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Relevant bibliographies by topics / Multi-class object detection

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Academic literature on the topic 'Multi-class object detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Multi-class object detection"

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Buenaposada, José Miguel, and Luis Baumela. "Improving multi-class Boosting-based object detection." Integrated Computer-Aided Engineering 28, no. 1 (2020): 81–96. http://dx.doi.org/10.3233/ica-200636.

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In recent years we have witnessed significant progress in the performance of object detection in images. This advance stems from the use of rich discriminative features produced by deep models and the adoption of new training techniques. Although these techniques have been extensively used in the mainstream deep learning-based models, it is still an open issue to analyze their impact in alternative, and computationally more efficient, ensemble-based approaches. In this paper we evaluate the impact of the adoption of data augmentation, bounding box refinement and multi-scale processing in the c

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Vieux, Remi, Jenny Benois-Pineau, Jean-Philippe Domenger, and Achille Braquelaire. "Segmentation-based multi-class semantic object detection." Multimedia Tools and Applications 60, no. 2 (2010): 305–26. http://dx.doi.org/10.1007/s11042-010-0611-2.

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Yao, Chao, Pengfei Sun, Ruicong Zhi, and Yanfei Shen. "Learning Coexistence Discriminative Features for Multi-Class Object Detection." IEEE Access 6 (2018): 37676–84. http://dx.doi.org/10.1109/access.2018.2852728.

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Mo, Nan, Li Yan, Ruixi Zhu, and Hong Xie. "Class-Specific Anchor Based and Context-Guided Multi-Class Object Detection in High Resolution Remote Sensing Imagery with a Convolutional Neural Network." Remote Sensing 11, no. 3 (2019): 272. http://dx.doi.org/10.3390/rs11030272.

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In this paper, the problem of multi-scale geospatial object detection in High Resolution Remote Sensing Images (HRRSI) is tackled. The different flight heights, shooting angles and sizes of geographic objects in the HRRSI lead to large scale variance in geographic objects. The inappropriate anchor size to propose the objects and the indiscriminative ability of features for describing the objects are the main causes of missing detection and false detection in multi-scale geographic object detection. To address these challenges, we propose a class-specific anchor based and context-guided multi-c

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Dou, Yumin, Pei Xu, Mao Ye, Xue Li, Lishen Pei, and Xudong Li. "Real-time multi-class object detection using two-dimensional index." Journal of Real-Time Image Processing 16, no. 2 (2015): 243–53. http://dx.doi.org/10.1007/s11554-015-0525-3.

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Khan, Rafflesia, and Rameswar Debnath. "Multi Class Fruit Classification Using Efficient Object Detection and Recognition Techniques." International Journal of Image, Graphics and Signal Processing 11, no. 8 (2019): 1–18. http://dx.doi.org/10.5815/ijigsp.2019.08.01.

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Dai, Y., J. S. Xiao, B. S. Yi, J. F. Lei, and Z. Y. Du. "DETECTION OF ARTIFICIAL OBJECTS IN REMOTE SENSING IMAGE BASED ON DEEP LEARNING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 321–26. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-321-2020.

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Abstract. Aiming at multi-class artificial object detection in remote sensing images, the detection framework based on deep learning is used to extract and localize the numerous targets existing in very high resolution remote sensing images. In order to realize rapid and efficient detection of the typical artificial targets on the remote sensing image, this paper proposes an end-to-end multi-category object detection method in remote sensing image based on the convolutional neural network to solve several challenges, including dense objects and objects with arbitrary direction and large aspect

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Rudraiah, Bhavya, and Geetha K S. "Multiple Detection and Tracking of Multi Class Vehicles using Locality Sensitive Histogram." International Journal of Recent Technology and Engineering 9, no. 5 (2021): 259–62. http://dx.doi.org/10.35940/ijrte.e5295.019521.

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Multiple object detection and tracking in a cluttered background is most important in vision-based applications. In this paper, the goal is to develop a classifier that detects and tracks multiple objects thereby ensuring robustness and accuracy. Locality Sensitive Histogram feature extraction is used, which adds contributions from all the pixels in an image. These features extracted are trained using decision tree classifier which performs with an accuracy of 97%. Experimental results demonstrate the objects tracked and detected under different scale and pose variations. Evaluation and compar

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Opelt, Andreas, Axel Pinz, and Andrew Zisserman. "Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection." International Journal of Computer Vision 80, no. 1 (2008): 16–44. http://dx.doi.org/10.1007/s11263-008-0139-3.

