The principal independent components of images

This paper proposes a new approach for the encoding of images by only a few important components. Classically, this is done by the Principal Component Analysis (PCA). Recently, the Independent Component Analysis (ICA) has found strong interest in the neural network community. Applied to images, we aim for the most important source patterns with the highest occurrence probability or highest information called principal independent components (PIC). For the example of a synthetic image composed by characters this idea selects the salient ones. For natural images it does not lead to an acceptable reproduction error since no a-priori probabilities can be computed. Combining the traditional principal component criteria of PCA with the independence property of ICA we obtain a better encoding. It turns out that this definition of PIC implements the classical demand of Shannon’s rate distortion theory

Image encoding by independent principal components

The encoding of images by semantic entities is still an unresolved task. This paper proposes the encoding of images by only a few important components or image primitives. Classically, this can be done by the Principal Component Analysis (PCA). Recently, the Independent Component Analysis (ICA) has found strong interest in the signal processing and neural network community. Using this as pattern primitives we aim for source patterns with the highest occurrence probability or highest information. For the example of a synthetic image composed by characters this idea selects the salient ones. For natural images it does not lead to an acceptable reproduction error since no a-priori probabilities can be computed. Combining the traditional principal component criteria of PCA with the independence property of ICA we obtain a better encoding. It turns out that the Independent Principal Components (IPC) in contrast to the Principal Independent Components (PIC) implement the classical demand of Shannon’s rate distortion theory

Project SEMACODE : a scale-invariant object recognition system for content-based queries in image databases

For the efficient management of large image databases, the automated characterization of images and the usage of that characterization for searching and ordering tasks is highly desirable. The purpose of the project SEMACODE is to combine the still unsolved problem of content-oriented characterization of images with scale-invariant object recognition and modelbased compression methods. To achieve this goal, existing techniques as well as new concepts related to pattern matching, image encoding, and image compression are examined. The resulting methods are integrated in a common framework with the aid of a content-oriented conception. For the application, an image database at the library of the university of Frankfurt/Main (StUB; about 60000 images), the required operations are developed. The search and query interfaces are defined in close cooperation with the StUB project “Digitized Colonial Picture Library”. This report describes the fundamentals and first results of the image encoding and object recognition algorithms developed within the scope of the project

MASCOT: a mechanism for attention-based scale-invariant object recognition in images

The efficient management of large multimedia databases requires the development of new techniques to process, characterize, and search for multimedia objects. Especially in the case of image data, the rapidly growing amount of documents prohibits a manual description of the images’ content. Instead, the automated characterization is highly desirable to support annotation and retrieval of digital images. However, this is a very complex and still unsolved task. To contribute to a solution of this problem, we have developed a mechanism for recognizing objects in images based on the query by example paradigm. Therefore, the most salient image features of an example image representing the searched object are extracted to obtain a scale-invariant object model. The use of this model provides an efficient and robust strategy for recognizing objects in images independently of their size. Further applications of the mechanism are classical recognition tasks such as scene decomposition or object tracking in video sequences

A Waveguide for Bose-Einstein Condensates

We report on the creation of Bose-Einstein condensates of $^{87}$Rb in a specially designed hybrid, dipole and magnetic trap. This trap naturally allows the coherent transfer of matter waves into a pure dipole potential waveguide based on a doughnut beam. Specifically, we present studies of the coherence of the ensemble in the hybrid trap and during the evolution in the waveguide by means of an autocorrelation interferometer scheme. By monitoring the expansion of the ensemble in the waveguide we observe a mean field dominated acceleration on a much longer time scale than in the free 3D expansion. Both the autocorrelation interference and the pure expansion measurements are in excellent agreement with theoretical predictions of the ensemble dynamics

Importance of second-order piezoelectric effects in zincblende semiconductors

We show that the piezoelectric effect that describes the emergence of an electric field in response to a crystal deformation in III-V semiconductors such as GaAs and InAs has strong contributions from second-order effects that have been neglected so far. We calculate the second-order piezoelectric tensors using density functional theory and obtain the piezoelectric field for [111]-oriented In$_x$Ga$_{1-x}$As quantum wells of realistic dimensions and concentration $x$. We find that the linear and the quadratic piezoelectric coefficients have the opposite effect on the field, and for large strains the quadratic terms even dominate. Thus, the piezoelectric field turns out to be a rare example of a physical quantity for which the first- and second-order contributions are of comparable magnitude.Comment: 4 pages, 3 figures, Submitted to Phys. Rev. Let