Object recognition and segmentation via shape models

Download

index.pdf

Date

2016

Author

Altınoklu, Metin Burak

Metadata

Show full item record

Item Usage Stats

299
views

152
downloads

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 pairwise relation between parts. For edge support function of lines, directional chamfer matching cost is calculated. The performance of the new method has been evaluated with experiments using databases especially suitable for shape based object detection methods. The proposed method performs well, and it is much faster as compared to related methods.

Subject Keywords

Computer vision., Image processing., Computer graphics., Image segmentation.

URI

http://etd.lib.metu.edu.tr/upload/12619841/index.pdf
https://hdl.handle.net/11511/25559

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

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...
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...
Object Detection with Minimal Supervision Demirel, Berkan; Cinbiş, Ramazan Gökberk; İkizler Cinbiş, Nazlı; Department of Computer Engineering (2023-1-18) Object detection is considered one of the most challenging problems in computer vision since it requires correctly predicting both the object classes and their locations. In the literature, object detection approaches are usually trained in a fully-supervised manner, with a large amount of annotated data for all classes. Since data annotation is costly in terms of both time and labor, there are also alternative object detection methods, such as weakly supervised or mixed supervised learning to reduce these ...
Part-based data-driven shape interpolation Aydınlılar, Melike; Sahillioğlu, Yusuf; Department of Computer Engineering (2018) An active problem in digital geometry processing is shape interpolation which aims to generate a continuous sequence of in-betweens for a given source and target shape. Unlike traditional approaches that interpolate source and target shapes in isolation, recent data-driven approaches utilize multiple interpolations through intermediate database shapes, and consequently perform better at the expense of a database requirement. In contrast to the existing data-driven approaches that consider intermediate shape...

Citation Formats

M. B. Altınoklu, “Object recognition and segmentation via shape models,” Ph.D. - Doctoral Program, Middle East Technical University, 2016.