What Happened in CLEF 2004?.- What Happened in CLEF 2004?.- I. Ad Hoc Text Retrieval Tracks.- CLEF 2004: Ad Hoc Track Overview and Results Analysis.- Selection and Merging Strategies for Multilingual Information Retrieval.- Using Surface-Syntactic Parser and Deviation from Randomness.- Cross-Language Retrieval Using HAIRCUT at CLEF 2004.- Experiments on Statistical Approaches to Compensate for Limited Linguistic Resources.- Application of Variable Length N-Gram Vectors to Monolingual and Bilingual Information Retrieval.- Integrating New Languages in a Multilingual Search System Based on a Deep Linguistic Analysis.- IR-n r2: Using Normalized Passages.- Using COTS Search Engines and Custom Query Strategies at CLEF.- Report on Thomson Legal and Regulatory Experiments at CLEF-2004.- Effective Translation, Tokenization and Combination for Cross-Lingual Retrieval.- Two-Stage Refinement of Transitive Query Translation with English Disambiguation for Cross-Language Information Retrieval: An Experiment at CLEF 2004.- Dictionary-Based Amharic - English Information Retrieval.- Dynamic Lexica for Query Translation.- SINAI at CLEF 2004: Using Machine Translation Resources with a Mixed 2-Step RSV Merging Algorithm.- Mono- and Crosslingual Retrieval Experiments at the University of Hildesheim.- University of Chicago at CLEF2004: Cross-Language Text and Spoken Document Retrieval.- UB at CLEF2004: Cross Language Information Retrieval Using Statistical Language Models.- MIRACLE's Hybrid Approach to Bilingual and Monolingual Information Retrieval.- Searching a Russian Document Collection Using English, Chinese and Japanese Queries.- Dublin City University at CLEF 2004: Experiments in Monolingual, Bilingual and Multilingual Retrieval.- Finnish, Portuguese and Russian Retrieval with Hummingbird SearchServerTM at CLEF 2004.- Data Fusion for Effective European Monolingual Information Retrieval.- The XLDB Group at CLEF 2004.- The University of Glasgow at CLEF 2004: French Monolingual Information Retrieval with Terrier.- II. Domain-Specific Document Retrieval.- The Domain-Specific Track in CLEF 2004: Overview of the Results and Remarks on the Assessment Process.- University of Hagen at CLEF 2004: Indexing and Translating Concepts for the GIRT Task.- IRIT at CLEF 2004: The English GIRT Task.- Ricoh at CLEF 2004.- GIRT and the Use of Subject Metadata for Retrieval.- III. Interactive Cross-Language Information Retrieval.- iCLEF 2004 Track Overview: Pilot Experiments in Interactive Cross-Language Question Answering.- Interactive Cross-Language Question Answering: Searching Passages Versus Searching Documents.- Improving Interaction with the User in Cross-Language Question Answering Through Relevant Domains and Syntactic Semantic Patterns.- Cooperation, Bookmarking, and Thesaurus in Interactive Bilingual Question Answering.- Summarization Design for Interactive Cross-Language Question Answering.- Interactive and Bilingual Question Answering Using Term Suggestion and Passage Retrieval.- IV. Multiple Language Question Answering.- Overview of the CLEF 2004 Multilingual Question Answering Track.- A Question Answering System for French.