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Conference on Human Vision and Electronic Imaging XVIII

作者： Dorr, Michael Vig, Eleonora Barth, Erhardt Harvard Univ Sch Med Schepens Eye Res Inst Boston MA USA

ISBN: (纸本)9780819494245

Interdisciplinary research in human vision and electronic imaging has greatly contributed to the current state of the art in imaging technologies. Image compression and image quality are prominent examples and the progress made in these areas relies on a better understanding of what natural images are and how they are perceived by the human visual system. A key research question has been: given the (statistical) properties of natural images, what are the most efficient and perceptually relevant image representations, what are the most prominent and descriptive features of images and videos? We give an overview of how these topics have evolved over the 25 years of HVEI conferences and how they have influenced the current state of the art. There are a number of striking parallels between human vision and electronic imaging. The retina does lateral inhibition, one of the early coders was using a Laplacian pyramid;primary visual cortical areas have orientation-and frequency-selective neurons, the current JPEG standard defines similar wavelet transforms;the brain uses a sparse code, engineers are currently excited about sparse coding and compressed sensing. Some of this has indeed happened at the HVEI conferences and we would like to distill that.

关键词： image coding sparse coding predictive coding intrinsic dimension nonlinear filters visual perception unsupervised learning gain control divisive normalization

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Efficient Learning from Explanation of Prediction Errors in Streaming Data

Efficient Learning from Explanation of Prediction Errors in ...

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IEEE International Conference on Big Data (Big Data)

作者： Banerjee, Bonny Dutta, Jayanta K. Univ Memphis Inst Intelligent Syst Memphis TN 38152 USA Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA

ISBN: (纸本)9781479912926;9781479912933

Streaming data from different kinds of sensors contributes to Big Data in a significant way. Recognizing the norms and abnormalities in such spatiotemporal data is a challenging problem. We present a general-purpose biologically-plausible computational model, called SELP, for learning the norms or invariances as features in an unsupervised and online manner from explanations of saliencies or surprises in the data. Given streaming data, this model runs a relentless cycle of Surprise -> Explain -> Learn -> Predict involving the real external world and its internal model, and hence the name. The key characteristic of the model is its efficiency, crucial for streaming Big Data applications, which stems from two functionalities exploited at each sampling instant - it operates on the change in the state of data between consecutive sampling instants as opposed to the entire state of data, and it learns only from surprise or prediction error to update its internal state as opposed to learning from the entire input. The former allows the model to concentrate its computational resources on spatial regions of the data changing most frequently and ignore others, while the latter allows it to concentrate on those instants of time when its prediction is erroneous and ignore others. The model is implemented in a neural network architecture. We show the performance of the network in learning and retaining sequences of handwritten numerals. When exposed to natural videos acquired by a camera mounted on a cat's head, the neurons learn receptive fields resembling simple cells in the primary visual cortex. The model leads to an agent-dependent framework for mining streaming data where the agent interprets and learns from the data in order to update its internal model.

关键词： surprise salience explain learn predict generative model predictive coding

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MULTIPLE DESCRIPTION coding WITH RANDOMLY OFFSET QUANTIZERS

MULTIPLE DESCRIPTION CODING WITH RANDOMLY OFFSET QUANTIZERS

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Meng, Lili Liang, Jie Samarawickrama, Upul Zhao, Yao Bai, Huihui Kaup, Andre Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Simon Fraser Univ Sch Engn Sci Burnaby BC Canada Microsoft Corp Redmond WA USA Univ Erlangen Nurnberg Erlangen Germany

ISBN: (纸本)9781467357623;9781467357609

A multiple description coding scheme based on prediction-induced randomly offset quantizers is proposed, where each description encodes one source subset with a small quantization stepsize, and other subsets are predictively coded with a large quantization stepsize. Due to the prediction, the quantization bins that a coefficient belongs to in different descriptions are randomly overlapped with each others. The optimal reconstruction is obtained by finding the intersection of all received quantization bins. Using the recently developed random quantization theory, the closed-form expression of the expected distortion is obtained. The proposed scheme is then applied to lapped transform-based multiple-description image coding, and an iterative optimization scheme is developed to find the optimal lapped transform. Experimental results show that the proposed scheme achieves better performance than other methods in this category.

