge amount of computation. This paper presents the concept of color energy in where it has great information, and extract large color energy regions, extracted SIFT feature points in them. Although losing some of the feature points, this method effectively reduces the computatio
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Liu, Ming Hua, Chuan Sheng Wang, and Xian Lun Wang. "Particle Filter Target Tracking Algorithm Based on the SIFT and Color Features Fusion." Applied Mechanics and Materials 734 (February 2015): 476–81. http://dx.doi.org/10.4028/www.scientific.net/amm.734.476.

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Aiming at the poor robustness problem of using single feature in the target tracking process, a novel tracking algorithm based on color and SIFT features fusion in particle filter framework is presented in complex environments. Color and SIFT features are selected to establish the target model according to their stability, The scale and rotation invariance of SIFT feature and resistance occlusion property of color feature has been fused in the particle filter framework adaptively. According to the dynamic change of the tracking scene, the fusion weights is updated adaptively. Experimental resu
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Zhang, Xiaoli, Punan Li, and Yibing Li. "Feature Point Extraction and Motion Tracking of Cardiac Color Ultrasound under Improved Lucas–Kanade Algorithm." Journal of Healthcare Engineering 2021 (August 3, 2021): 1–10. http://dx.doi.org/10.1155/2021/4959727.

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The purpose of this research is to study the application effect of Lucas–Kanade algorithm in right ventricular color Doppler ultrasound feature point extraction and motion tracking under the condition of scale invariant feature transform (SIFT). This study took the right ventricle as an example to analyze the extraction effect and calculation rate of SIFT algorithm and improved Lucas–Kanade algorithm. It was found that the calculation time before and after noise removal by the SIFT algorithm was 0.49 s and 0.46 s, respectively, and the number of extracted feature points was 703 and 698, respec
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Abu Doush, Iyad, and Sahar AL-Btoush. "Currency recognition using a smartphone: Comparison between color SIFT and gray scale SIFT algorithms." Journal of King Saud University - Computer and Information Sciences 29, no. 4 (2017): 484–92. http://dx.doi.org/10.1016/j.jksuci.2016.06.003.

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Kang, Nan Nan, Xiao Fang Wang, and Rong Rong Zhang. "Image Classification Based on Color Topic Model." Applied Mechanics and Materials 556-562 (May 2014): 4770–73. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4770.

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This paper addresses semantic image classification with topic model, which focusing on discovering a hidden semantic to solve the semantic gap between low-level visual feature and high-level feature. In our approach, Latent Dirichlet Allocation (LDA) model successfully reflect the high level features and the RGB SIFT features which integrating the Scale-invariant feature transform (SIFT) features with color features on the assumption that pictures generated by mixture of latent semantic which we called topics. The proposed approach has a sufficient theoretical basis and the experimental evalua
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Nosovskiy, Gleb. "Geometrical coding of color images." Publications de l'Institut Math?matique (Belgrade) 103, no. 117 (2018): 159–73. http://dx.doi.org/10.2298/pim1817159n.

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Formal analysis and computer recognition of 2D color images is important branch of modern computer geometry. However, existing algorithms, although they are highly developed, are not quite satisfactory and seem to be much worse than (unknown) algorithms, which our brain uses to analyze eye information. Almost all existing algorithms omit colors and deal with grayscale transformations only. But in many cases color information is important. In this paper fundamentally new method of coding and analyzing color digital images is suggested. The main point of this method is that a full-color digital
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Dissertations / Theses on the topic "Color SIFT"

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Mazin, Baptiste. "Méthodes robustes pour l'estimation d'illuminants et la prise en compte de la couleur en comparaison d'images." Thesis, Paris, ENST, 2014. http://www.theses.fr/2014ENST0016/document.

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Cette thèse traite de l’utilisation de la couleur en vision par ordinateur. Deux problèmes sont étudiés : - l’estimation d’illuminants, - la mise en correspondance de descripteurs locaux pour la comparaison d’images couleur. Les surfaces achromatiques renvoient un spectre lumineux ayant la même distribution fréquentielle que le spectre de l’illuminant. Les détecter permet donc de recouvrer l'illuminant. En supposant que l’ensemble des couleurs que peut prendre un illuminant est limité (équation de Planck), il est possible de sélectionner les pixels appartenant à une surface potentiellement gri
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Venezia, Luciano. "Localizzazione e riconoscimento di oggetti mediante descrittori locali." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/4999/.

