Euripides G. M. Petrakis

A survey on industrial vision systems, applications and tools

The state of the art in machine vision inspection and a critical overview of real-world applications are presented in this paper. Two independent ways to classify applications are proposed, one according to the inspected features of the industrial product or process and the other according to the inspection independent characteristics of the inspected product or process. The most contemporary software and hardware tools for developing industrial vision systems are reviewed. Finally, under the light of recent advances in image sensors, software and hardware technology, important issues and directions for designing and developing industrial vision systems are identified and discussed

Semantic similarity methods in wordNet and their application to information retrieval on the web

Semantic Similarity relates to computing the similarity between concepts which are not lexicographically similar. We investigate approaches to computing semantic similarity by mapping terms (concepts) to an ontology and by examining their relationships in that ontology. Some of the most popular semantic similarity methods are implemented and evaluated using WordNet as the underlying reference ontology. Building upon the idea of semantic similarity, a novel information retrieval method is also proposed. This method is capable of detecting similarities between documents containing semantically similar but not necessarily lexicographically similar terms. The proposed method has been evaluated in retrieval of images and documents on the Web. The experimental results demonstrated very promising performance improvements over state-of-the-art information retrieval methods.

Matching and retrieval of distorted and occluded shapes using dynamic programming

We propose an approach for matching distorted and possibly occluded shapes using dynamic programming (DP). We distinguish among various cases of matching such as cases where the shapes are scaled with respect to each other and cases where an open shape matches the whole or only a part of another open or closed shape. Our algorithm treats noise and shape distortions by allowing matching of merged sequences of consecutive small segments in a shape with larger segments of another shape, while being invariant to translation, scale, orientation, and starting point selection. We illustrate the effectiveness of our algorithm in retrieval of shapes on two data sets of two-dimensional open and closed shapes of marine life species. We demonstrate the superiority of our approach over traditional approaches to shape matching and retrieval based on Fourier descriptors and moments. We also compare our method with SQUID, a well-known method which is available on the Internet. Our evaluation is based on human relevance judgments following a well-established methodology from the information retrieval field.

Information Retrieval by Semantic Similarity

Semantic Similarity relates to computing the similarity between conceptually similar but not necessarily lexically similar terms. Typically, semantic similarity is computed by mapping terms to an ontology and by examining their relationships in that ontology. We investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic similarity methods are implemented and evaluated using WordNet and MeSH. Building upon semantic similarity, we propose the Semantic Similarity based Retrieval Model (SSRM), a novel information retrieval method capable for discovering similarities between documents containing conceptually similar terms. The most effective semantic similarity method is implemented into SSRM. SSRM has been applied in retrieval on OHSUMED (a standard TREC collection available on the Web). The experimental results demonstrated promising performance improvements over classic information retrieval methods utilizing plain lexical matching (e.g., Vector Space Model) and also over state-of-the-art semantic similarity retrieval methods utilizing ontologies.

Improving the performance of focused web crawlers

This work addresses issues related to the design and implementation of focused crawlers. Several variants of state-of-the-art crawlers relying on web page content and link information for estimating the relevance of web pages to a given topic are proposed. Particular emphasis is given to crawlers capable of learning not only the content of relevant pages (as classic crawlers do) but also paths leading to relevant pages. A novel learning crawler inspired by a previously proposed Hidden Markov Model (HMM) crawler is described as well. The crawlers have been implemented using the same baseline implementation (only the priority assignment function differs in each crawler) providing an unbiased evaluation framework for a comparative analysis of their performance. All crawlers achieve their maximum performance when a combination of web page content and (link) anchor text is used for assigning download priorities to web pages. Furthermore, the new HMM crawler improved the performance of the original HMM crawler and also outperforms classic focused crawlers in searching for specialized topics.

Design and evaluation of spatial similarity approaches for image retrieval

Abstract Similarity retrieval by spatial image content (i.e. using multiple objects and their relationships in space) is still an open problem which has received considerable attention in the literature. The most powerful approaches of spatial image content representation and matching are attributed relational graphs (ARGs) and symbolic projections (e.g. two-dimensional 2D strings). A framework is proposed for studying the performance of spatial similarity approaches in image databases (IDBs). The classical ARG and 2D string matching methods are evaluated. Several variants of ARG and 2D string methods for improving their accuracy and speeding-up their time responses are also proposed and evaluated. A critical analysis of the performance of all these methods is presented. The analysis indicates that in retrieving images by spatial content, retrieval response time and accuracy are traded-off.

Methodology for the representation, indexing and retrieval of images by content

Abstract This paper considers the requirements for the design and implementation of an image database system which supports the storage and retrieval of images by content. Attention is focused on a specific methodology for the efficient representation, indexing and retrieval of images based on spatial relationships and properties of objects. Images are first decomposed into groups of objects and are indexed by computing addresses to all such groups. This methodology supports the efficient processing of queries by image example, and avoids exhaustive searching through the entire image database. The performance of an image database system using the above methodology has been evaluated based on simulated images, as well as images obtained with computed tomography and magnetic resonance imaging. The results of this evaluation are presented and discussed.

iCluster: a self-organizing overlay network for P2P information retrieval

We present iCluster, a self-organizing peer-to-peer overlay network for supporting full-fledged information retrieval in a dynamic environment. iCluster works by organizing peers sharing common interests into clusters and by exploiting clustering information at query time for achieving low network traffic and high recall. We define the criteria for peer similarity and peer selection, and we present the protocols for organizing the peers into clusters and for searching within the clustered organization of peers. iCluster is evaluated on a realistic peer-to-peer environment using real-world data and queries. The results demonstrate significant performance improvements (in terms of clustering efficiency, communication load and retrieval accuracy) over a state-of-the-art peer-to-peer clustering method. Compared to exhaustive search by flooding, iCluster exchanged a small loss in retrieval accuracy for much less message flow.

The AMTEx approach in the medical document indexing and retrieval application

AMTEx is a medical document indexing method, specifically designed for the automatic indexing of documents in large medical collections, such as MEDLINE, the premier bibliographic database of the US National Library of Medicine (NLM). AMTEx combines MeSH, the terminological thesaurus resource of NLM, with a well-established method for extraction of terminology, the C/NC-value method. The performance evaluation of two AMTEx configurations is measured against the current state-of-the-art, the MetaMap Transfer (MMTx) method in four experiments, using two types of corpora: a subset of MEDLINE (PMC) full document corpus and a subset of MEDLINE (OHSUMED) abstracts, for each of the indexing and retrieval tasks, respectively. The experimental results demonstrate that AMTEx performs better in indexing in 20-50% of the processing time compared to MMTx, while for the retrieval task, AMTEx performs better in the full text (PMC) corpus.

TOQL: Temporal Ontology Querying Language

We introduce TOQL, a query language for querying time information in ontologies. TOQL is a high level query language that handles ontologies almost like relational databases. Queries are issued as SQL-like statements involving time (i.e., time points or intervals) or high-level ontology concepts that vary in time. Although independent from TOQL, this work suggests a mechanism for representing time evolving concepts in ontologies based on the four-dimensional perdurantist mechanism. However, TOQL prevents users from being familiar with the representation of time in ontologies. To show proof of concept, an application has been developed that supports translation and execution of TOQL queries on temporal ontologies combined with a reasoning mechanism based on event calculus. A real world temporal ontology is also implemented on which several TOQL example queries are processed and discussed.