Browsing by Subject "Data sets"

Now showing 1 - 20 of 29

Open Access
3D human pose search using oriented cylinders
(IEEE, 2009-09-10) Pehlivan, Selen; Duygulu, Pınar
In this study, we present a representation based on a new 3D search technique for volumetric human poses which is then used to recognize actions in three dimensional video sequences. We generate a set of cylinder like 3D kernels in various sizes and orientations. These kernels are searched over 3D volumes to find high response regions. The distribution of these responses are then used to represent a 3D pose. We use the proposed representation for (i) pose retrieval using Nearest Neighbor (NN) based classification and Support Vector Machine (SVM) based classification methods, and for (ii) action recognition on a set of actions using Dynamic Time Warping (DTW) and Hidden Markov Model (HMM) based classification methods. Evaluations on IXMAS dataset supports the effectiveness of such a robust pose representation. ©2009 IEEE.
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 rule learning exploiting morphological features for named entity recognition in Turkish
(2011) Tatar, S.; Cicekli I.
Named entity recognition (NER) is one of the basic tasks in automatic extraction of information from natural language texts. In this paper, we describe an automatic rule learning method that exploits different features of the input text to identify the named entities located in the natural language texts. Moreover, we explore the use of morphological features for extracting named entities from Turkish texts. We believe that the developed system can also be used for other agglutinative languages. The paper also provides a comprehensive overview of the field by reviewing the NER research literature. We conducted our experiments on the TurkIE dataset, a corpus of articles collected from different Turkish newspapers. Our method achieved an average F-score of 91.08% on the dataset. The results of the comparative experiments demonstrate that the developed technique is successfully applicable to the task of automatic NER and exploiting morphological features can significantly improve the NER from Turkish, an agglutinative language. © The Author(s) 2011.
Open Access
Bağlamsal çıkarımla nesne sezimi
(IEEE, 2009-04) Kalaycılar, Fırat; Aksoy, Selim
Bu 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.
Open Access
Carcinoma cell line discrimination in microscopic images using unbalanced wavelets
(IEEE, 2012-03) Keskin, Furkan; Suhre, Alexander; Erşahin, Tüli,; Çetin Atalay, Rengül; Çetin, A. Enis
Cancer cell lines are widely used for research purposes in laboratories all over the world. In this paper, we present a novel method for cancer cell line image classification, which is very costly by conventional methods. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a correlation descriptor utilizing the fractional unbalanced wavelet transform coefficients and several morphological attributes as pixel features is computed. Directionally selective textural features are preferred primarily because of their ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines. A Support Vector Machine (SVM) classifier with Radial Basis Function (RBF) kernel is employed for final classification. Over a dataset of 280 images, we achieved an accuracy of 88.2%, which outperforms the classical correlation based methods. © 2012 IEEE.
Open Access
Cost-aware strategies for query result caching in Web search engines
(Association for Computing Machinery, 2011) Ozcan, R.; Altingovde, I. S.; Ulusoy, O.
Search engines and large-scale IR systems need to cache query results for efficiency and scalability purposes. Static and dynamic caching techniques (as well as their combinations) are employed to effectively cache query results. In this study, we propose cost-aware strategies for static and dynamic caching setups. Our research is motivated by two key observations: (i) query processing costs may significantly vary among different queries, and (ii) the processing cost of a query is not proportional to its popularity (i.e., frequency in the previous logs). The first observation implies that cache misses have different, that is, nonuniform, costs in this context. The latter observation implies that typical caching policies, solely based on query popularity, can not always minimize the total cost. Therefore, we propose to explicitly incorporate the query costs into the caching policies. Simulation results using two large Web crawl datasets and a real query log reveal that the proposed approach improves overall system performance in terms of the average query execution time. © 2011 ACM.
Open Access
El yazısı belgelerde kelime tabanlı arama
(IEEE, 2008-04) Can, Ethem F.; Duygulu, Pınar
Bu çalışmada el yazısı belgelerde arama yapabilmek için yeni yöntemler önerilmiştir. Bu çalışmadaki en temel varsayım ve yola çıkış noktası; her bir kelimenin resim gibi ele alınabileceği ve dolayısıyla resim arama teknikleri ile sorgulama yapılabileceğidir. Özel olarak resim üzerindeki kenar noktalarının eğimlerinin yönlerinin dağılımı ve korelasyon katsayısı tabanlı iki yöntem önerilmiş, ayrıca bu iki yöntemin nasıl birleştirilebileceği anlatılmıştır. Deneyler George Washington'un el yazmaları veri kümesi üzerinde yapılmıştır. We present new methods to retrieve words in historical handwritten documents. With the assumption that the words can be seen as images, we used the word spotting idea and search for the words in the documents using image retrieval techniques. Specifically, we proposed two methods, one based on the histogram of gradient orientations and one based on the correlation coefficient. We also proposed a new method by combining these two methods. In the experiments the data set consisting of George Washington's handwritings is used. ©2008 IEEE.