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La tesi presenta un lavoro svolto nell'ambito dell'object recognition, in particolare riguardante l'analisi dei descrittori locali SIFT e BRIEF. Dopo aver implementato BRIEF, sono stati realizzati numerosi test al fine di presentare un esauriente confronto prestazionale tra i due descrittori. Infine, è stato realizzato un applicativo per la localizzazione e il riconoscimento di oggetti su ripiani.
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Mazin, Baptiste. "Méthodes robustes pour l'estimation d'illuminants et la prise en compte de la couleur en comparaison d'images." Electronic Thesis or Diss., Paris, ENST, 2014. http://www.theses.fr/2014ENST0016.

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Cette thèse traite de l’utilisation de la couleur en vision par ordinateur. Deux problèmes sont étudiés : - l’estimation d’illuminants, - la mise en correspondance de descripteurs locaux pour la comparaison d’images couleur. Les surfaces achromatiques renvoient un spectre lumineux ayant la même distribution fréquentielle que le spectre de l’illuminant. Les détecter permet donc de recouvrer l'illuminant. En supposant que l’ensemble des couleurs que peut prendre un illuminant est limité (équation de Planck), il est possible de sélectionner les pixels appartenant à une surface potentiellement gri
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Veľas, Martin. "Automatické třídění fotografií podle obsahu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236399.

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This thesis deals with content based automatic photo categorization. The aim of the work is to experiment with advanced techniques of image represenatation and to create a classifier which is able to process large image dataset with sufficient accuracy and computation speed. A traditional solution based on using visual codebooks is enhanced by computing color features, soft assignment of visual words to extracted feature vectors, usage of image segmentation in process of visual codebook creation and dividing picture into cells. These cells are processed separately. Linear SVM classifier with e
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Doert, Jillian Elizabeth. "A Library of Emotions." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/33344.

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This thesis is an investigation into the impact of design elements on human behavior as explored through the design of a library. A library was chosen for its role in the community and because of the diverse group of users a library affects. A library, defined as a collection of things, is also a metaphor for the role memory plays in determining the emotive response a person has to their surroundings. Memory acts as collection of internal associations and, when engaged through sensory experience, memory dictates an emotional reaction to a space based on previous experiences. This project i
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Wei-HauDu and 杜維豪. "A Color-SIFT Descriptor for Commodity Recognition Systems." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/jjz3uf.

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碩士<br>國立成功大學<br>電腦與通信工程研究所<br>104<br>The smart technologies realized in smart devices for human assistances and the demands of recognition technologies increase daily in recent years. The applications of recognition are wide, including robots recognition, face recognition, fingerprint recognition and commodity recognition. This thesis proposed a commodity recognition system which combines SIFT, Bag of Words and SVM. Because of traditional SIFT processes image in the gray scale, by this way, the color information is missing. In image processing, color information is an important data for an ima
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何相熹. "A SIFT descriptor with color information for object recognition based on log color space." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/81068722934459353523.

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碩士<br>國立臺灣海洋大學<br>資訊工程學系<br>99<br>The scale Invariant feature Transform (SIFT) is an effective and popular method of extracting key-point features of the gray image. Recently, one of the topic of improving SIFT is to incorporate the color information into SIFT. In this thesis, SIFT is carried out based on the log color space. It turns out that the proposed approach is more robust to the change of illumination condition. The proposed approach has tested against synthesized images and the ALOI image database, a public image data set. Experimental results have shown that the proposed approach is
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Prasad, S. "Signal Processing Algorithms For Digital Image Forensics." Thesis, 2008. http://hdl.handle.net/2005/655.

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Availability of digital cameras in various forms and user-friendly image editing softwares has enabled people to create and manipulate digital images easily. While image editing can be used for enhancing the quality of the images, it can also be used to tamper the images for malicious purposes. In this context, it is important to question the originality of digital images. Digital image forensics deals with the development of algorithms and systems to detect tampering in digital images. This thesis presents some simple algorithms which can be used to detect tampering in digital images. Out of
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Prasad, S. "Signal Processing Algorithms For Digital Image Forensics." Thesis, 2007. https://etd.iisc.ac.in/handle/2005/655.

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Availability of digital cameras in various forms and user-friendly image editing softwares has enabled people to create and manipulate digital images easily. While image editing can be used for enhancing the quality of the images, it can also be used to tamper the images for malicious purposes. In this context, it is important to question the originality of digital images. Digital image forensics deals with the development of algorithms and systems to detect tampering in digital images. This thesis presents some simple algorithms which can be used to detect tampering in digital images. Out of
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Books on the topic "Color SIFT"

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BEDE, Pradip. Sit and Colour: Colouring Joy. Independently Published, 2022.

