Browsing by Subject "Distance metrics"

Now showing 1 - 4 of 4

Open Access
A hybrid approach for line segmentation in handwritten documents
(2012) Adıgüzel, Hande; Şahin, Emre; Duygulu, Pınar
This paper presents an approach for text line segmentation which combines connected component based and projection based information to take advantage of aspects of both methods. The proposed system finds baselines of each connected component. Lines are detected by grouping baselines of connected components belonging to each line by projection information. Components are assigned to lines according to different distance metrics with respect to their size. This study is one of the rare studies that apply line segmentation to Ottoman documents. Further, it proposes a new method, Fourier curve fitting, to detect the peaks in a projection profile. The algorithm is demonstrated on different printed and handwritten Ottoman datasets. Results show that the method manages to segment lines both from printed and handwritten documents under different writing conditions at least with 92% accuracy.
Open Access
Rule based segmentation and subject identification using fiducial features and subspace projection methods
(Academy Publisher, 2007) Ince, E. A.; Ali, S. A.
This paper describes a framework for carrying out face recognition on a subset of standard color FERET database using two different subspace projection methods, namely PCA and Fisherfaces. At first, a rule based skin region segmentation algorithm is discussed and then details about eye localization and geometric normalization are given. The work achieves scale and rotation invariance by fixing the inter ocular distance to a selected value and by setting the direction of the eye-to-eye axis. Furthermore, the work also tries to avoid the small sample space (S3) problem by increasing the number of shots per subject through the use of one duplicate set per subject. Finally, performance analysis for the normalized global faces, the individual extracted features and for a multiple component combination are provided using a nearest neighbour classifier with Euclidean and/or Cosine distance metrics.
Open Access
Target detection and classification in SAR images using region covariance and co-difference
(SPIE, 2009-04) Duman, Kaan; Eryıldırım, Abdulkadir; Çetin, A. Enis
In this paper, a novel descriptive feature parameter extraction method from synthetic aperture radar (SAR) images is proposed. The new approach is based on region covariance (RC) method which involves the computation of a covariance matrix whose entries are used in target detection and classification. In addition the region co-difference matrix is also introduced. Experimental results of object detection in MSTAR (moving and stationary target recognition) database are presented. The RC and region co-difference method delivers high detection accuracy and low false alarm rates. It is also experimentally observed that these methods produce better results than the commonly used principal component analysis (PCA) method when they are used with different distance metrics introduced. © 2009 SPIE.
Open Access
Target detection in SAR images using codifference and directional filters
(SPIE, 2010) Duman, Kaan; Çetin, A. Enis
Target detection in SAR images using region covariance (RC) and codifference methods is shown to be accurate despite the high computational cost. The proposed method uses directional filters in order to decrease the search space. As a result the computational cost of the RC based algorithm significantly decreases. Images in MSTAR SAR database are first classified into several categories using directional filters (DFs). Target and clutter image features are extracted using RC and codifference methods in each class. The RC and codifference matrix features are compared using l 1 norm distance metric. Support vector machines which are trained using these matrices are also used in decision making. Simulation results are presented. © 2010 Copyright SPIE - The International Society for Optical Engineering.