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 ...
Edge strength functions as shape priors in image segmentation
Erdem, Erkut; Erdem, Aykut; Tarı, Zehra Sibel (2005-12-01)
Many applications of computer vision requires segmenting out of an object of interest from a given image. Motivated by unlevel-sets formulation of Raviv, Kiryati and Sochen [8] and statistical formulation of Leventon, Grimson and Faugeras [6], we present a new image segmentation method which accounts for prior shape information. Our method depends on Ambrosio-Tortorelli approximation of Mumford-Shah functional. The prior shape is represented by a by-product of this functional, a smooth edge indicator functi...
Hierarchical representations for visual object tracking by detection
Beşbınar, Beril; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2015)
Deep learning is the discipline of training computational models that are composed of multiple layers and these methods have improved the state of the art in many areas such as visual object detection, scene understanding or speech recognition. Rebirth of these fairly old computational models is usually related to the availability of large datasets, increase in the computational power of current hardware and more recently proposed unsupervised training methods that exploit the internal structure of very lar...
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.