A comparative study on pose estimation algorithms using visual data

Çetinkaya, Güven
Computation of the position and orientation of an object with respect to a camera from its images is called pose estimation problem. Pose estimation is one of the major problems in computer vision, robotics and photogrammetry. Object tracking, object recognition, self-localization of robots are typical examples for the use of pose estimation. Determining the pose of an object from its projections requires 3D model of an object in its own reference system, the camera parameters and 2D image of the object. Most of the pose estimation algorithms require the correspondences between the 3D model points of the object and 2D image points. In this study, four well-known pose estimation algorithms requiring the 2D-3D correspondences to be known a priori; namely, Orthogonal Iterations, POSIT, DLT and Efficient PnP are compared. Moreover, two other well-known algorithms that solve the correspondence and pose problems simultaneously; Soft POSIT and Blind- PnP are also compared in the scope of this thesis study. In the first step of the simulations, synthetic data is formed using a realistic motion scenario and the algorithms are compared using this data. In the next step, real images captured by a calibrated camera for an object with known 3D model are exploited. The simulation results indicate that POSIT algorithm performs the best among the algorithms requiring point correspondences. Another result obtained from the experiments is that Soft-POSIT algorithm can be considered to perform better than Blind-PnP algorithm.


A Comparative evaluation of foreground / background segmentation algorithms
Pakyürek, Muhammet; Akar, Gözde; Department of Electrical and Electronics Engineering (2012)
Foreground Background segmentation is a process which separates the stationary objects from the moving objects on the scene. It plays significant role in computer vision applications. In this study, several background foreground segmentation algorithms are analyzed by changing their critical parameters individually to see the sensitivity of the algorithms to some difficulties in background segmentation applications. These difficulties are illumination level, view angles of camera, noise level, and range of ...
Good features to correlate for visual tracking
Gündoğdu, Erhan; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2017)
Estimating object motion is one of the key components of video processing and the first step in applications which require video representation. Visual object tracking is one way of extracting this component, and it is one of the major problems in the field of computer vision. Numerous discriminative and generative machine learning approaches have been employed to solve this problem. Recently, correlation filter based (CFB) approaches have been popular due to their computational efficiency and notable perfo...
Investigation of the Effects of False Matches and Distribution of the Matched Keypoints on The PnP Algorithm
Demirtaş, Fatih; Gülmez, Baran; Yıldırım, İrem; Leloğlu, Uğur Murat; Yaman, Mustafa; Güneyi, Eylem Tuğçe (null; 2019-04-27)
Perspective-n-Point (PnP) problem is the estimation of the pose (location and orientation) of a calibrated camera from 3D-2D point correspondences, that is, three-dimensional coordinates of objects in a world coordinate system and corresponding pixels in two-dimensional images. PnP algorithms are used in computer vision, augmented reality, robotics, photogrammetry etc. Distribution of the points in the image and the accuracy of the 3D information are important on the accuracy of the estimated pose of the ca...
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...
A Formal Methods Approach to Pattern Recognition and Synthesis in Reaction Diffusion Networks
Bartocci, Ezio; Aydın Göl, Ebru; Haghighi, Iman; Belta, Calin (2018-03-01)
We introduce a formal framework for specifying, detecting, and generating spatial patterns in reaction diffusion networks. Our approach is based on a novel spatial superposition logic, whose semantics is defined over the quad-tree representation of a partitioned image. We demonstrate how to use rule-based classifiers to efficiently learn spatial superposition logic formulas for several types of patterns from positive and negative examples. We implement pattern detection as a model-checking algorithm and we ...
Citation Formats
G. Çetinkaya, “A comparative study on pose estimation algorithms using visual data,” M.S. - Master of Science, Middle East Technical University, 2012.