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
Large-Scale Video Retrieval Using Image Queries
Andre Araujo,Bernd Girod +1 more
TL;DR: A new retrieval architecture is introduced, in which the image query can be compared directly with database videos—significantly improving retrieval scalability compared with a baseline system that searches the database on a video frame level.
Book ChapterDOI
Bilateral Functions for Global Motion Modeling
TL;DR: This paper uses the bilateral domain to reformulate a piecewise smooth constraint as continuous global modeling constraint and demonstrates how the model can reliably obtain large numbers of good quality correspondences over wide baselines, while keeping outliers to a minimum.
BookDOI
3D imaging, analysis and applications
TL;DR: This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and processing 3D Shape Representation data.
Book ChapterDOI
Artistic image classification: an analysis on the PRINTART database
TL;DR: A new database of monochromatic artistic images containing 988 images with a global semantic annotation, a local compositional annotation, and a pose annotation of human subjects and animal types is presented.
Journal ArticleDOI
Deep Multi-View Feature Learning for Person Re-Identification
TL;DR: This paper proposes a novel scheme called deep multi-view feature learning (DMVFL), which exploits the collaboration between handcrafted and deep learning features in a simple but effective way, and proves that the XQDA is a robust algorithm.
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.