- Cross-Language French-English Question Answering Using the DLT System at CLEF 2004.- Experiments on Robust NL Question Interpretation and Multi-layered Document Annotation for a Cross-Language Question/Answering System.- Making Stone Soup: Evaluating a Recall-Oriented Multi-stream Question Answering System for Dutch.- The DIOGENE Question Answering System at CLEF-2004.- Cross-Lingual Question Answering Using Off-the-Shelf Machine Translation.- Bulgarian-English Question Answering: Adaptation of Language Resources.- Answering French Questions in English by Exploiting Results from Several Sources of Information.- Finnish as Source Language in Bilingual Question Answering.- miraQA: Experiments with Learning Answer Context Patterns from the Web.- Question Answering for Spanish Supported by Lexical Context Annotation.- Question Answering Using Sentence Parsing and Semantic Network Matching.- First Evaluation of Esfinge - A Question Answering System for Portuguese.- University of Evora in QA@CLEF-2004.- COLE Experiments at QA@CLEF 2004 Spanish Monolingual Track.- Does English Help Question Answering in Spanish?.- The TALP-QA System for Spanish at CLEF 2004: Structural and Hierarchical Relaxing of Semantic Constraints.- ILC-UniPI Italian QA.- Question Answering Pilot Task at CLEF 2004.- Evaluation of Complex Temporal Questions in CLEF-QA.- V. Cross-Language Retrieval in Image Collections.- The CLEF 2004 Cross-Language Image Retrieval Track.- Caption and Query Translation for Cross-Language Image Retrieval.- Pattern-Based Image Retrieval with Constraints and Preferences on ImageCLEF 2004.- How to Visually Retrieve Images from the St. Andrews Collection Using GIFT.- UNED at ImageCLEF 2004: Detecting Named Entities and Noun Phrases for Automatic Query Expansion and Structuring.- Dublin City University at CLEF 2004: Experiments with the ImageCLEF St. Andrew's Collection.- From Text to Image: Generating Visual Query for Image Retrieval.- Toward Cross-Language and Cross-Media Image Retrieval.- FIRE - Flexible Image Retrieval Engine: ImageCLEF 2004 Evaluation.- MIRACLE Approach to ImageCLEF 2004: Merging Textual and Content-Based Image Retrieval.- Cross-Media Feedback Strategies: Merging Text and Image Information to Improve Image Retrieval.- ImageCLEF 2004: Combining Image and Multi-lingual Search for Medical Image Retrieval.- Multi-modal Information Retrieval Using FINT.- Medical Image Retrieval Using Texture, Locality and Colour.- SMIRE: Similar Medical Image Retrieval Engine.- A Probabilistic Approach to Medical Image Retrieval.- UB at CLEF2004 Cross Language Medical Image Retrieval.- Content-Based Queries on the CasImage Database Within the IRMA Framework.- Comparison and Combination of Textual and Visual Features for Interactive Cross-Language Image Retrieval.- MSU at ImageCLEF: Cross Language and Interactive Image Retrieval.- VI. Cross-Language Spoken Document Retrieval.- CLEF 2004 Cross-Language Spoken Document Retrieval Track.- VII. Issues in CLIR and in Evaluation.- The Key to the First CLEF with Portuguese: Topics, Questions and Answers in CHAVE.- How Do Named Entities Contribute to Retrieval Effectiveness?.

