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.

Method and apparatus for managing and controlling manned and automated utility vehicles

Abstract: A method and apparatus for managing manned and automated utility vehicles, and for picking up and delivering objects by automated vehicles. A machine vision image acquisition apparatus determines the position and the rotational orientation of vehicles in a predefined coordinate space by acquiring an image of one or more position markers and processing the acquired image to calculate the vehicle's position and rotational orientation based on processed image data. The position of the vehicle is determined in two dimensions. Rotational orientation (heading) is determined in the plane of motion. An improved method of position and rotational orientation is presented. Based upon the determined position and rotational orientation of the vehicles stored in a map of the coordinate space, a vehicle controller, implemented as part of a computer, controls the automated vehicles through motion and steering commands, and communicates with the manned vehicle operators by transmitting control messages to each operator.

Methods for Quantifying Fibrillar Collagen Alignment

Abstract: Recent evidence has implicated collagen, particularly fibrillar collagen, in a number of diseases ranging from osteogenesis imperfecta and asthma to breast and ovarian cancer. A key property of collagen that has been correlated with disease has been the alignment of collagen fibers. Collagen can be visualized using a variety of imaging techniques including second-harmonic generation (SHG) microscopy, polarized light microscopy, and staining with dyes or antibodies. However, there exists a great need to easily and robustly quantify images from these modalities for individual fibers in specified regions of interest and with respect to relevant boundaries. Most currently available computational tools rely on calculation of pixel-wise orientation or global window-wise orientation that do not directly calculate or give visible fiber-wise information and do not provide relative orientation against boundaries. We describe and detail how to use a freely available, open-source MATLAB software framework that includes two separate but linked packages "CurveAlign" and "CT-FIRE" that can address this need by either directly extracting individual fibers using an improved fiber tracking algorithm or directly finding optimal representation of fiber edges using the curvelet transform. This curvelet-based framework allows the user to measure fiber alignment on a global, region of interest, and fiber basis. Additionally, users can measure fiber angle relative to manually or automatically segmented boundaries. This tool does not require prior experience of programming or image processing and can handle multiple files, enabling efficient quantification of collagen organization from biological datasets.

Systems for mobile image capture and remittance processing

Abstract: The present invention relates to automated document processing and more particularly, to methods and systems for document image capture and processing using mobile devices. In accordance with various embodiments, methods and systems for document image capture on a mobile communication device are provided such that the image is optimized and enhanced for data extraction from the document as depicted. These methods and systems may comprise capturing an image of a document using a mobile communication device; transmitting the image to a server; and processing the image to create a bi-tonal image of the document for data extraction. Additionally, these methods and systems may comprise capturing a first image of a document using the mobile communication device; automatically detecting the document within the image; geometrically correcting the image; binarizing the image; correcting the orientation of the image; correcting the size of the image; and outputting the resulting image of the document.

Detecting Keypoints with Stable Position, Orientation, and Scale under Illumination Changes

Abstract: Local feature approaches to vision geometry and object recognition are based on selecting and matching sparse sets of visually salient image points, known as 'keypoints' or 'points of interest'. Their performance depends critically on the accuracy and reliability with which corresponding keypoints can be found in subsequent images. Among the many existing keypoint selection criteria, the popular Forstner-Harris approach explicitly targets geometric stability, defining keypoints to be points that have locally maximal self-matching precision under translational least squares template matching. However, many applications require stability in orientation and scale as well as in position. Detecting translational key- points and verifying orientation/scale behaviour post hoc is suboptimal, and can be misleading when different motion variables interact. We give a more principled formulation, based on extending the Forstner-Harris approach to general motion models and robust template matching. We also incorporate a simple local appear- ance model to ensure good resistance to the most common illumination variations. We illustrate the resulting methods and quantify their performance on test images.