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2021 6th International Conference for Convergence in Technology (I2CT), 2021
SIFT method for the local description of images is introduced in this paper. After figuring out the various features of the images, this method is used to perform accurate comparison between different views of a scene or an object. The extracted characteristics are invariable to rotation of size, additional noise, change of light, cropped images ...
Shahid Eqbal +3 more
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SIFT method for the local description of images is introduced in this paper. After figuring out the various features of the images, this method is used to perform accurate comparison between different views of a scene or an object. The extracted characteristics are invariable to rotation of size, additional noise, change of light, cropped images ...
Shahid Eqbal +3 more
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Robust feature matching in 2.3µs
2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009In 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 ...
Simon Taylor +2 more
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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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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 GROUP MATCHING: A NOVEL METHOD TO FILTER OUT INCORRECT LOCAL FEATURE MATCHINGS
International Journal of Pattern Recognition and Artificial Intelligence, 2014The importance of finding correct correspondences between two images is the major aspect in problems such as appearance-based robot localization and content-based image retrieval. Local feature matching has become a commonly used method to compare images, despite being highly probable that at least some of the matchings/correspondences it detects are ...
Emanuele Frontoni +2 more
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Matching Images Using Linear Features
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984We describe techniques for matching two images or an image and a map. This operation is basic for machine vision and is needed for the tasks of object recognition, change detection, map up-dating, passive navigation, and other tasks. Our system uses line-based descriptions, and matching is accomplished by a relaxation operation which computes most ...
Gérard G. Medioni, Ramakant Nevatia
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Random projections for feature matching
Proceedings of the 29th International Conference on Image and Vision Computing New Zealand, 2014Several methods of random projection for feature matching are investigated. These include: random projections of established feature descriptors for dimensionality reduction; random projections of raw image data as feature descriptors; random projections of raw image data as keypoint detectors; and random projections as stereo keypoint detectors and ...
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Matching Affine Features with the SYBA Feature Descriptor
2014Many vision-based applications require a robust feature descriptor that works well with image deformations such as compression, illumination, and blurring. It remains a challenge for a feature descriptor to work well with image deformation caused by viewpoint change.
Alok Desai, Dah-Jye Lee, Dan Ventura
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Feature Point Matching with Matching Distribution
2015Most of the feature point matching techniques considers only the number of matches. The higher number of matches is, the better results are. However, reliability and quality of the matching is addressed in a few techniques. So, finding the good matches of the pairs of points from the two given point sets is one of the main issue of feature point ...
San Ratanasanya +2 more
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Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing, 2012
Computing the dense Approximate Nearest-Neighbour Field (ANNF) between a pair of images has become a major problem which is being tackled by the image processing community in the recent years. Two important papers viz. PatchMatch [3] and CSH [11] have been developed over the past few years based on the coherency between images, but one major problem ...
S. Avinash Ramakanth, R. Venkatesh Babu
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Computing the dense Approximate Nearest-Neighbour Field (ANNF) between a pair of images has become a major problem which is being tackled by the image processing community in the recent years. Two important papers viz. PatchMatch [3] and CSH [11] have been developed over the past few years based on the coherency between images, but one major problem ...
S. Avinash Ramakanth, R. Venkatesh Babu
openaire +2 more sources