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.

Shape and reflectance from natural illumination

Abstract: We introduce a method to jointly estimate the BRDF and geometry of an object from a single image under known, but uncontrolled, natural illumination. We show that this previously unexplored problem becomes tractable when one exploits the orientation clues embedded in the lighting environment. Intuitively, unique regions in the lighting environment act analogously to the point light sources of traditional photometric stereo; they strongly constrain the orientation of the surface patches that reflect them. The reflectance, which acts as a bandpass filter on the lighting environment, determines the necessary scale of such regions. Accurate reflectance estimation, however, relies on accurate surface orientation information. Thus, these two factors must be estimated jointly. To do so, we derive a probabilistic formulation and introduce priors to address situations where the reflectance and lighting environment do not sufficiently constrain the geometry of the object. Through extensive experimentation we show what this space looks like, and offer insights into what problems become solvable in various categories of real-world natural illumination environments.

Intelligent sensor method and apparatus for an optical wheel alignment machine

Abstract: A sensor method and apparatus for an optical wheel alignment machine utilizes one or more light sources, such as lasers, to project a laser line or other shaped light onto various locations about the sidewall of a tire undergoing measurement. The sensor includes a video camera or other light responsive receiver and a optical system that combines the reflected laser lines into a single image that is received by the camera. The optical system also rotates one or more of the reflected laser lines so that all of the reflected portions have the same general orientation upon entering the camera. The camera outputs a video data stream that is indicative of the image. The sensor has an electronic circuit that analyzes this video data stream in real time to determine the location in the image of a preselected feature of each of the laser lines. The circuit then outputs coordinate data indicative of the location of this feature.

Sketch based image retrieval using a soft computation of the histogram of edge local orientations (S-HELO)

Abstract: This paper introduces S-HELO (Soft-Histogram of Edge Local Orientations), an outperforming method for describing images in the context of sketch based image retrieval (SBIR). This proposal exploits the advantages provided by the HELO descriptor for describing sketches, and improves significantly its performance by using a soft computation of local orientations and taking into account spatial information. We experimentally demonstrate that a soft computation process together with a local estimation of orientations are very suitable for describing sketches in the context of image retrieval. Indeed, our results show that S-HELO significantly outperforms not only HELO but also classical orientation-based descriptors as HOG. We also show that S-HELO performs very close to the optimal when what we want to retrieve are target images. Moreover, our proposal also shows an outstanding performance for similarity search, i.e., retrieving images that belong to the same category of the query sketch.

Building extraction from high resolution imagery based on multi-scale object oriented classification and probabilistic Hough transform

Abstract: In this paper, we developed a new building extraction system applied on high resolution remote sensing imagery based on multi-scale object oriented classification and probabilistic Hough transform. This can be divided into two different phases: building roof extraction, and shape reconfiguration. For the first phase, the multispectral and panchromatic high resolution satellite imageries are firstly fused for spatial resolution improvement and color information enhancement. The multiresolution image segmentation is applied on the fused image, resulting in the formation of the different level of polygon primitives at different space scale, providing different view of the scene at different resolution. In addition to the spectral information, the tone, texture, shape, context information is evaluated in an object oriented manner. The classification is based on a fuzzy rule decision tree classifier. By fuzzy evaluating of the shape, texture, context and spectral information, building roofs are extracted by reconstruction and classification from an appropriate space scale of roof polygon primitives. For the shape reconfiguration phase, we adopt the probabilistic Hough transform to delineate the roof dominant line which shows the major orientation of the specific building roof. According to the dominant line, a building squaring algorithm is applied based on rectilinear fitting of the building boundary. It is shown by our experiment that most rectangular building roofs can be correctly detected, extracted and reconfigured, demonstrating the potential application of the method.