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Rajakumar, P. S., S. Geetha, and T. V. Ananthan. Fundamentals of Image Processing. Jupiter Publications Consortium, 2023. http://dx.doi.org/10.47715/jpc.b.978-93-91303-80-8.

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"Fundamentals of Image Processing" offers a comprehensive exploration of image processing's pivotal techniques, tools, and applications. Beginning with an overview, the book systematically categorizes and explains the multifaceted steps and methodologies inherent to the digital processing of images. The text progresses from basic concepts like sampling and quantization to advanced techniques such as image restoration and feature extraction. Special emphasis is given to algorithms and models crucial to image enhancement, restoration, segmentation, and application. In the initial segments, the i
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Tagoe, Belinda Rose. Cartoony Coloring Fest: Sit Back, Relax, and Color! CreateSpace Independent Publishing Platform, 2017.

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Kowling, K. K. Sit the F*ck down and Color Harry. Blurb, 2017.

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Jake, Elizabeth. Hello Friend! How Are You? - Color Sit and Listen Edition: A Rhyming Color Identification Book. Independently Published, 2019.

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Jake, Elizabeth. Hello Friend! How Are You? Color Learning Sit & Listen Edition : Cats: A Rhyming Color Identification Book. Independently Published, 2019.

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Jake, Elizabeth. Hello Friend! How Are You? Color Learning Sit & Listen Edition : Cats: A Rhyming Color Identification Book. Independently Published, 2019.

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Jake, Elizabeth. Hello Friend! How Are You? Color Learning Sit & Listen Edition : Dogs: A Rhyming Color Identification Book. Independently Published, 2019.

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Barrio Maestre, José María. Europa, en reinicio. Teseo, 2019. http://dx.doi.org/10.55778/ts508939159.

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&lt;p&gt;&lt;span&gt;&lt;span color="#000000" style="color: #000000;"&gt;Todo ser vivo, y toda comunidad viva, crece superando dificultades, atravesando momentos en los que se abren vías alternativas. De cómo se resuelva el dilema dependerá que la crisis devenga superación o depresión.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;&lt;p&gt;Además, avanzar implica integrar algo y dejar algo de lado; &lt;i&gt;sit venia verbo,&lt;/i&gt; incremento y excremento. La diferencia entre la superación y la depresión a menudo estriba en la recesión, digamos, depende de que haya suficiente &lt;i&gt;receso &lt;/i&gt;
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Jake, Elizabeth. Hello Friend! How Are You? Color Learning Sit and Listen Edition : Dogs: A Rhyming Color Identification Book. Independently Published, 2019.

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Book chapters on the topic "Color SIFT"

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Zhu, Tingge, Jiangbin Zheng, Mingchen Feng, Jinchang Ren, and Weihua Liu. "Blind Detection Based on Color SIFT." In Advances in Brain Inspired Cognitive Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39431-8_32.

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Ouddai, Ghada, Ines Hamdi, and Henda Ben Ghezala. "Color-SIFT Features for Histopathological Image Analysis." In Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2022-Winter. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26135-0_5.

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Brüning, Markus, Paul Wunderlich, and Helene Dörksen. "Advanced Feature Extraction Workflow for Few Shot Object Recognition." In Bildverarbeitung in der Automation. Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-66769-9_4.

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AbstractObject recognition is well known to have a high importance in various fields. Example applications are anomaly detection and object sorting. Common methods for object recognition in images divide into neural and non-neural approaches: Neural-based concepts, e.g. using deep learning techniques, require a lot of training data and involve a resource intensive learning process. Additionally, when working with a small number of images, the development effort increases. Common non-neural feature detection approaches, such as SIFT, SURF or AKAZE, do not require these steps for preparation. Th
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Verma, Abhishek, and Chengjun Liu. "SIFT Features in Multiple Color Spaces for Improved Image Classification." In Intelligent Systems Reference Library. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52081-0_7.

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Inomata, Ryo, Kenji Terabayashi, Kazunori Umeda, and Guy Godin. "Registration of 3D Geometric Model and Color Images Using SIFT and Range Intensity Images." In Advances in Visual Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24028-7_30.

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Wang, Yaru, HongXin Li, and LongKui Wang. "Similar Image Retrieval Using Color Histogram in HSV Space and SIFT Descriptor with FLANN." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54924-3_103.

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Weida, Courtney L. "Appendix 1." In The South Station Hoard. punctum books, 2014. https://doi.org/10.21983/p3.0085.1.10.

