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Teknik pemisahan  foreground  dari  background  pada citra statis merupakan penelitian yang sangat diperlukan dalam  computer vision . Teknik yang sering digunakan adalah  image segmentation,  namun hasil ekstraksinya masih kurang akurat.  Image matting  menjadi salah satu solusi untuk memperbaiki hasil dari  image segmentation . Pada metode  supervised ,  image matting  membutuhkan  scribbles  atau  trimap  sebagai  constraint  yang berfungsi untuk melabeli daerah tersebut adalah  foreground  atau  background . Pada makalah ini dibangun metode  unsupervised  dengan mengakuisisi  foreground  dan  background  sebagai  constraint  secara otomatis. Akuisisi  background  ditentukan dari varian nilai fitur DCT ( Discrete Cosinus Transform ) yang dikelompokkan menggunakan algoritme  k-means . Untuk mengakuisisi  foreground  ditentukan dari subset hasil klaster fitur DCT dengan fitur  edge detection.  Hasil dari proses akuisisi  foreground  dan  background  tersebut dijadikan sebagai  constraint . Perbedaan hasil dari penelitian diukur menggunakan MAE ( Mean Absolute Error ) dibandingkan dengan metode  supervised matting  maupun dengan metode  unsupervised matting  lainnya. Skor MAE dari hasil eksperimen menunjukkan bahwa nilai  alpha matte  yang dihasilkan mempunyai perbedaan 0,0336 serta selisih waktu proses 0,4 detik dibandingkan metode  supervised matting . Seluruh data citra berasal dari citra yang telah digunakan para peneliti sebelumnya    AbstractThe technique of separating the foreground and the background from a still image is widely used in computer vision. Current research in this technique is image segmentation. However, the result of its extraction is considered inaccurate. Furthermore, image matting is one solution to improve the effect of image segmentation. Mostly, the matting process used scribbles or trimap as a constraint, which is done manually as called a supervised method. The contribution offered in this paper lies in the acquisition of foreground and background that will be used to build constraints automatically. Background acquisition is determined from the variant value of the DCT feature that is clustered using the k-means algorithm. Foreground acquisition is determined by a subset resulting from clustering DCT values with edge detection features. The results of the two stages will be used as an automatic constraint method. The success of the proposed method, the constraint will be used in the supervised matting method. The difference in results from In the research experiment was measured using MAE (Mean Absolute Error) compared with the supervised matting method and with other unsupervised matting methods. The MAE score from the experimental results shows that the alpha matte value produced has a difference of 0.336, and the difference in processing time is 0.4 seconds compared to the supervised matting method. All image data comes from images that have been used by previous researchers.

Akuisisi Foreground dan Background Berbasis Fitur DTC pada Matting Citra secara Otomatis