Open Access
Distinctive Image Features from Scale-Invariant Keypoints
Reads0
Chats0
TLDR
The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.Abstract:
The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.read more
Citations
More filters
Journal ArticleDOI
Pattern recognition algorithm reveals how birds evolve individual egg pattern signatures
TL;DR: A computer vision tool for analysing visual patterns, NATUREPATTERNMATCH, is developed, which breaks new ground by mimicking visual and cognitive processes known to be involved in recognition tasks and reveals that recognizable signatures need not incorporate all three of these features.
Journal ArticleDOI
Content-based processing and analysis of endoscopic images and videos: A survey
TL;DR: This survey aims to introduce this research field to a broader audience in the Multimedia community to stimulate further research, to describe domain-specific characteristics of endoscopic videos that need to be addressed in a pre-processing step, and to systematically bring together the very diverse research results for the first time.
Journal ArticleDOI
LETRIST: Locally Encoded Transform Feature Histogram for Rotation-Invariant Texture Classification
TL;DR: Experimental results on the Outex, CUReT, KTH-TIPS, and UIUC texture data sets show that LETRIST consistently produces better or comparable classification results than the state-of-the-art approaches.
Proceedings ArticleDOI
Learning rotation-aware features: From invariant priors to equivariant descriptors
Uwe Schmidt,Stefan Roth +1 more
TL;DR: This paper describes a general framework for incorporating invariance to linear image transformations into product models for feature learning and shows the advantages of this approach in learning rotation-invariant image priors and in building rotation-equivariant and invariant descriptors of learned features.
Journal ArticleDOI
Efficient High Order Matching
Michael Chertok,Yosi Keller +1 more
TL;DR: This work presents a computational approach to high-order matching of data sets in Rd.grad, and shows that, based on the spectral properties of random matrices, affinity tensors can be randomly sparsified while retaining the matching accuracy.
References
More filters
Journal ArticleDOI
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.
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.
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
A performance evaluation of local descriptors
TL;DR: It is observed that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best and Moments and steerable filters show the best performance among the low dimensional descriptors.
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.