Research Output

Compact Composite Descriptors

The family of compact composite descriptors includes descriptors for 3 types of images. A group of 3 descriptors combines color and texture information in order to describe natural color images. On this page, you can read more details about CCDs and download the related libraries.

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MPEG-7 Visual Descriptors

From this page, you can download an open-source implementation (in C#) of the Scalable Color Descriptor, Color Layout Descriptor, Dominant Colors Descriptor, as well as the Edge Histogram Descriptor.

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Simple Descriptors

We employ the SURF detector to define salient image patches of blob-like textures and use the MPEG-7 Scalable Color (SC), Color Layout (CL), Edge Histogram (EH) and the Color and Edge Directivity Descriptor (CEDD) descriptors to produce the final local features’ vectors named SIMPLE-SC, SIMPLE-CL, SIMPLE-EH, and SIMPLE-CEDD or “LoCATE” respectively. 

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img(Rummager) brings into effect a number of new as well as state of the art descriptors. The application can execute an image search based on a query image, either from XML-based index files or directly from a folder containing image files, extracting the comparison features in real-time.

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In this web-site, a new set of feature descriptors is presented in a retrieval system. These descriptors have been designed with particular attention to their size and storage requirements, keeping them as small as possible without compromising their discriminating ability.

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The LIRE (Lucene Image REtrieval) library provides a simple way to retrieve images and photos based on their color and texture characteristics. LIRE creates a Lucene index of image features for content-based image retrieval (CBIR). Three of the available image features are taken from the MPEG-7 Standard: ScalableColor, ColorLayout, and EdgeHistogram a fourth one, the Auto Color Correlogram has been implemented based on recent research results.

Enorasis Robot

Enorasis robot implements a computer-vision-based line-tracking and following control technique for high-speed autonomous navigation.

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Current Research

ITSO: A novel Inverse Transform Sampling-based Optimization algorithm for stochastic search

Optimization algorithms appear in the core calculations of numerous Artificial Intelligence (AI) and Machine Learning methods, as well as Engineering and Business applications. Following recent works on the theoretical deficiencies of AI, a rigor context for the optimization problem of a black-box objective function is developed. The algorithm stems directly from the theory of probability, instead of a presumed inspiration, thus the convergence properties of the proposed methodology are inherently stable.

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A Gradient Free Neural Network Framework Based on Universal Approximation Theorem

This paper presents a numerical scheme for the computation of Artificial Neural Networks’ weights, without a laborious iterative procedure. The proposed algorithm adheres to the underlying theory, is highly fast, and results in remarkably low errors when applied for regression and classification of complex data-sets, such as the Griewank function of multiple variables x∈R^100 with random noise addition, and MNIST database for handwritten digits recognition, with 7×10^4 images.

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The Lagrangian remainder of Taylor’s series, distinguishes O(f(x)) time complexities to polynomials or not

The purpose of this letter is to investigate the time complexity consequences of the truncated Taylor series, known as Taylor Polynomials. In particular, it is demonstrated that the examination of P=NP equality, is associated with the determination of whether the n^th derivative of a particular solution is bounded or not. Accordingly, in some cases, this is not true, and hence in general.

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