Browsing by Subject "Spatial relationships"
Now showing 1 - 13 of 13
Results Per Page
Sort Options
Item 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.Item Open Access Automatic detection of compound structures by joint selection of region groups from multiple hierarchical segmentations(Bilkent University, 2016-09) Akçay, Hüseyin GökhanA challenging problem in remote sensing image interpretation is the detection of heterogeneous compound structures such as different types of residential, industrial, and agricultural areas that are comprised 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 unknown number of primitives appearing in different primitive object layers in large scenes. The modeling process starts with example structures, 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 reduced to the selection of multiple subsets of candidate regions from multiple hierarchical segmentations corresponding to different primitive object layers where each set of selected regions constitutes an instance of the example compound structures. The combinatorial selection problem is solved by joint sampling of groups of regions by maximizing the likelihood of their individual appearances and relative spatial arrangements under the model learned from the example structures of interest. Moreover, we incorporate linear equality and inequality constraints on the candidate regions to prevent the co-selection of redundant overlapping regions and to enforce a particular spatial layout that must be respected by the selected regions. The constrained selection problem is formulated as a linearly constrained quadratic program that is solved via a variant of the primal-dual algorithm called the Difference of Convex algorithm by rewriting the non-convex program as the difference of two convex programs. Extensive experiments using very high spatial resolution images show that the proposed method can provide good localization of unknown number of instances of different compound structures that cannot be detected by using spectral and shape features alone.Item Open Access Bağlamsal çıkarımla nesne sezimi(IEEE, 2009-04) Kalaycılar, Fırat; Aksoy, SelimBu bildiride, sezim başarımını arttırmada tek tek sezilmiş nesneler arasındaki bağlamsal ilişkilerden yararlanan bir nesne sezim sistemi tanıtılmaktadır. Bu çalışmadaki ilk katkı, iki boyutlu görüntü uzayında yapılan ölçümlerden olasılıksal çıkarım yaparak nesneler arası gerçek dünya ilişkilerinin (çevresinde, yakınında, üzerinde vb.) modellenmesidir. Diğer bir katkı ise, bireysel nesne etiketlerine ve nesne ikilileri arasındaki ilişkilere bağlı olan sahne olasılık fonksiyonunun enbüyütülerek, nesnelerin en son etiketlerinin atanmasıdır. En tutarlı sahne duzenleşimini bulmak için bu enbüyütme problemi, doğrusal eniyileme kullanılarak çözülmüştür. Ofis görüntüleri içeren iki farklı veri kümesinde yapılan deneylerde, gerçek dünya uzamsal ilişkileri bağlamsal bilgi olarak kullanıldığında genel sezim başarımının arttığı gözlemlenmiştir. In this paper, an object detection system that utilizes contextual relationships between individually detected objects to improve the overall detection performance is introduced. The first contribution in this work is the modelling of real world object relationships (beside, on, near etc.) that can be probabilistically inferred using measurements in the 2D image space. The other contribution is the assignment offinol lobe/s to the detected objects by maximizing a scene probability function that is defined jointly using both individual object labels and their pairwise spatial relationships. The most consistent scene configuration is obtained by solving the maximization problem using linear optimization. Experiments on two different office data sets showed that incorporation of the real world spatial relationships as can textual information improved the overall detection performance. ©2009 IEEE.Item Open Access Building detection using directional spatial constraints(IEEE, 2010) Akçay, H. Gökhan; Aksoy, SelimWe propose an algorithm for automatic detection of buildings with complex shapes and roof structures in very high spatial resolution remotely sensed images. First, an initial oversegmentation is obtained. Then, candidate building regions are found using shadow and sun azimuth angle information. Finally, the building regions are selected by clustering the candidate regions using minimum spanning trees. The experiments on Ikonos scenes show that the algorithm is able to detect buildings with complex appearances and shapes. © 2010 IEEE.Item Open Access Detection of compound structures by region group selection from hierarchical segmentations(IEEE, 2016-07) Akçay, H. Gökhan; Aksoy, SelimDetection of compound structures that are comprised of different arrangements of simpler primitive objects has been a challenging problem as commonly used bag-of-words models are limited in capturing spatial information. We have developed a generic method that considers the primitive objects as random variables, builds a contextual model of their arrangements using a Markov random field, and detects new instances of compound structures through automatic selection of subsets of candidate regions from a hierarchical segmentation by maximizing the likelihood of their individual appearances and relative spatial arrangements. In this paper, we extend the model to handle different types of primitive objects that come from multiple hierarchical segmentations. Results are shown for the detection of different types of housing estates in a WorldView-2 image. © 2016 IEEE.Item Open Access A framework for applying the principles of depth perception to information visualization(Association for Computing Machinery, 2013) Zeynep, C. Y.; Bulbul, A.; Capin, T.During the visualization of 3D content, using the depth cues selectively to support the design goals and enabling a user to perceive the spatial relationships