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Before I started elementary school, I remember being obsessed with the idea of treasure. I loved tales of pirates and jewels, and relished movies including secret compartments and magical amulets. An older sibling bought me beloved kitschy treasure chests from pet store aquarium sections. I also used to dig in the backyard, scour the basement, and sift through my mother’s jewelry in hopes to find some ancient, forgotten, secret objects. Treasure, with its preciousness and stories are interesting to children with good reason. The acts of discovering, discussing, defining, and creating treasure
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Klima, Gyula, Peter G. Sobol, Peter Hartman, and Jack Zupko. "Utrum color sit proprium obiectum visus." In John Buridan’s Questions on Aristotle’s De Anima – Iohannis Buridani Quaestiones in Aristotelis De Anima. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-94433-9_20.

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Rose, Deborah Bird. "Flying Foxes in Sydney." In Manifesto for Living in the Anthropocene. punctum books, 2015. https://doi.org/10.21983/p3.0100.1.16.

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Do city dwellers dream of wide open spaces like rangelands, or do they dream of tall buildings? Do their dreams entice them to look up into the great, blue depths above them, and do flying foxes flit across the night sky of their sleep? Do they ever think: “now, this is a biodiver-city!”?At least one city dweller, journalist James Woodford, looked up with delight. He wrote: “watching bats silhouetted against the stars is one of the greatest, but little known, pleasures of life” (Woodford 2003). Actually, many city dwellers find pleasure in the fact that their city and their lives are shared wi
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"Research of the color descriptor SIFT." In Computing, Control, Information and Education Engineering. CRC Press, 2015. http://dx.doi.org/10.1201/b18828-17.

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Conference papers on the topic "Color SIFT"

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Kumar Suman, Sunil, D. Kumaraswamy, Sunil Kumar Sahu, Prasanna Lakshmi Akella, S. P. Santhoshkumar, and R. Karthika. "Deep Learning - Based Prediction Models for Colon and Rectum Cancer Disease: Enhancing the Precision Diagnosis." In 2025 International Conference on Computing for Sustainability and Intelligent Future (COMP-SIF). IEEE, 2025. https://doi.org/10.1109/comp-sif65618.2025.10969862.

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Jalilvand, Ali, Hamidreza Shayegh Boroujeni, and Nasrollah Moghadam Charkari. "CH-SIFT: A local kernel color histogram SIFT based descriptor." In 2011 International Conference on Multimedia Technology (ICMT). IEEE, 2011. http://dx.doi.org/10.1109/icmt.2011.6001949.

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Cui, Yan, Alain Pagani, and Didier Stricker. "SIFT in perception-based color space." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5651165.

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Pan Xiao, Nian Cai, Bochao Tang, Shaowei Weng, and Han Wang. "Efficient SIFT descriptor via color quantization." In 2014 IEEE International Conference on Consumer Electronics – China. IEEE, 2014. http://dx.doi.org/10.1109/icce-china.2014.7029876.

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Ancuti, Cosmin, and Philippe Bekaert. "SIFT-CCH: Increasing the SIFT distinctness by Color Co-occurrence Histograms." In 2007 5th International Symposium on Image and Signal Processing and Analysis. IEEE, 2007. http://dx.doi.org/10.1109/ispa.2007.4383677.

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Nagar, Abhishek, Ankur Saxena, Serhat Bucak, Felix Fernandes, and Kong-Posh Bhat. "Low complexity image matching using color based SIFT." In 2013 Visual Communications and Image Processing (VCIP). IEEE, 2013. http://dx.doi.org/10.1109/vcip.2013.6706456.

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Dandan Li, Yongzhen Ke, and Guiling Zhang. "A SIFT descriptor with local kernel color histograms." In 2011 Second International Conference on Mechanic Automation and Control Engineering (MACE). IEEE, 2011. http://dx.doi.org/10.1109/mace.2011.5987099.

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Bo, Lu, and TaegKeun Whangbo. "A SIFT-Color Moments Descriptor for Object Recognition." In 2014 International Conference on IT Convergence and Security (ICITCS). IEEE, 2014. http://dx.doi.org/10.1109/icitcs.2014.7021716.

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Zhou, Xiao, Cai-Zhi Zhu, Shin'ichi Satoh, and Yu-Tang Guo. "Efficient quantization of color sift for image classification." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6115611.

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Zhou, Jindan, Steven Cadavid, and Mohamed Abdel-Mottaleb. "Exploiting color SIFT features for 2D ear recognition." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6116405.

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