Diurnal surface fuel moisture prediction model for Calabrian pine stands in Turkey

We would like to extend our appreciation and thanks to Mugla and Antalya Regional Forest Directorate and its staff. This study was supported by The Scientific and Technological Research Council of Turkey, project no. TOVAG-112O809. We are grateful to two anonymous reviewers for their useful suggestions and comments that greatly improved the manuscript.This study presents a dynamic model for the prediction of diurnal changes in the moisture content of dead surface fuels in normally stocked Calabrian pine stands under varying weather conditions. The model was developed based on several empirical relationships between moisture contents of dead surface fuels and weather variables, and calibrated using field data collected from three Calabrian stands from three different regions of Turkey (Mugla, southwest; Antalya, south; Trabzon, north-east). The model was tested and validated with independent measurements of fuel moisture from two sets of field observations made during dry and rainy periods. Model predictions showed a mean absolute error (MAE) of 1.19% for litter and 0.90% for duff at Mugla, and 3.62% for litter and 14.38% for duff at Antalya. When two rainy periods were excluded from the analysis at Antalya site, the MAE decreased from 14.38% to 4.29% and R-2 increased from 0.25 to 0.83 for duff fuels. Graphical inspection and statistical validation of the model indicated that the diurnal litter and duff moisture dynamics could be predicted reasonably. The model can easily be adapted for other similar fuel types in the Mediterranean region

İçerik tabanlı görüntü gerigetirim ve veri tabanı yönetim sistemi bütünleşmesi: Sayısal mamografiyle örnek bir çalışma

Bu tez kapsamında, içerik tabanlı görüntü gerigetirimi yaklaşımını veri tabanı sistemleri ile bütünleştirmek amacıyla yeni bir yöntem önerdik ve yöntemimizin başarımını ölçmek amacıyla bir mamografi gerigetirim sistemi geliştirdik. İlk olarak, 17'si literatürde mevcut, geri kalanı da meme kitlelerinin sınırını tanımlamak için önerdiğimiz, toplam 26 adet düşük seviyeli öznitelikleri inceledik. Ayrıca, meme kitlelerinin doğru sınırlarını bulmak için meme kitle sınır bölütlemesi adında yeni bir bölütleme algoritması önerdik. Mevcut mamografi veri kümelerini önerdiğimiz yaklaşımı sınamak için inceledik ve mevcut veri kümelerinin yetersiz betimleme seviyeleri sebebiyle yeni bir mamografi veri kümesi geliştirdik. Veri kümesinin geliştirilmesi sırasında, ontoloji tabanlı yeni bir betimleme aracı geliştirdik. Daha sonra, meme kitleleri için en iyi düşük seviyeli öznitelikleri, makine öğrenmesi yöntemlerini ve bölge seçim yöntemini belirlemek için iki farklı veri kümesi üzerinde bir dizi deneyler gerçekleştirdik. Son olarak, bütünleştirme yaklaşımımızı, seçilen düşük seviyeli öznitelikleri kullanarak PostgreSQL veri tabanı yönetim sistemi üzerinde gerçekleştirdik ve gerigetirim başarımını ölçtük. Deneylerden elde ettiğimiz sonuçlar, yaklaşımımız içerik tabanlı görüntü gerigetirimi ve veri tabanı yönetim sistemlerini birleştirilmesini başarı ile kullanıldığını gösterdi ve örnek bir çalışma olarak mamografi gerigetirim sistemi üzerine başarıyla uygulandı. In this thesis, we proposed a new integration method for content-based image retrieval and database systems, and developed a case study on mammography retrieval to measure performance of our approach. Initially, we investigated 26 low level features in total, 17 of them exist in the literature and rest of them is our proposal for mass contour description. Additionally, we proposed a new breast mass segmentation method called Breast Mass Contour Segmentation to determine accurate breast mass contours. Next, we reviewed available mammogram datasets to evaluate our proposal, and we also developed a new mammogram dataset due to insufficient annotation level of available datasets. During development of this dataset, we developed a new ontology based annotation tool. Then, we performed series of experimentations on two different mammogram datasets to identify the best low level features, machine learning and region selection methods for breast masses. Finally, we implemented our integration approach on PostgreSQL database management system using selected low-level features and evaluate the retrieval performance. Experimentation results showed that our integration approach of content-based image retrieval and Database Management Systems worked well and successfully applied to mammography mass retrieval system as case study

A Boolean Information Retrieval System For Mammography Reports

In this work it's presented a boolean information retrieval system that enhances an existing Turkish mammography report archive by enabling a retrospective search capability over it. With the help of this system, archived reports could be retrieved by using boolean queries, instead of being accessed only by patient id or study date. Moreover, this system can be used as a generic boolean information retrieval system for texts in Turkish. This article presents both the details of developed system and experiences in development perio

