Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Abstract: (2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.
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TL;DR: In this article, the authors proposed an entirely data-driven approach to estimate the 3D pose of a hand given a depth image using a feedback loop, which can correct the mistakes made by a Convolutional Neural Network trained to predict an estimate of the hand pose.
Abstract: We propose an entirely data-driven approach to estimating the 3D pose of a hand given a depth image. We show that we can correct the mistakes made by a Convolutional Neural Network trained to predict an estimate of the 3D pose by using a feedback loop. The components of this feedback loop are also Deep Networks, optimized using training data. They remove the need for fitting a 3D model to the input data, which requires both a carefully designed fitting function and algorithm. We show that our approach outperforms state-of-the-art methods, and is efficient as our implementation runs at over 400 fps on a single GPU.
212 citations
TL;DR: A novel joint sparse representation based multi-view automatic target recognition (ATR) method, which can not only handle multi- view ATR without knowing the pose but also has the advantage of exploiting the correlations among the multiple views of the same physical target for a single joint recognition decision.
Abstract: We introduce a novel joint sparse representation based multi-view automatic target recognition (ATR) method, which can not only handle multi-view ATR without knowing the pose but also has the advantage of exploiting the correlations among the multiple views of the same physical target for a single joint recognition decision. Extensive experiments have been carried out on moving and stationary target acquisition and recognition (MSTAR) public database to evaluate the proposed method compared with several state-of-the-art methods such as linear support vector machine (SVM), kernel SVM, as well as a sparse representation based classifier (SRC). Experimental results demonstrate that the proposed joint sparse representation ATR method is very effective and performs robustly under variations such as multiple joint views, depression, azimuth angles, target articulations, as well as configurations.
211 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...Extensive experiments are carried out in Section IV to evaluate the proposed method and compare its performance with linear support vector machine (SVM) classifier [18], kernel SVM classifier [19], as well as a sparse representation based classifier (SRC) [20]....
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TL;DR: A thorough analysis of various approaches that have been proposed for problems such as age estimation, appearance prediction, face verification, etc. are offered and offer insights into future research on this topic.
Abstract: Facial aging, a new dimension that has recently been added to the problem of face recognition, poses interesting theoretical and practical challenges to the research community. The problem which originally generated interest in the psychophysics and human perception community has recently found enhanced interest in the computer vision community. How do humans perceive age? What constitutes an age-invariant signature that can be derived from faces? How compactly can the facial growth event be described? How does facial aging impact recognition performance? In this paper, we give a thorough analysis on the problem of facial aging and further provide a complete account of the many interesting studies that have been performed on this topic from different fields. We offer a comparative analysis of various approaches that have been proposed for problems such as age estimation, appearance prediction, face verification, etc. and offer insights into future research on this topic.
211 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...Since obtaining a ‘complete aging pattern’ for each individual is difficult (the case when an individual’s face images are available for all the ages of interest), they developed the ‘aging pattern subspace’ drawing inspiration from methods that develop an eigenspace [40] using incomplete data (data with missing features)....
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TL;DR: This paper proposes a robust postprocessing model to infer the latent heart rate time series and applies the method to a wide range of heart rate data and obtains convincing predictions along with uncertainty estimates.
Abstract: Heart rate data collected during nonlaboratory conditions present several data-modeling challenges. First, the noise in such data is often poorly described by a simple Gaussian; it has outliers and errors come in bursts. Second, in large-scale studies the ECG waveform is usually not recorded in full, so one has to deal with missing information. In this paper, we propose a robust postprocessing model for such applications. Our model to infer the latent heart rate time series consists of two main components: unsupervised clustering followed by Bayesian regression. The clustering component uses auxiliary data to learn the structure of outliers and noise bursts. The subsequent Gaussian process regression model uses the cluster assignments as prior information and incorporates expert knowledge about the physiology of the heart. We apply the method to a wide range of heart rate data and obtain convincing predictions along with uncertainty estimates. In a quantitative comparison with existing postprocessing methodology, our model achieves a significant increase in performance.
211 citations
Cites background from "Pattern Recognition and Machine Lea..."
...P ({μc,Λc}c=1) and P (π), it is possible to derive a set of equations that allow an iterative update of the parameters of the approximate posterior distribution [18]....
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TL;DR: The proposed SFBCSP is a potential method for improving the performance of MI-based BCI by optimizing the spatial patterns and gives overall better MI classification accuracy in comparison with several competing methods.
210 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...Instead of cross-validation, Bayesian inference provides an effective approach to automatically and quickly estimate the model parameters under the so-called evidence framework [35, 36]....
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