Browsing by Subject "Independent component analysis"

Now showing 1 - 4 of 4

Open Access
Çizge kesit yöntemi ile hiperspektral görüntülerde anomali tabanlı hedef tespiti
(IEEE, 2015-05) Batı, E.; Erdinç, Acar; Çeşmeci, D.; Çalışkan, A.; Koz, A.; Aksoy, Selim; Ertürk, S.; Alatan, A. A.
Hiperspektral hedef tespiti için yürütülen çalışmalar genel olarak iki sınıfta degerlendirilebilir. İlk sınıf olan anomali tespit yöntemlerinde, hedefin görüntünün geri kalanından farklı oldugu bilgisi kullanılarak görüntü analiz edilmektedir. Diğer sınıfta ise daha önceden bilgisi edinilmiş hedefe ait spektral imza ile görüntüdeki herbir piksel arasındaki benzerlik bulunarak hedefin konumu tespit edimektedir. Her iki sınıf yöntemin de önemli bir dezavantajı hiperspektral görüntü piksellerini bagımsız olarak degerlendirip, aralarındaki komşuluk ilişkilerini gözardı etmesidir. Bu makalede anomali tespit ve imza tabanlı tespit yakla¸sımlarını, pikseller arası komşuluk ilişkilerini de göz önünde bulundurarak birleştiren çizge yaklaşımına dayalı yeni bir yöntem önerilmiştir. Hedeflerin hem imza bilgisine sahip olundugu hem de anomali sayılabilecek ölçülerde olduğu varsayılarak önerilen çizge yaklaşımında önplan için imza bilgisi kullanan özgün bir türev tabanlı uyumlu filtre önerilmiştir. Arkaplan için ise seyreklik bilgisi kullanarak Gauss karışım bileşeni kestirimi yapan yeni bir anomali tespit yöntemi geliştirilmiştir. Son olarak komşular arası benzerligi tanımlamak için ise spektral bir benzerlik ölçütü olan spektral açı eleştiricisi kullanılmıştır. Önerilen çizge tabanlı yöntemin önplan, arkaplan ve komşuluk ilişkilerini uygun şekilde birleştirdigi ve önceki yöntemlere göre hedefi gürültüden arınmış bir bütün şeklinde başarıyla tespit edebildigi gözlemlenmiştir. The studies on hyperspectral target detection until now, has been treated in two approaches. Anomaly detection can be considered as the first approach, which analyses the hyperspectral image with respect to the difference between target and the rest of the hyperspectral image. The second approach compares the previously obtained spectral signature of the target with the pixels of the hyperspectral image in order to localize the target. A distinctive disadvantage of the aforementioned approaches is to treat each pixel of the hyperspectral image individually, without considering the neighbourhood relations between the pixels. In this paper, we propose a target detection algorithm which combines the anomaly detection and signature based hyperspectral target detection approaches in a graph based framework by utilizing the neighbourhood relations between the pixels. Assuming that the target signature is available and the target sizes are in the range of anomaly sizes, a novel derivative based matched filter is first proposed to model the foreground. Second, a new anomaly detection method which models the background as a Gaussian mixture is developed. The developed model estimates the optimal number of components forming the Gaussian mixture by means of utilizing sparsity information. Finally, the similarity of the neighbouring hyperspectral pixels is measured with the spectral angle mapper. The overall proposed graph based method has successfully combined the foreground, background and neighbouring information and improved the detection performance by locating the target as a whole object free from noises. © 2015 IEEE.
Open Access
Fast insect damage detection in wheat kernels using transmittance images
(IEEE, 2004-07) Çataltepe, Z.; Pearson, T.; Cetin, A. Enis
We used transmittance images and different learning algorithms to classify insect damaged and un-damaged wheat kernels. Using the histogram of the pixels of the wheat images as the feature, and the linear model as the learning algorithm, we achieved a False Positive Rate (1-specificity) of 0.12 at the True Positive Rate (sensitivity) of 0.8 and an Area Under the ROC Curve (AUC) of 0.90 ± 0.02. Combining the linear model and a Radial Basis Function Network in a committee resulted in a FP Rate of 0.09 at the TP Rate of 0.8 and an AUC of 0.93 ± 0.03.
Open Access
Real time noise-cancellation using ICA, PSO and PE
(IEEE, 2012) Bor, R. İrem; Ider, Y. Ziya; Arıkan, Orhan; Ertan, Erdem
In order to provide noiseless transmission of speech in wireless communication systems a real-time implementable noise cancellation algorithm is developed. Speech and noise sources are not known but only their mixtures are observed. That system is modeled with instantaneous mixture model. Combination of independent component analysis (ICA) and particle swarm optimization (PSO) algorithms is used to separate speech and noise. However, ICA has an ambiguity such that it is not possible to know which one of the separated signals is speech or noise. As a result, the transmitted signal can be noise, instead of speech. To overcome this ambiguity problem, a pitch extraction (PE) algorithm is developed and combined with ICA-PSO. ICAPSO-PE algorithm is implemented in MATLAB. Contributions of this work are modifying objective functions of ICA algorithm to make them more robust, combining ICA with PSO to make it work fast and robust, and overcoming the ambiguity problem using PE algorithm. © 2012 IEEE.
Open Access
Sparse binarised statistical dynamic features for spatio-temporal texture analysis
(Springer, 2019) Arashloo, Shervin Rahimzadeh
The paper presents a new spatio-temporal learning-based descriptor called binarised statistical dynamic features (BSDF) for representation and classification of dynamic texture. The BSDF descriptor operates by applying three-dimensional spatio-temporal filters on local voxels of an image sequence where the filters are learned via an independent component analysis, maximising independence over spatial and temporal domains concurrently. The BSDF representation is formed by binarising filter responses which are then converted into codewords and summarised using histograms. A robust representation of the BSDF descriptor is finally obtained via a sparse representation approach yielding very discriminative features for classification. The effects of different hyper-parameters on performance including the number of filters, the number of scales, temporal depth, number of samples drawn are also investigated. The proposed approach is evaluated on the most commonly used dynamic texture databases and shown to perform very well compared to the existing methods.