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For many real world problems we must perform classification under widely varying amounts of computational resources. For example, if asked to classify an instance taken from a bursty stream, we may have from milliseconds to minutes to return a class prediction. For such problems an anytime algorithm may be especially useful. In this work we show how we can(More)
In recent years, there has been a rapid increase in the size and number of medical image collections. Thus, the development of appropriate methods for medical information retrieval is especially important. In a large collection of spine X-ray images, maintained by the National Library of Medicine, vertebral boundary shape has been determined to be relevant(More)
FPGA devices have often found use as higher-performance alternatives to programmable processors for implementing a variety of computations. Applications successfully implemented on FPGAs have typically contained high levels of parallelism and have often used simple statically-scheduled control and modest arithmetic. Recently introduced computing devices(More)
Optical flow algorithms are difficult to apply to robotic vision applications in practice because of their extremely high computational and frame rate requirements. In most cases, traditional general purpose processors and sequentially executed software cannot compute optical flow in real time. In this paper, a tensor-based optical flow algorithm is(More)
Efficient content-based image retrieval of biomedical images is a challenging problem of growing research interest. Feature representation algorithms used in indexing medical images on the pathology of interest have to address conflicting goals of reducing feature dimensionality while retaining important and often subtle biomedical features. At the Lister(More)
Micro Unmanned Air Vehicles are well suited for a wide variety of applications in agriculture, homeland security, military, search and rescue, and surveillance. In response to these opportunities, a quad-rotor micro UAV has been developed at the Robotic Vision Lab at Brigham Young University. The quad-rotor UAV uses a custom, low-power FPGA platform to(More)
Feature matching is an important step for many computer vision applications. This presentation introduces the development of a new feature descriptor, called SYnthetic BAsis (SYBA), for feature point description and matching. SYBA is built on the basis of the compressed sensing theory that uses synthetic basis functions to encode or reconstruct a signal. It(More)
A significant amount of research in the field of stereo vision has been published in the past decade. Considerable progress has been made in improving accuracy of results as well as achieving real-time performance in obtaining those results. This work provides a comprehensive review of stereo vision algorithms with specific emphasis on real-time performance(More)
Scale-invariant feature transform (SIFT) transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale, rotation, and changing viewpoints. Because of its scale-invariant properties, SIFT has been successfully used for object recognition and content-based image retrieval. The biggest drawback of SIFT is(More)