Implementasi Fitur Haar-like dalam Mendeteksi dan Menghitung Jumlah Orang pada Noised Digital Image
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Abstract
Mendeteksi objek pada citra digital bagi manusia pada umumnya merupakan hal yang tidak sulit, namun tidak bagi komputer. Komputer membutuhkan teknologi khusus untuk mengolah suatu citra hingga dapat mendeteksi objek pada citra digital. Visi komputer memberikan solusi pada komputer untuk dapat mendeteksi objek pada citra digital. Salah satu fitur yang dapat diimplementasikan adalah Fitur Haar-like. Dengan fitur Haar-like, komputer akan mengekstraksi citra digital yang nantinya akan dideteksi suatu objek pada citra tersebut dengan algoritma Viola-Jones, yaitu algoritma cascade classifier yang berfungsi untuk mendeteksi suatu objek pada citra berdasarkan data citra terlatih (trained data). Pada penelitian ini, Fitur Haar dapat mendeteksi objek pada citra digital dengan tiga kondisi yang berbeda, yaitu saat kondisi normal, kondisi citra dengan derau, dan kondisi citra dengan pencahayaan rendah. Visi komputer dengan Fitur Haar-like dapat mendeteksi objek pada ketiga kondisi citra tersebut dengan prosentase kesalahan yang cukup rendah.
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