This paper presents a secure (tamper-resistant) algorithm for watermarking images, and a methodology for digital watermarking that may be generalized to audio, video, and multimedia data. We advocate that a watermark should be constructed as an independent and identically distributed (i.i.d.) Gaussian random vector that is imperceptibly inserted in a spread-spectrum-like fashion into the perceptually most significant spectral components of the data. We argue that insertion of a watermark under this regime makes the watermark robust to signal processing operations (such as lossy compression, filtering, digital-analog and analog-digital conversion, requantization, etc.), and common geometric transformations (such as cropping, scaling, translation, and rotation) provided that the original image is available and that it can be succesfully registered against the transformed watermarked image. In these cases, the watermark detector unambiguously identifies the owner. Further, the use of Gaussian noise, ensures strong resilience to multiple-document, or collusional, attacks. Experimental results are provided to support these claims, along with an exposition of pending open problems.

Abstract--In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k-Nearest Neighbours, and a Quadratic Discriminant Function) on six "real world " medical diagnostics data sets. We compare and discuss the use of AUC to the more conventional overall accuracy and find that AUC exhibits a number of desirable properties when compared to overall accuracy: increased sensitivity in Analysis of Variance (ANOVA) tests; a standard error that decreased as both AUC and the number of test samples increased; decision threshold independent; and it is invariant to a priori class probabilities. The paper concludes with the recommendation that AUC be used in preference to overall accuracy for "single number " evaluation of machine

Motion correspondence is a fundamental problem in computer vision and many other disciplines. This article describes statistical data association techniques originally developed in the context of target tracking and surveillance and now beginning to be used in dynamic motion analysis by the computer vision community. The Mahalanobis distance measure is first introduced before discussing the limitations of nearest neighbor algorithms. Then, the track-splitting, joint likelihood, multiple hypothesis algorithms are described, each method solving an increasing-ly more complicated optimization. Real-time constraints may prohibit the application of these optimal methods. The suboptimal joint probabilistic data association algorithm is therefore described. The advantages, limitations, and relationships between the approaches are discussed. 1

This paper addresses the problem that a listener experiences when attempting to capture information presented during a lecture, meeting, or interview. Listeners must divide their attention between the talker and their notetaking activity. We propose a new device -- the Audio Notebook -- for taking notes and interacting with a speech recording. The Audio Notebook is a combination of a digital audio recorder and paper notebook, all in one device. Audio recordings are structured using two techniques: user structuring based on notetaking activity, and acoustic structuring based on a talker's changes in pitch, pausing, and energy. A field study showed that the interaction techniques enabled a range of usage styles, from detailed review to high speed skimming. The study motivated the addition of phrase detection and topic suggestions to improve access to the audio recordings. Through these audio interaction techniques, the Audio Notebook defines a new approach for navigation in the audio domain.

The contribution of this paper is threefold: (1) to formulate a decision fusion problem encountered in the design of a multi-modal identity verification system as a particular classification problem, (2) to propose three simple classifiers to solve this problem, (3) to compare the relative performances of the proposed classifiers. The multi-modal identity verification system under consideration is built of d modalities in parallel, each one delivering as output a scalar number, called score, stating how well the claimed identity is verified. A fusion module receiving as input the d scores has to take a binary decision: accept or reject identity. This fusion problem has been solved using three different classifiers, respectively based on the k-nearest- neighbor (k-NN) classifier, decision trees and logistic regression. The performances of these different fusion modules have been evaluated and compared on a multi-modal database, containing both vocal and visual modalities. Keywords: ...

. The segmentation problem arises in many applications in data mining, A.I. and statistics, including segmenting time series, decision tree algorithms and image processing. In this paper, we consider a range of criteria which may be applied to determine if some data should be segmented into two or regions. We develop a information theoretic criterion (MML) for the segmentation of univariate data with Gaussian errors. We perform simulations comparing segmentation methods (MML, AIC, MDL and BIC) and conclude that the MML criterion is the preferred criterion. We then apply the segmentation method to financial time series data. 1 Introduction We consider a particular instance of the segmentation problem. The segmentation problem arises wherever it is desired to partition data into distinct homogeneous segments (or regions). The segmentation problem is to decide whether to divide a segment into one or more sub-segments and to choose where to make the divisions. The segmentation problem ari...

Abstract—We advance new active object recognition algorithms that classify rigid objects and estimate their pose from intensity images. Our algorithms automatically detect if the class or pose of an object is ambiguous in a given image, reposition the sensor as needed, and incorporate data from multiple object views in determining the final object class and pose estimate. A probabilistic feature space trajectory (FST) in a global eigenspace is used to represent 3D distorted views of an object and to estimate the class and pose of an input object. Confidence measures for the class and pose estimates, derived using the probabilistic FST object representation, determine when additional observations are required as well as where the sensor should be positioned to provide the most useful information. We demonstrate the ability to use FSTs constructed from images rendered from computer-aided design models to recognize real objects in real images and present test results for a set of metal machined parts. Index Terms—Active vision, classification, object recognition, pose estimation. 1

This report uses a bark scale, which has been replaced here with a mel-scale. CHAPTER 3. THE ABBOT SPEECH RECOGNITION SYSTEM 32 where, ¯ i = 1

The segmentation problem arises in many applications in data mining, A.I. and statistics. In this paper, we consider segmenting simple time series. We develop two Bayesian approaches for segmenting a time series, namely the Bayes Factor approach, and the Minimum Message Length (MML) approach. We perform simulations comparing these Bayesian approaches, and then perform a comparison with other classical approaches, namely AIC, MDL and BIC. We conclude that the MML criterion is the preferred criterion. We then apply the segmentation method to financial time series data. 1 Introduction In this paper, we consider the problem of segmenting simple time series. We consider time series of the form: y t+1 = y t + ¯ j + ffl t where we are given N data points (y 1 : : : ; yN ) and we assume there are C + 1 segments (j 2 f0; : : : Cg), and that each ffl t is Gaussian with mean zero and variance oe 2 j . We wish to estimate -- the number of segments, C + 1, -- the segment boundaries, fv 1 ; : :...

This paper presents a probabilistic framework for off-line multiple object tracking. At each timestep, a small set of deterministic candidates is generated which is guaranteed to contain the correct solution. Tracking an object within video then becomes possible using the Viterbi algorithm. In contrast with particle filter methods where candidates are numerous and random, the proposed algorithm involves a few candidates and results in a deterministic solution. Moreover, we consider here off-line applications where past and future information is exploited. This paper shows that, although basic and very simple, this candidate selection allows the solution of many tracking problems in different real-world applications and offers a good alternative to particle filter methods for off-line applications. 1.