Multi-Directional Detection of Scratches in Digitized Images

Publication in the conference proceedings of EUSIPCO, Glasgow, Scotland, 2009

Grounded Human-Robot Interaction

Learning through observation and imitation: An overview of the ConSCIS architecture

A Set of Low-Level Descriptors for Images Affected by Foxing

Old printed photos are affected by several typical damages, due to age and bad preservation. “Foxing” defects look like red-brownish spots onto the paper of the printed photo. Similar features can be seen in the digitized copies. In this paper we propose a set of low level descriptors to extract features from digitized photos affected by foxing. An image retrieval application, based on information extracted by the proposed descriptors, is developed to discriminate, through comparison, if an image is affected by foxing. Results are compared to those obtained using some standard color descriptors.

Filling-in Gaps in Textured Images Using Bit-Plane Statistics

In this paper we propose a novel approach for the texture analysis-synthesis problem, with the purpose to restore missing zones in greyscale images. Bit-plane decomposition is used, and a dictionary is build with bit-blocks statistics for each plane. Gaps are reconstructed with a conditional stochastic process, to propagate texture global features into the damaged area, using information stored in the dictionary. Our restoration method is simple, easy and fast, with very good results for a large set of textured images. Results are compared with a state-of-the-art restoration algorithm.

Damages of Digitized Historical Images as Objects for Content Based Applications

This work presents the preliminary results achieved within a FIRB project aimed to develop innovative support tools for automatic or semi-automatic restoration of damaged digital images oncerning archaeological and monumental inheritance of Mediterranean coast. In particular, this paper is focused on a methodology for describing image degradation and its meta-representation for content based storing and retrieval. Our innovative idea is to decompose and store in a conventional RDBMS the images content, considering the damages as objects of the images. Moreover, a set of descriptors(a subset of MPEG7 descriptors) is used for the damage meta representation aimed to content based application. …

Autonomous acquisition of natural language

An important part of human intelligence is the ability to use language. Humans learn how to use language in a society of language users, which is probably the most effective way to learn a language from the ground up. Principles that might allow an artificial agents to learn language this way are not known at present. Here we present a framework which begins to address this challenge. Our auto-catalytic, endogenous, reflective architecture (AERA) supports the creation of agents that can learn natural language by observation. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime mock television interview, using…