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  • 1
    Electronic Resource
    Electronic Resource
    Real-time multiple vehicle detection and tracking from a moving vehicle (2000)
    Springer
    Machine vision and applications 12 (2000), S. 69-83 
    Details
    ISSN: 1432-1769
    Keywords: Key words: Real-time computer vision – Vehicle detection and tracking – Object recognition under ego-motion – Intelligent vehicles
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract. A real-time vision system has been developed that analyzes color videos taken from a forward-looking video camera in a car driving on a highway. The system uses a combination of color, edge, and motion information to recognize and track the road boundaries, lane markings and other vehicles on the road. Cars are recognized by matching templates that are cropped from the input data online and by detecting highway scene features and evaluating how they relate to each other. Cars are also detected by temporal differencing and by tracking motion parameters that are typical for cars. The system recognizes and tracks road boundaries and lane markings using a recursive least-squares filter. Experimental results demonstrate robust, real-time car detection and tracking over thousands of image frames. The data includes video taken under difficult visibility conditions.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s001380050126
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Learned Models for Estimation of Rigid and Articulated Human Motion from Stationary or Moving Camera (2000)
    Springer
    International journal of computer vision 36 (2000), S. 5-30 
    Details
    ISSN: 1573-1405
    Keywords: tracking ; optical flow ; camera motion ; non-rigid motion ; motion learning ; human motion
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We propose an approach for modeling, measurement and tracking of rigid and articulated motion as viewed from a stationary or moving camera. We first propose an approach for learning temporal-flow models from exemplar image sequences. The temporal-flow models are represented as a set of orthogonal temporal-flow bases that are learned using principal component analysis of instantaneous flow measurements. Spatial constraints on the temporal-flow are then incorporated to model the movement of regions of rigid or articulated objects. These spatio-temporal flow models are subsequently used as the basis for simultaneous measurement and tracking of brightness motion in image sequences. Then we address the problem of estimating composite independent object and camera image motions. We employ the spatio-temporal flow models learned through observing typical movements of the object from a stationary camera to decompose image motion into independent object and camera motions. The performance of the algorithms is demonstrated on several long image sequences of rigid and articulated bodies in motion.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008173322902
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    Paper (German National Licenses)
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  • 3
    Electronic Resource
    Electronic Resource
    Temporal Multi-Scale Models for Flow and Acceleration (1999)
    Springer
    International journal of computer vision 32 (1999), S. 147-163 
    Details
    ISSN: 1573-1405
    Keywords: motion estimation ; optical flow ; non-rigid motion
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A model for computing image flow in image sequences containing a very wide range of instantaneous flows is proposed. This model integrates the spatio-temporal image derivatives from multiple temporal scales to provide both reliable and accurate instantaneous flow estimates. The integration employs robust regression and automatic scale weighting in a generalized brightness constancy framework. In addition to instantaneous flow estimation the model supports recovery of dense estimates of image acceleration and can be readily combined with parameterized flow and acceleration models. A demonstration of performance on image sequences of typical human actions taken with a high frame-rate camera is given.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008109516258
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    Paper (German National Licenses)
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  • 4
    Electronic Resource
    Electronic Resource
    Egomotion analysis based on the Frenet-Serret motion model (1995)
    Springer
    International journal of computer vision 15 (1995), S. 105-122 
    Details
    ISSN: 1573-1405
    Keywords: constraint intersection ; curvature and torsion ; dominant motion ; egomotion analysis ; Frenet-Serret motion model ; normal optical flow ; screw-motion equations
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper we propose a new model,Frenet-Serret motion, for the motion of an observer in a stationary environment. This model relates the motion parameters of the observer to the curvature and torsion of the path along which the observer moves. Screw-motion equations for Frenet-Serret motion are derived and employed for geometrical analysis of the motion. Normal flow is used to derive constraints on the rotational and translational velocity of the observer and to compute egomotion by intersecting these constraints in the manner proposed in (Durić and Aloimonos 1991) The accuracy of egomotion estimation is analyzed for different combinations of observer motion and feature distance. We explain the advantages of controlling feature distance to analyze egomotion and derive the constraints on depth which make either rotation or translation dominant in the perceived normal flow field. The results of experiments on real image sequences are presented.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01450851
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    Paper (German National Licenses)
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  • 5
    Electronic Resource
    Electronic Resource
    Model-based object pose in 25 lines of code (1995)
    Springer
    International journal of computer vision 15 (1995), S. 123-141 
    Details
    ISSN: 1573-1405
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper, we describe a method for finding the pose of an object from a single image. We assume that we can detect and match in the image four or more noncoplanar feature points of the object, and that we know their relative geometry on the object. The method combines two algorithms; the first algorithm,POS (Pose from Orthography and Scaling) approximates the perspective projection with a scaled orthographic projection and finds the rotation matrix and the translation vector of the object by solving a linear system; the second algorithm,POSIT (POS with ITerations), uses in its iteration loop the approximate pose found by POS in order to compute better scaled orthographic projections of the feature points, then applies POS to these projections instead of the original image projections. POSIT converges to accurate pose measurements in a few iterations. POSIT can be used with many feature points at once for added insensitivity to measurement errors and image noise. Compared to classic approaches making use of Newton's method, POSIT does not require starting from an initial guess, and computes the pose using an order of magnitude fewer floating point operations; it may therefore be a useful alternative for real-time operation. When speed is not an issue, POSIT can be written in 25 lines or less in Mathematica; the code is provided in an Appendix.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01450852
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    Paper (German National Licenses)
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  • 6
    Electronic Resource
    Electronic Resource
    Parallel algorithms for image enhancement and segmentation by region growing, with an experimental study (1996)
    Springer
    The journal of supercomputing 10 (1996), S. 141-168 
    Details
    ISSN: 1573-0484
    Keywords: Parallel algorithms ; image processing ; region growing ; image enhancement ; image segmentation ; Symmetric Neighborhood Filter ; connected components ; parallel performance
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper presents efficient and portable implementations of a powerful image enhancement process, the Symmetric Neighborhood Filter (SNF), and an image segmentation technique that makes use of the SNF and a variant of the conventional connected components algorithm which we call δ-Connected Components. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The image segmentation algorithm makes use of an efficient connected components algorithm based on a novel approach for parallel merging. The algorithms have been coded in Split-C and run on a variety of platforms, including the Thinking Machines CM-5, IBM SP-1 and SP-2, Cray Research T3D, Meiko Scientific CS-2, Intel Paragon, and workstation clusters. Our experimental results are consistent with the theoretical analysis (and provide the best known execution times for segmentation, even when compared with machine-specific implementations). Our test data include difficult images from the Landsat Thematic Mapper (TM) satellite data.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00130707
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    Paper (German National Licenses)
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