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.

The influence of vision on the absolute identification of sound-source position

Abstract: The influence of vision on auditory localization was assessed in an absolute identification paradigm using sighted and blindfolded subjects. Vision improved the accuracy of judgments directly in front of, to the side of, and behind the head of subjects in the horizontal plane, but had little relevance to vertical-plane localization. The exact form of the observed facilitation depended on the orientation of the speaker array to the head. In a second experiment involving sound localization in 10 visual environments, there was evidence for the operation of two distinct influences of vision on directional hearing. One result supported the hypothesis that vision provides a frame of reference for judgments, and a second indicated the importance of vision to the maintenance of spatial memory.

Adaptive Structure Tensors and their Applications

Abstract: The structure tensor, also known as second moment matrix or Forstner interest operator, is a very popular tool in image processing. Its purpose is the estimation of orientation and the local analysis of structure in general. It is based on the integration of data from a local neighborhood. Normally, this neighborhood is defined by a Gaussian window function and the structure tensor is computed by the weighted sum within this window. Some recently proposed methods, however, adapt the computation of the structure tensor to the image data. There are several ways how to do that. This chapter wants to give an overview of the different approaches, whereas the focus lies on the methods based on robust statistics and nonlinear diffusion. Furthermore, the data-adaptive structure tensors are evaluated in some applications. Here the main focus lies on optic flow estimation, but also texture analysis and corner detection are considered.

Corneal Imaging System: Environment from Eyes

Abstract: This paper provides a comprehensive analysis of exactly what visual information about the world is embedded within a single image of an eye. It turns out that the cornea of an eye and a camera viewing the eye form a catadioptric imaging system. We refer to this as a corneal imaging system. Unlike a typical catadioptric system, a corneal one is flexible in that the reflector (cornea) is not rigidly attached to the camera. Using a geometric model of the cornea based on anatomical studies, its 3D location and orientation can be estimated from a single image of the eye. Once this is done, a wide-angle view of the environment of the person can be obtained from the image. In addition, we can compute the projection of the environment onto the retina with its center aligned with the gaze direction. This foveated retinal image reveals what the person is looking at. We present a detailed analysis of the characteristics of the corneal imaging system including field of view, resolution and locus of viewpoints. When both eyes of a person are captured in an image, we have a stereo corneal imaging system. We analyze the epipolar geometry of this stereo system and show how it can be used to compute 3D structure. The framework we present in this paper for interpreting eye images is passive and non-invasive. It has direct implications for several fields including visual recognition, human-machine interfaces, computer graphics and human affect studies.

Object detection and analysis via unmanned aerial vehicle

Abstract: An unmanned aerial vehicle (UAV) can include one or more cameras for capturing image data within a field of view that depends in part upon the location and orientation of the UAV. At least a portion of the image data can be processed on the UAV to locate objects of interest, such as people or cars, and use that information to determine where to fly the drone in order to capture higher quality image data of those or other such objects. Once identified, the objects of interest can be counted, and the density, movement, location, and behavior of those objects identified. This can help to determine occurrences such as traffic congestion or unusual patterns of pedestrian movement, as well as to locate persons, fires, or other such objects. The data can also be analyzed by a remote system or service that has additional resources to provide more accurate results.

A statistical analysis of natural images matches psychophysically derived orientation tuning curves.

Abstract: A neural net method is used to extract principal components from real-world images. The initial components are a Gaussian followed by horizontal and vertical operators, starting with the first derivative and moving to successively higher orders. Two of the components are 'bar-detectors'. Their measured orientation selectivity is similar to that suggested by Foster & Ward (Proc. R. Soc. Lond. B 243, 75 (1991] to account for brief-exposure psychophysical data. In tests with noise images, the ratio of sensitivity between the two components is controlled by the degree of anisotropy in the image.