36 research outputs found

    Crosstalk Cascades for Frame-rate Pedestrian Detection

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    Cascades help make sliding window object detection fast, nevertheless, computational demands remain prohibitive for numerous applications. Currently, evaluation of adjacent windows proceeds independently; this is suboptimal as detector responses at nearby locations and scales are correlated. We propose to exploit these correlations by tightly coupling detector evaluation of nearby windows. We introduce two opposing mechanisms: detector excitation of promising neighbors and inhibition of inferior neighbors. By enabling neighboring detectors to communicate, crosstalk cascades achieve major gains (4-30x speedup) over cascades evaluated independently at each image location. Combined with recent advances in fast multi-scale feature computation, for which we provide an optimized implementation, our approach runs at 35-65 fps on 640 x 480 images while attaining state-of-the-art accuracy

    Forming-free resistive switching of tunable ZnO films grown by atomic layer deposition

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    Undoped ZnO thin films with tunable electrical properties have been achieved by adjusting the O2 plasma time in the plasma enhanced atomic layer deposition process. The structural, compositional and electrical properties of the deposited ZnO films were investigated by various characterization techniques. By tuning the plasma exposure from 2 to 8 s, both resistivities and carrier concentrations of the resultant ZnO films can be modulated by up to 3 orders of magnitude. Forming-free TiN/ZnO/TiN resistive memory devices have been achieved by choosing the ZnO film with the plasma exposure time of 6 s. This deposition method offers a great potential for producing other un-doped metal oxides with tunable properties as well as complex multilayer structures in a single deposition

    An Optimal Non-Orthogonal Separation of the Anisotropic Gaussian Convolution Filter

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    We give an analytical and geometrical treatment of what it means to sepa rate a Gaussian kernel along arbitrary axes in Rn, and we present a separation scheme that allows to efficiently implement anisotropic Gaussian convolution filters in arbitrary dimension. Based on our previous analysis we show that this scheme is optimal with regard to the number of memory accesses and nterpolation operations needed. Our method relies on non-orthogonal convolution axes and works com- pletely in image space. Thus, it avoids the need for an FFT-subroutine. Depending on the accuracy and speed requirements, different interpolation schemes and methods to implement the one-dimensional Gaussian (FIR, IIR) can be integrated. The algorithm is also feasible for hardware that does not contain a floating-point unit. Special emphasis is laid on analyzing the performance and accuracy of our method. In particular, we show that withot any special optimization of the source code, our method can perform anisotropic Gaussian filtering faster than methods relyin on the Fast Fourier Transform

    An Optimal Non-Orthogonal Separation of the Anisotropic Gaussian Convolution Filter

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    We give an analytical and geometrical treatment of what it means to sepa rate a Gaussian kernel along arbitrary axes in Rn, and we present a separation scheme that allows to efficiently implement anisotropic Gaussian convolution filters in arbitrary dimension. Based on our previous analysis we show that this scheme is optimal with regard to the number of memory accesses and nterpolation operations needed. Our method relies on non-orthogonal convolution axes and works com- pletely in image space. Thus, it avoids the need for an FFT-subroutine. Depending on the accuracy and speed requirements, different interpolation schemes and methods to implement the one-dimensional Gaussian (FIR, IIR) can be integrated. The algorithm is also feasible for hardware that does not contain a floating-point unit. Special emphasis is laid on analyzing the performance and accuracy of our method. In particular, we show that withot any special optimization of the source code, our method can perform anisotropic Gaussian filtering faster than methods relyin on the Fast Fourier Transform

    Fast sub-window search with square shape

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    Object Localization and Detection Using Variance Filter

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    Semi-paired Probabilistic Canonical Correlation Analysis

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