Browsing by Author "Aksoy, S."

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Open Access
2010 IAPR workshop on pattern recognition in remote sensing, PRRS 2010: preface
(2010) Aksoy, S.; Younan, N. H.; Forstner, W.
Open Access
Automatic detection and segmentation of orchards using very high resolution imagery
(Institute of Electrical and Electronics Engineers, 2012-08) Aksoy, S.; Yalniz, I. Z.; Tasdemir, K.
Spectral information alone is often not sufficient to distinguish certain terrain classes such as permanent crops like orchards, vineyards, and olive groves from other types of vegetation. However, instances of these classes possess distinctive spatial structures that can be observable in detail in very high spatial resolution images. This paper proposes a novel unsupervised algorithm for the detection and segmentation of orchards. The detection step uses a texture model that is based on the idea that textures are made up of primitives (trees) appearing in a near-regular repetitive arrangement (planting patterns). The algorithm starts with the enhancement of potential tree locations by using multi-granularity isotropic filters. Then, the regularity of the planting patterns is quantified using projection profiles of the filter responses at multiple orientations. The result is a regularity score at each pixel for each granularity and orientation. Finally, the segmentation step iteratively merges neighboring pixels and regions belonging to similar planting patterns according to the similarities of their regularity scores and obtains the boundaries of individual orchards along with estimates of their granularities and orientations. Extensive experiments using Ikonos and QuickBird imagery as well as images taken from Google Earth show that the proposed algorithm provides good localization of the target objects even when no sharp boundaries exist in the image data. © 2012 IEEE.
Open Access
Automatic detection of compound structures by joint selection of region groups from a hierarchical segmentation
(Institute of Electrical and Electronics Engineers, 2016) Akçay, H. G.; Aksoy, S.
A challenging problem in remote sensing image analysis is the detection of heterogeneous compound structures such as different types of residential, industrial, and agricultural areas that are composed of spatial arrangements of simple primitive objects such as buildings and trees. We describe a generic method for the modeling and detection of compound structures that involve arrangements of an unknown number of primitives in large scenes. The modeling process starts with a single example structure, considers the primitive objects as random variables, builds a contextual model of their arrangements using a Markov random field, and learns the parameters of this model via sampling from the corresponding maximum entropy distribution. The detection task is formulated as the selection of multiple subsets of candidate regions from a hierarchical segmentation where each set of selected regions constitutes an instance of the example compound structure. The combinatorial selection problem is solved by the joint sampling of groups of regions by maximizing the likelihood of their individual appearances and relative spatial arrangements. Experiments using very high spatial resolution images show that the proposed method can effectively localize an unknown number of instances of different compound structures that cannot be detected by using spectral and shape features alone.
Open Access
Automatic detection of geospatial objects using multiple hierarchical segmentations
(Institute of Electrical and Electronics Engineers, 2008-07) Akçay, H. G.; Aksoy, S.
The object-based analysis of remotely sensed imagery provides valuable spatial and structural information that is complementary to pixel-based spectral information in classification. In this paper, we present novel methods for automatic object detection in high-resolution images by combining spectral information with structural information exploited by using image segmentation. The proposed segmentation algorithm uses morphological operations applied to individual spectral bands using structuring elements in increasing sizes. These operations produce a set of connected components forming a hierarchy of segments for each band. A generic algorithm is designed to select meaningful segments that maximize a measure consisting of spectral homogeneity and neighborhood connectivity. Given the observation that different structures appear more clearly at different scales in different spectral bands, we describe a new algorithm for unsupervised grouping of candidate segments belonging to multiple hierarchical segmentations to find coherent sets of segments that correspond to actual objects. The segments are modeled by using their spectral and textural content, and the grouping problem is solved by using the probabilistic latent semantic analysis algorithm that builds object models by learning the object-conditional probability distributions. The automatic labeling of a segment is done by computing the similarity of its feature distribution to the distribution of the learned object models using the Kullback-Leibler divergence. The performances of the unsupervised segmentation and object detection algorithms are evaluated qualitatively and quantitatively using three different data sets with comparative experiments, and the results show that the proposed methods are able to automatically detect, group, and label segments belonging to the same object classes. © 2008 IEEE.
Open Access
Automatic mapping of linear woody vegetation features in agricultural landscapes using very high resolution imagery
(Institute of Electrical and Electronics Engineers, 2010) Aksoy, S.; Akçay H. G.; Wassenaar, T.
