Browsing by Subject "Salient points"

Now showing 1 - 2 of 2

Open Access
Osmanlıca kelimeleri eşleme
(IEEE, 2007-06) Ataer, Esra; Duygulu, Pınar
Osmanlı arşivleri dünyanın pek çok yerinden araştırmacının ilgi alanına girmektedir. Fakat bu belgelerin elle çevirisi zor bir iş olduğu için, bu arşivler kullanılamaz durumdadır. Otomatik çeviri gerekmektedir, fakat Osmanlıca’nın yazma özelliklerinden dolayı karakter tabanlı tanıma sistemleri istenen başarıyı gösterememektedir. Ayrıca, belgeler minyatür ve tuğra gibi önemli kısımlar içerdiği için, imge formatında saklanmaları gerekmektedir. Bu nedenle, bu çalışmada Osmanlıca kelimeleri imge olarak görerek probleme imge erişim problemi olarak yaklaşıldı ve kelime eşleme tekniği üzerine bir çözüm önerisinde bulunuldu. Nesne tanımada başarılı olan görsel öğeler kümesi (bag-of-visterms) tekniği kelime eşleme işlemine uyarlandı ve böylece her kelime imgesi taç noktalarından çıkarılan SIFT özelliklerinin ¨ vektor¨ nicemlemesiyle sembolize edildi. Benzer kelimeler görsel ögelerin dağılımına göre eşlendi. Deneyler 10,000 kelimenin üzerindeki matbu ve elyazması belge üzerinde yapıldı. Sonuçlar sistemin benzer kelimeleri yüksek doğrulukla eşlediğini ve anlamsal benzerlikleri bulduğunu gösteriyor Large archives of Ottoman documents are challenging to many historians all over the world. However, these archives remain inaccessible since manual transcription of such a huge volume is difficult. Automatic transcription is required, but due to the characteristics of Ottoman documents, character recognition based systems may not yield satisfactory results. It is also desirable to store the documents in image form since the documents may contain important drawings, especially the signatures. Due to these reasons, in this study we treat the problem as an image retrieval problem with the view that Ottoman words are images, and we propose a solution based on image matching techniques. The bag-of-visterms approach, which is shown to be successful to classify objects and scenes, is adapted for matching word images. Each word image is represented by a set of visual terms which are obtained by vector quantization of SIFT descriptors extracted from salient points. Similar words are then matched based on the similarity of the distributions of the visual terms. The experiments are carried out on printed and handwritten documents which included over 10,000 words. The results show that, the proposed system is able to retrieve words with high accuracies, and capture the semantic similarities between words.
Open Access
Region covariance descriptors calculated over the salient points for target tracking
(IEEE, 2012) Çakir, S.; Aytaç, T.; Yildirim, A.; Beheshti, S.; Gerek Ö.N.; Çetin, A. Enis
Features extracted at salient points in the image are used to construct region covariance descriptor (RCD) for target tracking purposes. In the classical approach, the RCD is computed by using the features at each pixel location and thus, increases the computational cost in the scenarios where large targets are tracked. The approach in which the features at each pixel location are used, is redundant in cases where image statistics do not change significantly between neighboring pixels. Furthermore, this may decrease the tracking accuracy while tracking large targets which have background dominating structures. In the proposed approach, the salient points are extracted via the Shi and Tomasi's minimum eigenvalue method and a descriptor based target tracking structure is constructed based on the features extracted only at these salient points. Experimental results indicate that the proposed method provides comparable and in some cases even better tracking results compared to the classical method while providing a computationally more efficient structure. © 2012 IEEE.