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.

A Shearlet Approach to Edge Analysis and Detection

Abstract: It is well known that the wavelet transform provides a very effective framework for analysis of multiscale edges. In this paper, we propose a novel approach based on the shearlet transform: a multiscale directional transform with a greater ability to localize distributed discontinuities such as edges. Indeed, unlike traditional wavelets, shearlets are theoretically optimal in representing images with edges and, in particular, have the ability to fully capture directional and other geometrical features. Numerical examples demonstrate that the shearlet approach is highly effective at detecting both the location and orientation of edges, and outperforms methods based on wavelets as well as other standard methods. Furthermore, the shearlet approach is useful to design simple and effective algorithms for the detection of corners and junctions.

A Model-Based Vision System for Industrial Parts

Abstract: A vision system has been developed which can determine the position and orientation of complex curved objects in gray-level noisy scenes. The system organizes and reduces the image data from a digitized picture to a compact representation having the appearance of a line drawing. This compact image representation can be used for forming a model under favorable viewing conditions or for locating a part under poor viewing conditions by a matching process that uses a previously formed model. Thus, models are formed automatically by having the program view the part under favorable lighting and background conditions. The compact image representation describes the boundaries of the part.

Topography of contextual modulations mediated by short-range interactions in primary visual cortex

Abstract: Neurons in primary visual cortex (V1) respond differently to a simple visual element presented in isolation from when it is embedded within a complex image. This difference, a specific modulation by surrounding elements in the image, is mediated by short- and long-range connections within V1 and by feedback from other areas. Here we study the role of short-range connections in this process, and relate it to the layout of local inhomogeneities in the cortical maps of orientation and space. By measuring correlation between neuron pairs located in optically imaged maps of V1 orientation columns we show that the strength of local connections between cells is a graded function of lateral separation across cortex, largely radially symmetrical and relatively independent of orientation preferences. We then show the contextual influence of flanking visual elements on neuronal responses varies systematically with a neuron's position within the cortical orientation map. The strength of this contextual influence on a neuron can be predicted from a model of local connections based on simple overlap with particular features of the orientation map. This indicates that local intracortical circuitry could endow neurons with a graded specialization for processing angular visual features such as corners and T junctions, and this specialization could have its own functional cortical map, linked with the orientation map.

Relative Orientation

Abstract: Before corresponding points in images taken with two cameras can be used to recover distances to objects in a scene, one has to determine the position and orientation of one camera relative to the other. This is the classic photogrammetric problem of {\it relative orientation}, central to the interpretation of binocular stereo information. Described here is a particularly simple iterative scheme for recovering relative orientation that, unlike existing methods, does not require a good initial guess for the baseline and the rotation.

Physically Based Simulation Model for Acoustic Sensor Robot Navigation

Abstract: A computer model is described that combines concepts from the fields of acoustics, linear system theory, and digital signal processing to simulate an acoustic sensor navigation system using time-of-flight ranging. By separating the transmitter/receiver into separate components and assuming mirror-like reflectors, closed-form solutions for the reflections from corners, edges, and walls are determined as a function of transducer size, location, and orientation. A floor plan consisting of corners, walls, and edges is efficiently encoded to indicate which of these elements contribute to a particular pulse-echo response. Sonar maps produced by transducers having different resonant frequencies and transmitted pulse waveforms can then be simulated efficiently. Examples of simulated sonar maps of two floor plans illustrate the performance of the model. Actual sonar maps are presented to verify the simulation results.