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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High-Performance SIFT Hardware Accelerator for Real-Time Image Feature Extraction
TL;DR: A segment buffer scheme is successfully developed that could not only feed data to the computing modules in a data-streaming manner, but also reduce about 50% memory requirement than a previous work.
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Feature Extraction Methods for Palmprint Recognition: A Survey and Evaluation
TL;DR: A unified framework is proposed to use a unified framework to classify palmprint images into four categories: 1) the contact-based; 2) contactless; 3) high-resolution; and 4) 3-D palm print images.
Journal ArticleDOI
Training-Free, Generic Object Detection Using Locally Adaptive Regression Kernels
Hae Jong Seo,Peyman Milanfar +1 more
TL;DR: The proposed method operates using a single example of an object of interest to find similar matches, does not require prior knowledge about objects being sought, anddoes not require any preprocessing step or segmentation of a target image.
Proceedings ArticleDOI
Exemplar-Based Face Parsing
TL;DR: This work proposes an exemplar-based face image segmentation algorithm, taking inspiration from previous works on image parsing for general scenes, that first selects a subset of exemplar images from the database, then computes a nonrigid warp for each exemplar image to align it with the test image.
Journal ArticleDOI
A Comprehensive Study Over VLAD and Product Quantization in Large-Scale Image Retrieval
Eleftherios Spyromitros-Xioufis,Symeon Papadopoulos,Ioannis Kompatsiaris,Grigorios Tsoumakas,Ioannis Vlahavas +4 more
TL;DR: An in-depth analysis of the state-of-the-art framework of VLAD and Product Quantization proposed by Jegou is made, which develops an enhanced framework that significantly outperforms the previous best reported accuracy results on standard benchmarks and is more efficient.
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.