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
Proceedings ArticleDOI
Minimal Scene Descriptions from Structure from Motion Models
Song Cao,Noah Snavely +1 more
TL;DR: A new method for computing compact models that takes into account both image-point relationships and feature distinctiveness is introduced, and it is shown that this method produces small models that yield better recognition performance than previous model reduction techniques.
Journal ArticleDOI
Heterogeneous Feature Selection With Multi-Modal Deep Neural Networks and Sparse Group LASSO
Lei Zhao,Qinghua Hu,Wenwu Wang +2 more
TL;DR: This framework is composed of two modules, namely, multi-modal deep neural networks and feature selection with sparse group LASSO, and shows that the proposed approach is effective in selecting the relevant feature groups and achieves competitive classification performance as compared with several recent baseline methods.
Journal ArticleDOI
Feature description with SIFT, SURF, BRIEF, BRISK, or FREAK? A general question answered for bone age assessment
TL;DR: The aim of this study is to compare the five leading keypoint descriptors on BAA, and, in doing so, presenting a generic approach for a specific task.
Proceedings ArticleDOI
From Google Street View to 3D city models
TL;DR: A large-scale reconstruction computed from 4,799 images of the Google Street View Pittsburgh Research Data Set is presented, showing the robust and stable camera poses estimated by PROSAC with soft voting and by scale selection using a visual cone test bring high quality initial structure for bundle adjustment.
Journal ArticleDOI
SAR and Optical Image Registration Using Nonlinear Diffusion and Phase Congruency Structural Descriptor
TL;DR: A novel image registration method, which combines nonlinear diffusion and phase congruency structural descriptor (PCSD), is proposed for the registration of SAR and optical images that is more robust against speckle noise and nonlinear intensity differences and improves the registration accuracy effectively.
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.