Automatic mapping and monitoring of agricultural landscapes using remotely sensed imagery has been an important research problem. This paper describes our work on developing automatic methods for the detection of target landscape features in very high spatial resolution images. The target objects of interest consist of linear strips of woody vegetation that include hedgerows and riparian vegetation that are important elements of the landscape ecology and biodiversity. The proposed framework exploits the spectral, textural, and shape properties of objects using hierarchical feature extraction and decision-making steps. First, a multifeature and multiscale strategy is used to be able to cover different characteristics of these objects in a wide range of landscapes. Discriminant functions trained on combinations of spectral and textural features are used to select the pixels that may belong to candidate objects. Then, a shape analysis step employs morphological top-hat transforms to locate the woody vegetation areas that fall within the width limits of an acceptable object, and a skeletonization and iterative least-squares fitting procedure quantifies the linearity of the objects using the uniformity of the estimated radii along the skeleton points. Extensive experiments using QuickBird imagery from three European Union member states show that the proposed algorithms provide good localization of the target objects in a wide range of landscapes with very different characteristics. © 2009 IEEE.
Open Access
COST292 experimental framework for TRECVID 2006
(National Institute of Standards and Technology, 2006) Ćalić J.; Krämer P.; Naci, U.; Vrochidis, S.; Aksoy, S.; Zhangk Q.; Benois-Pineau J.; Saracoglu, A.; Doulaverakis, C.; Jarina, R.; Campbell, N.; Mezaris V.; Kompatsiaris I.; Spyrou, E.; Koumoulos G.; Avrithis, Y.; Dalkilic, A.; Alatan, A.; Hanjalic, A.; Izquierdo, E.
In this paper we give an overview of the four TRECVID tasks submitted by COST292, European network of institutions in the area of semantic multimodal analysis and retrieval of digital video media. Initially, we present shot boundary evaluation method based on results merged using a confidence measure. The two SB detectors user here are presented, one of the Technical University of Delft and one of the LaBRI, University of Bordeaux 1, followed by the description of the merging algorithm. The high-level feature extraction task comprises three separate systems. The first system, developed by the National Technical University of Athens (NTUA) utilises a set of MPEG-7 low-level descriptors and Latent Semantic Analysis to detect the features. The second system, developed by Bilkent University, uses a Bayesian classifier trained with a "bag of subregions" for each keyframe. The third system by the Middle East Technical University (METU) exploits textual information in the video using character recognition methodology. The system submitted to the search task is an interactive retrieval application developed by Queen Mary, University of London, University of Zilina and ITI from Thessaloniki, combining basic retrieval functionalities in various modalities (i.e. visual, audio, textual) with a user interface supporting the submission of queries using any combination of the available retrieval tools and the accumulation of relevant retrieval results over all queries submitted by a single user during a specified time interval. Finally, the rushes task submission comprises a video summarisation and browsing system specifically designed to intuitively and efficiently presents rushes material in video production environment. This system is a result of joint work of University of Bristol, Technical University of Delft and LaBRI, University of Bordeaux 1.
Open Access
Detection of compound structures using a gaussian mixture model with spectral and spatial constraints
(Institute of Electrical and Electronics Engineers Inc., 2014) Arı, C.; Aksoy, S.
Increasing spectral and spatial resolution of new-generation remotely sensed images necessitate the joint use of both types of information for detection and classification tasks. This paper describes a new approach for detecting heterogeneous compound structures such as different types of residential, agricultural, commercial, and industrial areas that are comprised of spatial arrangements of primitive objects such as buildings, roads, and trees. The proposed approach uses Gaussian mixture models (GMMs), in which the individual Gaussian components model the spectral and shape characteristics of the individual primitives and an associated layout model is used to model their spatial arrangements. We propose a novel expectation-maximization (EM) algorithm that solves the detection problem using constrained optimization. The input is an example structure of interest that is used to estimate a reference GMM and construct spectral and spatial constraints. Then, the EM algorithm fits a new GMM to the target image data so that the pixels with high likelihoods of being similar to the Gaussian object models while satisfying the spatial layout constraints are identified without any requirement for region segmentation. Experiments using WorldView-2 images show that the proposed method can detect high-level structures that cannot be modeled using traditional techniques. © 1980-2012 IEEE.
Open Access
Foreword to the special issue on pattern recognition in remote sensing
(Institute of Electrical and Electronics Engineers, 2012) Younan, N. H.; Aksoy, S.; King, R. L.
The nine papers in this special issue focus on covering different aspects of remote sensing image analysis.
