Yazar "Yildiz, Ferruh" seçeneğine göre listele

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    Feature Extraction from Aerial Photos
    (WORLD ACAD SCI, ENG & TECH-WASET, 2006) Guenduez, Mesut; Yildiz, Ferruh; Onat, Ayse
    In Geographic Information System, one of the sources of obtaining needed geographic data is digitizing analog maps and evaluation of aerial and satellite photos. In this study, a method will be discussed which can be used to extract vectorial features and creating vectorized drawing files for aerial photos. At the same time a software developed for these purpose. Converting from raster to vector is also known as vectorization and it is the most important step when creating vectorized drawing files. In the developed algorithm, first of all preprocessing on the aerial photo is done. These are; converting to grayscale if necessary, reducing noise, applying some filters and determining the edge of the objects etc. After these steps, every pixel which constitutes the photo are followed from upper left to right bottom by examining its neighborhood relationship and one pixel wide lines or polylines obtained. The obtained lines have to be erased for preventing confusion while continuing vectorization because if not erased they can be perceived as new line, but if erased it can cause discontinuity in vector drawing so the image converted from 2 bit to 8 bit and the detected pixels are expressed as a different bit. In conclusion, the aerial photo can be converted to vector form which includes lines and polylines and can be opened in any CAD application.
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    Ottoman Script Recognition Using Hidden Markov Model
    (WORLD ACAD SCI, ENG & TECH-WASET, 2006) Onat, Ayse; Yildiz, Ferruh; Guenduez, Mesut
    In this study, an OCR system for segmentation, feature extraction and recognition of Ottoman Scripts has been developed using handwritten characters. Detection of handwritten characters written by humans is a difficult process. Segmentation and feature extraction stages are based on geometrical feature analysis, followed by the chain code transformation of the main strokes of each character. The output of segmentation is well-defined segments that can be fed into any classification approach. The classes of main strokes are identified through left-right Hidden Markov Model (HMM).
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    PHOTOGRAMMETRIC STUDY FOR SIRCALI MEDRESE DOOR
    (INT SCIENTIFIC CONFERENCE SGEM, 2009) Yakar, Murat; Yildiz, Ferruh; Alyilmaz, Cengiz; Yilmaz, H. Murat
    Sircali Medrese(Madrasa- moslem theological school), which is one of the most important medreses of Konya and Anatolia is in Konya Province, Meram District, Gazialemsah Quarter. Sircali Medrese, that is among the medreses having an open courtyard, two liwans and two floors, has been had constructed by Bedreddin Muslih in the Period of Glyaseddin Keyhusrev the 2nd. In the front parts of the building cut stone is used, while in other parts rubble stone is used. It has been used as medrese until 1924 with various changes. The front side of the medrese lying in east - west direction is made of cut stone. The throne door making a projection forward is ornamented with geometric and Anatolian ornaments. In the part on the entrance door, there is an inscription. At two sides of this inscription, there are ornamental workmanship samples. Furthermore, as we see in classical Seljuk throne doors, there are two niches at both sides of the door. After the entrance, cradle vaulted liwan is located. In this study photogrammetric study of the sircali medrese door have been completed. All details have been obtained in 3d model.
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    Pixel-versus object-based classification of forest and agricultural areas from multiresolution satellite images
    (TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2017) Boyaci, Dijle; Erdogan, Mustafa; Yildiz, Ferruh
    Managing of natural resources including agriculture and forestry is a very important subject for governments and decision makers. Up-to-date, accurate, and timely geospatial information about natural resources is needed in the management process. Remote sensing technology plays a significant role in the production of this geospatial information. Compared to terrestrial work, the analysis of larger areas with remote sensing techniques can be done on a shorter timescale and at lower cost. Image classification in remote sensing is one of the most popular methods used for the detection of forest and agricultural areas. However, the accuracy of classification changes according to the source and reference data, the classification method, and the producer's knowledge and experience. In this research, the identification of forests and agricultural areas was studied in terms of both their geometry and attribution using different classification methods and different source data. Landsat, Aster, and RapidEye images, which have different spatial and spectral resolution, were used as the source data. Pixel- and object-based classification algorithms were also tested. Classification accuracy results were evaluated at 300 stratified random pixels. It was found that the best overall accuracy was obtained from Aster imagery with object-based classification using the nearest neighbor method. The results also showed that spatial resolution is important for discrimination of classes and spectral resolution is important for definition of features, and confirmed the well-established paradigm of remote sensing that there are no perfect source data or method of classification for all situations.