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
More filters
Journal ArticleDOI
A Novel Interest-Point-Matching Algorithm for High-Resolution Satellite Images
Zhen Xiong,Yun Zhang +1 more
TL;DR: The proposed algorithm can successfully process local distortion in high-resolution satellite images and can avoid ambiguity in matching the smooth areas and is simple, fast, and accurate.
Journal ArticleDOI
Medical image classification based on multi-scale non-negative sparse coding
TL;DR: The experimental results demonstrate that the proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance.
Journal ArticleDOI
Automatic Building Extraction on High-Resolution Remote Sensing Imagery Using Deep Convolutional Encoder-Decoder With Spatial Pyramid Pooling
TL;DR: A light-weight deep learning model integrating spatial pyramid pooling with an encoder-decoder structure that has the potential to deliver automatic building segmentation from high-resolution remote sensing images at an accuracy that makes it a useful tool for practical application scenarios is proposed.
Journal ArticleDOI
3D Shape Matching via Two Layer Coding
TL;DR: This work proposes a two layer coding (TLC) framework to conduct shape matching much more efficiently and achieves state-of-the-art performance in both retrieval accuracy and efficiency.
Journal ArticleDOI
A Lightweight and Discriminative Model for Remote Sensing Scene Classification With Multidilation Pooling Module
TL;DR: This paper proposes a lightweight and effective CNN which is capable of maintaining high accuracy and uses MobileNet V2 as a base network and introduces the dilated convolution and channel attention to extract discriminative features.
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.