关键词： Multiple description coding predictive coding random quantization

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IMPROVING THE EFFICIENCY OF predictive CODERS VIA ADAPTIVE MULTIPLE PREDICTOR COOPERATION

IMPROVING THE EFFICIENCY OF PREDICTIVE CODERS VIA ADAPTIVE M...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sar, Huseyin Topal, Cihan At, Nuray Gerek, Omer Nezih Anadolu Univ Dept Elect & Elect Engn Eskisehir Turkey

ISBN: (纸本)9781479903566

Due to the popularity of the prediction concept in time series analysis, predictive coding has been an attractive approach, particularly in lossless image compression. Utilization of prediction in time series not only makes use of residual encoding of the prediction error, but also describes and models the behavior of the underlying process. Unfortunately, this approach seems to have limited most of the scientists in the compression society to focus only to causal (or windowed) predictors, which are fine tuned to particular signal patterns. This work considers the fundamental formulation of finite extent data compression by making use of "adaptive multi-channel" prediction that is constructed by comparing prediction values of separate predictors (called, the multiple predictor cooperation). The deliberately generated channels are observed to have sharp error distributions with different bias centers. These biases are centered in a second pass, to produce plausible experimental predictive compression results.

关键词： predictive coding predictive error distribution bias

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Lossless to lossy compression for hyperspectral imagery based on wavelet and integerKLT transforms with 3D binary EZW

Lossless to lossy compression for hyperspectral imagery base...

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Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX

作者： Cheng, Kai-jen Dill, Jeffrey Ohio Univ Sch Elect Engn & Comp Sci Russ Coll Engn & Technol Stocker Ctr 329 Athens OH 45701 USA

ISBN: (纸本)9780819495341

In this paper, a lossless to lossy transform based image compression of hyperspectral images based on Integer Karhunen-Loeve Transform (IKLT) and Integer Discrete Wavelet Transform (IDWT) is proposed. Integer transforms are used to accomplish reversibility. The IKLT is used as a spectral decorrelator and the 2D-IDWT is used as a spatial decorrelator. The three-dimensional Binary Embedded Zerotree Wavelet (3D-BEZW) algorithm efficiently encodes hyperspectral volumetric image by implementing progressive bitplane coding. The signs and magnitudes of transform coefficients are encoded separately. Lossy and lossless compressions of signs are implemented by conventional EZW algorithm and arithmetic coding respectively. The efficient 3D-BEZW algorithm is applied to code magnitudes. Further compression can be achieved using arithmetic coding. The lossless and lossy compression performance is compared with other state of the art predictive and transform based image compression methods on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images. Results show that the 3D-BEZW performance is comparable to predictive algorithms However, its computational cost is comparable to transform- based algorithms

关键词： predictive coding transform based coding hyperspectral images EZW SPIHT wavelet transform Karhunen-Loeve transform matrix factorization

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Cognitive resource depletion in religious interactions

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RELIGION BRAIN & BEHAVIOR 2013年第1期3卷 39-55页

作者： Schjoedt, Uffe Sorensen, Jesper Nielbo, Kristoffer Laigaard Xygalatas, Dimitris Mitkidis, Panagiotis Bulbulia, Joseph Aarhus Univ Aarhus Denmark Victoria Univ Wellington Wellington New Zealand

We explore the cognitive effects of three common features of religious interactions: (1) demand for the expressive suppression of emotion;(2) exposure to goal-demoted and causally opaque actions;and (3) the presence of a charismatic authority. Using a cognitive resource model of executive function, we argue that these three features affect the executive system in ways that limit the capacity for individual processing of religious events. We frame our analysis in the context of a general assumption that collective rituals facilitate the transmission of cultural ideas. Building on recent experiments, we suggest that these three features increase participants' susceptibility to authoritative narratives and interpretations by preventing individuals from constructing their own accounts of the ritual event.

关键词： resource depletion predictive coding ritualized behavior emotion regulation authority

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Anisotropic connectivity implements motion-based prediction in a spiking neural network