Open Access
Guest editorial: foreword to the special issue on pattern recognition in remote sensing
(2007) Clausi, D. A.; Aksoy, S.; Tilton, J. C.
Open Access
Image mining using directional spatial constraints
(Institute of Electrical and Electronics Engineers, 2010-01) Aksoy, S.; Cinbiş, R. G.
Spatial information plays a fundamental role in building high-level content models for supporting analysts' interpretations and automating geospatial intelligence. We describe a framework for modeling directional spatial relationships among objects and using this information for contextual classification and retrieval. The proposed model first identifies image areas that have a high degree of satisfaction of a spatial relation with respect to several reference objects. Then, this information is incorporated into the Bayesian decision rule as spatial priors for contextual classification. The model also supports dynamic queries by using directional relationships as spatial constraints to enable object detection based on the properties of individual objects as well as their spatial relationships to other objects. Comparative experiments using high-resolution satellite imagery illustrate the flexibility and effectiveness of the proposed framework in image mining with significant improvements in both classification and retrieval performance.
Open Access
Impairment of vestibulo-collic reflex and linear vestibulo-ocular reflex in pediatric-onset multiple sclerosis patients
(Elsevier Ireland Ltd., 2021-08) Ertuğrul, G.; Aksoy, S.; Konuşkan, B.; Eskandarian, L.; Oğuz, Kader Karlı; Anlar, B.
Objectives, This study aimed to examine the vestibulo-collic reflex (VCR) and linear vestibulo-ocular reflex (lVOR) and their correlation with brain lesions in pediatric-onset multiple sclerosis (POMS). Methods, The study group consisted of 17 patients (34 ears) with POMS (mean age 18.73 ± 2.02, mean age at disease onset 14.64 ± 1.36 years), and the control group included 11 age-matched healthy subjects (22 ears). Ocular and cervical Vestibular Evoked Myogenic Potentials (oVEMP and cVEMP) were performed to assess IVOR and VCR pathways. Magnetic Resonance Imaging was evaluated in the study group. Results, In the POMS group, 47.05 % of oVEMPs and 17.64 % of the cVEMPs were abnormal, while all VEMPs were normal in the control group. The oVEMP amplitude was associated with infratentorial lesion volume (r = −0.459, p = 0.01) and total lesion volume of the brainstem and cerebellum (r = −0.450, p = 0.01). The cVEMP asymmetry ratio was correlated with the deep white matter lesion volume (r = 0.683, p < 0.001). The MVEMP scores were found to correlate only with lesion volumes in the cerebellum (r = 0.488, p = 0.04) and infratentorial region (r = 0.573, p = 0.01). Conclusions, Ocular and cervical VEMP abnormalities confirm that lVOR and VCR pathways may be affected in early POMS. Significance, Routine use of the VEMP test, especially the oVEMP test is recommended as a useful tool in the follow-up of POMS patients.
Open Access
Land cover classification with multi-sensor fusion of partly missing data
(American Society for Photogrammetry and Remote Sensing, 2009-05) Aksoy, S.; Koperski, K.; Tusk, C.; Marchisio, G.
We describe a system that uses decision tree-based tools for seamless acquisition of knowledge for classification of remotely sensed imagery. We concentrate on three important problems in this process: information fusion, model understandability, and handling of missing data. Importance of multi-sensor information fusion and the use of decision tree classifiers for such problems have been well-studied in the literature. However, these studies have been limited to the cases where all data sources have a full coverage for the scene under consideration. Our contribution in this paper is to show how decision tree classifiers can be learned with alternative (surrogate) decision nodes and result in models that are capable of dealing with missing data during both training and classification to handle cases where one or more measurements do not exist for some locations. We present detailed performance evaluation regarding the effectiveness of these classifiers for information fusion and feature selection, and study three different methods for handling missing data in comparative experiments. The results show that surrogate decisions incorporated into decision tree classifiers provide powerful models for fusing information from different data layers while being robust to missing data. © 2009 American Society for Photogrammetry and Remote Sensing.
Open Access
Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics): preface
(2010) Ünay, D.; Çataltepe, Z.; Aksoy, S.
Open Access
Localization of diagnostically relevant regions of interest in whole slide images: a comparative study
(Springer New York LLC, 2016-08) Mercan, E.; Aksoy, S.; Shapiro, L. G.; Weaver, D. L.; Brunyé, T. T.; Elmore, J. G.
Whole slide digital imaging technology enables researchers to study pathologists’ interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists’ actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-of-words model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors. © 2016, Society for Imaging Informatics in Medicine.