Open Access
Extraction of sparse spatial filters using Oscillating Search
(IEEE, 2012) Onaran, İbrahim; İnce, N. Fırat; Abosch, A.; Çetin, A. Enis
Common Spatial Pattern algorithm (CSP) is widely used in Brain Machine Interface (BMI) technology to extract features from dense electrode recordings by using their weighted linear combination. However, the CSP algorithm, is sensitive to variations in channel placement and can easily overfit to the data when the number of training trials is insufficient. Construction of sparse spatial projections where a small subset of channels is used in feature extraction, can increase the stability and generalization capability of the CSP method. The existing 0 norm based sub-optimal greedy channel reduction methods are either too complex such as Backward Elimination (BE) which provided best classification accuracies or have lower accuracy rates such as Recursive Weight Elimination (RWE) and Forward Selection (FS) with reduced complexity. In this paper, we apply the Oscillating Search (OS) method which fuses all these greedy search techniques to sparsify the CSP filters. We applied this new technique on EEG dataset IVa of BCI competition III. Our results indicate that the OS method provides the lowest classification error rates with low cardinality levels where the complexity of the OS is around 20 times lower than the BE. © 2012 IEEE.
Open Access
Generic text summarization for Turkish
(Oxford University Press, 2010) Kutlu, M.; Cığır, C.; Cicekli, I.
In this paper, we propose a generic text summarization method that generates summaries of Turkish texts by ranking sentences according to their scores. Sentence scores are calculated using their surface-level features, and summaries are created by extracting the highest ranked sentences from the original documents. To extract sentences which form a summary with an extensive coverage of the main content of the text and less redundancy, we use features such as term frequency, key phrase (KP), centrality, title similarity and sentence position. The sentence rank is computed using a score function that uses its feature values and the weights of the features. The best feature weights are learned using machine-learning techniques with the help of human-constructed summaries. Performance evaluation is conducted by comparing summarization outputs with manual summaries of two newly created Turkish data sets. This paper presents one of the first Turkish summarization systems, and its results are promising. We introduce the usage of KP as a surface-level feature in text summarization, and we show the effectiveness of the centrality feature in text summarization. The effectiveness of the features in Turkish text summarization is also analyzed in detail. © The Author 2008. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.
Open Access
Histogram of oriented rectangles: a new pose descriptor for human action recognition
(Elsevier BV, 2009-09-02) İkizler, N.; Duygulu, P.
Most of the approaches to human action recognition tend to form complex models which require lots of parameter estimation and computation time. In this study, we show that, human actions can be simply represented by pose without dealing with the complex representation of dynamics. Based on this idea, we propose a novel pose descriptor which we name as Histogram-of-Oriented-Rectangles (HOR) for representing and recognizing human actions in videos. We represent each human pose in an action sequence by oriented rectangular patches extracted over the human silhouette. We then form spatial oriented histograms to represent the distribution of these rectangular patches. We make use of several matching strategies to carry the information from the spatial domain described by the HOR descriptor to temporal domain. These are (i) nearest neighbor classification, which recognizes the actions by matching the descriptors of each frame, (ii) global histogramming, which extends the idea of Motion Energy Image proposed by Bobick and Davis to rectangular patches, (iii) a classifier-based approach using Support Vector Machines, and (iv) adaptation of Dynamic Time Warping on the temporal representation of the HOR descriptor. For the cases when pose descriptor is not sufficiently strong alone, such as to differentiate actions "jogging" and "running", we also incorporate a simple velocity descriptor as a prior to the pose based classification step. We test our system with different configurations and experiment on two commonly used action datasets: the Weizmann dataset and the KTH dataset. Results show that our method is superior to other methods on Weizmann dataset with a perfect accuracy rate of 100%, and is comparable to the other methods on KTH dataset with a very high success rate close to 90%. These results prove that with a simple and compact representation, we can achieve robust recognition of human actions, compared to complex representations. © 2009 Elsevier B.V. All rights reserved.
