Emre Sümer

Building-based damage detection due to earthquake using the watershed segmentation of the post-event aerial images

This paper presents an approach for detecting the damaged buildings due to earthquake using the watershed segmentation of the post‐event aerial images. The approach utilizes the relationship between the buildings and their cast shadows. It is based on an idea that if a building is damaged, it will not produce shadows. The cast shadows of the buildings are detected through an immersion‐based watershed segmentation. The boundaries of the buildings are available and stored in a GIS as vector polygons. The vector‐building boundaries are used to match the shadow casting edges of the buildings with their corresponding shadows and to perform assessments on a building‐specific manner. For each building, a final decision on the damage condition is taken, based on the assessments carried out for that building only. The approach was implemented in Golcuk, one of the urban areas most strongly hit by the 1999 Izmit, Turkey earthquake. To implement the approach, a system called the Building‐Based Earthquake Damage Assessment System was developed in MATLAB. Of the 284 buildings processed and analysed, 229 were correctly labelled as damaged and undamaged, providing an overall accuracy of 80.63%.

Building damage detection from post-earthquake aerial imagery using building grey-value and gradient orientation analyses

The collapsed buildings due to 1999 Kocaeli earthquake were detected from post-event panchromatic aerial imagery based on grey-value and the gradient orientation of the buildings. The building boundaries were available and stored in a GIS as vector polygons. The building polygons were utilized to perform the assessments in a building specific manner. The approach was implemented in a selected area of Golcuk, which is one of the urban areas most strongly hit by the earthquake. First, the buildings were selected one-by-one from the integrated vector (building boundaries) and raster (aerial photo) data set. The building damage detection process was then divided into two branches. In the first branch, the detection was performed using the building grey-value information. To do that, a greyvalue threshold (T1) was determined for discriminating the collapsed buildings from the un-collapsed ones. In the second branch, a group of operations including the gradient calculation and the determination of gradient orientation were performed. By utilizing the orientation information, an optimum threshold level (T2) was determined for the standard deviation of the angle distribution of the building pixels. When assessing the condition of a building, the results of the two branches were combined and a final decision was made in an integrated manner. Of the 284 buildings analyzed, 254 were labeled correctly as collapsed or un-collapsed providing an overall accuracy of 89.44%. The results reveal that the collapsed buildings due to the earthquake can be successfully detected from post-event aerial images.

Eliminating rib shadows in chest radiographic images providing diagnostic assistance

A major difficulty with chest radiographic analysis is the invisibility of abnormalities caused by the superimposition of normal anatomical structures, such as ribs, over the main tissue to be examined. Suppressing the ribs with no information loss about the original tissue would therefore be helpful during manual identification or computer-aided detection of nodules on a chest radiographic image. In this study, we introduce a two-step algorithm for eliminating rib shadows in chest radiographic images. The algorithm first delineates the ribs using a novel hybrid self-template approach and then suppresses these delineated ribs using an unsupervised regression model that takes into account the change in proximal thickness (depth) of bone in the vertical axis. The performance of the system is evaluated using a benchmark set of real chest radiographic images. The experimental results determine that proposed method for rib delineation can provide higher accuracy than existing methods. The knowledge of rib delineation can remarkably improve the nodule detection performance of a current computer-aided diagnosis (CAD) system. It is also shown that the rib suppression algorithm can increase the nodule visibility by eliminating rib shadows while mostly preserving the nodule intensity.

Unsupervised Rib Delineation in Chest Radiographs by an Integrative Approach

We address the problem of segmenting ribs in a chest radiography image as an intermediate step for eliminating rib shadows for an effective Computer-Aided Diagnosis System (CAD). To this end, we introduce a complete framework that takes an unprocessed x-ray image and reports the entire rib regions. The system offers a novel strategy to fit a parabola curve to all rib seeds obtained through a log Gabor filtering approach and extend the center curve by a problem-specific region growing technique to delineate the entire rib, which does not necessarily follow a general parabolic model of rib cage. The visual examinations of predicted rib delineations in a common dataset have demonstrated that the system can achieve a reasonably good performance to be used in practice.

Promoting development through a geographic information system-based Lodging Property Query System (LPQS) for Antalya, Turkey

Information technology currently plays an important role in many industries and has enabled the development of different sectors and economies. Geographic information system (GIS) is an information technology that triggers improvements in many countries, and this paper presents a method of using GIS in the retrieval of lodging properties. A Lodging Property Query System (LPQS) is a novel system proposed for use by travel agencies to perform spatial queries. The proposed system was tested on a sample dataset that contains lodging properties selected from five different regions located along the shoreline of Antalya, Turkey. The data layers were prepared with the MapInfo software package, and the spatial queries and graphical user interface were developed with the MapXtreme software development kit. This study aims to contribute to the development of the travel agencies by offering useful information that fits customer expectations and needs by means of spatial context.

