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arXiv 2018年

作者： Prabhushankar, Mohit Temel, Dogancan AlRegib, Ghassan Center for Signal and Information Processing School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

In this paper, we train independent linear decoder models to estimate the perceived quality of images. More specifically, we calculate the responses of individual non-overlapping image patches to each of the decoders and scale these responses based on the sharpness characteristics of filter set. We use multiple linear decoders to capture different abstraction levels of the image patches. Training each model is carried out on 100,000 image patches from the imageNet database in an unsupervised fashion. Color space selection and ZCA Whitening are performed over these patches to enhance the descriptiveness of the data. The proposed quality estimator is tested on the LIVE and the TID 2013 image quality assessment databases. Performance of the proposed method is compared against eleven other state of the art methods in terms of accuracy, consistency, linearity, and monotonic behavior. Based on experimental results, the proposed method is generally among the top performing quality estimators in all categories. Copyright © 2018, The Authors. All rights reserved.

关键词： Decoding

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Generating adaptive and robust filter sets using an unsupervised learning framework

arXiv

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arXiv 2018年

In this paper, we introduce an adaptive unsupervised learning framework, which utilizes natural images to train filter sets. The applicability of these filter sets is demonstrated by evaluating their performance in two contrasting applications - image quality assessment and texture retrieval. While assessing image quality, the filters need to capture perceptual differences based on dissimilarities between a reference image and its distorted version. In texture retrieval, the filters need to assess similarity between texture images to retrieve closest matching textures. Based on experiments, we show that the filter responses span a set in which a monotonicity-based metric can measure both the perceptual dissimilarity of natural images and the similarity of texture images. In addition, we corrupt the images in the test set and demonstrate that the proposed method leads to robust and reliable retrieval performance compared to existing methods. Copyright © 2018, The Authors. All rights reserved.

关键词： image quality

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Assessment of depth of anesthesia using principal component analysis

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Journal of Biomedical Science and engineering 2009年第1期2卷 9-15页

作者： Mina Taheri Behzad Ahmadi Rassoul Amirfattahi Mojtaba Mansouri Digital Signal Processing Research Lab Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156-83111 Iran Medical Image and Signal Processing Research Center Isfahan University of Medical Sciences Isfahan Iran. Correspondence should be addressed to Mina Taheri

A new approach to estimating level of uncon-sciousness based on Principal Component Analysis (PCA) is proposed. The Electroen-cephalogram (EEG) data was captured in both Intensive Care Unit (ICU) and operating room, using different anesthetic drugs. Assuming the central nervous system as a 20-tuple source, window length of 20 seconds is applied to EEG. The mentioned window is considered as 20 nonoverlapping mixed-signals (epoch). PCA algorithm is applied to these epochs, and larg-est remaining eigenvalue (LRE) and smallest remaining eigenvalue (SRE) were extracted. Correlation between extracted parameters (LRE and SRE) and depth of anesthesia (DOA) was measured using Prediction probability (PK). The results show the superiority of SRE than LRE in predicting DOA in the case of ICU and isoflurane, and the slight superiority of LRE than SRE in propofol induction. Finally, a mixture model containing both LRE and SRE could predict DOA as well as Relative Beta Ratio (RBR), which expresses the high capability of the proposed PCA based method in estimating DOA.

关键词： Bispectral Index Depth of Anesthesia Eignevalue Decomposition Principal Component Analysis

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On the Structures of Representation for the Robustness of Semantic Segmentation to Input Corruption

On the Structures of Representation for the Robustness of Se...

