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.

Observations on Cortical Mechanisms for Object Recognition andsLearning

This paper sketches a hypothetical cortical architecture for visual 3D object recognition based on a recent computational model. The view-centered scheme relies on modules for learning from examples, such as Hyperbf-like networks. Such models capture a class of explanations we call Memory-Based Models (MBM) that contains sparse population coding, memory-based recognition, and codebooks of prototypes. Unlike the sigmoidal units of some artificial neural networks, the units of MBMs are consistent with the description of cortical neurons. We describe how an example of MBM may be realized in terms of cortical circuitry and biophysical mechanisms, consistent with psychophysical and physiological data.

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.

Global Depth Perception from Familiar Scene Structure

In the absence of cues for absolute depth measurements as binocular disparity, motion, or defocus, the absolute distance between the observer and a scene cannot be measured. The interpretation of shading, edges and junctions may provide a 3D model of the scene but it will not inform about the actual "size" of the space. One possible source of information for absolute depth estimation is the image size of known objects. However, this is computationally complex due to the difficulty of the object recognition process. Here we propose a source of information for absolute depth estimation that does not rely on specific objects: we introduce a procedure for absolute depth estimation based on the recognition of the whole scene. The shape of the space of the scene and the structures present in the scene are strongly related to the scale of observation. We demonstrate that, by recognizing the properties of the structures present in the image, we can infer the scale of the scene, and therefore its absolute mean depth. We illustrate the interest in computing the mean depth of the scene with application to scene recognition and object detection.

Apatite-Polymer Composites for the Controlled Dual Delivery of BMP-2 and BMP-6 for Bone Tissue Engineering

The release of growth factors from tissue engineering scaffolds provides signals that influence the migration, differentiation, and proliferation of cells. The incorporation of a drug delivery platform that is capable of tunable release will give tissue engineers greater versatility in the direction of tissue regeneration. We have prepared a novel composite of two biomaterials with proven track records - apatite and poly(lactic-co-glycolic acid) (PLGA) – as a drug delivery platform with promising controlled release properties. These composites have been tested in the delivery of a model protein, bovine serum albumin (BSA), as well as therapeutic proteins, recombinant human bone morphogenetic protein-2 (rhBMP-2) and rhBMP-6. The controlled release strategy is based on the use of a polymer with acidic degradation products to control the dissolution of the basic apatitic component, resulting in protein release. Therefore, any parameter that affects either polymer degradation or apatite dissolution can be used to control protein release. We have modified the protein release profile systematically by varying the polymer molecular weight, polymer hydrophobicity, apatite loading, apatite particle size, and other material and processing parameters. Biologically active rhBMP-2 was released from these composite microparticles over 100 days, in contrast to conventional collagen sponge carriers, which were depleted in approximately 2 weeks. The released rhBMP-2 was able to induce elevated alkaline phosphatase and osteocalcin expression in pluripotent murine embryonic fibroblasts. To augment tissue engineering scaffolds with tunable and sustained protein release capabilities, these composite microparticles can be dispersed in the scaffolds in different combinations to obtain a superposition of the release profiles. We have loaded rhBMP-2 into composite microparticles with a fast release profile, and rhBMP-6 into slow-releasing composite microparticles. An equi-mixture of these two sets of composite particles was then injected into a collagen sponge, allowing for dual release of the proteins from the collagenous scaffold. The ability of these BMP-loaded scaffolds to induce osteoblastic differentiation in vitro and ectopic bone formation in a rat model is being investigated. We anticipate that these apatite-polymer composite microparticles can be extended to the delivery of other signalling molecules, and can be incorporated into other types of tissue engineering scaffolds.