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Yang, Feng, Wentong Li, Haiwei Hu, Wanyi Li, and Peng Wang. "Multi-Scale Feature Integrated Attention-Based Rotation Network for Object Detection in VHR Aerial Images." Sensors 20, no. 6 (2020): 1686. http://dx.doi.org/10.3390/s20061686.

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Accurate and robust detection of multi-class objects in very high resolution (VHR) aerial images has been playing a significant role in many real-world applications. The traditional detection methods have made remarkable progresses with horizontal bounding boxes (HBBs) due to CNNs. However, HBB detection methods still exhibit limitations including the missed detection and the redundant detection regions, especially for densely-distributed and strip-like objects. Besides, large scale variations and diverse background also bring in many challenges. Aiming to address these problems, an effective

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Dissertations / Theses on the topic "Multi-class object detection"

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Zehnder, Philipp. "Efficient multi-class object detection." Konstanz Hartung-Gorre, 2009. http://d-nb.info/998364045/04.

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Chiu, Han-Pang. "Models for multi-view object class detection." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53197.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 99-105).<br>Learning how to detect objects from many classes in a wide variety of viewpoints is a key goal of computer vision. Existing approaches, however, require excessive amounts of training data. Implementors need to collect numerous training images not only to cover changes in the same object's shape due to the viewpoint variation, but also to accommodate the variability in appearance among

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khan, saad. "MULTI-VIEW APPROACHES TO TRACKING, 3D RECONSTRUCTION AND OBJECT CLASS DETECTION." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4066.

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Multi-camera systems are becoming ubiquitous and have found application in a variety of domains including surveillance, immersive visualization, sports entertainment and movie special effects amongst others. From a computer vision perspective, the challenging task is how to most efficiently fuse information from multiple views in the absence of detailed calibration information and a minimum of human intervention. This thesis presents a new approach to fuse foreground likelihood information from multiple views onto a reference view without explicit processing in 3D space, thereby circumventing

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Odabai, Fard Seyed Hamidreza. "Efficient multi-class objet detection with a hierarchy of classes." Thesis, Clermont-Ferrand 2, 2015. http://www.theses.fr/2015CLF22623/document.

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Dans cet article, nous présentons une nouvelle approche de détection multi-classes basée sur un parcours hiérarchique de classifieurs appris simultanément. Pour plus de robustesse et de rapidité, nous proposons d’utiliser un arbre de classes d’objets. Notre modèle de détection est appris en combinant les contraintes de tri et de classification dans un seul problème d’optimisation. Notre formulation convexe permet d’utiliser un algorithme de recherche pour accélérer le temps d’exécution. Nous avons mené des évaluations de notre algorithme sur les benchmarks PASCAL VOC (2007 et 2010). Comparé à

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Verschae, Tannenbaum Rodrigo. "Object Detection Using Nested Cascades of Boosted Classifiers. A Learning Framework and Its Extension to The Multi-Class Case." Tesis, Universidad de Chile, 2010. http://www.repositorio.uchile.cl/handle/2250/102398.

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Book chapters on the topic "Multi-class object detection"

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Lee, Byungjae, Enkhbayar Erdenee, Songguo Jin, Mi Young Nam, Young Giu Jung, and Phill Kyu Rhee. "Multi-class Multi-object Tracking Using Changing Point Detection." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48881-3_6.

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Azimi, Seyed Majid, Eleonora Vig, Reza Bahmanyar, Marco Körner, and Peter Reinartz. "Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery." In Computer Vision – ACCV 2018. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20893-6_10.

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Gall, Juergen, Nima Razavi, and Luc Van Gool. "An Introduction to Random Forests for Multi-class Object Detection." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34091-8_11.

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Ulusoy, Ilkay. "A Generative Model for Multi Class Object Recognition and Detection." In Artificial Intelligence and Neural Networks. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11803089_4.

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Matsugu, Masakazu, and Pierre Cardon. "Unsupervised Feature Selection for Multi-class Object Detection Using Convolutional Neural Networks." In Advances in Neural Networks – ISNN 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28647-9_142.

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Zhu, Xinge, Yuexin Ma, Tai Wang, Yan Xu, Jianping Shi, and Dahua Lin. "SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58595-2_35.

