Transparent haptic rendering of the contact between a tool and its environment requires very frequent update of the contact forces acting on the tool. Given a rigid tool and a deformable environment, we define contact...
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Transparent haptic rendering of the contact between a tool and its environment requires very frequent update of the contact forces acting on the tool. Given a rigid tool and a deformable environment, we define contact constraints by solving a constrained dynamic simulation problem, typically at a low update rate. A generalized contact Jacobian defines velocities at the constraints given the velocity of the rigid tool. We define an inverse of the contact Jacobian that is dynamically consistent with the constraints and, once we know other forces acting on the rigid tool, it allows a fast update of the accumulated contact forces, and thereby highly transparent rendering
In recent years many powerful computer vision algorithms have been invented, making automatic or semiautomatic solutions to many popular vision tasks, such as visual object recognition or camera calibration, possible....
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In recent years many powerful computer vision algorithms have been invented, making automatic or semiautomatic solutions to many popular vision tasks, such as visual object recognition or camera calibration, possible. On the other hand embedded vision platforms and solutions such as smart cameras have successfully emerged, however, only offering limited computational and memory resources. The first contribution of this paper is the investigation of a set of robust local feature detectors and descriptors for application on embedded systems. We briefly describe the methods involved, i.e. the DoG (difference of Gaussian) and MSER (maximally stable extremal regions) detector as well as the PCA-SIFT descriptor, and discuss their suitability for smart systems and their qualification for given tasks. The second contribution of this work is the experimental evaluation of these methods on two challenging tasks, namely fully embedded object recognition on a moderate size database and on the task of robust camera calibration. Our approach is fortified by encouraging results we present at length.
In this paper we present a full-featured license plate detection and recognition system. The system is implemented on an embedded DSP platform and processes a video stream in real-time. It consists of a detection and ...
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In this paper we present a full-featured license plate detection and recognition system. The system is implemented on an embedded DSP platform and processes a video stream in real-time. It consists of a detection and a character recognition module. The detector is based on the AdaBoost approach presented by Viola and Jones. Detected license plates are segmented into individual characters by using a region-based approach. Character classification is performed with support vector classification. In order to speed up the detection process on the embedded device, a Kalman tracker is integrated into the system. The search area of the detector is limited to locations where the next location of a license plate is predicted. Furthermore, classification results of subsequent frames are combined to improve the class accuracy. The major advantages of our system are its real-time capability and that it does not require any additional sensor input (e.g. from infrared sensors) except a video stream. We evaluate our system on a large number of vehicles and license plates using bad quality video and show that the low resolution can be partly compensated by combining classification results of subsequent frames.
Object reacquisition or reidentification is the process of matching objects between images taken from separate cameras. In this paper, we present our work on feature based object reidentification performed on autonomo...
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Object reacquisition or reidentification is the process of matching objects between images taken from separate cameras. In this paper, we present our work on feature based object reidentification performed on autonomous embedded smart cameras and applied to traffic scenarios. We present a novel approach based on PCA-SIFT features and a vocabulary tree. By building unique object signatures from visual features, reidentification can be done efficiently coevally minimizing the communication overhead between separate camera nodes. Applied to large-scale traffic scenarios, important parameters including travel time, travel time variability, section density, and partial dynamic origin/destination demands can be obtained. The proposed approach works on spatially separated, un-calibrated, non-overlapping cameras, is highly scalable and solely based on appearance-based optical features. The entire system is implemented and evaluated with regard to a typical embedded smart camera platform featuring one single Texas Instruments™ fixed-point DSP.
Institutions of higher education are self-governing bodies of which participating members must serve on various committees from time to time. To this end, nominations and elections of schoolwide committees are the vit...
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We present a new approach for strongly compressing a relatively small amount of poorly structured data, as is required when embedding fingerprint template information in images of ID‐cards by means of watermarking te...
We present a new approach for strongly compressing a relatively small amount of poorly structured data, as is required when embedding fingerprint template information in images of ID‐cards by means of watermarking techniques. The approach is based on the construction of a directed tree spanning a selected part of the data points and a codebook of template arcs used for a compact encoding of relative point positions. The selection of data points, the tree structure, and the codebook are simultaneously optimized by a new exact branch‐and‐cut approach or, alternatively, a faster greedy randomized adaptive search procedure (GRASP) to maximize compression. Experiments indicate that the new method can encode the required information in less space than several standard compression algorithms.
Osteoarthritis is a chronic and crippling disease affecting an increasing number of people each year. With no known cure, it is expected to reach epidemic proportions in the near future. Accurate segmentation of knee ...
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Osteoarthritis is a chronic and crippling disease affecting an increasing number of people each year. With no known cure, it is expected to reach epidemic proportions in the near future. Accurate segmentation of knee cartilage from magnetic resonance imaging (MRI) scans facilitates the measurement of cartilage volume present in a patient's knee, thus enabling medical clinicians to detect the onset of osteoarthritis and also crucially, to study its effects. This paper compares four model-based segmentation methods popular for medical data segmentation, namely Active Shape Models (ASM) (Cootes et al., 1995), Active Appearance Models (AAM) (Cootes et al., 2001), Patch-based Active Appearance Models (PAAM) (Faggian et al., 2006), and Active Feature Models (AFM) (Langs et al., 2006). A comprehensive analysis of how accurately these methods segment human tibial cartilage is presented. The results obtained were benchmarked against the current "gold standard" (cartilage segmented manually by trained clinicians) and indicate that modeling local texture features around each landmark provides the best results for segmenting human tibial cartilage.
This paper describes "search in speech" techniques developed in the Speech@FIT research group at FIT BUT in the last couple of years. It concentrates on spoken term detection (STD) and presents our system fo...
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ISBN:
(纸本)1424408210
This paper describes "search in speech" techniques developed in the Speech@FIT research group at FIT BUT in the last couple of years. It concentrates on spoken term detection (STD) and presents our system for NIST STD 2006 evaluations in detail. It also briefly mentions our systems for speaker and language recognition.
Image segmentation methods like active shape models, active appearance models or snakes require an initialisation that guarantees a considerable overlap with the object to be segmented. In this paper we present an app...
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ISBN:
(纸本)1901725340
Image segmentation methods like active shape models, active appearance models or snakes require an initialisation that guarantees a considerable overlap with the object to be segmented. In this paper we present an approach that localises anatomical structures in a global manner by means of Markov Random Fields (MRF). It does not need initialisation, but finds the most plausible match of the query structure in the image. It provides for precise, reliable and fast detection of the structure and can serve as initialisation for more detailed segmentation steps. Sparse MRF Appearance Models (SAMs) encode a priori information about the geometric configurations of interest points, local features at these points and local features along the edges of adjacent points. This information is used to formulate a Markov Random Field and the mapping of the modeled object (e.g. a sequence of vertebrae) to the query image interest points is performed by the MAX-SUM algorithm. The local image information is captured by novel symmetry-based interest points and local descriptors derived from Gradient Vector Flow. Experimental results are reported for two data-sets showing the applicability to complex medical data.
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