Rancang Bangun Game Aksi dengan Integrasi dan Pengenalan Gambar Menggunakan Algoritma Ekstraksi Fitur SURF dan Klasifikasi SVM pada Perangkat Android

Saat ini perangkat mobile berbasis Android dan iOS sangat digemari oleh anak-anak. Mereka yang telah memiliki perangkat mobile akan menghabiskan seluruh waktunya untuk menatap layar perangkat saat bermain. Akibat yang ditimbulkan adalah penurunan kesehatan mata dan kurangnya bersosialisasi dengan teman sebaya. Menggambar adalah hal yang disukai anak-anak. Dengan menggabungkan menggambar dengan permainan perangkat mobile, anak-anak akan tetap merasa bermain meski mereka sedang tidak menatap layar untuk menggambar. Tugas akhir ini bertujuan untuk membangun sebuah permainan yang dapat mengenali gambar dan menggunakan gambar tersebut dalam permainan. Metode pengenalan gambar dalam tugas akhir ini dilakukan dengan menggunakan metode Edge Detection dengan algoritma ekstraksi fitur SURF (Speeded-Up Robust Features) dan klasifikasi SVM (Support Vector Machine). Jenis permainan yang dibangun merupakan gabungan dari aksi dan tower defense. Hasil dari tugas akhir ini dibagi menjadi 3 yaitu hasil fungsionalitas permainan, hasil daya tarik permainan dan hasil akurasi pengenalan gambar. Semua fungsi yang dibuat pada permainan dapat berjalan dengan baik, dan permainan juga telah memiliki daya tarik yang memikat. Namun hasil akurasi pengenalan gambar masih kurang dari harapan penulis

Sparse Coding-Based Method Comparison for Land-Use Classification

Land-use classification utilize high-resolution remote sensing image. The image is utilized for improving the classification problem. Nonetheless, in other side, the problem becomes more challenging cause the image is too complex. We have to represent the image appropriately. On of the common method to deal with it is Bag of Visual Word (BOVW). The method needs a coding process to get the final data interpretation. There are many methods to do coding such as Hard Quantization Coding (HQ), Sparse Coding (SC), and Locality-constrained Linear Coding (LCC). However, that coding methods use a different assumption. Therefore, we have to compare the result of each coding method. The coding method affects classification accuracy. The best coding method will produce the better classification result. Dataset UC Merced consisted 21 classes is used in this research. The experiment result shows that LCC got better performance / accuracy than SC and HQ. LCC method got 86.48 % accuracy. Furthermore, LCC also got the best performance on various number of training data for each class

Deep learning for animal recognition

Deep learning has obtained many successes in different computer vision tasks such as classification, detection, and segmentation of objects or faces. Many of these successes can be ascribed to training deep convolutional neural network architectures on a dataset containing many images. Limited research has explored deep learning methods for performing recognition or detection of animals using a limited number of images. This thesis examines the use of different deep learning techniques and conventional computer vision methods for performing animal recognition or detection with relatively small training datasets and has the following objectives: 1) Analyse the performance of deep learning systems compared to classical approaches when there exists a limited number of images of animals; 2) Develop an algorithm for effectively dealing with rotation variation naturally present in aerial images; 3) Construct a computer vision system that is more robust to illumination variation; 4) Analyse how important the use of different color spaces is in deep learning; 5) Compare different deep convolutional neural-network algorithms for detecting and recognizing individual instances (identities) in a group of animals, for example, badgers. For most of the experiments, effectively reduced neural network recognition systems are used, which are derived from existing architectures. These reduced systems are compared to standard architectures and classical computer vision methods. We also propose a color transformation algorithm, a novel rotation-matrix data-augmentation algorithm and a hybrid variant of such a method, that factors color constancy with the aim to enhance images and construct a system that is more robust to different kinds of visual appearances. The results show that our proposed algorithms aid deep learning systems to become more accurate in classifying animals for a large number of different animal datasets. Furthermore, the developed systems yield performances that significantly surpass classical computer vision techniques, even with limited amounts of available images for training