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Fidler, Sanja, Marko Boben, and Aleš Leonardis. "A Coarse-to-Fine Taxonomy of Constellations for Fast Multi-class Object Detection." In Computer Vision – ECCV 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15555-0_50.

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Mühling, Markus, Ralph Ewerth, Bing Shi, and Bernd Freisleben. "Multi-class Object Detection with Hough Forests Using Local Histograms of Visual Words." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23672-3_47.

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Kabani, AbdulWahab, and Mahmoud R. El-Sakka. "Object Detection and Localization Using Deep Convolutional Networks with Softmax Activation and Multi-class Log Loss." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41501-7_41.

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Zolghadr, Esfandiar, and Borko Furht. "Context-Based Scene Understanding." In Computer Vision. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch029.

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Context plays an important role in performance of object detection. There are two popular considerations in building context models for computer vision applications; type of context (semantic, spatial, scale) and scope of the relations (pairwise, high-order). In this paper, a new unified framework is presented that combines multiple sources of context in high-order relations to encode semantical coherence and consistency of the scenes. This framework introduces a new descriptor called context relevance score to model context-based distribution of the response variables and apply it to two distributions. First model incorporates context descriptor along with annotation response into a supervised Latent Dirichlet Allocation (LDA) built on multi-variate Bernoulli distribution called Context-Based LDA (CBLDA). The second model is based on multi-variate Wallenius' non-central Hyper-geometric distribution and is called Wallenius LDA (WLDA). WLDA incorporates context knowledge as bias parameter. Scene context is modeled as a graph and effectively used in object detection framework to maximize semantical consistency of the scene. The graph can also be used in recognition of out-of-context objects. Annotation metadata of Sun397 dataset is used to construct the context model. Performance of the proposed approaches was evaluated on ImageNet dataset. Comparison between proposed approaches and state-of-art multi-class object annotation algorithm shows superiority of presented approach in labeling of scene content.

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Conference papers on the topic "Multi-class object detection"

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Razavi, Nima, Juergen Gall, and Luc Van Gool. "Scalable multi-class object detection." In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995441.

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Yang, Yi, Sam Hallman, Deva Ramanan, and Charless Fowlkes. "Layered object detection for multi-class segmentation." In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010. http://dx.doi.org/10.1109/cvpr.2010.5540070.

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"Joint Learning for Multi-class Object Detection." In International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004692401040112.

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Mayershofer, Christopher, Ala Hammami, and Johannes Fottner. "Multi-Class Object Detection Using 2D Poses." In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2020. http://dx.doi.org/10.1109/icmla51294.2020.00107.

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Selvaraj, K., A. Annis Fathima, and V. Vaidehi. "Multi-class object detection by part based approach." In 2012 International Conference on Recent Trends in Information Technology (ICRTIT). IEEE, 2012. http://dx.doi.org/10.1109/icrtit.2012.6206837.

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Fard, Hamidreza Odabai, Mohamed Chaouch, Quoc-Cuong Pham, Antoine Vacavant, and Thierry Chateau. "Joint hierarchical learning for efficient multi-class object detection." In 2014 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2014. http://dx.doi.org/10.1109/wacv.2014.6836090.

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Advani, Siddharth, Yasuki Tanabe, Kevin Irick, Jack Sampson, and Vijaykrishnan Narayanan. "A scalable architecture for multi-class visual object detection." In 2015 25th International Conference on Field Programmable Logic and Applications (FPL). IEEE, 2015. http://dx.doi.org/10.1109/fpl.2015.7293961.

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Kanezaki, Asako, Yasuo Kuniyoshi, and Tatsuya Harada. "Weakly-supervised multi-class object detection using multi-type 3D features." In the 21st ACM international conference. ACM Press, 2013. http://dx.doi.org/10.1145/2502081.2502159.

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Liebelt, Joerg, and Cordelia Schmid. "Multi-view object class detection with a 3D geometric model." In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010. http://dx.doi.org/10.1109/cvpr.2010.5539836.

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Wang, Yanjuan, Haijun Niu, Xiao Wang, and Liang Chen. "Multi-class Object Detection Algorithm Based on Convolutional Neural Network." In 2019 IEEE International Conference of Intelligent Applied Systems on Engineering (ICIASE). IEEE, 2019. http://dx.doi.org/10.1109/iciase45644.2019.9074015.

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