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2013年第2013期7卷 112-112页

作者： Kaplan, Bernhard A. Lansner, Anders Masson, Guillaume S. Perrinet, Laurent U. Royal Inst Technol Dept Computat Biol Stockholm Sweden Karolinska Inst Stockholm Brain Inst Stockholm Sweden Stockholm Univ Dept Numer Anal & Comp Sci S-10691 Stockholm Sweden CNRS Inst Neurosci Timone UMR7289 F-13385 Marseille 5 France Aix Marseille Univ Marseille France

predictive coding hypothesizes that the brain explicitly infers upcoming sensory input to establish a coherent representation of the world. Although it is becoming generally accepted, it is not clear on which level spiking neural networks may implement predictive coding and what function their connectivity may have. We present a network model of conductance-based integrate-and-fire neurons inspired by the architecture of retinotopic cortical areas that assumes predictive coding is implemented through network connectivity, namely in the connection delays and in selectiveness for the tuning properties of source and target cells. We show that the applied connection pattern leads to motion-based prediction in an experiment tracking a moving dot. In contrast to our proposed model, a network with random or isotropic connectivity fails to predict the path when the moving dot disappears. Furthermore, we show that a simple linear decoding approach is sufficient to transform neuronal spiking activity into a probabilistic estimate for reading out the target trajectory.

关键词： motion detection motion extrapolation probabilistic representation predictive coding network of spiking neurons large-scale neuromorphic systems

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Interoceptive inference, emotion, and the embodied self

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TRENDS IN COGNITIVE SCIENCES 2013年第11期17卷 565-573页

作者： Seth, Anil K. Univ Sussex Sackler Ctr Consciousness Sci Brighton BN1 9QJ E Sussex England Univ Sussex Sch Engn & Informat Brighton BN1 9QJ E Sussex England

The concept of the brain as a prediction machine has enjoyed a resurgence in the context of the Bayesian brain and predictive coding approaches within cognitive science. To date, this perspective has been applied primarily to exteroceptive perception (e.g., vision, audition), and action. Here, I describe a predictive, inferential perspective on interoception: 'interoceptive inference' conceives of subjective feeling states (emotions) as arising from actively-inferred generative (predictive) models of the causes of interoceptive afferents. The model generalizes 'appraisal' theories that view emotions as emerging from cognitive evaluations of physiological changes, and it sheds new light on the neurocognitive mechanisms that underlie the experience of body ownership and conscious selfhood in health and in neuropsychiatric illness.

关键词： interoception predictive coding emotion experience of body ownership rubber hand illusion active inference

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predictive coding explains binocular rivalry: An epistemological review

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COGNITION 2008年第3期108卷 687-701页

作者： Hohwy, Jakob Roepstorff, Andreas Friston, Karl J. Monash Univ Dept Philosophy Clayton Vic 3800 Australia Univ Aarhus Dept Social Anthropol DK-8000 Aarhus C Denmark Univ Aarhus Ctr Functionally Integrat Neurosci Danish Natl Res Fdn DK-8000 Aarhus C Denmark UCL Wellcome Trust Ctr Neuroimaging Inst Neurol London England

Binocilar rivalry occurs when the eyes are presented with different stimuli and subjective perception alternates between them. Though recent years have seen a number of models of this phenomenon, the mechanisms behind binocular rivalry are still debated and we still lack a principled understanding of why a cognitive system such as the brain should exhibit this striking kind of behaviour. Furthermore, psychophysical and neurophysiological (single cell and imaging) studies of rivalry are not unequivocal and have proven difficult to reconcile within one framework. This review takes an epistemological approach to rivalry that considers the brain as engaged in probabilistic unconscious perceptual inference about the causes of its sensory input. We describe a simple empirical Bayesian framework, implemented with predictive coding, which seems capable of explaining binocular rivalry and reconciling many findings. The core of the explanation is that selection of one stimulus, and subsequent alternation between stimuli in rivalry occur when: (i) there is no single model or hypothesis about the causes in the environment that enjoys both high likelihood and high prior probability and (ii) when one stimulus dominates, the bottom-up driving, signal for that stimulus is explained away while, crucially, the bottom-up signal for the suppressed stimulus is not, and remains as an unexplained but explainable prediction error signal. This induces instability in perceptual dynamics that can give rise to perceptual transitions or alternations during rivalry. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Binocular rivalry Consciousness predictive coding Empirical Bayes Perceptual inference Learning Free-energy Psychophysics Neurophysiology

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IMPROVING THE EFFICIENCY OF predictive CODERS VIA ADAPTIVE MULTIPLE PREDICTOR COOPERATION

IMPROVING THE EFFICIENCY OF PREDICTIVE CODERS VIA ADAPTIVE M...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Huseyin Sar Cihan Topal Nuray At Omer Nezih Gerek Department of Electrical and Electronics Engineering Anadolu University

ISBN: (纸本)9781479903573

关键词： predictive coding predictive error distribution Bias

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