EYE MOVEMENT CONTROLLED PERIPHERALS FOR THE HANDICAPPED-PARALYZED PEOPLE AND ALS PATIENTS

Controlling some devices in their daily life for the handicapped-paralyzed people and ALS (Amyotrophic Lateral Sclerosis) patients is an important challenge. In this study, a wearable system, called SmartEyes, is developed. The system is controlled by the eye movements of the user. With the help of this system, two groups of facilities are provided. The first is: communicating with predefined voiced messages, valuable especially for people who are unable to talk, and the second is: controlling some peripherals which are in the range around the user. The novelty of the developed system is that it navigates among the menus by means of the eye movements with the help of synthesized voice messages and without a need to sit across a monitor. In the control part, both the infrared (IR) and radio frequency (RF) wireless technologies were employed. The details of the peripheral control operations, namely: controlling the desk light, rolling curtain, TV, air conditioner and the sickbed, are explained in detail. The test results show that the system works quite satisfactorily in tracing and implementing the commands given by the user’s pupil gaze directions. We found that the overall satisfaction is quite high by yielding a total average survey score of 4.7 out of 5. We believe that the developed system offers a practical and efficient solution for making the lives of handicapped-paralyzed people and ALS patients easier. We carry on improving the skills of our SmartEyes system.

Context-sensitive model learning for lung nodule detection

Nodule detection in chest radiographs is a main component of current Computer Aided Diagnosis (CAD) systems. The problem is usually approached as a supervised classification task of candidate nodule segments. To this end, a discriminative model is learnt from predefined set of features. A key concern with this approach is the fact that some normal tissues are also imaged and these regions can overlap with the lung tissue as to hide the nodules. These overlaps may reduce the discriminative ability of extracted features and increase the number of false positives accordingly. In this study, we offer to learn distinct models for bone and normal tissue regions following to the segmentation of ribs, which are often the major reason for false positives. Thus, the nodule candidates in bone and normal tissue regions can be assessed in context-sensitive way. The experiments on a common benchmark set determine that the proposed approach can significantly rescue the false positives while preserving the sensitivity of detections.

Automatic near-photorealistic 3-D modelling and texture mapping for rectilinear buildings

Abstract Three-dimensional (3-D) representations of urban regions have gained much attention because of recent developments in remote sensing and computer graphics technologies. In particular, textured 3-D building reconstruction for a variety of applications has been a popular research topic in recent years. In this study, we present the reconstruction of 3-D building models along with texture selection and mapping. Extracted two-dimensional building patches and normalized digital surface model (nDSM) data are used to generate the 3-D models. To build near-photorealistic 3-D models, the acquired geo-referenced facade textures are associated with the corresponding building facades using an automated GPS-assisted approach. On the other hand, the modelling and texture mapping of the roof structures were carried out manually. The study area is composed of eight housing estates (blocks), where a total of 110 buildings were analysed. The whole study area was modelled, with facade textures, in less than 1 min of processor running time with an acceptable level of accuracy. The texture mapping was carried out using MATLAB’s Virtual Reality Toolbox™.

Weighted feature fusion for content-based image retrieval

The feature descriptors such as SIFT (Scale Invariant Feature Transform), SURF (Speeded-up Robust Features) and ORB (Oriented FAST and Rotated BRIEF) are known as the most commonly used solutions for the content-based image retrieval problems. In this paper, a novel approach called ”Weighted Feature Fusion” is proposed as a generic solution instead of applying problem-specific descriptors alone. Experiments were performed on two basic data sets of the Inria in order to improve the precision of retrieval results. It was found that in cases where the descriptors were used alone the proposed approach yielded 10-30% more accurate results than the ORB alone. Besides, it yielded 9-22% and 12-29% less False Positives compared to the SIFT alone and SURF alone, respectively.

Performance analysis of spatial and frequency domain filtering in high resolution images

High resolution imagery have become more popular day by day in many areas such as satellite imagery, aerial photography and in electron microscopy scanners. Processing of these images in various applications can be complex and time consuming due to the number of pixels to be processed. In this paper, the runtime performances of filtering methods are evaluated in the spatial and frequency domain. Detailed performance analysis are carried out with experimental results. An aerial photo was used as the high resolution data in the present study. Experimental results show that spatial domain filtering takes less time than frequency domain filtering for high resolution images.