DEU at ImageCLEFmed 2009: Evaluating re-ranking and integrated retrieval model

This paper presents DEU team participation in imageCLEF2009med. Main goal of our participation is to evaluate two different approaches: First, a new re-ranking method which aims to model information system behaviour depending on several aspects of both documents and query. Secondly, we compare a new retrieval approach which integrates textual and visual contents into one single model. Initially we extract textual features of all documents using standard vector space model and assume as a baseline. Then this baseline is combined with re-ranking and integrated retrieval model. Re- ranking approach is trained using ImageCLEFmed 2008 ground truth data. However, re-ranking approach did not produced satisfactory results. On the other hand, our integrated retrieval model resulted top rank among all submissions in automatic mixed runs

Expansion and Re-ranking Approaches for Multimodal Image Retrieval using Text-based Methods

In this chapter, we present an approach to handle multi-modality in image retrieval using a Vector Space Model (VSM), which is extensively used in text retrieval. We simply extended the model with visual terms aiming to close the semantic gap by helping to map low-level features into high level textual semantic concepts. Moreover, this combination of textual and visual modality into one space also helps to query a textual database with visual content, or a visual database with textual content. Alongside this, in order to improve the performance of text retrieval we propose a novel expansion and re-ranking method, applied both to the documents and the query. When textual annotations of images are acquired automatically, they may contain too much information, and document expansion adds more noise to retrieval results. We propose a re-ranking phase to discard such noisy terms. The approaches introduced in this chapter were evaluated in two sub-tasks of ImageCLEF2009. First, we tested the multi-modality part in ImageCLEFmed and obtained the best rank in mixed retrieval, which includes textual and visual modalities. Secondly, we tested expansion and re-ranking methods in ImageCLE-FWiki and the results were superior to others and obtained the best four positions in text-only retrieval. The results showed that the handling of multi-modality in text retrieval using a VSM is promising, and document expansion and re-ranking plays an important role in text based image retrieval

MPEG-7 based service guide for mobile TV

Copyright 2007 ICST.In this paper, we propose MPEG-7 based Electronic Service Guide (ESG) within Multimedia Broadcast Multicast System (MBMS). Our prototype covers OMA BCAST ESG fragments defined for MBMS and extends content fragment of ESG by MPEG-7. In order to demonstrate the usefulness of our approach, we implement a test application that provides a multimedia query for MBMS services and sessions, and retrieve a tree view of available services and a categorized view according to the genre grouping criteria

Breast mass contour segmentation algorithm in digital mammograms

Many computer aided diagnosis (CAD) systems help radiologist on difficult task of mass detection in a breast mammogram and, besides, they also provide interpretation about detected mass. One of the most crucial information of a mass is its shape and contour, since it provides valuable information about spread ability of a mass. However, accuracy of shape recognition of a mass highly related with the precision of detected mass contours. In this work, we introduce a new segmentation algorithm, breast mass contour segmentation, based on classical seed region growing algorithm to enhance contour of a mass from a given region of interest with ability to adjust threshold value adaptively. The new approach is evaluated over a dataset with 260 masses whose contours are manually annotated by expert radiologists. The performance of the method is evaluated with respect to a set of different evaluation metrics, such as specificity, sensitivity, balanced accuracy, Yassnoff and Hausdorrf error distances. The results obtained from experimentations shows that our method outperforms the other compared methods. All the findings and details of approach are presented in detail. (c) 2012 Elsevier Ireland Ltd. All rights reserved

Etiological Evaluation of Congenital Hypothyroidism Cases

Objective: The aim of this study was to determine, (i) the cause of congenital hypothyroidism (permanent or transient), (ii) the etiological cause of persistent congenital hypothyroidism and (iii) to investigate the role of clinical and laboratory data in predicting persistent and transient congenital hypothyroidism

DEMIR at image CLEF wiki 2011: Evaluating different weighting schemes in information retrieval

This paper present the participation details of DEMIR (Dokuz Eylul University Multimedia Information Retrieval) research team at ImageCLEF wiki2011. This year we investigate on evaluating of different weighting models on text retrieval performance. In the case of low-level feature selection, we extracted different features and examined their performance to choose the proper feature for our experiments. Thereupon to apply late fusion for best gained result of image and textual features. In these experiments we found that choice of proper weighting model may crucially affect the performance of any information retrieval system and also we found that although linear weighted fusion is simplest and frequently used method. The results clearly show that combining text-based and content-based image retrieval with a proper fusion technique improves the performance

DEMIR at image CLEF Med 2011: Evaluation of fusion techniques for multimodal content-based medical image retrieval

This paper present the details of participation of DEMIR (Dokuz Eylul University Multimedia Information Retrieval) research team to the context of our participation to the Image CLEF 2011 Medical Retrieval task. This year, we evaluated fusion and re-ranking method which is based on the best low level feature of images with best text retrieval result. We improved results by examination of different weighting models for retrieved text data and low level features. We tested multi-modality image retrieval in Image CLEF 2011 medical retrieval task and obtained the best seven ranks in mixed retrieval, which includes textual and visual modalities. The results clearly show that proper fusion of different modalities improve the overall retrieval performance