between the objects are important concerns. In this novel solution, we automate this process by proposing a framework that determines important depth cues for the input scene and the rendering methods to provide these cues. While determining the importance of the cues, we consider the user's tasks and the scene's spatial layout. The importance of each depth cue is calculated using a fuzzy logic-based decision system. Then, suitable rendering methods that provide the important cues are selected by performing a cost-profit analysis on the rendering costs of the methods and their contribution to depth perception. Possible cue conflicts are considered and handled in the system. We also provide formal experimental studies designed for several visualization tasks. A statistical analysis of the experiments verifies the success of our framework. © 2013 ACM.Item Open Access Image information mining using spatial relationship constraints(Bilkent University, 2012) Karakuş, FatihThere is a huge amount of data which is collected from the Earth observation satellites and they are continuously sending data to Earth receiving stations day by day. Therefore, mining of those data becomes more important for effective processing of collected multi-spectral images. The most popular approaches for this problem use the meta-data of the images such as geographical coordinates etc. However, these approaches do not offer a good solution for determining what those images contain. Some researches make a big step from the meta-data based approaches in this area by moving the focus of the study to content based approaches such as utilizing the region information of the sensed images. In this thesis, we propose a novel, generic and extendable image information mining system that uses spatial relationship constraints. In this system, we use not only the region content, but also relationships of those regions. First, we extract the region information of the images and then extract pairwise relationship information of those regions such as left, right, above, below, near, far and distance etc. This feature extraction process is defined as a generic process which is independent from how the region segmentation is obtained. In addition to these, since new features and new approaches are continuously being developed by the image information mining researchers, extendability feature of the our system plays a big role while we are designing our system. In this thesis, we also propose a novel feature vector structure in which a feature vector consists of several sub-feature vectors. In the proposed feature vector structure, each sub-feature vector can be exclusively selected to be used for search process and they can have different distance metrics to be used in comparisons between the same sub-feature vector of the other feature vector structures. Therefore, the system gives ability to users to choose which information about the region and its pairwise relationship with other regions to be used when they perform a search on the system. The proposed system is illustrated by using region based retrieval scenarios on very high spatial resolution satellite images.Item 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.Item Open Access Konuma bağlı uzamsal ilişkilerin biçimbilimsel modellenmesi(IEEE, 2007-06) Cinbiş, R. Gökberk; Aksoy, SelimUzamsal bilgi, görüntü analizi modellerinde çok önemli bir yer tutmaktadır. Bu bildiride, ikili ve üçlü uzamsal ilişkileri bulmak için, matemaktiksel biçimbilim kullanarak, özelleştirilebilir, gerçekçi ve hızlı yöntemler öneriyoruz. Bu ilişkiler,resmin her noktasında, referans nesneye veya nesnelere göre, istenilen ilişkinin değerini veren bir matris hesaplanarak gösterilmektedir. Modelimiz, bir nesnenin istenilen yönlerden gözükmeyecek kısımlarını da dikkate almayı mümkün kılmakta, ayrıca, nesnelerin uzamsal olarak çok farklı olduğu durumlara da başarılı olmaktadır. Yapay ve gerçek görüntülerde yaptığımız deneyler ise modelimizin diğer yöntemlere olan üstünlüğünü ortaya koymaktadır. Spatial information plays a very important role in image understanding. Fuzzy mathematical morphology provides an effective basis for extracting binary and ternary spatial relationships by creating a fuzzy landscape where the value at each point corresponds to the relationship degree according to its position with respect to the reference object(s). We improve existing morphological approaches in terms of flexibility and efficiency while also obtaining more intuitive results. Our morphological definitions are sensitive to relative visibility of areas based on partial occlusions, and can also cope with the cases where some objects extend significantly differently relative to others. We show the effectiveness of the proposed definitions using synthetic and real images.Item Open Access Mining of remote sensing image archives using spatial relationship histograms(IEEE, 2008-07) Kalaycılar, Fırat; Kale, Aslı; Zamalieva, Daniya; Aksoy, SelimWe describe a new image representation using spatial relationship histograms that extend our earlier work on modeling image content using attributed relational graphs. These histograms are constructed by classifying the regions in an image, computing the topological and distance-based spatial relationships between these regions, and counting the number of times different groups of regions are observed in the image. We also describe a selection algorithm that produces very compact representations by identifying the distinguishing region groups that are frequently found in a particular class of scenes but rarely exist in others. Experiments using Ikonos scenes illustrate the effectiveness of the proposed representation in retrieval of images containing complex types of scenes such as dense and sparse urban areas. © 2008 IEEE.Item Open Access An object recognition framework using contextual interactions among objects(Bilkent University, 