|
News & Notices -
Research News
|
|
Written by Andrew Willis
|
|
Tuesday, 24 August 2010 17:24 |
|
UI Engineering alumnus Thaddeus Thomas (BS 2004, MS 2007 biomedical engineering) recently received the Clinical Biomechanics Award at the 34th annual meeting of the American Society of Biomechanics. Thomas was recognized for outstanding new biomechanics research targeting a contemporary clinical problem. The award is sponsored by Elsevier Science, Ltd., publishers of Clinical Biomechanics, an international multidisciplinary journal of musculoskeletal biomechanics.
Two finalists were selected from the top 10th percentile of over 500 abstracts submitted to the annual meeting of the Society’s annual meeting, who then made competitive podium presentations judged by the ASB Awards Committee. The award consists of an engraved plaque and a check for $1,000.
The ASB was founded in October 1977 by a group of 53 scientists and clinicians, and its first annual meeting was held that year in Iowa City. The ASB mission is to encourage and foster the exchange of information and ideas among biomechanists working in different disciplines and fields of application and to facilitate the development of biomechanics as a basic and applied science.
Thomas is a graduate research assistant in the Department of Orthopaedics and Rehabilitation, as well as a PhD candidate in the Department of Biomedical Engineering at The University of Iowa. His presentation was entitled “Virtual pre-operative reconstruction planning for comminuted articular fractures,” co-authored by Donald D. Anderson, J. Lawrence Marsh, and Thomas D. Brown (University of Iowa), and by Andrew R. Willis (University of North Carolina at Charlotte).
Also at the annual meeting, Donald Anderson, research associate professor of orthopaedics and rehabilitation, and biomedical engineering, was elected president of the ASB. |
|
|
News & Notices -
Research News
|
|
Written by Andrew Willis
|
|
Friday, 09 July 2010 16:21 |
Research at the UNCC visionlab has produced an inexpensive 3D scanner that is portable, accurate and is capable of "wrapping" photographs over the 3D meshes produced by the scanner. The system is powered by a SICK LMS 200 LIDAR sensor that captures 3D (x,y,z) coordinates at a rate of up to 27k 3D points per second. Each measurement records the (x,y,z) position of surfaces within the line- o f-sight of the scanner. The 3D surface samples are integrated with photographs from a webcamera in real-time to create a 3D mesh of the scene in the vicinity of the scanner. The system is high ly configurable and allows the users to specify the region of interest for data capture that can range from a small surface patch (~1 sq. m.) to a 360-degree view of all surfaces within 60m. of the scanning sensor. A dense 360-degree scan can take up to 2-3 minutes to capture and less dense scans covering smaller areas may be captured much faster. The SICK sensor provides measurements that average 2 cm. of error. Surface (x,y,z) measurements are integrated in real-time with images produced by a web camera that is also controlled by the scanning software. The scanner output is a sequence of Alias-Wavefront (Maya-compatible) OBJ files. Each output OBJ file includes a portion of the 3D scan and a image from the web camera that is overlaid onto the mesh using texture-mapping. The system was successfully used to capture data from Mayan architecture in the Puuc region of the Yucatan peninsula in Mexico in May 2010.
===============
Technical Specs
===============
DataRate: up to 27k 3D points/sec
Vertical Field of view: Configurable from straight up (0 degrees) to almost straight down (150 degrees) -- occlusion occurs due to tripod mount.
Horizontal Field of view: Configurable up to 360 degrees
Accuracy: ~2 cm.
Weight: ~22 kg.
Output: OBJ format 3D files and JPG images (for texture mapping)
Two views of a scan of a Mayan facade from the Kiuic archaeological site are shown below.
|
|
Last Updated on Friday, 09 July 2010 16:42 |
|
News & Notices -
Research News
|
|
Written by Andrew Willis
|
|
Wednesday, 24 March 2010 18:03 |
During the period September 22 to October 4 Yunfeng Sui attended the International Conference on Computer Vision (ICCV) where he presented joint work with Dr. Willis on the development of a fast corner detector. This work is described in detail in the paper entitled An Algebraic Model for fast Corner Detection. This paper revisits the classical problem of detecting interest points, popularly known as “corners,” in 2D images by proposing a technique based on fitting algebraic shape models to contours in the edge image. Our method for corner detection is targeted for use on structural images, i.e., images that contain man-made structures for which corner detection algorithms are known to perform well. Further, our detector seeks to find image regions that contain two distinct linear contours that intersect. We define the intersection point as the corner, and, in contrast to previous approaches such as the Harris detector, we consider the spatial coherence of the edge points, i.e., the fact that the edge points must lie close to one of the two intersecting lines, an important aspect to stable corner detection. Comparisons between results for the proposed method and that for several popular feature detectors are provided using input images exhibiting a number of standard image variations, including blurring, affine transformation, scaling, rotation, and illumination variation. A modified version of the repeatability rate is proposed for evaluating the stability of the detector under these variations which requires a 1-to-1 mapping between matched features. Using this performance metric, our method is found to perform well in contrast to several current methods for corner detection. Discussion is provided that motivates our method of evaluation and provides an explanation for the observed performance of our algorithm in contrast to other algorithms. Our approach is distinct from other contour-based methods since we need only compute the edge image, from which we explicitly solve for the unknown linear contours and their intersections that provide image corner location estimates. The key benefits to this approach are: (1) performance (in space and time); since no image pyramid (space) and no edge-linking (time) is required and (2) compactness; the estimated model includes the corner location, and direction of the incoming contours in space, i.e., a complete model of the local corner geometry. |
|
Last Updated on Thursday, 26 August 2010 12:31 |
|
|
|
|
Page 1 of 8 |
