Recognition by Linear Combinations of Models

Visual object recognition requires the matching of an image with a set of models stored in memory. In this paper we propose an approach to recognition in which a 3-D object is represented by the linear combination of 2-D images of the object. If M = {M1,...Mk} is the set of pictures representing a given object, and P is the 2-D image of an object to be recognized, then P is considered an instance of M if P = Eki=aiMi for some constants ai. We show that this approach handles correctly rigid 3-D transformations of objects with sharp as well as smooth boundaries, and can also handle non-rigid transformations. The paper is divided into two parts. In the first part we show that the variety of views depicting the same object under different transformations can often be expressed as the linear combinations of a small number of views. In the second part we suggest how this linear combinatino property may be used in the recognition process.

Localization and Positioning Using Combinations of Model Views

A method for localization and positioning in an indoor environment is presented. The method is based on representing the scene as a set of 2D views and predicting the appearances of novel views by linear combinations of the model views. The method is accurate under weak perspective projection. Analysis of this projection as well as experimental results demonstrate that in many cases it is sufficient to accurately describe the scene. When weak perspective approximation is invalid, an iterative solution to account for the perspective distortions can be employed. A simple algorithm for repositioning, the task of returning to a previously visited position defined by a single view, is derived from this method.

Physically-Based Combinations of Views: Representing Rigid and Nonrigid Motion

Nonrigid motion can be described as morphing or blending between extremal shapes, e.g., heart motion can be described as transitioning between the systole and diastole states. Using physically-based modeling techniques, shape similarity can be measured in terms of forces and strain. This provides a physically-based coordinate system in which motion is characterized in terms of physical similarity to a set of extremal shapes. Having such a low-dimensional characterization of nonrigid motion allows for the recognition and the comparison of different types of nonrigid motion.

Combinations of Deformable Shape Prototypes

We propose to investigate a model-based technique for encoding non-rigid object classes in terms of object prototypes. Objects from the same class can be parameterized by identifying shape and appearance invariants of the class to devise low-level representations. The approach presented here creates a flexible model for an object class from a set of prototypes. This model is then used to estimate the parameters of low-level representation of novel objects as combinations of the prototype parameters. Variations in the object shape are modeled as non-rigid deformations. Appearance variations are modeled as intensity variations. In the training phase, the system is presented with several example prototype images. These prototype images are registered to a reference image by a finite element-based technique called Active Blobs. The deformations of the finite element model to register a prototype image with the reference image provide the shape description or shape vector for the prototype. The shape vector for each prototype, is then used to warp the prototype image onto the reference image and obtain the corresponding texture vector. The prototype texture vectors, being warped onto the same reference image have a pixel by pixel correspondence with each other and hence are "shape normalized". Given sufficient number of prototypes that exhibit appropriate in-class variations, the shape and the texture vectors define a linear prototype subspace that spans the object class. Each prototype is a vector in this subspace. The matching phase involves the estimation of a set of combination parameters for synthesis of the novel object by combining the prototype shape and texture vectors. The strengths of this technique lie in the combined estimation of both shape and appearance parameters. This is in contrast with the previous approaches where shape and appearance parameters were estimated separately.

El coeficiente de gastos de conservación y reparaciones del tractor agrícola

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Synergistic phage-antibiotic combinations for the control of Escherichia coli biofilms in vitro.

The potential application of phage therapy for the control of bacterial biofilms has received increasing attention as resistance to conventional antibiotic agents continues to increase. The present study identifies antimicrobial synergy between bacteriophage T4 and a conventional antibiotic, cefotaxime, via standard plaque assay and, importantly, in the in vitro eradication of biofilms of the T4 host strain Escherichia coli 11303. Phage-antibiotic synergy (PAS) is defined as the phenomenon whereby sub-lethal concentrations of certain antibiotics can substantially stimulate the host bacteria's production of virulent phage. Increasing sub-lethal concentrations of cefotaxime resulted in an observed increase in T4 plaque size and T4 concentration. The application of PAS to the T4 one-step growth curve also resulted in an increased burst size and reduced latent period. Combinations of T4 bacteriophage and cefotaxime significantly enhanced the eradication of bacterial biofilms when compared to treatment with cefotaxime alone. The addition of medium (10(4) PFU mL(-1) ) and high (10(7) PFU mL(-1) ) phage titres reduced the minimum biofilm eradication concentration value of cefotaxime against E. coli ATCC 11303 biofilms from 256 to 128 and 32 µg mL(-1) , respectively. Although further investigation is needed to confirm PAS, this study demonstrates, for the first time, that synergy between bacteriophage and conventional antibiotics can significantly improve biofilm control in vitro.