Topic
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.
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08 Oct 2003
TL;DR: In this paper, a computer-based 3D modeling system for constructing a virtual 3D representation from a plurality of data images of 2D cross sections having a mutual spatial relationship is presented.
Abstract: A computer-based 3D modeling system for constructing a virtual 3D representation from a plurality of data images of 2D cross sections having a mutual spatial relationship. The plurality of data images and the associated orientation and positioning information are extractable from a data source module (22). A frame creation module constructs a rectangular frame for each image slice. A texture-mapping module (26) maps the image slice onto the associated frame as a texture. A rotation transform module (28) rotates each frame appropriately about one or more axes based upon the orientation information associated with each data image to achieve the correct orientation in 3D space. A translation transform module (30) translates each frame based upon the positioning information associated with each data image to achieve the correct position in 3D space.
312 citations
TL;DR: While S- and C-operators are optimised for the representation of 1-D features such as edges and lines, the end-stopped operator responses at the key-points make explicit 2-D signal variations such as line ends, corners and segments of strong curvature.
312 citations
TL;DR: Methods to measure the following properties of gray level corners: subtended angle, orientation, contrast, bluntness (or rounding of the apex), and boundary curvature (for cusps) are described.
311 citations
TL;DR: In this paper, an edge operator based on two-dimensional spatial moments is presented, which can be implemented for virtually any size of window and has been shown to locate edges in digitized images to a twentieth of a pixel.
Abstract: Recent results in precision measurements using computer vision are presented. An edge operator based on two-dimensional spatial moments is given. The operator can be implemented for virtually any size of window and has been shown to locate edges in digitized images to a twentieth of a pixel. This accuracy is unaffected by additive or multiplicative changes to the data values. The precision is achieved by correcting for many of the deterministic errors caused by nonideal edge profiles using a lookup table to correct the original estimates of edge orientation and location. This table is generated using a synthesized edge which is located at various subpixel locations and various orientations. The operator is extended to accommodate nonideal edge profiles and rectangularly sampled pixels. The technique is applied to the measurement of imaged machined metal parts. Theoretical and experimental noise analyses show that the operator has relatively small bias in the presence of noise. >
311 citations
12 May 2009
TL;DR: This paper presents an approach for building metric 3D models of objects using local descriptors from several images, optimized to fit a set of calibrated training images, thus obtaining the best possible alignment between the 3D model and the real object.
Abstract: Robust perception is a vital capability for robotic manipulation in unstructured scenes. In this context, full pose estimation of relevant objects in a scene is a critical step towards the introduction of robots into household environments. In this paper, we present an approach for building metric 3D models of objects using local descriptors from several images. Each model is optimized to fit a set of calibrated training images, thus obtaining the best possible alignment between the 3D model and the real object. Given a new test image, we match the local descriptors to our stored models online, using a novel combination of the RANSAC and Mean Shift algorithms to register multiple instances of each object. A robust initialization step allows for arbitrary rotation, translation and scaling of objects in the test images. The resulting system provides markerless 6-DOF pose estimation for complex objects in cluttered scenes. We provide experimental results demonstrating orientation and translation accuracy, as well a physical implementation of the pose output being used by an autonomous robot to perform grasping in highly cluttered scenes.
310 citations