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Error analysis for quadtree image formation

Error analysis for quadtree image formation

引用

IEEE International Conference on image processing

作者： L.M. Kaplan Seung-Mok Oh M. Cobb J.H. McClellan Department of Engineering Clark Atlanta University Atlanta GA USA Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)0780362977

The quadtree image formation technique is a computationally efficient approximation to standard backprojection. Where the computational load of backprojection is O(N/sup 3/) for N sensors forming an N/spl times/N image, the quadtree method uses a divide-and-conquer strategy similar to the fast Fourier transform (FFT) to reduce the computational load down to O(N/sup 2/ log(N)). However, the quadtree introduces errors in the relative time shifts used to focus pulses. These errors reduce the signal gain in the mainlobe response for isotropic point-like targets. In addition, the oscillations of the sidelobes increase from stage to stage. This paper develops performance bounds for the mainlobe losses under far field conditions and relates these bounds to the slow-time Nyquist rate.

关键词： Error analysis Synthetic aperture radar Radar scattering Layout Radar imaging Frequency image sensors Ultra wideband radar Motion compensation Geometry

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Nonlinear Diffusion Adaptation with Bounded Transmission over Distributed Networks

Nonlinear Diffusion Adaptation with Bounded Transmission ove...

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IEEE International Conference on Communications

作者： Jongmin Lee Cihan Tepedelenlioglu Mahesh K. Banavar Andreas Spanias Signal and Information Processing (SenSIP) Center School of Electrical Computer and Energy Engineering Arizona State University Department of Electrical and Computer Engineering Clarkson University

ISBN: (纸本)9781467364300

This paper introduces diffusion adaptation strategies over distributed networks with nonlinear transmissions, motivated by the necessity for bounded transmit power. Local information is exchanged in real-time with neighboring nodes in order to estimate a common parameter vector via constrained nonlinear transmissions, using an adaptive learning algorithm. We propose nonlinear diffusion strategies for such an adaptive estimation. We will study convergence properties of the proposed algorithm in the mean and the mean-square sense. Simulations support the performance analysis and show that the proposed algorithm performs close to the linear case with the added advantage of power savings.

关键词： Nonlinear diffusion Bounded transmission Adaptive networks Consensus Distributed sensor networks

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Exploiting linear block codes for blind co-channel interference reduction in TDMA based wireless communication systems

Exploiting linear block codes for blind co-channel interfere...

引用

Asilomar Conference on signals, Systems & computers

作者： J.D. Terry D.B. Williams School of Electrical and Computer Engineering Georgia Institute of Technology USA Texas Instruments Inc. Dallas TX USA Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

In this paper, we present a novel array processing technique for co-channel interference reduction for TDMA based mobile satellite communication systems using linear block codes. This technique combines least squares projection with syndrome detection (LSPSD) to compute the optimum multi-antenna linear receiver for a signal of interest (SOI) encoded with a linear block coding scheme. The algorithm is shown to reduce co-channel interference and to improve bit error rate performance. Further, we show that the algorithm achieves SNR improvement by coherently adding multipath components of the SOI.

关键词： Block codes Interchannel interference Time division multiple access Vectors Linear antenna arrays Array signal processing Sensor arrays Wireless communication Least squares methods Fading

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Motion compensated compression of computer animation frames 93

Motion compensated compression of computer animation frames

引用

20th International Conference on computer Graphics and Interactive Techniques, SIGGRAPH 1993

作者： Guenter, Brian K. Yun, Hee Cheol Mersereau, Russell M. Computer Animation Laboratory GVU Center College of Computing Georgia Institute of Technology AtlantaGA30332 United States Digital Signal Processing Labratory School of Electrical Engineering Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)0897916018

This paper presents a new lossless compression algorithm for computer animation image sequences. The algorithm uses transformation information available in the animation script and floating point depth and object number information stored at each pixel to perform highly accurate motion prediction with very low computation. The geometric data, i.e., the depth and object number, is very efficiently compressed using motion prediction and a new technique called direction coding, typically to 1 to 2 bits per pixel. The geometric data is also useful in z-buffer image compositing and this new compression algorithm offers a very low storage overhead method for saving the information needed for z-buffer image compositing. The overall compression ratio of the new algorithm, including the geometric data overhead, is compared to conventional spatial linear prediction compression and is shown to be consistently better, by a factor of 1.4 or more, even with large frame-to-frame motion. © 1993 ACM.

关键词： Pixels

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A space-time coded transmitter diversity technique for frequency selective fading channels

A space-time coded transmitter diversity technique for frequ...

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Sensor Array and Multichannel signal processing Workshop

作者： K.F. Lee D.B. Williams Motorola Inc. Boynton Beach FL USA Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

A simple space-time coded orthogonal frequency division multiplexing (OFDM) transmitter diversity technique for wireless communications over frequency selective fading channels is presented. The proposed technique utilizes OFDM to transform frequency selective fading channels into multiple flat fading subchannels on which space-time coding is applied. A two-branch transmitter diversity system is implemented without bandwidth expansion and with a small increase in complexity beyond that of a conventional OFDM system. Simulations verify that the two-branch transmitter diversity system achieves a diversity gain equivalent to that of the optimal maximal ratio combining (MRC) receiver diversity system.

