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Orientation (computer vision)

About: Orientation (computer vision) is a research topic. Over the lifetime, 17196 publications have been published within this topic receiving 358181 citations.


Papers
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Journal ArticleDOI
TL;DR: A localized dictionaries-based orientation field estimation algorithm, in which noisy orientation patch at a location output by a local estimation approach is replaced by real orientation patch in the local dictionary at the same location.
Abstract: Dictionary based orientation field estimation approach has shown promising performance for latent fingerprints. In this paper, we seek to exploit stronger prior knowledge of fingerprints in order to further improve the performance. Realizing that ridge orientations at different locations of fingerprints have different characteristics, we propose a localized dictionaries-based orientation field estimation algorithm, in which noisy orientation patch at a location output by a local estimation approach is replaced by real orientation patch in the local dictionary at the same location. The precondition of applying localized dictionaries is that the pose of the latent fingerprint needs to be estimated. We propose a Hough transform-based fingerprint pose estimation algorithm, in which the predictions about fingerprint pose made by all orientation patches in the latent fingerprint are accumulated. Experimental results on challenging latent fingerprint datasets show the proposed method outperforms previous ones markedly.

78 citations

Book ChapterDOI
07 Oct 2015
TL;DR: It is demonstrated that a convolutional network can learn subtle features to predict the canonical orientation of images, and this approach runs in real-time and can be applied also to live video streams.
Abstract: Rectifying the orientation of scanned documents has been an important problem that was solved long ago. In this paper, we focus on the harder case of estimating and correcting the exact orientation of general images, for instance, of holiday snapshots. Especially when the horizon or other horizontal and vertical lines in the image are missing, it is hard to find features that yield the canonical orientation of the image. We demonstrate that a convolutional network can learn subtle features to predict the canonical orientation of images. In contrast to prior works that just distinguish between portrait and landscape orientation, the network regresses the exact orientation angle. The approach runs in real-time and, thus, can be applied also to live video streams.

78 citations

Journal ArticleDOI
TL;DR: The theory which governs constraints under orthography which governs shadows cast by polyhedra and curved surfaces is described, and some methods are presented for combining shadow geometry with other gradient space techniques for 3D shape inference.
Abstract: Given a line drawing from an image with shadow regions identified, the shapes of the shadows can be used to generate constraints on the orientations of the surfaces involved. This paper describes the theory which governs those constraints under orthography. A “Basic Shadow Problem” is first posed, in which there is a single light source, and a single surface casts a shadow on another (background) surface. There are six parameters to determine: the orientation (two parameters) for each surface, and the direction of the vector (two parameters) pointing at the light source. If some set of three of these are given in advance, the remaining three can then be determined geometrically. The solution method consists of identifying “illumination surfaces” consisting of illumination vectors, assigning Huffman-Clowes line labels to their edges, and applying the corresponding constraints in gradient space. The analysis is extended to shadows cast by polyhedra and curved surfaces. In both cases, the constraints provided by shadows can be analyzed in a manner analogous to the Basic shadow Problem. When the shadow falls upon a polyhedron or curved surface, similar techniques apply. The consequences of varying the position and number of light sources are also discussed. Finally, some methods are presented for combining shadow geometry with other gradient space techniques for 3D shape inference.

78 citations

Journal ArticleDOI
TL;DR: It is demonstrated that the efficiency of the alignment methods can be improved if approximate centers of gravity of particle views are known and the accuracy of the methods considered is largely affected by the order in which interpolation steps are applied.

78 citations

Journal ArticleDOI
TL;DR: The potential of three‐dimensional ultrasound as a technique for visualization of fetal surfaces, extremities and internal anatomy is demonstrated and potential advantages over existing diagnostic methods since it is non‐invasive and offers interactive review and viewing optimization after fetal scanning.
Abstract: This paper presents preliminary results of techniques which permit acquisition and display of three-dimensional fetal anatomy using ultrasound image data collected as two-dimensional planar images with commercially available equipment. A precision translational stage was used which permitted the transducer position and orientation in the volume to be accurately determined. Ultrasound video image data were digitized into a personal computer together with transducer position and orientation information. All image and position data were subsequently transferred to a graphics workstation. Gray-scale ultrasound data were three-dimensionally filtered, thresholded and analyzed using maximum intensity, variable transparency and surface rendering techniques to evaluate the fetal surface. Images of arbitrary orientation were displayed interactively on a graphics workstation. Our results demonstrate the potential of three-dimensional ultrasound as a technique for visualization of fetal surfaces, extremities and internal anatomy. Three-dimensional ultrasound offers potential advantages over existing diagnostic methods since it is non-invasive and offers interactive review and viewing optimization after fetal scanning. Copyright © 1992 International Society of Ultrasound in Obstetrics and Gynecology

78 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202212
2021535
2020771
2019830
2018727
2017691