Open Access
İçerik tabanlı görüntü erişimi için sahne sınıflandırması
(IEEE, 2008-04) Çavuş, Özge; Aksoy, Selim
Son yıllarda çok geniş veri tabanlarının kullanımıyla birlikte içerik tabanlı görüntü indekslemesi ve erişimi önemli bir araştırma konusu halini almıştır. Bu çalışmada, görüntü indekslemesi için sahne sınıflandırmasını baz alan bir görüntü erişim sistemi tanımlanmıştır. Görüntülerden çıkarılan alt düzey öznitelikler görüntü indekslemesinde doğrudan kullanılmak yerine, bu öznitelikler sahne sınıflandırması için kullanılmış ve görüntüler sınıflandırma sonucunda elde edilen anlamsal sınıf bilgileriyle indekslenmiştir. Sahne sınıflandırması için “kelime kümesi” (bag of words) dokuman analizi yöntemi olarak bilinen tekniğin bir uyarlaması kullanılmıştır. Görüntü erişim sistemini insan algısıyla desteklemek ve anlambilimsel uçurumu en aza indirgemek için erişim senaryosuna tek sınıf sınıflandırıcı bazlı ilgililik geri beslemesi eklenmiştir. Bunun için, ilgili görüntüleri çok iyi modelleyen, ilgili olmayan görüntülerden de bir o kadar uzak duran bir hiperkure oluşturan destek vektör veri tanımlaması kullanılmıştır. Önerilen yöntemler Corel veri kümesinde denenmiş ve başarılı sonuçlar elde edilmiştir. Content-based image indexing and retrieval have become important research problems with the use of large databases in a wide range of areas. In this study, a content-based image retrieval system that is based on scene classification for image indexing is proposed. Instead of using low-level features directly, semantic class information that is obtained as a result of scene classification is used during indexing. The traditional "bag of words" approach is modified for classifying the scenes. In order to minimize the semantic gap, a relevance feedback approach that is based on one-class classification is also integrated. The support vector data description is used for learning during feedback iterations. The experiments using the Corel data set show good results for both classification and retrieval. ©2008 IEEE.
Open Access
Image classification using subgraph histogram representation
(IEEE, 2010) Özdemir, Bahadır; Aksoy, Selim
We describe an image representation that combines the representational power of graphs with the efficiency of the bag-of-words model. For each image in a data set, first, a graph is constructed from local patches of interest regions and their spatial arrangements. Then, each graph is represented with a histogram of subgraphs selected using a frequent subgraph mining algorithm in the whole data. Using the subgraphs as the visual words of the bag-of-words model and transforming of the graphs into a vector space using this model enables statistical classification of images using support vector machines. Experiments using images cut from a large satellite scene show the effectiveness of the proposed representation in classification of complex types of scenes into eight high-level semantic classes. © 2010 IEEE.
Open Access
Kişi ilişkileri kullanılarak fotoğraflardaki yüzlerin isimlendirilmesi
(IEEE, 2009-04) Bulut, Muhammed Fatih; Duygulu, Pınar
Bu çalışmamızda internet üzerinden topladığımız fotoğraflardaki yüzleri isimlendirmeyi amaçladık. İnternet üzerinde bulunan fotoğraflarda bazı kişiler çok sık, bazı kişiler ise diğerlerine göre daha az sıklıkta geçmektedir. Fotoğraflarda çok geçen kişileri isimlendirmek az geçen kişileri isimlendirmeye göre daha kolay bir problem, çünkü bu kişilerle ilgili çok sayıda örnek mevcut. Fakat, fotoğraflarda az sayıda geçen kişileri isimlendirmek problemli bir konu olarak karşımıza çıkmaktadır. Bu çalışmamızda, bu konuya yüksek oranda doru çalışan bir çözüm getirmeyi hedefledik. Bu amaçla, ilk önce fotoğraflarda çok sayıda geçen kişilerin yüzlerini isimlerle eşleştirmeye çalıştık. Bundan sonraki aşamada eğer veri kümelerimizde isim kaldıysa bu ismin fotoğraflarda az sayıda geçen kişilere ait olduğu varsayımıyla, ismi olmayan ve az sayıda geçen kişileri yüz benzerliklerini kullanarak isimlendirmeye çalıştık. Bu amaçla ikili ilişkilerin kolayca elde edilebileceği politikacı fotoğraflarını kullandık. In this study, we are aiming to name faces of people in photographs which are collected from web. In photographs from the web, some people appear more frequently than some other people. In addition to that, since there are so many examples of more frequently appearing people, naming of these people is simpler than naming of other less frequently appearing people. Therefore, naming of less common people on photographs seems to be problematic. In this study, we are aiming to propose a method which increases the correctness of naming infrequently appearing people. By this purpose, we first try to match the names of most frequently appearing people with their faces. After that, if there are any names remaining, then we assume that these names match with people who are appearing less frequently in the photographs. Therefore, we try to name those people by using the similarity of their faces. For this purpose, we use the politicians' photographs in order to build our dataset, because pair relations can be easily exploited in these photographs. ©2009 IEEE.
