Browsing by Subject "Vector quantization"

Now showing 1 - 8 of 8

Open Access
Adaptive prediction and vector quantization based very low bit rate video codec
(1993) Ulukuş, Şennur
A very low bit rate video codec (coder/decoder) based on motion compensation, adaptive prediction, vector quantization (VQ) and entropy coding, and a new prediction scheme based on Gibbs random field (GRF) model are presented. The codec is specifically designed for the video-phone application for which the main constraint is to transmit the coded bit stream via the existing telephone lines. Proposed codec can operate in the transmission bit rate interval ranging from 8 to 32 Kbits/s which is defined as the very low bit rates for video coding. Four different coding strategies are adapted to the system, and depending on the characteristics of the image data in the block one of these coding methods is chosen by the coder. Linear prediction is implemented in the codec, and the performances of the two prediction schemes are compared at several transmission bit rates. The need for any prediction is also questioned, by implementing the same codec structure without prediction and comparing the performances of the codecs with prediction and without prediction. It is proved that the presented codec can be used in transmitting the video signal via the existing telephone network for the video-phone applications. Also, it is observed that the codec with GRF model based non-linear prediction has a better performance compared to the codec with linear prediction.
Open Access
Classification of closed-and open-shell pistachio nuts using voice-recognition technology
(American Society of Agricultural and Biological Engineers, 2004) Çetin, A. Enis; Pearson, T. C.; Tewfik, A. H.
An algorithm using speech recognition technology was developed to distinguish pistachio nuts with closed shells from those with open shells. It was observed that upon impact with a steel plate, nuts with closed shells emit different sounds than nuts with open shells. Features extracted from the sound signals consisted of mel-cepstrum coefficients and eigenvalues obtained from the principle component analysis (PCA) of the autocorrelation matrix of the sound signals. Classification of a sound signal was performed by linearly combining the mel-cepstrum and PCA feature vectors. An important property of the algorithm is that it is easily trainable, as are most speech-recognition algorithms. During the training phase, sounds of nuts with closed shells and with open shells were used to obtain a representative vector of each class. During the recognition phase, the feature vector from the sample under question was compared with representative vectors. The classification accuracy of closed-shell nuts was more than 99% on the validation set, which did not include the training set.
Open Access
Coding of fingerprint images using binary subband decomposition and vector quantization
(SPIE, 1998-01) Gerek, Ömer N.; Çetin, A. Enis
In this paper, compression of binary digital fingerprint images is considered. High compression ratios for fingerprint images is essential for handling huge amount of images in databases. In our method, the fingerprint image is first processed by a binary nonlinear subband decomposition filter bank and the resulting subimages are coded using vector quantizers designed for quantizing binary images. It is observed that the discriminating properties of the fingerprint, images are preserved at very low bit rates. Simulation results are presented.
Open Access
Matching ottoman words: an image retrieval approach to historical document indexing
(ACM, 2007-07) Ataer, Esra; Duygulu, Pınar
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. Copyright 2007 ACM.
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
QR-RLS algorithm for error diffusion of color images
(SPIE, 2000) Unal, G. B.; Yardimci, Y.; Arıkan, Orhan; Çetin, A. Enis
Printing color images on color printers and displaying them on computer monitors requires a significant reduction of physically distinct colors, which causes degradation in image quality. An efficient method to improve the display quality of a quantized image is error diffusion, which works by distributing the previous quantization errors to neighboring pixels, exploiting the eye's averaging of colors in the neighborhood of the point of interest. This creates the illusion of more colors. A new error diffusion method is presented in which the adaptive recursive least-squares (RLS) algorithm is used. This algorithm provides local optimization of the error diffusion filter along with smoothing of the filter coefficients in a neighborhood. To improve the performance, a diagonal scan is used in processing the image.
Open Access
Subband analysis for robust speech recognition in the presence of car noise
(IEEE, 1995-05) Çetin, A. Enis; Yardımcı, Y.; Erzin, Engin
In this paper, a new set of speech feature representations for robust speech recognition in the presence of car noise are proposed. These parameters are based on subband analysis of the speech signal. Line Spectral Frequency (LSF) representation of the Linear Prediction (LP) analysis in subbands and cepstral coefficients derived from subband analysis (SUBCEP) are introduced, and the performances of the new feature representations are compared to mel scale cepstral coefficients (MELCEP) in the presence of car noise. Subband analysis based parameters are observed to be more robust than the commonly employed MELCEP representations.
Open Access
Wavelet based detection of moving tree branches and leaves in video
(IEEE, 2006-05) Töreyin, B. Uğur; Çetin, A. Enis
A method for detection of tree branches and leaves in video is proposed. It is observed that the motion vectors of tree branches and leaves exhibit random motion. On the other hand regular motion of green colored objects has well-defined directions. In this paper, the wavelet transform of motion vectors are computed and objects are classified according to the wavelet coefficients of motion vectors. Color information is also used to reduce the search space in a given image frame of the video. Motion trajectories of moving objects are modeled as Markovian processes and Hidden Markov Models (HMMs) are used to classify the green colored objects in the final step of the algorithm. © 2006 IEEE.