Browsing by Subject "Automatic detection"
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Item 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.Item Open Access Automatic detection of salient objects and spatial relations in videos for a video database system(Elsevier BV, 2008-10) Sevilmiş, T.; Baştan M.; Güdükbay, Uğur; Ulusoy, ÖzgürMultimedia databases have gained popularity due to rapidly growing quantities of multimedia data and the need to perform efficient indexing, retrieval and analysis of this data. One downside of multimedia databases is the necessity to process the data for feature extraction and labeling prior to storage and querying. Huge amount of data makes it impossible to complete this task manually. We propose a tool for the automatic detection and tracking of salient objects, and derivation of spatio-temporal relations between them in video. Our system aims to reduce the work for manual selection and labeling of objects significantly by detecting and tracking the salient objects, and hence, requiring to enter the label for each object only once within each shot instead of specifying the labels for each object in every frame they appear. This is also required as a first step in a fully-automatic video database management system in which the labeling should also be done automatically. The proposed framework covers a scalable architecture for video processing and stages of shot boundary detection, salient object detection and tracking, and knowledge-base construction for effective spatio-temporal object querying. © 2008 Elsevier B.V. All rights reserved.Item Open Access Camera tamper detection using wavelet analysis for video surveillance(IEEE, 2007-09) Aksay, A.; Temizel, A.; Çetin, A. EnisIt is generally accepted that video surveillance system operators lose their concentration after a short period of time and may miss important events taking place. In addition, many surveillance systems are frequently left unattended. Because of these reasons, automated analysis of the live video feed and automatic detection of suspicious activity have recently gained importance. To prevent capture of their images, criminals resort to several techniques such as deliberately obscuring the camera view, covering the lens with a foreign object, spraying or defocusing the camera lens. In this paper, we propose some computationally efficient wavelet domain methods for rapid camera tamper detection and identify some real-life problems and propose solutions to these. © 2007 IEEE.Item Open Access Tarım alanlarında doğrusal odunsu bitki gruplarının otomatik sezimi(IEEE, 2009-04) Akçay, H. Gökhan; Aksoy, SelimTarım alanlarının otomatik haritalanması ve izlenmesi önemli bir araştırma konusudur. Bu bildiride, çok yüksek çözünürlükteki uydu görüntülerinde doğrusal şeritler halindeki odunsu bitki gruplarının otomatik olarak sezilmesi için bir yöntem sunulmaktadır. Yötem, öznitelik çıkarma ve karar verme adımlarını sıradüzensel bir şekilde uygulayarak spektral, doku ve nesne şekil bilgisini bir arada kullanmaktadır. Farklı özellikte alanlardan elde edilen Quickbird görüntüleri üzerinde yapılan deneyler tatmin edici başarım göstermektedir. Automatic mapping and monitoring of agricultural landscapes is an important research problem. In this paper, we present a method for automatic mapping of linear strips of woody vegetation in very high-resolution imagery. The method combines spectral, textural and object shape information using hierarchical feature extraction and decision making steps. Experiments on Quickbird imagery from different sites show promising detection performance. ©2009 IEEE.