2009) Kalaycılar, FıratObject recognition is one of the fundamental tasks in computer vision. The main endeavor in object recognition research is to devise techniques that make computers understand what they see as precise as human beings. The state of the art recognition methods utilize low-level image features (color, texture, etc.), interest points/regions, filter responses, etc. to find and identify objects in the scene. Although these work well for specific object classes, the results are not satisfactory enough to accept these techniques as universal solutions. Thus, the current trend is to make use of the context embedded in the scene. Context defines the rules for object - object and object - scene interactions. A scene configuration generated by some object recognizers can sometimes be inconsistent with the scene context. For example, observing a car in a kitchen is not likely in terms of the kitchen context. In this case, knowledge of kitchen can be used to correct this inconsistent recognition. Motivated by the benefits of contextual information, we introduce an object recognition framework that utilizes contextual interactions between individually detected objects to improve the overall recognition performance. Our first contribution arises in the object detector design. We define three methods for object detection. Two of these methods, shape based and pixel classification based object detection, mainly use the techniques presented in the literature. However, we also describe another method called surface orientation based object detection. The goal of this novel detection technique is to find objects whose shape, color and texture features are not discriminative while their surface orientations (horizontality or verticality) are consistent across different instances. Wall, table top, and road are typical examples for such objects. The second contribution is a probabilistic contextual interaction model for objects based on their spatial relationships. In order to represent the spatial relationships between objects, we propose three features that encode the relative position/location, scale and orientation of a given object pair. Using these features and our object interaction likelihood model, we achieve to encode the semantic, spatial, and pose context of a scene concurrently. Our third main contribution is a contextual agreement maximization framework that assigns final labels to the detected objects by maximizing a scene probability function that is defined jointly using both the individual object labels and their pairwise contextual interactions. The most consistent scene configuration is obtained by solving the maximization problem using linear optimization. We performed experiments on the LabelMe [27] and Bilkent data sets by both utilizing and not utilizing the scene type (indoor or outdoor) information. While the average F2 score increased from 0.09 to 0.20 without the scene type assumption, it increased from 0.17 to 0.25 when the scene type is known on the LabelMe dataset. The results are similar for the experiments performed on the Bilkent data set. F2 score increased from 0.16 to 0.36 when the scene type information is not available and it increased from 0.31 to 0.44 when this additional information is used. It is clear that the incorporation of the contextual interactions improves the overall recognition performance.Item Open Access Relative position-based spatial relationships using mathematical morphology(IEEE, 2007-09-10) Cinbiş, R. Gökberk; Aksoy, SelimSpatial information is a crucial aspect of image understanding for modeling context as well as resolving the uncertainties caused by the ambiguities in low-level features. We describe intuitive, flexible and efficient methods for modeling pairwise directional spatial relationships and the ternary between relation using fuzzy mathematical morphology. First, a fuzzy landscape is constructed where each point is assigned a value that quantifies its relative position according to the reference object(s) and the type of the relationship. Then, the degree of satisfaction of this relation by a target object is computed by integrating the corresponding landscape over the support of the target region. Our models support sensitivity to visibility to handle areas that are partially enclosed by objects and are not visible from image points along the direction of interest. They can also cope with the cases where one object is significantly spatially extended relative to others. Experiments using synthetic and real images show that our models produce more intuitive results than other techniques. ©2007 IEEE.Item Open Access Scene classification using bag-of-regions representations(IEEE, 2007-06) Gökalp, Demir; Aksoy, SelimThis paper describes our work on classification of outdoor scenes. First, images are partitioned into regions using one-class classification and patch-based clustering algorithms where one-class classifiers model the regions with relatively uniform color and texture properties, and clustering of patches aims to detect structures in the remaining regions. Next, the resulting regions are clustered to obtain a codebook of region types, and two models are constructed for scene representation: a "bag of individual regions" representation where each region is regarded separately, and a "bag of region pairs" representation where regions with particular spatial relationships are considered, together. Given these representations, scene classification is done using Bayesian classifiers. We also propose a novel region selection algorithm that identifies region types that are frequently found in a particular class of scenes but rarely exist in other classes, and also consistently occur together in the same class of scenes. Experiments on the LabelMe data set showed that the proposed models significantly out-perform a baseline global feature-based approach. © 2007 IEEE.