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ABSTRACT: We present a hybrid neural network architecture that supports the estimation of binocular disparity in a cyclopean, head-centric coordinate system without explicitly establishing retinal correspondences. Instead the responses of binocular energy neurons are gain-modulated by oculomotor signals. The network can handle the full six degrees of freedom of binocular gaze and operates directly on image pairs of possibly varying contrast. Furthermore, we show that in the absence of an oculomotor signal the same architecture is capable of estimating the epipolar geometry directly from the population response. The increased complexity of the scenarios considered in this work provides an important step towards the application of computational models centered on gain modulation mechanisms in real-world robotic applications. The proposed network is shown to outperform a standard computer vision technique on a disparity estimation task involving real-world stereo images.
International Journal of Neural Systems 06/2012; 22(3):1250007. · 4.28 Impact Factor
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Irene Markelic,
Anders Kjær-Nielsen,
Karl Pauwels,
Lars Baunegaard With Jensen,
Nikolay Chumerin,
Ausra Vidugiriene,
Minija Tamosiunaite,
Alexander Rotter, Marc M. Van Hulle,
Norbert Krüger,
Florentin Wörgötter
IEEE Transactions on Intelligent Transportation Systems. 01/2011; 12:1135-1146.
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VISAPP 2010 - Proceedings of the Fifth International Conference on Computer Vision Theory and Applications, Angers, France, May 17-21, 2010 - Volume 1; 01/2010
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J. Real-Time Image Processing. 01/2010; 5:291-304.
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ABSTRACT: Walking through a crowd or driving on a busy street requires monitoring your own movement and that of others. The segmentation of these other, independently moving, objects is one of the most challenging tasks in vision as it requires fast and accurate computations for the disentangling of independent motion from egomotion, often in cluttered scenes. This is accomplished in our brain by the dorsal visual stream relying on heavy parallel-hierarchical processing across many areas. This study is the first to utilize the potential of such design in an artificial vision system. We emulate large parts of the dorsal stream in an abstract way and implement an architecture with six interdependent feature extraction stages (e.g., edges, stereo, optical flow, etc.). The computationally highly demanding combination of these features is used to reliably extract moving objects in real time. This way-utilizing the advantages of parallel-hierarchical design-we arrive at a novel and powerful artificial vision system that approaches richness, speed, and accuracy of visual processing in biological systems.
Journal of Vision 01/2010; 10(10):18. · 3.38 Impact Factor
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ABSTRACT: The problem of representing the visual signal in the harmonic space guaranteeing a complete characterization of its 2D local structure is investigated. Specifically, the efficacy of anisotropic versus isotropic filtering is analyzed with respect to general phase-based metrics for early vision attributes. We verified that the spectral information content gathered through channeled oriented frequency bands is characterized by high compactness and flexibility, since a wide range of visual attributes emerge from different hierarchical combinations of the same channels. We observed that constructing a multichannel, multiorientation representation is preferable than using a more compact one based on an isotropic generalization of the analytic signal. Maintaining a channeled (i.e., distributed) representation of the harmonic content results in a more complete structural analysis of the visual signal, and allows us to enable a set of “constraints” that are often essential to disambiguate the perception of the different features. The complete harmonic content is then combined in the phase-orientation space at the final stage, only, to come up with the ultimate perceptual decisions, thus avoiding an “early condensation” of basic features. The resulting algorithmic solutions reach high performance in real-world situations at an affordable computational cost.
Computer Vision and Image Understanding 01/2010; 114:681-699. · 1.34 Impact Factor
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Image Vision Comput. 01/2009; 27:579-587.
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VISAPP 2009 - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications, Lisboa, Portugal, February 5-8, 2009 - Volume 1; 01/2009
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International Journal of Computer Vision. 01/2007; 72:5-7.
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International Journal of Computer Vision. 01/2007; 72:67-78.
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VISAPP 2007: Proceedings of the Second International Conference on Computer Vision Theory and Applications, Barcelona, Spain, March 8-11, 2007 - Volume 1; 01/2007
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Proceedings of the British Machine Vision Conference 2006, Edinburgh, UK, September 4-7, 2006; 01/2006
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2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), 20-26 June 2005, San Diego, CA, USA; 01/2005
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Natural Computing. 01/2004; 3:293-321.
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ABSTRACT: Low-level computer vision algorithms have extreme computational requirements. In this work, we compare two real-time architectures developed using FPGA and GPU devices for the computation of phase-based optical flow, stereo, and local image features (energy, orientation, and phase). The presented approach requires a massive degree of parallelism to achieve real-time performance and allows us to compare FPGA and GPU design strategies and trade-offs in a much more complex scenario than previous contributions. Based on this analysis, we provide suggestions to real-time system designers for selecting the most suitable technology, and for optimizing system development on this platform, for a number of diverse applications.
IEEE Transactions on Computers 61:999-1012. · 1.10 Impact Factor
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ABSTRACT: A novel method is introduced for optimal estimation of rigid camera motion from instantaneous velocity measurements. The error surface associated with this problem is highly complex and existing algorithms suffer heavily from local minima. Repeated minimization with different random initializations and selection of the minimum-cost solution are a common (albeit ad hoc) procedure to increase the likelihood of finding the global minimum. We instead show that the optimal estimation problem can be transformed into one of arbitrary complexity, which allows for a gradual regularization of the error function. A simple reweighting scheme is presented that smoothly increases the problem complexity at each iteration. We show that the resulting method retains all the desirable properties of optimal algorithms, such as unbiasedness and minimal variance of the parameter estimates, but is substantially more robust to local minima. This robustness comes at the expense of a slightly increased computational complexity.
Computer Vision and Image Understanding.
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ABSTRACT: We introduce a novel video stabilization method that enables the extraction of optic flow from short unstable sequences. Contrary to traditional stabilization techniques that use approximative global motion models to estimate the full camera motion, our method estimates the unstable component of the camera motion only. This allows for the use of simpler global motion models, and at the same time extends the validity to more complex environments, such as close scenes that contain independently moving objects. The unstable component of the camera motion is derived from a maximization of the temporal local velocity constancy over the entire short sequence. The method, embedded within a phase-based optic flow algorithm, is tested on both synthetic and complex real-world sequences. The optic flow obtained using our technique is denser than that extracted directly from the original sequence, and from a sequence stabilized with a more traditional stabilization technique.
Image and Vision Computing.
