The neocognitron is a neural model which can recognize a pattern even when it is shifted in position, changed in size, or distorted. However, the complexity of its structure and its algorithm makes it difficult to tun...
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The neocognitron is a neural model which can recognize a pattern even when it is shifted in position, changed in size, or distorted. However, the complexity of its structure and its algorithm makes it difficult to tune its parameters and to specify the detail of its structure. The characteristics of this model are measured as the relationship between the degree of distortion of the input and the tolerance of the model. Computational experiments show that the pyramid has a suitable structure for implementing a neocognitron, provided that the input has a relatively low spatial frequency.< >
Building a dense and accurate environment model out of range image data faces problems like sensor noise, extensive memory consumption or computation time. We present an approach which reconstructs 3D environments usi...
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Building a dense and accurate environment model out of range image data faces problems like sensor noise, extensive memory consumption or computation time. We present an approach which reconstructs 3D environments using a probabilistic occupancy grid in real-time. Operating on depth image pyramids speeds up computation time, whereas a weighted interpolation scheme between neighboring pyramid layers boosts accuracy. In our experiments we compare our method with a state-of-the-art mapping procedure. Our results demonstrate that we achieve better results. Finally, we present its viability by mapping a large indoor environment.
Object identification from local information has recently been investigated with respect to its potential for robust recognition, e.g., in case of partial object occlusions, scale variation, noise, and background clut...
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Object identification from local information has recently been investigated with respect to its potential for robust recognition, e.g., in case of partial object occlusions, scale variation, noise, and background clutter in detection tasks. This work contributes to this research by a thorough analysis of the discriminative power of local appearance patterns and by proposing to exploit local information content for object representation and recognition. In a first processing stage, we localize discriminative regions in the object views from a posterior entropy measure, and then derive object models from selected discriminative local patterns. Object recognition is then applied to test patterns with associated low entropy using an efficient voting process. The method is evaluated by various degrees of partial occlusion and Gaussian image noise, resulting in highly robust recognition even in the presence of severe occlusion effects.
In this paper we introduce and describe a novel generic and semiconductor-technology independent hardware development environment for a class of statistical signal- and imageprocessing models. The statistical signal-...
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In this paper we introduce and describe a novel generic and semiconductor-technology independent hardware development environment for a class of statistical signal- and imageprocessing models. The statistical signal- and imageprocessing approach under consideration formally adopts the Bayesian paradigm and uses discrete Markov Random Field (MRF) methods for the processing models to derive the joint distribution of signal- and image-processing problems by means of mathematically and computationally tractable conditional distributions. We experimentally demonstrate and prove the capabilities respectively the concepts of the proposed novel high-level design environment by detailed chip-layouts of different neighbourhood topologies and a single processing element of a MRF- architecture, which solves the imageprocessing problem of noise removing, restoration and intensity-level preserving.
Traditional display fashions of housing design mainly depend on static pictures, which are short of the intuition of house space and do not support interaction during design periods. This paper proposes a virtual hous...
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As a popular evolutionary algorithm, artificial bee colony (ABC) algorithm has been successfully applied into the threshold-based image segmentation problem. Based on our analysis, we find that the Otsu segmentation f...
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Face aging has been studied for decades. Determining age from a facial shot is key to our technique for diagnosing abnormal behavior. Security monitoring, forensics, biometrics, and Human-computer Interface (HCI) use ...
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Spline curve and surface play an important role in CAD and computergraphics. In this paper, we propose several extensions of cubic uniform B-spline. Then, we present the extensions of interpolating α-B-spline based ...
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Spline curve and surface play an important role in CAD and computergraphics. In this paper, we propose several extensions of cubic uniform B-spline. Then, we present the extensions of interpolating α-B-spline based on the new B-splines and the singular blending technique. The advantage of the extensions is that they have global and local shape parameters. Furthermore, we also investigate their applications in data interpolation and polygonal shape deformation.
The popular formulas currently used to evaluate the similarity of digital watermarks have serious drawbacks: when the similarity degree is 1, the watermarks are not unique. This paper firstly analyze the drawbacks in ...
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