Model-Based Matching by Linear Combinations of Prototypes

We describe a method for modeling object classes (such as faces) using 2D example images and an algorithm for matching a model to a novel image. The object class models are "learned'' from example images that we call prototypes. In addition to the images, the pixelwise correspondences between a reference prototype and each of the other prototypes must also be provided. Thus a model consists of a linear combination of prototypical shapes and textures. A stochastic gradient descent algorithm is used to match a model to a novel image by minimizing the error between the model and the novel image. Example models are shown as well as example matches to novel images. The robustness of the matching algorithm is also evaluated. The technique can be used for a number of applications including the computation of correspondence between novel images of a certain known class, object recognition, image synthesis and image compression.

Example Based Image Analysis and Synthesis

Image analysis and graphics synthesis can be achieved with learning techniques using directly image examples without physically-based, 3D models. In our technique: -- the mapping from novel images to a vector of "pose" and "expression" parameters can be learned from a small set of example images using a function approximation technique that we call an analysis network; -- the inverse mapping from input "pose" and "expression" parameters to output images can be synthesized from a small set of example images and used to produce new images using a similar synthesis network. The techniques described here have several applications in computer graphics, special effects, interactive multimedia and very low bandwidth teleconferencing.

Sparse Correlation Kernel Analysis and Reconstruction

This paper presents a new paradigm for signal reconstruction and superresolution, Correlation Kernel Analysis (CKA), that is based on the selection of a sparse set of bases from a large dictionary of class- specific basis functions. The basis functions that we use are the correlation functions of the class of signals we are analyzing. To choose the appropriate features from this large dictionary, we use Support Vector Machine (SVM) regression and compare this to traditional Principal Component Analysis (PCA) for the tasks of signal reconstruction, superresolution, and compression. The testbed we use in this paper is a set of images of pedestrians. This paper also presents results of experiments in which we use a dictionary of multiscale basis functions and then use Basis Pursuit De-Noising to obtain a sparse, multiscale approximation of a signal. The results are analyzed and we conclude that 1) when used with a sparse representation technique, the correlation function is an effective kernel for image reconstruction and superresolution, 2) for image compression, PCA and SVM have different tradeoffs, depending on the particular metric that is used to evaluate the results, 3) in sparse representation techniques, L_1 is not a good proxy for the true measure of sparsity, L_0, and 4) the L_epsilon norm may be a better error metric for image reconstruction and compression than the L_2 norm, though the exact psychophysical metric should take into account high order structure in images.

Non-linear mechanical behavior of the elastomer polydimethylsiloxane (PDMS) used in the manufacture of microfluidic devices

Polydimethylsiloxane (PDMS) is the elastomer of choice to create a variety of microfluidic devices by soft lithography techniques (eg., [1], [2], [3], [4]). Accurate and reliable design, manufacture, and operation of microfluidic devices made from PDMS, require a detailed characterization of the deformation and failure behavior of the material. This paper discusses progress in a recently-initiated research project towards this goal. We have conducted large-deformation tension and compression experiments on traditional macroscale specimens, as well as microscale tension experiments on thin-film (≈ 50µm thickness) specimens of PDMS with varying ratios of monomer:curing agent (5:1, 10:1, 20:1). We find that the stress-stretch response of these materials shows significant variability, even for nominally identically prepared specimens. A non-linear, large-deformation rubber-elasticity model [5], [6] is applied to represent the behavior of PDMS. The constitutive model has been implemented in a finite-element program [7] to aid the design of microfluidic devices made from this material. As a first attempt towards the goal of estimating the non-linear material parameters for PDMS from indentation experiments, we have conducted micro-indentation experiments using a spherical indenter-tip, and carried out corresponding numerical simulations to verify how well the numerically-predicted P(load-h(depth of indentation) curves compare with the corresponding experimental measurements. The results are encouraging, and show the possibility of estimating the material parameters for PDMS from relatively simple micro-indentation experiments, and corresponding numerical simulations.