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IEEE International Conference on image processing

作者： Charles Lehman 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 USA

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Semantic segmentation is a scene understanding task at the heart of safety-critical applications where robustness to corrupted inputs is essential. Implicit Background Estimation (IBE) has demonstrated to be a promising technique to improve the robustness to out-of-distribution inputs for semantic segmentation models for little to no cost. In this paper, we provide analysis comparing the structures learned as a result of optimization objectives that use Softmax, IBE, and Sigmoid in order to improve understanding their relationship to robustness. As a result of this analysis, we propose combining Sigmoid with IBE (SCrIBE) to improve robustness. Finally, we demonstrate that SCrIBE exhibits superior segmentation performance aggregated across all corruptions and severity levels with a mIOU of 42.1 compared to both IBE 40.3 and the Softmax Baseline 37.5.

关键词： Robustness Semantics Entropy Training Estimation Task analysis Machine learning

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A Performance Study of Spatial Modulation Systems Under Vehicle-to-Vehicle Channel Models

A Performance Study of Spatial Modulation Systems Under Vehi...

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IEEE Vehicular Technology Conference

作者： Yu Fu Cheng-Xiang Wang Raed Mesleh Xiang Cheng Harald Haas Yejun He Joint Research Institute for Signal and Image Processing Heriot-Watt University School of Information Science and Engineering Shandong University Electrical Engineering Department and SNCS Research Center University of Tabuk School of Electronics Engineering & Computer Science Peking University Joint Research Institute for Signal and Image Processing University of Edinburgh College of Information Engineering Shenzhen University

ISBN: (纸本)9781479944811

Spatial modulation (SM) is a relatively new multiple-input multiple-output (MIMO) technology that can provide high data rate with reasonable spectral efficiency. In this paper, the bit error rate (BER) performance of SM systems under vehicle-to-vehicle (V2V) channel models is investigated. The theoretical BER expression is given. The impact of some V2V channel model parameters on the underlying space-time correlation function (STCF) and the BER performance of SM systems are also studied. Simulation results indicate that modulation schemes, maximum Doppler frequency, the distance between the transmitter (Tx) and receiver (Rx), and antenna element spacings can affect the performance of SM systems.

关键词： Spatial modulation Vehicle-to-vehicle channel model Bit error rate Space-time correlation function

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Efficient FPGA implementation of a knowledge-based automatic speech classifier 1

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2nd International Conference on Embedded Software and Systems, ICESS 2005

作者： Siniscalchi, Sabato M. Gennaro, Fulvio Vitabile, Salvatore Gentile, Antonio Sorbello, Filippo 90128 Palermo Italy Dipartimento di Biotecnologie Mediche e Medicina Legale Università di Palermo Via del Vespro 90127 Palermo Italy Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 United States 90128 Palermo Italy

ISBN: (数字)9783540322979

ISBN: (纸本)3540308814

Speech recognition has become common in many application domains, from dictation systems for professional practices to vocal user interfaces for people with disabilities or hands-free system control. However, so far the performance of Automatic Speech Recognition (ASR) systems are comparable to Human Speech Recognition (HSR) only under very strict working conditions, and in general far lower. Incorporating acoustic-phonetic knowledge into ASR design has been proven a viable approach to rise ASR accuracy. Manner of articulation attributes such as vowel, stop, fricative, approximant, nasal, and silence are examples of such knowledge. Neural networks have already been used successfully as detectors for manner of articulation attributes starting from representations of speech signal frames. In this paper an optimized digital Knowledge-based Automatic Speech Classifier for real-time applications is implemented on FPGA using six attribute scoring Multi-Layer Perceptrons (MLP). Digital MLP key features are a virtual neuron architecture and use of sinusoidal activation functions for the hidden layer. Implementation results on FPGA show that use of sinusoidal activation functions decrease hardware resource usage of more than 50% for slices, FFs, LUTs and more than 35% for FPGA RAM blocks when compared with the standard sigmoid-based neuron implementation. Furthermore, neuron virtualization allows for a significant decrease of concurrent memory access, resulting in improved performance for the entire attribute scoring module. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Speech recognition

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S6: Semi-Supervised Self-Supervised Semantic Segmentation

S6: Semi-Supervised Self-Supervised Semantic Segmentation

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IEEE International Conference on image processing

