KONSEP SETTING DAN STRATEGI PENGELOLAAN RUANG BERMAIN DALAM PENGEMBANGAN KREATIVITAS ANAK USIA DINI

Penelitian ini untuk mengetahui: 1) konsep setting ruang bermain yang dapat menunjang kreativitas anak; 2) penataan ruang bermain yang dapat menunjang kreativitas anak yang sesuai dengan perkembangannya; 3) pengelolaan elemen fsik ruang bermain dalam mengembangkan kreativitas anak; 4) pola seting ruang bermain (indoor) yang dilakukan di dua tempat penitipan anak (TPA) besar di Yogyakarta. Jenis penelitian ini adalah penelitian lapangan (field research) bersifat deskriptif kualitatif non statistik dengan menggunakan studi kasus (case study). Data diperoleh melalui observasi, dokumentasi, dan wawancara kemudian dianalisis dengan analisis tringulasi Moleong, yakni tringulasi sumber, tringulasi dengan metode, dan tringulasi dengan teori. Penelitian ini dilakukan di dua tempat peneitipan anak (TPA) besar yang ada di Yogyakarta yaitu Taman Pengasuhan Anak (TPA) Beringharjo dan Lembaga Pendidikan Islam Terpadu Untuk Anak Usia Dini Tiara Candra. Hasil penelitian ini menemukan bahwa: 1) konsep setting ruang bermain yang pada setiap lembaga yaitu menggunakan konsep home schooling dengan beyound centre and circle times (BCCT). 2) penataan ruang bermain dengan menggunakan sistem pembelajaran berdasarkan BCCT dan sudut ruang bermain yang disesuaikan dengan peralatan  dan sesuai dengan masa perkembangannya. 3) pengelolaan elemen fisik ruang bermain seperti pemilihan warna dan furniture disesuaikan dengan kebutuhan anak serta tidak mengandung bahan yang berbahaya. 4) pola setting ruang bermain ditata sesuai dengan konsep ruang bermain seperti yang telah diprogramkan. Kata kunci: Ruang Bermain, Anak Usia Dini, Kreativitas

Development of New Image Fusion Techniques

Image fusion techniques merge the complementary information of several images (multi-focus, multi-exposure and multi-modal). Each of these scenarios poses different challenges for image fusion techniques, which are being extensively researched. However, most of these works assume that source images are preregistered, which is a less practical scenario. Both registered and unregistered image fusion algorithms are considered in this thesis. The registration involves the geometrical / spatial alignment of source images taken using different sensors or a sensor in different operating conditions. This research is concerned with the reliable fusion schemes of several scenario images (including muti-focus, Infra Red (IR) and visible, Computed Tomography (CT) and Magnetic Resonance (MR), and multi-exposure images) demonstrating high quality fused results without loss of useful information. The first scheme is a textural registration based multi-focus scheme involving the Gabor filtering (with specific frequency and orientation) for extracting texture features from the images. The filtered images are aligned/registered using affine transformation. Noise and blur play an important role in image fusion and need to be classified and treated for quality image fusion. The next two fusion schemes deal with multi-exposure noisy (real and synthetic both) and blur images. In the first algorithm, the noisy, blurry and clean images are classified using Laplacian filter and histogram spread. The noise is reduced in the frequency domain. Heavy weights are assigned to noise free pixels and the blur images are passed through the Wiener filter. In the second algorithm, a noise resistant image fusion scheme for multi-exposure sensors using color dissimilarity (for motion detection and removal), median and noise maps is proposed. A well exposed image is obtained as a result of weighted average of multi-exposure source images. Higher valued weights are assigned to pixels containing low values of noises, high values of color dissimilarity and median maps. The next work (two schemes) involve pre-registered visible and IR images. In the first one, a three stage image fusion scheme using Genetic Algorithm (GA) is presented. In the first stage, it segments the image into homogeneous regions and generates segmentation maps. In the second stage, the segmentation maps are combined by an adaptive weight adjustment procedure. The third stage fuses the input images and segmentation maps via GA based multi- objective optimization strategy. The second image fusion scheme uses Un-Decimated Dual Tree Complex Wavelet Transform (UDTCWT) for astronomical images. The UDTCWT reduces noise effects and improves object classification due to its inherited shift invariance property. Local standard deviation and distance transforms are used to extract useful information, especially small objects. In the medical (CT and MR) image fusion scheme, the source images are contrast enhanced using histogram equalization. It is a sparse decomposition based fusion technique that uses the dictionary learnt from input images and k-mean singular value decomposition algorithm. The scheme splits CT and MR images into texture and gradient images. The texture decomposition improves the overall performance of the sparse representation based fusion. The quantitative analysis performed using mutual information, structural similarity measure and edge dependent based performance metrics, yields improved results for proposed schemes, as compared to existing schemes.