Open Access
Laboratuar hayvanlarının davranışlarının görü tabanlı çözümlenmesi: 3 Boyutlu gradyan tabanlı bir yaklaşım
(IEEE, 2009-04) Sandıkçı, Selçuk; Duygulu-Şahin, Pınar; Özgüler, Arif Bülent
In pharmacological experiments behavior pattern of laboratory mice, which are under the influence of psychotherapeutic drugs, reveals important clues about effects of the drug. Behavior analysis of laboratory mice by video processing saves both time and labor. In this work a method which was previously used to recognize human behaviors is adapted to laboratory mice case. Method is based on fitting histograms of spatio-temporal gradients extracted from 3D space-time volumes to multidimensional statistical distributions and class(lj'ing according to distances between the distributions. In this work the method is tested on a common mice video dataset, compared to other methods in the literature and found to be successful. ©2009 IEEE.
Open Access
Nearest-neighbor based metric functions for indoor scene recognition
(Academic Press, 2011) Cakir, F.; Güdükbay, Uğur; Ulusoy, Özgür
Indoor scene recognition is a challenging problem in the classical scene recognition domain due to the severe intra-class variations and inter-class similarities of man-made indoor structures. State-of-the-art scene recognition techniques such as capturing holistic representations of an image demonstrate low performance on indoor scenes. Other methods that introduce intermediate steps such as identifying objects and associating them with scenes have the handicap of successfully localizing and recognizing the objects in a highly cluttered and sophisticated environment. We propose a classification method that can handle such difficulties of the problem domain by employing a metric function based on the Nearest-Neighbor classification procedure using the bag-of-visual words scheme, the so-called codebooks. Considering the codebook construction as a Voronoi tessellation of the feature space, we have observed that, given an image, a learned weighted distance of the extracted feature vectors to the center of the Voronoi cells gives a strong indication of the image's category. Our method outperforms state-of-the-art approaches on an indoor scene recognition benchmark and achieves competitive results on a general scene dataset, using a single type of descriptor. © 2011 Elsevier Inc. All rights reserved.
Open Access
A new approach to search result clustering and labeling
(Springer, Berlin, Heidelberg, 2011) Türel, Anıl; Can, Fazlı
Search engines present query results as a long ordered list of web snippets divided into several pages. Post-processing of retrieval results for easier access of desired information is an important research problem. In this paper, we present a novel search result clustering approach to split the long list of documents returned by search engines into meaningfully grouped and labeled clusters. Our method emphasizes clustering quality by using cover coefficient-based and sequential k-means clustering algorithms. A cluster labeling method based on term weighting is also introduced for reflecting cluster contents. In addition, we present a new metric that employs precision and recall to assess the success of cluster labeling. We adopt a comparative strategy to derive the relative performance of the proposed method with respect to two prominent search result clustering methods: Suffix Tree Clustering and Lingo. Experimental results in the publicly available AMBIENT and ODP-239 datasets show that our method can successfully achieve both clustering and labeling tasks. © 2011 Springer-Verlag Berlin Heidelberg.
Open Access
On recognizing actions in still images via multiple features
(Springer, Berlin, Heidelberg, 2012) Şener, Fadime; Bas, C.; Ikizler-Cinbis, N.
We propose a multi-cue based approach for recognizing human actions in still images, where relevant object regions are discovered and utilized in a weakly supervised manner. Our approach does not require any explicitly trained object detector or part/attribute annotation. Instead, a multiple instance learning approach is used over sets of object hypotheses in order to represent objects relevant to the actions. We test our method on the extensive Stanford 40 Actions dataset [1] and achieve significant performance gain compared to the state-of-the-art. Our results show that using multiple object hypotheses within multiple instance learning is effective for human action recognition in still images and such an object representation is suitable for using in conjunction with other visual features. © 2012 Springer-Verlag.
