Face segmentation in thermal images /

Eryılmaz, Melis
Automatic face segmentation is a key issue in many applications such as machine vision, coding, etc. Therefore, the accuracy of the segmentation algorithms results has a strong impact on the later stages. These algorithms should also be computationally efficient and robust against changing environments. The aim of this thesis is to analyze different approaches for face segmentation and compare them in terms of the robustness and computational efficiency. Four different face segmentation methods are chosen to be compared in the scope of this thesis. Experiments are performed on IRIS and Terravic databases. Implemented face segmentation methods are compared according to their classification performances and error rates.


Object recognition and segmentation via shape models
Altınoklu, Metin Burak; Ulusoy, İlkay; Tarı, Zehra Sibel; Department of Electrical and Electronics Engineering (2016)
In this thesis, the problem of object detection, recognition and segmentation in computer vision is addressed with shape based methods. An efficient object detection method based on a sparse skeleton has been proposed. The proposed method is an improved chamfer template matching method for recognition of articulated objects. Using a probabilistic graphical model structure, shape variation is represented in a skeletal shape model, where nodes correspond to parts consisting of lines and edges correspond to pa...
Moving hot object detection in airborne thermal videos
Kaba, Utku; Akar, Gözde; Department of Electrical and Electronics Engineering (2012)
In this thesis, we present an algorithm for vision based detection of moving objects observed by IR sensors on a moving platform. In addition we analyze the performance of different approaches in each step of the algorithm. The proposed algorithm is composed of preprocessing, feature detection, feature matching, homography estimation and difference image analysis steps. First, a global motion estimation based on planar homography model is performed in order to compensate the motion of the sensor and moving ...
Environment generation tool for enabling aspect verification
Aldanmaz, Şenol Lokman; Betin Can, Aysu; Department of Information Systems (2010)
Aspects are units of aspect oriented programming developed for influencing the software behavior. In order to use an aspect confidently in any software, first it should be verified. For verification of an aspect, the mock classes for the original software should be prepared. These mock classes are a model of the aspect environment which the aspect is woven. In this study, considering that there are not enough tools for supporting the aspect oriented programming developers, we have developed a tool for enabl...
Visual object detection and tracking using local convolutional context features and recurrent neural networks
Kaya, Emre Can; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2018)
Visual object detection and tracking are two major problems in computer vision which have important real-life application areas. During the last decade, Convolutional Neural Networks (CNNs) have received significant attention and outperformed methods that rely on handcrafted representations in both detection and tracking. On the other hand, Recurrent Neural Networks (RNNs) are commonly preferred for modeling sequential data such as video sequences. A novel convolutional context feature extension is introduc...
Compressive sensing methods for multi-contrast magnetic resonance imaging
Güngör, Alper; Yarman Vural, Fatoş Tunay; Çukur, Tolga; Department of Computer Engineering (2017)
Compressive sensing (CS) is a signal processing tool that allows reconstruction of sparse signals from highly undersampled data. This study investigates application of CS to magnetic resonance imaging (MRI). In this study, first, an optimization framework for single contrast CS MRI is presented. The method relies on an augmented Lagrangian based method, specifically alternating direction method of multipliers (ADMM). The ADMM framework is used to solve a constrained optimization problem with an objective fu...
Citation Formats
M. Eryılmaz, “Face segmentation in thermal images /,” M.S. - Master of Science, Middle East Technical University, 2015.