Orman yangını gözetleme amaçlı video işleme algoritmaları

We propose various image and video processing algorithms for wildfire surveillance. The proposed methods include; classifier fusion, online learning, real-time feature extraction, image registration and optimization. We develop an entropy functional based online classifier fusion framework. We use Bregman divergences as the distance measure of the projection operator onto the hyperplanes describing the output decisions of classifiers. We test the performance of the proposed system in a wildfire detection application with stationary cameras that scan predefined preset positions. In the second part of this thesis, we investigate different formulations and mixture applications for passive-aggressive online learning algorithms. We propose a classifier fusion method that can be used to increase the performance of multiple online learners or the same learners trained with different update parameters. We also introduce an aerial wildfire detection system to test the real-time performance of the analyzed algorithms. In the third part of the thesis we propose a real-time dynamic texture recognition method using random hyperplanes and deep neural networks. We divide dynamic texture videos into spatio-temporal blocks and extract features using local binary patterns (LBP). We reduce the computational cost of the exhaustive LBP method by using randomly sampled subset of pixels in the block. We use random hyperplanes and deep neural networks to reduce the dimensionality of the final feature vectors. We test the performance of the proposed method in a dynamic texture database. We also propose an application of the proposed method in real-time detection of flames in infrared videos. Using the same features we also propose a fast wildfire detection system using pan-tilt-zoom cameras and panoramic background subtraction. We use a hybrid method consisting of speeded-up robust features and mutual information to register consecutive images and form the panorama. The next step for multi-modal surveillance applications is the registration of images obtained with different devices. We propose a multi-modal image registration algorithm for infrared and visible range cameras. A new similarity measure is described using log-polar transform and mutual information to recover rotation and scale parameters. Another similarity measure is introduced using mutual information and redundant wavelet transform to estimate translation parameters. The new cost function for translation parameters is minimized using a novel lifted projections onto convex sets method. Keywords: Projections onto convex sets, classifier fusion, online learning, entropymaximization, wildfire detection, adaptive filtering, LMS, Bregman divergence,image Processing, infrared, mutual information, wavelet transform, image registration,log-polar transform, supporting hyperplanes..