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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Journal ArticleDOI
Vehicle Type Classification Using a Semisupervised Convolutional Neural Network
TL;DR: This paper introduces sparse Laplacian filter learning to obtain the filters of the network with large amounts of unlabeled data and proposes a vehicle type classification method using a semisupervised convolutional neural network from vehicle frontal-view images.
Proceedings Article
Evaluation of local detectors and descriptors for fast feature matching
TL;DR: A performance evaluation of recent feature detectors is provided and compares their matching precision and speed in randomized kd-trees setup as well as an evaluation of binary descriptors with efficient computation of Hamming distance.
Proceedings ArticleDOI
CHoG: Compressed histogram of gradients A low bit-rate feature descriptor
TL;DR: A framework for computing low bit-rate feature descriptors with a 20× reduction in bit rate is proposed and it is shown how to efficiently compute distances between descriptors in their compressed representation eliminating the need for decoding.
Proceedings Article
Surpassing human-level face verification performance on LFW with gaussian face
Chaochao Lu,Xiaoou Tang +1 more
TL;DR: A principled multi-task learning approach based on Discriminative Gaussian Process Latent Variable Model (DGPLVM), named GaussianFace, for face verification, which achieved an impressive accuracy rate and introduced a more efficient equivalent form of Kernel Fisher Discriminant Analysis to DGPLVM.
Journal ArticleDOI
Feature Learning for Image Classification Via Multiobjective Genetic Programming
Ling Shao,Li Liu,Xuelong Li +2 more
TL;DR: Experimental results verify that the proposed evolutionary learning methodology significantly outperforms many state-of-the-art hand-designed features and two feature learning techniques in terms of classification accuracy.
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.
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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.