Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TLDR
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.Abstract:
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. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.read more
Citations
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Proceedings ArticleDOI
Towards optimal bag-of-features for object categorization and semantic video retrieval
TL;DR: This paper evaluates various factors which govern the performance of Bag-of-features, and proposes a novel soft-weighting method to assess the significance of a visual word to an image and experimentally shows it can consistently offer better performance than other popular weighting methods.
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Finding action tubes
Georgia Gkioxari,Jitendra Malik +1 more
TL;DR: In this article, the authors proposed a method to extract spatio-temporal feature representations to build strong classifiers using Convolutional Neural Networks and link their predictions to produce detections consistent in time.
Proceedings ArticleDOI
MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking
TL;DR: Inspired by the well-known Atkinson-Shiffrin Memory Model, this work proposes MUlti-Store Tracker (MUSTer), a dual-component approach consisting of short- and long-term memory stores to process target appearance memories.
Proceedings ArticleDOI
Joint Deep Learning for Pedestrian Detection
Wanli Ouyang,Xiaogang Wang +1 more
TL;DR: This paper forms these four important components in pedestrian detection into a joint deep learning framework and proposes a new deep network architecture that achieves a 9% reduction in the average miss rate compared with the current best-performing pedestrian detection approaches on the largest Caltech benchmark dataset.
Proceedings ArticleDOI
Large-Scale Image Retrieval with Attentive Deep Local Features
TL;DR: An attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELE (DEep Local Feature), based on convolutional neural networks, which are trained only with image-level annotations on a landmark image dataset.
References
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Proceedings ArticleDOI
Object recognition from local scale-invariant features
TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
Book
Multiple view geometry in computer vision
Richard Hartley,Andrew Zisserman +1 more
TL;DR: In this article, the authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly in a unified framework, including geometric principles and how to represent objects algebraically so they can be computed and applied.
Multiple View Geometry in Computer Vision.
TL;DR: This book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts and it will show the best book collections and completed collections.
Proceedings ArticleDOI
A Combined Corner and Edge Detector
Chris Harris,Mike Stephens +1 more
TL;DR: The problem the authors are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work.
Journal ArticleDOI
Robust wide-baseline stereo from maximally stable extremal regions
TL;DR: The high utility of MSERs, multiple measurement regions and the robust metric is demonstrated in wide-baseline experiments on image pairs from both indoor and outdoor scenes.