Open Access
Distinctive Image Features from Scale-Invariant Keypoints
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
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Object detection using Non-Redundant Local Binary Patterns
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Object Retrieval Using Visual Query Context
TL;DR: An object retrieval method that exploits the information about the visual context of the query object and employ it to compensate for possible uncertainty in feature-based query object representation is proposed.
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Large Scale Retrieval and Generation of Image Descriptions
Vicente Ordonez,Xufeng Han,Polina Kuznetsova,Girish Kulkarni,Margaret Mitchell,Kota Yamaguchi,Karl Stratos,Amit Goyal,Jesse Dodge,Alyssa Mensch,Hal Daumé,Alexander C. Berg,Yejin Choi,Tamara L. Berg +13 more
TL;DR: The end result is two simple, but effective, methods for harnessing the power of big data to produce image captions that are altogether more general, relevant, and human-like than previous attempts.
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Edge-SIFT: Discriminative Binary Descriptor for Scalable Partial-Duplicate Mobile Search
TL;DR: A novel binary local descriptor named Edge-SIFT is proposed, which shows superior retrieval accuracy to Oriented BRIEF and is superior to SIFT in the aspects of retrieval precision, efficiency, compactness, and transmission cost.
Journal ArticleDOI
Segmenting CT prostate images using population and patient-specific statistics for radiotherapy
TL;DR: A new deformable model using both population and patient-specific statistics to segment the prostate from CT images, using a modified scale invariant feature transform (SIFT) local descriptor to characterize the image features.
References
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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.