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Visual Object Recognition
Kristen Grauman,Bastian Leibe +1 more
- 19 Apr 2011
TL;DR: This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization, with an emphasis on recent advances in the field.
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Abstract: The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions
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Citations
Deep Learning for Generic Object Detection: A Survey
Li Liu,Li Liu,Wanli Ouyang,Xiaogang Wang,Paul Fieguth,Jie Chen,Xinwang Liu,Matti Pietikäinen +7 more
TL;DR: A comprehensive survey of the recent achievements in this field brought about by deep learning techniques, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics.
Multimodal distributional semantics
TL;DR: This work proposes a flexible architecture to integrate text- and image-based distributional information, and shows in a set of empirical tests that the integrated model is superior to the purely text-based approach, and it provides somewhat complementary semantic information with respect to the latter.
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R2D2: Repeatable and Reliable Detector and Descriptor.
Jerome Revaud,Philippe Weinzaepfel,César Roberto de Souza,Noe Pion,Gabriela Csurka,Yohann Cabon,Martin Humenberger +6 more
TL;DR: This work argues that salient regions are not necessarily discriminative, and therefore can harm the performance of the description, and proposes to jointly learn keypoint detection and description together with a predictor of the local descriptor discriminativeness.
552
An affine invariant salient region detector
Timor Kadir,Andrew Zisserman,J. Michael Brady +2 more
- 01 Dec 2004
TL;DR: In this article, a novel technique for detecting salient regions in an image is described, which is a generalization to affine invariance of the method introduced by Kadir and Brady.
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References
ImageNet: A large-scale hierarchical image database
Jia Deng,Wei Dong,Richard Socher,Li-Jia Li,Kai Li,Li Fei-Fei +5 more
- 20 Jun 2009
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
- 20 Jun 2005
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
Rapid object detection using a boosted cascade of simple features
Paul A. Viola,Michael Jones +1 more
- 01 Dec 2001
TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.