作者： Moamen Soliman Charles Lehman Ghassan AlRegib OLIVES at the Center for Signal and Information Processing (CSIP) School of Electrical and Computer Engineering Georgia Institute of Technology GA Atlanta

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Semi-supervised learning provides a means to leverage unlabeled data when labels are expensive to obtain. In this work, we propose a constrained framework that better learns from unlabeled data. The proposed algorithm adds a self-supervised task, image reconstruction, to the target segmentation task. The extra reconstruction task improves the model's geometric reasoning about different textures in an image. It happens that this improvement is transferable from reconstruction to segmentation since they both share some common parts of the architecture. Such extra task allows the trained model to have richer representations and better geometric understanding. Our results show that the proposed constrained framework achieves an improvement in mean Intersection over Union by 18% over unconstrained one, using only 2% of labeled examples. The performance gain and reduction in amounts of labeled data is crucial for applications in which obtaining labels is expensive and labor intensive such as Biomedical Imaging and Seismic Interpretation.

关键词： image reconstruction image segmentation Task analysis Data models Decoding Training Semisupervised learning

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Study-flow: Studying effective student-content interaction in signal processing education

Study-flow: Studying effective student-content interaction i...

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IEEE International Conference on Acoustics, Speech and signal processing

作者： Gregory Krudysz James McClellan Center for Signal and Information Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States of America

ISBN: (纸本)9781509041183

Throughout a semester students use textbooks to reference key fragments especially in response to assigned homework problems. We present an extension to an existing web-based Question and Answer (Q&A) system by incorporating the course textbook as a collection of annotated and chunked resources in a website. To characterize student interaction with the textbook contents, we capture the progress of each student's self-study through their content usage and construct a map of conceptual understanding which defines an individual's organization of course information into relevant concept hierarchies for efficient recall. This process consists of a sequence of enhancing learning activities, or study-flow.

关键词： tutoring system self-study educational data-mining personalized learning concept maps Concept mapping Textbooks Students signal processing quality assurance system

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A comparative study of computational aesthetics

A comparative study of computational aesthetics

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IEEE International Conference on image processing

作者： Dogancan Temel Ghassan AlRegib Center for Signal and Information Processing (CSIP) School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Objective metrics model image quality by quantifying image degradations or estimating perceived image quality. However, image quality metrics do not model what makes an image more appealing or beautiful. In order to quantify the aesthetics of an image, we need to take it one step further and model the perception of aesthetics. In this paper, we examine computational aesthetics models that use hand-crafted, generic and hybrid descriptors. We show that generic descriptors can perform as well as state of the art hand-crafted aesthetics models that use global features. However, neither generic nor hand-crafted features is sufficient to model aesthetics when we only use global features without considering spatial composition or distribution. We also follow a visual dictionary approach similar to state of the art methods and show that it performs poorly without the spatial pyramid step.

关键词： image color analysis Feature extraction Accuracy Visualization Measurement Dictionaries Training

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Guest Editorial: Vehicular communications and networks - Part I

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IEEE Journal on Selected Areas in Communications 2011年第1期29卷 1-6页

作者： Wang, Cheng-Xiang Vasilakos, Athanasios V. Murch, Ross D. Stüber, Gordon Shen, Xuemin Sherman Chen, Wai Kosch, Timo Joint Research Institute for Signal and Image Processing School of Engineering and Physical Sciences Heriot-Watt University Edinburgh EH14 4AS United Kingdom Dept. of Computer and Telecommunications Engineering University of Western Macedonia Kozani 50100 Greece Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 United States Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada Applied Research Telcordia Technologies Inc. Piscataway NJ 07054 United States BMW Group Research and Technology Hanauer Strasse 46 D-80992 Munich Germany

Part I of this special issue is devoted to the physical layer, MAC layer, and physical-link cross-layer design technologies of vehicular communications and networks. There are 21 papers in this issue.

关键词： Communication Research iodine VEHICULAR COMMUNICATIONS

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