Steganography and Steganalysis in Digital Multimedia: Hype or Hallelujah?

In this tutorial, we introduce the basic theory behind Steganography and Steganalysis, and present some recent algorithms and developments of these fields. We show how the existing techniques used nowadays are related to Image Processing and Computer Vision, point out several trendy applications of Steganography and Steganalysis, and list a few great research opportunities just waiting to be addressed.In this tutorial, we introduce the basic theory behind Steganography and Steganalysis, and present some recent algorithms and developments of these fields. We show how the existing techniques used nowadays are related to Image Processing and Computer Vision, point out several trendy applications of Steganography and Steganalysis, and list a few great research opportunities just waiting to be addressed

Cue Integration Using Affine Arithmetic and Gaussians

In this paper we describe how the connections between affine forms, zonotopes, and Gaussian distributions help us devise an automated cue integration technique for tracking deformable models. This integration technique is based on the confidence estimates of each cue. We use affine forms to bound these confidences. Affine forms represent bounded intervals, with a well-defined set of arithmetic operations. They are constructed from the sum of several independent components. An n-dimensional affine form describes a complex convex polytope, called a zonotope. Because these components lie in bounded intervals, Lindeberg\u27s theorem, a modified version of the central limit theorem,can be used to justify a Gaussian approximation of the affine form. We present a new expectation-based algorithm to find the best Gaussian approximation of an affine form. Both the new and the previous algorithm run in O(n2m) time, where n is the dimension of the affine form, and m is the number of independent components. The constants in the running time of new algorithm, however, are much smaller, and as a result it runs 40 times faster than the previous one for equal inputs. We show that using the Berry-Esseen theorem it is possible to calculate an upper bound for the error in the Gaussian approximation. Using affine forms and the conversion algorithm, we create a method for automatically integrating cues in the tracking process of a deformable model. The tracking process is described as a dynamical system, in which we model the force contribution of each cue as an affine form. We integrate their Gaussian approximations using a Kalman filter as a maximum likelihood estimator. This method not only provides an integrated result that is dependent on the quality of each on of the cues, but also provides a measure of confidence in the final result. We evaluate our new estimation algorithm in experiments, and we demonstrate our deformable model-based face tracking system as an application of this algorithm

Prefácio à Seção Especial dos Tutoriais do SIBGRAPI'08

Sejam bem-vindos à seção especial dos tutoriais do SIBGRAPI 2008!Os participantes do SIBGRAPI de 2008 tiveram a oportunidade de presenciar um conjunto excepcional de tutoriais que cobriram uma vasta gama de assuntos de interesse da comunidade. Tivemos dois tutoriais introdutórios: o primeiro sobre o uso do OpenCV, uma biblioteca já estabelecida para desenvolvimento de aplicações em visão computacional, e o segundo sobre o uso de processamento de imagens para a elaboração de efeitos especiais. Nossos tutoriais avançados foram também bem diversos. O primeiro tratou sobre o uso de CUDA, uma nova linguagem desenvolvida pela NVIDIA para o uso das placas gráficas de última geração em problemas de computação geral, em aplicações de realidade aumentada.Nosso segundo tutorial avançado lidou com o ajuste de superfícies utilizando pseudo-variedades parametrizadas. Desde a conferência, os autores deste tutorial publicaram o material em outras conferências e revistas, e acharam melhor disponibilizar o material do tutorial juntamente com estas publicações no portal do projeto, localizado em http://w3.impa.br/~lvelho/ppm09/.Desejamos então que todos aproveitem o material dos três tutoriais publicados aqui nesta seção

First steps toward image phylogeny

Abstract—In this paper, we introduce and formally define a new problem, Image Phylogeny Tree (IPT): to find the structure of transformations, and their parameters, that generate a given set of near duplicate images. This problem has direct applications in security, forensics, and copyright enforcement. We devise a method for calculating an asymmetric dissimilarity matrix from a set of near duplicate images. We also describe a new algorithm to build an IPT. We also analyze our algorithm’s computational complexity. Finally, we perform experiments that show near-perfect reconstructed IPT results when using an appropriate dissimilarity function. I

A gentle introduction to predictive filters

Abstract: Predictive filters are essential tools in modern science. They perform state prediction and parameter estimation in fields such as robotics, computer vision, and computer graphics. Sometimes also called Bayesian filters, they apply th

A Gentle Introduction to Predictive Filters

Predictive filters are essential tools in modern science. They perform state prediction and parameter estimation in fields such as robotics, computer vision, and computer graphics. Sometimes also called Bayesian filters, they apply the Bayesian rule of conditional probability to combine a predicted behavior with some corrupted indirect observation. When we study and solve a problem, we first need its proper mathematical formulation. Finding the essential parameters that best describe the system is hard; modeling their behaviors over time is even more challenging. Usually, we also have an inspection mechanism that provides us with indirect measurements, the observations, of the hidden underlying parameters. We also need to deal with the concept of uncertainty, and use random variables to represent both the state and the observations. Predictive filters are a family of estimation techniques. They combine the uncertain prediction from the system’s dynamics and the corrupted observation. There are many different predictive filters, each dealing with different types of mathematical representations for random variables and system dynamics. Here, the reader will find a dense introduction to predictive filters. After a general introduction, we discuss briefly discussion about mathematical modeling of systems: state representation, dynamics, and observation. Then, we expose some basic issues related to random variables and uncertainty modeling, and discuss four implementations of predictive filters, in order of complexity: the Kalman filter, the extended Kalman filter, the particle filter, and the unscented Kalman filter. Keywords: Predictive Filters, Density Estimators, Kalman Filter, Particle Filter, Unscented Kalman Filter