Open Access
Oscillatory synchronization model of attention to moving objects
(Elsevier, 2012) Yilmaz, O.
The world is a dynamic environment hence it is important for the visual system to be able to deploy attention on moving objects and attentively track them. Psychophysical experiments indicate that processes of both attentional enhancement and inhibition are spatially focused on the moving objects; however the mechanisms of these processes are unknown. The studies indicate that the attentional selection of target objects is sustained via a feedforward-feedback loop in the visual cortical hierarchy and only the target objects are represented in attention-related areas. We suggest that feedback from the attention-related areas to early visual areas modulates the activity of neurons; establishes synchronization with respect to a common oscillatory signal for target items via excitatory feedback, and also establishes de-synchronization for distractor items via inhibitory feedback. A two layer computational neural network model with integrate-and-fire neurons is proposed and simulated for simple attentive tracking tasks. Consistent with previous modeling studies, we show that via temporal tagging of neural activity, distractors can be attentively suppressed from propagating to higher levels. However, simulations also suggest attentional enhancement of activity for distractors in the first layer which represents neural substrate dedicated for low level feature processing. Inspired by this enhancement mechanism, we developed a feature based object tracking algorithm with surround processing. Surround processing improved tracking performance by 57% in PETS 2001 dataset, via eliminating target features that are likely to suffer from faulty correspondence assignments. © 2012 Elsevier Ltd.
Open Access
Osmanlica belgelerde kelime erişimi
(IEEE, 2011-04) Arifoǧlu, Damla; Duygulu, Pınar
Bu çalışmada, Osmanlıca arşivlerinin analizi amacıyla, kelime erişimi problemi iki farklı resim eşleme yöntemi ile çözülmeye çalışılmaktadır. Bu amaçla (1) Dinamik Zaman Bükmesi (DZB) tabanlı kelime eşleme yöntemi [7] ve (2) Şekil İçeriği (shape context) tanımlayıcısı [10] Osmanlıca belgeler üzerinde uyarlanmıştır. Öncelikle, verilen bir Osmanlıca belgedeki tüm alt-kelimeler bulunmuştur. Birinci yöntemde, her alt-kelime grubu için, üst ve alt kelime profili, siyah pikselden beyaz piksele geçiş sayısı ve dikey izdüşüm özniteliklerinden oluşturulmuş 4 parçalı öznitelik vektörü çıkartılmış, bu özniteliklerin birbirine olan uzaklığı DZB algoritmasıyla bulunmuştur. İkinci yöntemde ise, Şekil İçeriği tanımlayıcısı kullanılarak, alt-kelimelerin birbirine olan uzaklıkları hesaplanmıştır. Uygulanan yöntemler, Fuzuli’nin Leyla ve Mecnun divanının 10 sayfasından oluşan bir Osmanlıca veri kümesi üzerinde denenmiştir. In this paper, two image matching methods are adapted to retrieve words in Ottoman documents. The first method is based on Dynamic Time Warping (DTW) method proposed in [7], while the second method is based on the Shape Context descriptor [10]. Firstly, all sub-words in a given Ottoman document are extracted. In the first method, a 4-variant feature vector (upper and lower word profiles, background to ink transition, vertical projection) is calculated for each subword and feature vectors' distance to each other is found by DTW algorithm. In the second method, shape context descriptor is used to calculate the distances of sub-word images. The methods are tested on an Ottoman data set, which consists of 10 pages of Leyla and Mecnun Divan of Fuzuli. © 2011 IEEE.
Open Access
Pap smear test görüntülerinde hücre çekirdeklerinin bölütlenmesi
(IEEE, 2009-04) Kale, Aslı; Aksoy, Selim; Önder, S.
Cervical cancer is a preventable disease and the dysplasia it causes can be scanned by using a pap smear test. It can be beneficial to develop a computer-assisted diagnosis system to make the pap smear test robust and widespread. The most fundamental part of such a system is the segmentation of nuclei and cytoplasm in cervical cell images. The aim of this study is to segment the nuclei in such images. First, markers on the nuclei are found by using mathematical morphology operations. Based on the obtained markers, marker-based watershed segmentation and balloon snake model are applied to find the nuclei contours in a data set consisting of cervical cell images. The data set is composed of six classes ranging according to the dysplasia degree of the cells. The results are evaluated according to the relative distance error measure, and the strengths and weakness of the methods are discussed. ©2009 IEEE.