The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses "factored sampling", previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust tracking of agile motion. Notwithstanding the use of stochastic methods, the algorithm runs in near real-time. Contents 1 Tracking curves in clutter 2 2 Discrete-time propagation of state density 3 3 Factored sampling 6 4 The Condensation algorithm 8 5 Stochastic dynamical models for curve motion 10 6 Observation model 13 7 Applying the Condensation algorithm to video-streams 17 8 Conclusions 26 A Non-line...

We introduce a technique for analyzing the behavior of sophisticated A.I. search programs working on realistic, large-scale problems. This approach allows us to predict where, in a space of problem instances, the hardest problems are to be found and where the fluctuations in difficulty are greatest. Our key insight is to shift emphasis from modelling sophisticated algorithms directly to modelling a search space that captures their principal effects. We compare our model’s predictions with actual data on real problems obtained independently and show that the agreement is quite good. By systematically relaxing our underlying modelling assumptions we identify their relative contribution to the remaining error and then remedy it. We also discuss further applications of our model and suggest how this type of analysis can be generalized to other kinds of A.I. problems. Chapter 1

journal = {Signal Processing}, publisher = {Elsevier}, volume = {31}, number = {2}, year = {1993}, pages = {165--180}} This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by the authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder. document created on: December 20, 2006 created from file: sp93cdcoverpage.tex cover page automatically created with CoverPage.sty (available at your favourite CTAN mirror) L

The proliferation of online information sources has led to an increased use of wrappers for extracting data from Web sources. While most of the previous research has focused on quick and e#cient generation of wrappers, the development of tools for wrapper maintenance has received less attention. This is an important research problem because Web sources often change in ways that prevent the wrappers from extracting data correctly. We present an e#cient algorithm that learns structural information about data from positive examples alone. We describe how this information can be used for two wrapper maintenance applications: wrapper verification and reinduction. The wrapper verification system detects when a wrapper is not extracting correct data, usually because the Web source has changed its format. The reinduction algorithm automatically recovers from changes in the Web source by identifying data on Web pages so that a new wrapper may be generated for this source. To validate our approach, we monitored 27 wrappers over a period of a year.

The complexity of circuit designs has necessitated a top-down approach to layout synthesis. A large body of work shows that a good layout hierarchy, or partitioning tree, as measured by the associated Rent parameter, will correspond to an area-efficient layout. We define the intrinsic Rent parameter of a netlist to be the minimum possible Rent parameter of any partitioning tree for the netlist. Experimental results show that spectra-based ratio cut partitioning algorithms yield partitioning trees with the lowest observed Rent parameter over all benchmarks and over all algorithms tested. For examples where the intrinsic Rent parameter is known, spectral ratio cut partitioning yields a partitioning tree with Rent parameter essentially identical to this theoretical optimum. These results have deep implications withrespect to both the choice of partitioning algorithms for top-down layout, as well as new approaches to layout area estimation. The paper concludes with directions for future research, including several promising techniques for fast estimation of the (intrinsic) Rent parameter.

An unsupervised iterative approach for extracting a new lexicon (or unknown words) from a Chinese text corpus is proposed in this paper. Instead of using a non-iterative segmentation-mergingfiltering -and-disambiguation approach, the proposed method iteratively integrates the contextual constraints (among word candidates) and a joint character association metric to progressively improve the segmentation results of the input corpus (and thus the new word list.) An augmented dictionary, which includes potential unknown words (in addition to known words), is used to segment the input corpus, unlike traditional approaches which use only known words for segmentation. In the segmentation process, the augmented dictionary is used to impose contextual constraints over known words and potential unknown words within input sentences; an unsupervised Viterbi Training process is then applied to ensure that the selected potential unknown words (and known words) maximize the likelihood of the input ...

We propose a new statistical timing analysis algorithm, which produces arrival-time random variables for all internal signals and primary outputs for cell-based designs with all cell delays modeled as random variables. Our algorithm propagates probabilistic timing events through the circuit and obtains final probabilistic events (distributions) at all nodes. The new algorithm is deterministic and flexible in controlling run time and accuracy. However, the algorithm has exponential time complexity for circuits with reconvergent fanouts. In order to solve this problem, we further propose a fast approximate algorithm. Experiments show that this approximate algorithm speeds up the statistical timing analysis by at least an order of magnitude and produces results with small errors when compared with Monte Carlo methods. 1.

The watermarking of digital images, audio, video and multimedia products in general has been proposed for resolving copyright ownership and verifying originality of content. This paper studies the contribution of watermarking for developing protection schemes. A general watermarking framework (GWF) is studied and the fundamental demands are listed. The watermarking algorithms, namely watermark generation, embedding and detection, are analyzed and necessary conditions for a reliable and efficient protection are stated. Although the GWF satisfies the majority of requirements for copyright protection and content verification, there are unsolved problems inside a pure watermarking framework. Particular solutions, based on product registration and related network services, are suggested to overcome such problems. 1 1 Introduction The digital form of photographs, paintings, speech, music, video etc. became very popular in the last decade. Digital facilities for creating, processi...

The rapid evolution of digital image manipulation and transmission techniques has created a pressing need for the protection of the intellectual property rights on images. A copyright protection method that is based on hiding an "invisible" signal, known as digital watermark, in the image is presented in this paper. Watermark casting is performed in the spatial domain by slightly modifying the intensity of randomly selected image pixels. Watermark detection does not require the existence of the original image and is carried out by comparing the mean intensity value of the marked pixels against that of the not marked pixels. Statistical hypothesis testing is used for this purpose. Pixel modifications can be done in such a way that the watermark is resistant to JPEG compression and lowpass filtering. This is achieved by minimizing the energy content of the watermark signal in higher frequencies while taking into account properties of the human visual system. A variation that generates im...

in this article we present symmetric diffusion networks, a family of networks that instantiate the principles of continuous, stochastic, adaptive and interactive pro-pagation of information. Using methods of Markovlon diffusion theory, we for-malize the activation dynamics of these networks and then show that they can be trained to reproduce entire muitivariote probability distributions an their outputs using the contrastive Hebbian learning rule (CHL).,We show that CHL performs gradient descent on an error function that captures differences between desired and obtolned continuous multivoriate probability distributions. This allows the learning algorithm to go beyond expected values of output units and to approxi-mate complete probability distributions on continuous muitivariote activation spaces. We argue that learning continuous distributions is an important task underlying a variety of real-life situations that were beyond the scope of previous connectionist networks. Deterministic networks, like back propagation, cannot ieorn this task because they ore limited to learning average values of indepen-dent output units. Previous stochastic connectionist networks could learn pro-bobility distributions but they were limited to discrete variables. Simulations show that symmetric diffusion networks can be trained with the CHL rule to op-proximate discrete and continuous probability distributions of various types. 1.