# Browsing by Subject "Belief propagation"

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Item Facial feature localization using highly flexible yet sufficiently strict shape models(2014-08) Tamersoy, Birgi; Aggarwal, J.K. (Jagdishkumar Keshoram), 1936-Show more Accurate and efficient localization of facial features is a crucial first step in many face-related computer vision tasks. Some of these tasks include, but not limited to: identity recognition, expression recognition, and head-pose estimation. Most effort in the field has been exerted towards developing better ways of modeling prior appearance knowledge and image observations. Modeling prior shape knowledge, on the other hand, has not been explored as much. In this dissertation I primarily focus on the limitations of the existing methods in terms of modeling the prior shape knowledge. I first introduce a new pose-constrained shape model. I describe my shape model as being "highly flexible yet sufficiently strict". Existing pose-constrained shape models are either too strict, and have questionable generalization power, or they are too loose, and have questionable localization accuracies. My model tries to find a good middle-ground by learning which shape constraints are more "informative" and should be kept, and which ones are not-so-important and may be omitted. I build my pose-constrained facial feature localization approach on this new shape model using a probabilistic graphical model framework. Within this framework, observed and unobserved variables are defined as the local image observations, and the feature locations, respectively. Feature localization, or "probabilistic inference", is then achieved by nonparametric belief propagation. I show that this approach outperforms other popular pose-constrained methods through qualitative and quantitative experiments. Next, I expand my pose-constrained localization approach to unconstrained setting using a multi-model strategy. While doing so, once again I identify and address the two key limitations of existing multi-model methods: 1) semantically and manually defining the models or "guiding" their generation, and 2) not having efficient and effective model selection strategies. First, I introduce an approach based on unsupervised clustering where the models are automatically learned from training data. Then, I complement this approach with an efficient and effective model selection strategy, which is based on a multi-class naive Bayesian classifier. This way, my method can have many more models, each with a higher level of expressive power, and consequently, provides a more effective partitioning of the face image space. This approach is validated through extensive experiments and comparisons with state-of-the-art methods on state-of-the-art datasets. In the last part of this dissertation I discuss a particular application of the previously introduced techniques; facial feature localization in unconstrained videos. I improve the frame-by-frame localization results, by estimating the actual head-movement from a sequence of noisy head-pose estimates, and then using this information for detecting and fixing the localization failures.Show more Item Graphical models and message passing receivers for interference limited communication systems(2013-08) Nassar, Marcel; Evans, Brian L. (Brian Lawrence), 1965-Show more In many modern wireless and wireline communication networks, the interference power from other communication and non-communication devices is increasingly dominating the background noise power, leading to interference limited communication systems. Conventional communication systems have been designed under the assumption that noise in the system can be modeled as additive white Gaussian noise (AWGN). While appropriate for thermal noise, the AWGN model does not always capture the interference statistics in modern communication systems. Interference from uncoordinated users and sources is particularly harmful to communication performance because it cannot be mitigated by current interference management techniques. Based on previous statistical-physical models for uncoordinated wireless interference, this dissertation derives similar models for uncoordinated interference in PLC networks. The dissertation then extends these models for wireless and powerline interference to include temporal dependence among amplitude samples. The extensions are validated with measured data. The rest of this dissertation utilizes the proposed models to design receivers in interference limited environments. Prior designs generally adopt suboptimal approaches and often ignore the problem of channel estimation which limits their applicability in practical systems. This dissertation uses the graphical model representation of the OFDM system to propose low-complexity message passing OFDM receivers that leverage recent results in soft-input soft-output decoding, approximate message passing, and sparse signal recovery for joint channel/interference estimation and data decoding. The resulting receivers provide huge improvements in communication performance (more than 10dB) over the conventional receivers at a comparable computational complexity. Finally, this dissertation addresses the design of robust receivers that