Sketch Plus Colorization Deep Convolutional Neural Networks for Photos Generation from Sketches

In this paper, we introduce a method to generate photos from sketches using Deep Convolutional Neural Networks (DCNN). This research proposes a method by combining a network to invert sketches into photos (sketch inversion net) with a network to predict color given grayscale images (colorization net). By using this method, the quality of generated photos is expected to be more similar to the actual photos. We first artificially constructed uncontrolled conditions for the dataset. The dataset, which consists of hand-drawn sketches and their corresponding photos, were pre-processed using several data augmentation techniques to train the models in addressing the issues of rotation, scaling, shape, noise, and positioning. Validation was measured using two types of similarity measurements: pixel- difference based and human visual system (HVS) which mimics human perception in evaluating the quality of an image. The pixel- difference based metric consists of Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) while the HVS consists of Universal Image Quality Index (UIQI) and Structural Similarity (SSIM). Our method gives the best quality of generated photos for all measures (844.04 for MSE, 19.06 for PSNR, 0.47 for UIQI, and 0.66 for SSIM)

Gray Level Co-ocurence Matrix untuk Pengekstrasian Ciri Topeng Cirebon

Topeng Cirebon merupakan salah satu kekayaan budaya dalam Indonesia. Salah satunya adalah Topeng Cirebon yang merupakan salah satu identitas yang tidak terpisahkan dalam budaya kota Cirebon. Sayangnya, orang-orang tidak terlalu tertarik dengan kebudayaan daerah dan kurangnya bantuan pemerintahan setempat membuat topeng Cirebon semakin ditinggalkan. Hal ini ditakutkan dikemudian hari topeng Cirebon akan dilupakan. Tujuan utama dari penelitian ini adalah melakukan digitalisasi topeng Cirebon dan identifikasi secara automatis menggunakan pendekatan pengolahan citra. Penelitian ini menggunakan Gray Level Co-occurence Matrix sebagai fitur utama dan K-Nearest Neighbour sebagai klasifier. Hasil akurasi terbaik dari penelitian ini adalah sebesar 40.67%</jats:p

Perbandingan Distance Metric pada Nearest Neighbour untuk Klasifikasi Sel Darah Putih

White blood cells, have a function to protect human body from viruses, bacteria and any other harmful substance. In this research, Local Binary Pattern was proposed for feature extraction using Euclidean distance, Chebyshev distance and Minkowski distance as classifier.</jats:p