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Yayın Rate-distortion and complexity joint optimization for fast motion estimation in H.264 video coding(IEEE, 2006) Ateş, Hasan Fehmi; Kanberoğlu, Berkay; Altunbaşak, YücelH.264 video coding standard offers several coding modes including inter-prediction modes that use macroblock partitions with variable block sizes. Choosing a rate-distortion optimal mode among these possibilities contributes significantly to the superior coding efficiency of the H.264 encoder. Unfortunately, searching for optimal motion vectors of each possible subblock incurs a heavy computational cost. In this paper, in order to reduce the complexity of integer-pel motion estimation, we propose a rate-distortion and complexity joint optimization method that selects for each MB a subset of partitions to evaluate during motion estimation. This selection is based on simple measures of spatio-temporal activity within the MB. The procedure is optimized to minimize mode estimation error at a certain level of computational complexity. Simulation results show that the algorithm speeds up the motion estimation module by a factor of up to 20 with little loss in coding efficiency.Yayın Plaka tanıma sistemi için farklı bir yaklaşım(IEEE, 2009-06-26) Tamer, Engin; Çizmeci, BurakBu bildiride, bilgisayarlı görü ve örüntü tanıma alanlarında çok popüler olan plaka tanıma sistemi için farklı bir yakla¸sım sunuyoruz. Plaka tanıma sistemi genellikle üç ana bölüme ayrılır: plakanın yerinin saptanması, karakter bölütleme ve karakter tanıma. Plaka tanıma sisteminin en önemli bölümü olan plaka yerinin saptanmasında, yatay tarama ile arama alanını daralttıktan sonra, Türk plakalarında yer alan TR işaretini kullanan yeni ve özgün bir algoritma öneriyoruz. Yeri saptanan plakanın karakterlerinin bölütlenmesi için ikili imge üzerinde morfolojik işlemler uyguluyoruz. Son olarak, karakter tanıma işleminde ise, harf ve sayı yapay sinir ağlarını ayırarak hata oranını en küçültmeyi hedefliyoruz.Yayın Feature extraction from discrete attributes(IEEE, 2010) Yıldız, Olcay TanerIn many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we extract new features by combining k discrete attributes, where for each subset of size k of the attributes, we generate all orderings of values of those attributes exhaustively. We then apply the usual univariate decision tree classifier using these orderings as the new attributes. Our simulation results on 16 datasets from UCI repository [2] show that the novel decision tree classifier performs better than the proper in terms of error rate and tree complexity. The same idea can also be applied to other univariate rule learning algorithms such as C4.5Rules [7] and Ripper [3].Yayın Searching for the optimal ordering of classes in rule induction(IEEE, 2012-11-15) Ata, Sezin; Yıldız, Olcay TanerRule induction algorithms such as Ripper, solve a K > 2 class problem by converting it into a sequence of K - 1 two-class problems. As a usual heuristic, the classes are fed into the algorithm in the order of increasing prior probabilities. In this paper, we propose two algorithms to improve this heuristic. The first algorithm starts with the ordering the heuristic provides and searches for better orderings by swapping consecutive classes. The second algorithm transforms the ordering search problem into an optimization problem and uses the solution of the optimization problem to extract the optimal ordering. We compared our algorithms with the original Ripper on 8 datasets from UCI repository [2]. Simulation results show that our algorithms produce rulesets that are significantly better than those produced by Ripper proper.Yayın A novel representation method for electromyogram (EMG) signal with predefined signature and envelope functional bank(IEEE, 2004) Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık BinboğaIn this paper, a new method to model EMG signals by means of "Predefined Signature and Envelope Functional Banks (PSEB)" is presented. Since EMG signals present quasi-stationary behavior, any EMG signal Xi is modeled by the form of Xi ? Ci?K?R on a frame bases in this work. In this model, ?R is defined as the Predefined Signature Vector (PSV); ?K is referred to as Predefined Envelope Vector (PEV) and Ci is called the Frame-Scaling Coefficient (FSC). EMG signal for each frame is described in terms of the two indices "R" and "K" of PSEB and the frame -scaling coefficient Ci. Furthermore, It has been shown that the new method of modeling provides significant data compression while preserving the clinical information in the reconstructed signal.
