SİMAFOR BAYRAK TANIMA İÇİN KÜMELEME TABANLI ÖZNİTELİK ÇIKARMA YÖNTEMLERİ
Simafor ile bayrak muhaberesi elektronik harp esnasında yayın kontrolü yapılırken gemiler arasında çokça kullanılan bir görsel muhabere yöntemidir. Bu makalede RGB kamera imgeleri ile çalışan, yıüksek performanslı ve düşük maliyetli bir otomatik simafor tanıma sistemi önerilmektedir. Bu maksatla İngilizce simafor alfabesindeki harflere tekabül gelen bir vei seti oluşturulmuş ve morfolojik işlemler ile sancaklar arasındaki açılar otomatik olarak tespit edilmiştir. Sancak mevkilerinin özetlemesinde üç metot kullanılmıştır: ikili erozyon ile küçülme, kortalamalar ile öbekleme ve hiyerarşik toplayıcı öbekleme. Çok küçük boyutlu uzayda elde edilen öznitelikler ile karar destek makinaları kullanılarak sınıflandırma gerçekleştirilmiştir. Çapraz doğrulama deneyleri ile Kinct algılayıcıya ve hesap bakımından maliyetli sinir ağlarının çalışacağı GPU donanımlarına gereksinim duyulmaksızın %99.76’lık bir başarıma ulaşıldığı gözlemlenmiştir.
Anahtar Kelimeler:
Simafor Sancak Tanıma, Yönlü Gradyenlerin Histogramı, Morfolojik İmge İşleme, Karar Destek Makinası
CLUSTERING BASED FEATURE EXTRACTION METHODS FOR SEMAPHORE FLAG RECOGNITION
Semaphore flag signaling is a visual communication system used by Naval vessels when the radio emissions are under strict control during electronic warfare. This paper presents an autonomous semaphore flag signaling recognition system that translates the RGB images into letters with a high performance at a low cost. A dataset is created for the semaphore flag signals for the English alphabet and the relative angles of the flags are acquired via morphological operations. The summarization of the flag locations is conducted with three methods: binary erosion the shrinking, kmeans clustering and hierarchical agglomerative clustering. The resulting features with low dimensionality are then classified with support vector machine classifier with polynomial kernel. The cross-validation experiments show that the proposed methodology yields 99.76% accuracy, with no need for a Kinect sensor and computationally expensive neural network training that requires GPUs, as proposed by the similar works in the literature.
Keywords:
Semaphore Flag Recognition, Histogram of Oriented Gradients, Morphological Image Processing, Support Vector Machines,
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- ISSN: 1304-2025
- Yayın Aralığı: Yılda 2 Sayı
- Başlangıç: 2003
- Yayıncı: Milli Savunma Üniversitesi Deniz Harp Okulu Dekanlığı
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