关键词： Transmitters Frequency diversity Fading Diversity reception Delay effects OFDM modulation Discrete Fourier transforms Wireless communication Base stations Receiving antennas

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Non-iterative rate-constrained motion estimation for OBMC

Non-iterative rate-constrained motion estimation for OBMC

引用

IEEE International Conference on image processing

作者： J.K. Su R.M. Mersereau Center for Signal and Image Processing Georgia Tech Research Institute Georgia Institute of Technology Atlanta GA USA School of Electrical and Computer Engineering Atlanta GA USA

For many video compression systems, overlapped block motion compensation (OBMC) provides an effective extension of traditional block MC (BMC). However, because OBMC causes noncausal spatial interaction between blocks, iterative methods have typically been used for motion estimation (ME) for OBMC. For compression purposes, the rate for the motion vector field may also be constrained. This paper considers several rate-constrained ME methods for OBMC. Experiments demonstrate that a simple raster-scan algorithm is effective as a suboptimal non-iterative solution, with better rate-distortion performance and equal or lower computational complexity than several iterative methods.

关键词： Motion estimation Iterative methods Video compression Motion compensation Iterative algorithms Rate-distortion Computational complexity

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Gradients as a Measure of Uncertainty in Neural Networks

Gradients as a Measure of Uncertainty in Neural Networks

引用

IEEE International Conference on image processing

作者： Jinsol Lee Ghassan AlRegib OLIVES at the Center for Signal and Information Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Despite tremendous success of modern neural networks, they are known to be overconfident even when the model encounters inputs with unfamiliar conditions. Detecting such inputs is vital to preventing models from making naive predictions that may jeopardize real-world applications of neural networks. In this paper, we address the challenging problem of devising a simple yet effective measure of uncertainty in deep neural networks. Specifically, we propose to utilize backpropagated gradients to quantify the uncertainty of trained models. Gradients depict the required amount of change for a model to properly represent given inputs, thus providing a valuable insight into how familiar and certain the model is regarding the inputs. We demonstrate the effectiveness of gradients as a measure of model uncertainty in applications of detecting unfamiliar inputs, including out-of-distribution and corrupted samples. We show that our gradient-based method outperforms state-of-the-art methods by up to 4.8% of AUROC score in out-of-distribution detection and 35.7% in corrupted input detection.

关键词： Uncertainty Training Measurement uncertainty Computational modeling Neural networks Data models Predictive models

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Contrastive Explanations In Neural Networks

Contrastive Explanations In Neural Networks

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IEEE International Conference on image processing

作者： Mohit Prabhushankar Gukyeong Kwon Dogancan Temel Ghassan AlRegib OLIVES at the Center for Signal and Information Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Visual explanations are logical arguments based on visual features that justify the predictions made by neural networks. Current modes of visual explanations answer questions of the form `Why P.7'. These Why questions operate under broad contexts thereby providing answers that are irrelevant in some cases. We propose to constrain these Why questions based on some context Q so that our explanations answer contrastive questions of the form `Why P, rather than Q.7'. In this paper, we formalize the structure of contrastive visual explanations for neural networks. We define contrast based on neural networks and propose a methodology to extract defined contrasts. We then use the extracted contrasts as a plug-in on top of existing `Why P?' techniques, specifically Grad-CAM. We demonstrate their value in analyzing both networks and data in applications of large-scale recognition, fine-grained recognition, subsurface seismic analysis, and image quality assessment.

关键词： Visualization Neural networks Manifolds image recognition image quality Automobiles image color analysis

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Novelty Detection Through Model-Based Characterization of Neural Networks

Novelty Detection Through Model-Based Characterization of Ne...

引用

IEEE International Conference on image processing

作者： Gukyeong Kwon Mohit Prabhushankar Dogancan Temel Ghassan AlRegib OLIVES at the Center for Signal and Information Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

In this paper, we propose a model-based characterization of neural networks to detect novel input types and conditions. Novelty detection is crucial to identify abnormal inputs that can significantly degrade the performance of machine learning algorithms. Majority of existing studies have focused on activation-based representations to detect abnormal inputs, which limits the characterization of abnormality from a data perspective. However, a model perspective can also be informative in terms of the novelties and abnormalities. To articulate the significance of the model perspective in novelty detection, we utilize back-propagated gradients. We conduct a comprehensive analysis to compare the representation capability of gradients with that of activation and show that the gradients outperform the activation in novel class and condition detection. We validate our approach using four image recognition datasets including MNIST, Fashion-MNIST, CIFAR10, and CURE-TSR. We achieve a significant improvement on all four datasets with an average AUROC of 0.953, 0.918, 0.582, and 0.746, respectively.

关键词： image reconstruction Neural networks Training Feature extraction Loss measurement Backpropagation Decoding

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Adaptive Video Subsampling For Energy-Efficient Object Detection

Adaptive Video Subsampling For Energy-Efficient Object Detec...

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Asilomar Conference on signals, Systems & computers

作者： Divya Mohan Sameeksha Katoch Suren Jayasuriya Pavan Turaga Andreas Spanias Sensor Signal and Information Processing (SenSIP) Center School of Electrical Computer and Energy Engineering Arizona State University

ISBN: (数字)9781728143002

ISBN: (纸本)9781728143019

Energy-efficient computer vision is vitally important for embedded and mobile platforms where a longer battery life can allow increased deployment in the field. In image sensors, one of the primary causes of energy expenditure is the sampling and digitization process. Smart subsampling of the image-array in a manner that is task-specific, can result in significant savings of energy. We present an adaptive algorithm for video subsampling, which is aimed at enabling accurate object detection, while saving sampling energy. The approach utilizes objectness measures, which we show can be accurately estimated even from sub-sampled frames, and then uses that information to determine the adaptive sampling for the subsequent frame. We show energy savings of 18 - 67% with only a slight degradation in object detection accuracy in experiments. These results motivated us to further explore energy-efficient subsampling using advanced techniques such as, reinforcement learning and Kalman filtering. The experiments using these techniques are underway and provide ample support for adaptive subsampling as a promising avenue for embedded computer vision in the future.

关键词： Object detection Streaming media computer vision image sensors Optical imaging Task analysis Adaptive algorithms

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