Browsing by Subject "Sparse representation"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
Item Open Access A compression method based on compressive sampling for 3-D laser range scans of indoor environments(Springer, Dordrecht, 2010) Dobrucalı, Oğuzcan; Barshan, BillurWhen 3-D models of environments need to be transmitted or stored, they should be compressed efficiently to increase the capacity of the communication channel or the storage medium. We propose a novel compression technique based on compressive sensing, applied to sparse representations of 3-D range measurements. We develop a novel algorithm to generate sparse innovations between consecutive range measurements along the axis of the sensor's motion, since the range measurements do not have highly sparse representations in common domains. Compared with the performances of widely used compression techniques, the proposed method offers the smallest compression ratio and provides a reasonable balance between reconstruction error and processing time. © 2011 Springer Science+Business Media B.V.Item Open Access A compression method for 3-D laser range scans of indoor environments based on compressive sensing(IEEE, 2011-08-09) Dobrucalı, Oğuzcan; Barshan, BiilurModeling and representing 3-D environments require the transmission and storage of vast amount of measurements that need to be compressed efficiently. We propose a novel compression technique based on compressive sensing for 3-D range measurements that are found to be correlated with each other. The main issue here is finding a highly sparse representation of the range measurements, since they do not have highly sparse representations in common domains, such as the frequency domain. To solve this problem, we generate sparse innovations between consecutive range measurements along the axis of the sensor's motion. We obtain highly sparse innovations compared with other possible ones generated by estimation and filtering. Being a lossy technique, the proposed method performs reasonably well compared with widely used compression techniques. © 2011 EURASIP.Item Open Access Seyrek sinyallerin geri çatımına özyineli bir yaklaşım(IEEE, 2014-04) Teke, Oğuzhan; Arıkan, Orhan; Gürbüz, A. C.Sıkıştırılmış Algılama (SA) kuramı, bilinen bir tabanda seyrek olan bir sinyalin az sayıda ölçüm ile nasıl geri çatılacağını inceler. Çoğu pratik sistemdeki ölçüm sinyallerinin sürekli bir parametre uzayında seyrek bir tanıma sahip olması, SA kuramı altında geliştirilmiş tekniklerin kullanılabilme olasılığını ortaya çıkarır. Ancak, SA tekniklerinin uygulanabilmesi için sürekli parametre uzayının ayrıklaştırılması gerekir. Bu ayrıklaştırma sonucunda da iyi bilinen ızgara-dışılık problemi ortaya çıkar. Izgara-dışılık problemini engellemek için bu çalışmada, parametre alanını degişken ve uyarlamalı bir şekilde ayrıklaştıran özyineli bir yaklaşım sunulmuştur. Önerilen yaklaşımın çok yakın şekilde konumlanmış hedefleri dahi yüksek hassasiyetle kestirebildiği benzetim çalışmalarıyla gösterilmiştir.Item Open Access Sparse binarised statistical dynamic features for spatio-temporal texture analysis(Springer, 2019) Arashloo, Shervin RahimzadehThe 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.