Robust Feature Matching with Spatial Smoothness Constraints
نویسندگان
چکیده
منابع مشابه
Multimodal surface matching with higher-order smoothness constraints
In brain imaging, accurate alignment of cortical surfaces is fundamental to the statistical sensitivity and spatial localisation of group studies, and cortical surface-based alignment has generally been accepted to be superior to volume-based approaches at aligning cortical areas. However, human subjects have considerable variation in cortical folding, and in the location of functional areas re...
متن کاملRobust feature matching in 2.3µs
In this paper we present a robust feature matching scheme in which features can be matched in 2.3μs. For a typical task involving 150 features per image, this results in a processing time of 500μs for feature extraction and matching. In order to achieve very fast matching we use simple features based on histograms of pixel intensities and an indexing scheme based on their joint distribution. Th...
متن کاملRobust Foreground Detection Using Smoothness and Arbitrariness Constraints
Foreground detection plays a core role in a wide spectrum of applications such as tracking and behavior analysis. It, especially for videos captured by fixed cameras, can be posed as a component decomposition problem, the background of which is typically assumed to lie in a low dimensional subspace. However, in real world cases, dynamic backgrounds like waving trees and water ripples violate th...
متن کاملEfficient algorithms for robust feature matching
One of the basic building blocks in any point-based registration scheme involves matching feature points that are extracted from a sensed image to their counterparts in a reference image. This leads to the fundamental problem of point matching: Given two sets of points, find the (affine) transformation that transforms one point set so that its distance from the other point set is minimized. Bec...
متن کاملRobust Feature Matching and Selection Methods
1. INTRODUCTION Multisensor image registration is necessary in many applications of remote sensing imagery, whose crucial problem is how to establish the correspondence between the features extracted from the reference and input images. Most existing methods only consider how to extract features, however, the quality of the features are ignored. In this paper, we combine scale invariant feature...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Remote Sensing
سال: 2020
ISSN: 2072-4292
DOI: 10.3390/rs12193158