CSIE

Telah dibuat suatu sistem pendeteksi kulit manusia melalui citra HSV (Hue Saturation Value) menggunakan program Matlab. Penelitian ini dilakukan karena masih minimnya penelitian untuk citra HSV pada Matlab. Berbedanya nilai Hue dan Saturation pada program matlab maka penelitian ini akan berguna untuk pendeteksian kulit m anusia menggunakan program Matlab. Penelitian ini dimulai dengan mencari nilai Hue dan Saturation pada citra HSV dan menentukan range nilai kulit manusia yang dapat dibedakan juga dengan objek yang memiliki warna mirip dengan kulit manusia. Hasil dari segmentasi kemudian di filter menggunakan low pass filter untuk menghilangkan noise. Hasilnya akan dideteksi untuk membedakan kulit manusia yang berupa muka dan selain muka. Penelitian ini menghasilkan kesimpulan range kulit manusia memiliki nilai Hue sebesar 0.021 sampai 0.080 dan Nilai Saturation sebesar 0.23 sampai 0.68. Metode ini dapat bekerja dengan sangat baik dengan kecepatan rata-rata 1.4 frame per second dengan tingkat akurasi 87.86 %. Akurasi keberhasilan dipengaruhi oleh gerakan objek, posisi dan arah objek wajah terhadap kamera, jumlah objek orang dalam satu frame, jarak objek kulit manusia dengan kamera serta intensitas cahaya pencahayaan.

Different color material and extreme lighting change pose a problem for single image depth prediction on archeological artifacts. These conditions can lead to misprediction on the surface of the foreground depth reconstruction. We propose a new method, the Color-Light Multi-Cascade Network, to overcome single image depth prediction limitations under these influences. Two feature extractions based on Multi-Cascade Networks (MCNet) are trained to deal with light and color problems individually for this new approach. By concatenating both of the features, we create a new architecture capable of reducing both color and light problems. Three datasets are used to evaluate the method with respect to color and lighting variations. Our experiments show that the individual Color-MCNet can improve the performance in the presence of color variations and fails to handle extreme light changes; the Light-MCNet, on the other hand, shows consistent results under changing lighting conditions but lacks detail. When joining the feature maps of Color-MCNet and Light-MCNet, we obtain a detailed surface both in the presence of different material colors in relief images, and under different lighting conditions. These results prove that our networks outperform state-of-the-art in limited number dataset. Finally, we also evaluate our joined network on the NYU Depth V2 Dataset to compare it with other state-of-the-art methods and obtain comparable performance.

The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are deep learning algorithms commonly used in wide applications. CNN is often used for image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. CNN has four layers: convolution layer, pooling layer, fully connected layer, and non-linear layer. The convolutional layer uses kernel filters to calculate the convolution of the input image by extracting the fundamental features. The pooling layer combines two successive convolutional layers. The third layer is the fully connected layer, commonly called the convolutional output layer. The activation function defines the o...