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Oblivious Transfer for Privacy-Preserving in VANET’s Feature Matching
IEEE transactions on intelligent transportation systems (Print), 2021In the feature matching of some Vehicular Ad Hoc Network (VANET) technologies, users’ privacy disclosure issue has seriously threatened personal safety and caused considerable economic loss. In this paper, we proposed Oblivious Transfer (OT) protocol and
Xianmin Wang +5 more
semanticscholar +1 more source
Robust Feature Matching Using Spatial Clustering With Heavy Outliers
IEEE Transactions on Image Processing, 2020This paper focuses on removing mismatches from given putative feature matches created typically based on descriptor similarity. To achieve this goal, existing attempts usually involve estimating the image transformation under a geometrical constraint ...
Xingyu Jiang +3 more
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Learning Relaxed Neighborhood Consistency for Feature Matching
IEEE Transactions on Geoscience and Remote Sensing, 2021Feature matching is a critical prerequisite in many applications of remote sensing, and its aim is to establish reliable correspondences between two sets of features.
Shuang Chen +7 more
semanticscholar +1 more source
IEEE Transactions on Instrumentation and Measurement, 2020
This paper presents a new online detection approach for rolling bearing’s incipient fault based on self-adaptive deep feature matching (SDFM). This approach includes offline and online stages. At the offline stage, a new health state assessment algorithm
Wentao Mao +3 more
semanticscholar +1 more source
This paper presents a new online detection approach for rolling bearing’s incipient fault based on self-adaptive deep feature matching (SDFM). This approach includes offline and online stages. At the offline stage, a new health state assessment algorithm
Wentao Mao +3 more
semanticscholar +1 more source
A Brief Introduction of Feature Matching
2008 IEEE Region 5 Conference, 2008This paper presents several useful techniques about feature matching. As we know, for virtual worlds 3D reconstruction, we need many good 2D images with more precious correspondent matching points, thus, more realistic 3D models could be reconstructed for computer vision use, medical use or many other vision applications.
Dianchao Liu, Samuel Cheng
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2012
In many visual effects applications, we need to relate images taken from different perspectives or at different times. For example, we often want to track a point on a set as a camera moves around during a shot so that a digital creature can be later inserted at that location.
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In many visual effects applications, we need to relate images taken from different perspectives or at different times. For example, we often want to track a point on a set as a camera moves around during a shot so that a digital creature can be later inserted at that location.
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Feature Detection and Matching
2010Feature detection and matching are an essential component of many computer vision applications. Consider the two pairs of images shown in Figure 4.2. For the first pair, we may wish to align the two images so that they can be seamlessly stitched into a composite mosaic (Chapter 9).
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Feature Extraction and Geomagnetic Matching
Journal of Navigation, 2013In this paper, some improvements in feature extraction for geomagnetic matching are made. Geomagnetic entropy is extracted from the total intensity of the geomagnetic field and its performance for navigation is tested with real geomagnetic data. The matching progress is divided into two phrases: rough matching and precise matching.
Caifa Guo +2 more
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Automated 3d feature matching [PDF]
The Photogrammetric Record, 2015 AbstractThree‐dimensional (3D) feature‐matching techniques, which are essential for progress towards an automated feature‐based procedure, have attracted considerable research attention in both the photogrammetry and computer vision communities. This study introduces a novel matching approach, called RSTG, that comprises four major phases: rotation ...
Tzu Yi Chuang, Jen Jer Jaw
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Feature Detection and Matching
2017This chapter continues the exploration of motion from the previous chapter with more sophisticated tracking methods. In the previous chapter, you compared and analyzed the whole image between frames to identify movement information. As a result, the motion details tracked from these methods are general, without making use of the specific structural ...
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