Image based recognition of the monuments in Prizren

Image classification application has recently been covering a high number of research fields. In the other hand as the performance of the mobile devices is being updated day by day, the implementation of image recognition algorithms in them, is not only being trendy but very helpful in everyday tasks. With the automatic monument recognition, visiting a city is easy and fun. This application recognizes the captured monument, gives useful information and describes that particular landmark. In this thesis there are used four historical monuments of the city of Prizren, Kosovo. The images where taken specially for the research from the different angles of the city and the dataset for the training and testing process has been created. Although these monuments differ from one another in the archaeological structure, the classification process is not an easy approach. Here will be presented an approach for the recognition of these particular monuments by using computer vision and machine learning methods on images. The image processing classification techniques and algorithms used in the literatures not only for the landmark recognition but overall the methods, will be described

Denoising single images by feature ensemble revisited

Image denoising is still a challenging issue in many computer vision sub-domains. Recent studies show that significant improvements are made possible in a supervised setting. However, few challenges, such as spatial fidelity and cartoon-like smoothing remain unresolved or decisively overlooked. Our study proposes a simple yet efficient architecture for the denoising problem that addresses the aforementioned issues. The proposed architecture revisits the concept of modular concatenation instead of long and deeper cascaded connections, to recover a cleaner approximation of the given image. We find that different modules can capture versatile representations, and concatenated representation creates a richer subspace for low-level image restoration. The proposed architecture's number of parameters remains smaller than the number for most of the previous networks and still achieves significant improvements over the current state-of-the-art networks