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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: Important research areas in active vision include attention, foveal sensing, gaze control, eye-hand coordination, and integration with robot architectures.
Abstract: Active vision systems have mechanisms that can actively control camera parameters such as position, orientation, focus, zoom, aperture, and vergence (in a two-camera system) in response to the requirements of the task and external stimuli. They may also have features such as spatially variant (foveal) sensors. More broadly, active vision encompassesattention, selective sensing in space, resolution, and time, whether it is achieved by modifying physical camera parameters or the way data is processed after leaving the camera. In the active-vision paradigm, the basic components of the visual system are visual behaviors tightly integrated with the actions they support; these behaviors may not require elaborate categorical representations of the 3-D world. Because the cost of generating and updating a complete, detailed model of most environments is too high, this approach to vision is vital for achieving robust, real-time perception in the real world. In addition, active control of imaging parameters has been shown to simplify scene interpretation by eliminating the ambiguity present in single images. Important research areas in active vision include attention, foveal sensing, gaze control, eye-hand coordination, and integration with robot architectures.

201 citations

Journal ArticleDOI
TL;DR: Various vector-valued techniques for detecting discontinuities in color images are discussed, mainly based on vector order statistics, followed by presentation by examples of a couple of results of color edge detection.
Abstract: Up to now, most of the color edge detection methods are monochromatic-based techniques, which produce, in general, better than when traditional gray-value techniques are applied. In this overview, we focus mainly on vector-valued techniques because it is easy to understand how to apply common edge detection schemes to every color component. Opposed to this, vector-valued techniques are new and different. The second part of the article addresses the topic of edge classification. While edges are often classified into step edges and ramp edges, we address the topic of physical edge classification based on their origin into shadow edges, reflectance edges, orientation edges, occlusion edges, and specular edges. In the rest of this article we discuss various vector-valued techniques for detecting discontinuities in color images. Then operators are presented based on vector order statistics, followed by presentation by examples of a couple of results of color edge detection. We then discuss different approaches to a physical classification of edges by their origin.

201 citations

Dissertation
01 Jan 2006
TL;DR: The approach couples topic models originally developed for text analysis with spatial transformations, and thus consistently accounts for geometric constraints by building integrated scene models, which may discover contextual relationships, and better exploit partially labeled training images.
Abstract: We develop statistical methods which allow effective visual detection, categorization, and tracking of objects in complex scenes. Such computer vision systems must be robust to wide variations in object appearance; the often small size of training databases, and ambiguities induced by articulated or partially occluded objects. Graphical models provide a powerful framework for encoding the statistical structure of visual scenes, and developing corresponding learning and inference algorithms. In this thesis, we describe several models which integrate graphical representations with nonparametric statistical methods. This approach leads to inference algorithms which tractably recover high-dimensional, continuous object pose variations, and learning procedures which transfer knowledge among related recognition tasks. Motivated by visual tracking problems, we first develop a nonparametric extension of the belief propagation (BP) algorithm. Using Monte Carlo methods, we provide general procedures for recursively updating particle-based approximations of continuous sufficient statistics. Efficient multiscale sampling methods then allow this nonparametric BP algorithm to be flexibly adapted to many different applications. As a particular example, we consider a graphical model describing the hand's three-dimensional (3D) structure, kinematics, and dynamics. This graph encodes global hand pose via the 3D position and orientation of several rigid components, and thus exposes local structure in a high-dimensional articulated model. Applying nonparametric BP, we recover a hand tracking algorithm which is robust to outliers and local visual ambiguities. Via a set of latent occupancy masks, we also extend our approach to consistently infer occlusion events in a distributed fashion. In the second half of this thesis, we develop methods for learning hierarchical models of objects, the parts composing them, and the scenes surrounding them. Our approach couples topic models originally developed for text analysis with spatial transformations, and thus consistently accounts for geometric constraints. By building integrated scene models, we may discover contextual relationships, and better exploit partially labeled training images. We first consider images of isolated objects, and show that sharing parts among object categories improves accuracy when learning from few examples. Turning to multiple object scenes, we propose nonparametric models which use Dirichlet processes to automatically learn the number of parts underlying each object category, and objects composing each scene. Adapting these transformed Dirichlet processes to images taken with a binocular stereo camera, we learn integrated, 3D models of object geometry and appearance. This leads to a Monte Carlo algorithm which automatically infers 3D scene structure from the predictable geometry of known object categories. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

200 citations

Journal ArticleDOI
TL;DR: High-resolution imaging results demonstrate a reliable millimeters-scale orientation signal, likely emerging from irregular spatial arrangements of orientation columns and their supporting vasculature, and fMRI pattern analysis methods are thus likely to be sensitive to signals originating from other irregular columnar structures elsewhere in the brain.
Abstract: Although orientation columns are less than a millimeter in width, recent neuroimaging studies indicate that viewed orientations can be decoded from cortical activity patterns sampled at relatively coarse resolutions of several millimeters One proposal is that these differential signals arise from random spatial irregularities in the columnar map However, direct support for this hypothesis has yet to be obtained Here, we used high-field, high-resolution functional magnetic resonance imaging (fMRI) and multivariate pattern analysis to determine the spatial scales at which orientation-selective information can be found in the primary visual cortex (V1) of cats and humans We applied a multiscale pattern analysis approach in which fine- and coarse-scale signals were first removed by ideal spatial lowpass and highpass filters, and the residual activity patterns then analyzed by linear classifiers Cat visual cortex, imaged at 03125 mm resolution, showed a strong orientation signal at the scale of individual columns Nonetheless, reliable orientation bias could still be found at spatial scales of several millimeters In the human visual cortex, imaged at 1 mm resolution, a majority of orientation information was found on scales of millimeters, with small contributions from global spatial biases exceeding ∼1 cm Our high-resolution imaging results demonstrate a reliable millimeters-scale orientation signal, likely emerging from irregular spatial arrangements of orientation columns and their supporting vasculature fMRI pattern analysis methods are thus likely to be sensitive to signals originating from other irregular columnar structures elsewhere in the brain

200 citations

01 Jan 1972
TL;DR: The application of statistical methods for determining the areas of animal orientation and navigation are discussed and mathematical models are included to support the theoretical considerations and tables of data are developed to show the value of information obtained by statistical analysis.
Abstract: The application of statistical methods for determining the areas of animal orientation and navigation are discussed. The method employed is limited to the two-dimensional case. Various tests for determining the validity of the statistical analysis are presented. Mathematical models are included to support the theoretical considerations and tables of data are developed to show the value of information obtained by statistical analysis.

200 citations


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