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Feature Identities, Descriptors, and Handles
1999Finding the “right” geographic feature is a common source of interoperability difficulties. This paper reviews the issues and discusses how persistent feature identifiers can be used to support relationships and incremental updating in dispersed inter-operating information systems. Using such identifiers requires common definitions for concepts such as
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Harris feature vector descriptor (HFVD)
2008 19th International Conference on Pattern Recognition, 2008A new image feature called Harris feature vector is defined in this paper, which effectively describes the image gradient distribution. By computing the mean and the standard deviation of the Harris feature vector in key point neighborhood, a novel descriptor for key points matching is constructed, which is invariant to image rigid transformation and ...
Xuguang Wang, Fuchao Wu, Zhiheng Wang
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Robust synthetic basis feature descriptor
2017 IEEE International Conference on Image Processing (ICIP), 2017Feature detection and matching is an important step in many object detection and tracking algorithms. This paper discusses methods to improve upon our previous work on the SYnthetic BAsis feature descriptor (SYBA) algorithm, which describes and compares image features in an efficient and discrete manner. SYBA utilizes synthetic basis images overlaid on
Lindsey Raven, Dah-Jye Lee, Alok Desai
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A comparison of feature descriptors for visual SLAM
2013 European Conference on Mobile Robots, 2013Feature detection and feature description plays an important part in Visual Simultaneous Localization and Mapping (VSLAM). Visual features are commonly used to efficiently estimate the motion of the camera (visual odometry) and link the current image to previously visited parts of the environment (place recognition, loop closure).
Jan Hartmann +2 more
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Active Descriptor Learning for Feature Matching
2019Feature descriptor extraction lies at the core of many computer vision tasks including image retrieval and registration. In this paper, we present an active learning method for extracting efficient features to be used in matching image patches. We train a Siamese deep neural network by optimizing a triplet loss function. We develop a more efficient and
Aziz Koçanaogullari +1 more
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An Experimental Evaluation of Binary Feature Descriptors
2017Efficient and compact representation of local image patches in the form of features descriptors that are distinctive/robust as well as fast to compute and match is an essential and inevitable step for many computer vision applications. One category of these representations is the binary descriptors which have been shown to be successful alternatives ...
Hammam A. Alshazly +3 more
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Texture descriptors for representing feature vectors
Expert Systems with Applications, 2019Abstract Pattern representation affects classification performance. Although discovering “universal” features that work for many classification problems is ideal, most representations are problem specific. In this paper, we improve the classification performance of a classifier system by transforming a one-dimensional input descriptor into a two ...
Loris Nanni +2 more
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Using affine features for an efficient binary feature descriptor
2014 Southwest Symposium on Image Analysis and Interpretation, 2014A feature descriptor that is robust to a number of image deformations is a basic requirement for vision based applications. Most feature descriptors work well in image deformations such as compression artifacts, illumination changes, and blurring. To develop a feature descriptor that works well apart from these image deformations like transformations ...
Alok Desai, Dah-Jye Lee, Craig Wilson
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Fast image segmentation for local feature descriptors
2017 25th Signal Processing and Communications Applications Conference (SIU), 2017Local feature descriptors are the most frequently used feature representation in many Computer Vision problems. In particular, high level semantic information extraction from low-level features in classification and retrieval is also quite successful. Region based approaches to classification and retrieval have become very popular.
BİLGE, HASAN ŞAKİR, Celik, Ceyhun
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Geodesic Invariant Feature: A Local Descriptor in Depth
IEEE Transactions on Image Processing, 2015Different from the photometric images, depth images resolve the distance ambiguity of the scene, while the properties, such as weak texture, high noise, and low resolution, may limit the representation ability of the well-developed descriptors, which are elaborately designed for the photometric images.
Yazhou Liu +3 more
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