Sharif Digital Repository

Image classification is one of the most challenging issues in computer vision. One sort of such classifications is Scene Classification. To perform automatic classification reserchers used many aproches.The general approach used features directly extracted from the image, such as color and texture or features extracted by the SIFT algorithmetc. Another method is based on recognizing object of the Scene (espessially indoor scene). This method is based on finding of a limited number of prespecified objects. In the proposed method, first a window surrounding each objects, (regardless of the type of object) founded. Then the SIFT feature is extracted from that window. All features (corresponding... 

Classification is one the contrivesial problems in machine vision and pattern recongnition. Traditional feature extraction methods which are based on low level feature extraction do not have high classification accuracy, thus they do not have the ability to represent images in feature space in discriminative way. In this thesis we have proposed a grid base method and used hidden Markov model (HMM) to include topological and spatial information in feature vectors. Then the classifiers created based on HMM feature extraction are combind. Combination of classifiers is based on designing a convex goal function. The goal of this optimization is to determine the wight of each classifier for... 

Scene classification is one of the most controversial fields in computer vision. It has many applications such as robot navigation and control, content-based image retrieval (CBIR), semantic organization of image databases, depth estimation and multimedia services. In fact the outcome of any classification system depends on the ability of the feature vector defined for the problem, by means of its distinguishing strength. In this research we focus on efficient feature extraction methods. In recent years, methods based on bags of features and special pyramid approach, have shown good performance in scene classification comparison to the others. So we based our proposed method on these ideas.... 

Scene classification and understanding is one of the most important fields in computer vision. Its applications are such as exploring robot navigation enviroment, content-based image retrieval (CBIR), organization in image databases, highly semantic describing images and videos and content extraction of videos.Many methods and algorithm are proposed till today to deal with diversity of this field by emphesizing on feature based methods or machine learning based methods. In this research we have focoused on proposing a new algorithm which is using principals of NBNN image classification method but major changes in how to exract distance metric from Nearest neighbour and how to use local... 

Traffic density estimation is one of the most challenging problems in Intelligent Transportation Systems. One of the important traffic information that is broadcasted to drivers is Traffic Density information. In many traffic control centers; human operators are responsible for estimating traffic density from captured video data. Increasing traffic cameras and constraint number of operators introduce an updating delay to broadcasted information. So it is important to have an automatic traffic density estimation system. In this thesis, machine vision is used to solve this problem. Supervised Image classification is our approach. In supervised Image classification, images are classified to...