can be deployed in rapidly varying environments where the interference statistics are constantly changing.Show more Item Multi-scale error-correcting codes and their decoding using belief propagation(2014-05) Yoo, Yong Seok; Fiete, Ila; Vishwanath, SriramShow more This work is motivated from error-correcting codes in the brain. To counteract the effect of representation noise, a large number of neurons participate in encoding even low-dimensional variables. In many brain areas, the mean firing rates of neurons as a function of represented variable, called the tuning curve, have unimodal shape centered at different values, defining a unary code. This dissertation focuses on a new type of neural code where neurons have periodic tuning curves, with a diversity of periods. Neurons that exhibit this tuning are grid cells of the entorhinal cortex, which represent self-location in two-dimensional space. First, we investigate mutual information between such multi-scale codes and the coded variable as a function of tuning curve width. For decoding, we consider maximum likelihood (ML) and plausible neural network (NN) based models. For unary neural codes, Fisher information increases with narrower tuning, regardless of the decoding method. By contrast, for the multi-scale neural code, the optimal tuning curve width depends on the decoding method. While narrow tuning is optimal for ML decoding, a finite width, matched to statistics of the noise, is optimal with a NN decoder. This finding may explain why actual neural tuning curves have relatively wide tuning. Next, motivated by the observation that multi-scale codes involve non-trivial decoding, we examine a decoding algorithm based on belief propagation (BP) because BP promises certain gains in decoding efficiency. The decoding problem is first formulated as a subset selection problem on a graph and then approximately solved by BP. Even though the graph has many cycles, BP converges to a fixed point after few iterations. The mean square error of BP approaches to that of ML at high signal-to-noise ratios. Finally, using the multi-scale code, we propose a joint source-channel coding scheme that allows separate senders to transmit complementary information over additive Gaussian noise channels without cooperation. The receiver decodes one sender's codeword using the other as side information and achieves a lower distortion using the same number of transmissions. The proposed scheme offers a new framework to design distributed joint source-channel codes for continuous variables.Show more Item On a class of distributed algorithms over networks and graphs(2011-05) Lee, Sang Hyun, 1977-; Vishwanath, Sriram; Vikalo, Haris; Powers, Edward J.; Ghosh, Joydeep; Sanghavi, Sujay; Qiu, LiliShow more Distributed iterative algorithms are of great importance, as they are known to provide low-complexity and approximate solutions to what are otherwise high-dimensional intractable optimization problems. The theory of message-passing based algorithms is fairly well developed in the coding, machine learning and statistical physics literatures. Even though several applications of message-passing algorithms have already been identified, this work aims at establishing that a plethora of other applications exist where it can be of great importance. In particular, the goal of this work is to develop and demonstrate applications of this class of algorithms in network communications and computational biology. In the domain of communications, message-passing based algorithms provide distributed ways of inferring the optimal solution without the aid of a central agent for various optimization problems that happen in the resource allocation of communication networks. Our main framework is Affinity Propagation (AP), originally developed for clustering problems. We reinterpret this framework to unify the development of distributed algorithms for discrete resource allocation problems. Also, we consider a network-coded communication network, where continuous rate allocation is studied. We formulate an optimization problem with a linear cost function, and then utilize a Belief Propagation (BP) approach to determine a decentralized rate allocation strategy. Next, we move to the domain of computational biology, where graphical representations and computational biology play a major role. First, we consider the motif finding problem with several DNA sequences. In effect, this is a sequence matching problem, which can be modeled using various graphical representations and also solved using low-complexity algorithms based on message-passing techniques. In addition, we address the application of message-passing algorithms for a DNA sequencing problem where the one dimensional structure of a single DNA sequence is identified. We reinterpret the problem as being equivalent to the decoding of a nonlinear code. Based on the iterative decoding framework, we develop an appropriate graphical model which enables us to derive a message-passing algorithm to improve the performance of the DNA sequencing problem. Although this work consists of disparate application domains of communications, networks and computational biology, graphical models and distributed message-passing algorithms form a common underlying theme.Show more