Multilingual information access for text, speech and images : 5th workshop of the cross-language evaluation forum, CLEF 2004, Bath, UK, September 15-17, 2004, revised selected papers

Detection of events is an essential task in surveillance applications. This task requires finding a general event representation method and developing efficient recognition algorithms dealing with this representation. In this paper, we describe an interactive system for querying surveillance video about events. The queries may not be known in advance and have to be composed from primitive events and previously defined queries. We propose using Petri nets as both representation and recognition methods. The Petri net representation for users' queries is derived automatically from simpler event nets. Recognition is then performed by tokens moving through the Petri nets.

Representation and Recognition of Events in Surveillance Video Using Petri Nets

Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.

http://ijece.iaescore.com/index.php/IJECE/article/download/5889/5549

An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding

The segmentation process represents a first step necessary for any automatic method of extracting information from an image. In the case of X-ray images, through segmentation we can di ...

/pdf/segmentation-of-bone-structure-in-x-ray-images-using-4erv803ce1.pdf

Segmentation of Bone Structure in X-ray Images using Convolutional Neural Network

In the last few years, the Internet of Things (IoT) is gaining more and more attention both in the academic and in the industrial worlds. IoT is a concept describing a vision in which everyday objects will be connected to the Internet, will be identified, and will, possibly, communicate with other devices. These objects are typically referred as “smart objects”, which can be defined as real artifacts augmented with computing, communication, sensing/actuation and storing functionalities. Their importance resides in the capabilities they have to make physical environments “smart” so as to provide novel cyberphysical services to people. In the last years, several middlewares for SOs were proposed. Middlewares, widely used in conventional distributed systems, are fundamental tools for the design and implementation of smart objects as well as of smart environment applications. They provide general and specific abstractions (e.g. object computation model, inter-object communication, sensory/actuation interfaces, discovery service, knowledge management) through which smart objects and their related applications can be easily built up. In this chapter, we present an overview of middlewares for smart objects and smart environments and compare them according to the most important general and specific requirements that have been identified in the literature so far. Moreover, such middlewares are also compared according to a feature-oriented framework to better highlight their distinctive properties. The comparison therefore provides a clear picture about the suitability of such middlewares to support the development of SO-based IoT systems. Finally, the chapter will briefly discuss on-going challenges in this research area.

Middlewares for Smart Objects and Smart Environments: Overview and Comparison

This paper presents an original low-level system for color image segmentation considering the Hue-Saturation-Value (HSV) color space. Many difficulties of color image segmentation may be resolved using the correct color space in order to increase the effectiveness of color components to discriminate color data. The technique proposed in the article uses new data structures that lead to simpler and more efficient segmentation algorithms. We introduce a flexible hexagonal network structure on the pixels image and we extract for each segmented region the syntactic features that can be used in the shape recognition process. Our technique has a time complexity lower than the methods studied from specialized literature and the experimental results on Berkeley Segmentation Dataset color image database show that the performance of method is robust.

A New Method for Segmentation of Images Represented in a HSV Color Space

This paper presents a new method for image representation based on object-oriented graph structure used for formalizing the process of image retrieval based on content considering the information extracted from the segmentation process and the semantic interpretation of this information. We used an image domain ontology for interpreting the image contents and constructed an Object Oriented Graphs (OOG) for describing each image. The instances of the ontology together with the OOG’s corresponding to the low level features extracted from the images after segmentation image are stored in an object-oriented database. The object-oriented native query system is used for the retrieval of the images from the database. Our technique, which combines the image low-level descriptors with image domain ontologies, has a good time complexity and the experiments showed that the retrieval can be conducted with good results.

An Object Oriented Graph Approach for Image Representation and Query Based on Content

The paper presents a framework that implements our original algorithm of automatic mapping a MySQL relational database to a MongoDB NoSQL database. The algorithm uses the metadata stored in the MySQL system tables. It takes into consideration the concepts from Entity-Relationship (ER) model: entity type represented by a relation in the Relational Model (RM), 1∶1 and 1∶M relationship type represented with Foreign Keys (FK) in the RM and N∶M relationship type represented in RM with a join table that contains the Primary Keys (PK) from the original tables, each representing a FK and two 1∶M relationships between the original tables and the join table. The initial results of our algorithm that was tested on small size databases (10–15 tables with many relationships and 100 records/ table) are presented in this paper.

/pdf/automatic-mapping-of-mysql-databases-to-nosql-mongodb-1h3hjr81uc.pdf

Automatic mapping of MySQL databases to NoSQL MongoDB

The aim of this research survey note is to provide a comprehensive overview of Smart Home Environments with a focus on their architectures and application areas, as well as utilized technologies, infrastructures, and standards. The main source of information was provided by past and existing research and development projects carried out in this area, while the main result of our survey is a classification of Smart Home Environments, as revealed by this projects’ survey.

/pdf/an-overview-of-smart-home-environments-architectures-271dlrvem4.pdf

Marius Brezovan

Papers

A New Method for Segmentation of Images Represented in a HSV Color Space

An Object Oriented Graph Approach for Image Representation and Query Based on Content

Automatic mapping of MySQL databases to NoSQL MongoDB

An Overview of Smart Home Environments: Architectures, Technologies and Applications.

Automatic image annotation and semantic based image retrieval for medical domain