Open Access
Maximum likelihood estimation of Gaussian mixture models using stochastic search
(Elsevier BV, 2012) Ar, C.; Aksoy, S.; Arıkan, Orhan
Gaussian mixture models (GMM), commonly used in pattern recognition and machine learning, provide a flexible probabilistic model for the data. The conventional expectationmaximization (EM) algorithm for the maximum likelihood estimation of the parameters of GMMs is very sensitive to initialization and easily gets trapped in local maxima. Stochastic search algorithms have been popular alternatives for global optimization but their uses for GMM estimation have been limited to constrained models using identity or diagonal covariance matrices. Our major contributions in this paper are twofold. First, we present a novel parametrization for arbitrary covariance matrices that allow independent updating of individual parameters while retaining validity of the resultant matrices. Second, we propose an effective parameter matching technique to mitigate the issues related with the existence of multiple candidate solutions that are equivalent under permutations of the GMM components. Experiments on synthetic and real data sets show that the proposed framework has a robust performance and achieves significantly higher likelihood values than the EM algorithm. © 2012 Elsevier Ltd. All rights reserved.
Open Access
Modeling urbanization using building patterns
(2007) Doǧrusöz, E.; Aksoy, S.
Automatic extraction of buildings and modeling of their spatial arrangements provide essential information for urban applications. This paper describes our work on modeling urbanization using spatial building patterns. Building detection is done using Bayesian classification of multi-spectral information. The individual buildings are used as textural primitives, and co-occurrence based spatial domain features and Fourier spectrum-based frequency domain features are used to model their repetitiveness and periodicity at particular orientations. These features are used to classify image neighborhoods as organized (regular) and unorganized (irregular). Experiments with high-resolution Ikonos imagery show that the proposed technique can be used for automatic segmentation of urban scenes and extraction of valuable information about urban growth.
Open Access
Patern recognition in remote sensing
(Elsevier BV, 2010-07-15) Aksoy, S.; Younan, N. H.; Bruzzone, L.
Open Access
Performance measures for object detection evaluation
(Elsevier BV, 2010) Özdemir, B.; Aksoy, S.; Eckert, S.; Pesaresi, M.; Ehrlich, D.
We propose a new procedure for quantitative evaluation of object detection algorithms. The procedure consists of a matching stage for finding correspondences between reference and output objects, an accuracy score that is sensitive to object shapes as well as boundary and fragmentation errors, and a ranking step for final ordering of the algorithms using multiple performance indicators. The procedure is illustrated on a building detection task where the resulting rankings are consistent with the visual inspection of the detection maps. © 2009 Elsevier B.V. All rights reserved.
Open Access
Segmentation of cervical cell images
(IEEE, 2010) Kale, A.; Aksoy, S.
The key step of a computer-assisted screening system that aims early diagnosis of cervical cancer is the accurate segmentation of cells. In this paper, we propose a two-phase approach to cell segmentation in Pap smear test images with the challenges of inconsistent staining, poor contrast, and overlapping cells. The first phase consists of segmenting an image by a non-parametric hierarchical segmentation algorithm that uses spectral and shape information as well as the gradient information. The second phase aims to obtain nucleus regions and cytoplasm areas by classifying the segments resulting from the first phase based on their spectral and shape features. Experiments using two data sets show that our method performs well for images containing both a single cell and many overlapping cells. © 2010 IEEE.
Open Access
Sixth International Workshop on spatial and spatiotemporal data mining: preface
(2011) Shekar, S.; Agouris, P.; Vatsavai, R. R.; Stefanidis, A.; Bhaduri, B.; Chandola, V.; Aksoy, S.; Appice, A.; Atzori, M.; Baumann, P.; Bogorny, V.; Boriah, S.; Cheriyadat, A.; Croitoru, A.; Cuzzocrea, A.; Deng, K.; Ding, W.; Durbha, S.; Filippi, A.; Franzese, P.; Gunopulos, D.; Ganguly, A.; Gutierrez, A. G.; Guo, D.; Hoffman, F.; Janeja, V.; Jun, G.; Kao, Shih-Chieh; Keogh, E.; Li, Ki-Joune; Lozano, A.; Manco, G.; May, M.; McGovern, A.; Mignet, L.; Mokbel, M.; Pagh, R.; Panait, L.; Papadias, D.; Ratanamahatana, C. A.; Rafaeta, A.; Schmid, F.; Sorokine, A.