UTILIZATION OF SPATIAL INFORMATION FOR POINT CLOUD SEGMENTATION

Date

2010-06-09

Author

Akman, Oytun
Bayramoglu, Neslihan
Alatan, Abdullah Aydın
Jonker, Pieter

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

204
views

0
downloads

Object segmentation has an important role in the field of computer vision for semantic information inference. Many applications such as 3DTV archive systems, 3D/2D model fitting, object recognition and shape retrieval are strongly dependent to the performance of the segmentation process. In this paper we present a new algorithm for object localization and segmentation based on the spatial information obtained via a Time-of-Flight (TOF) camera. 3D points obtained via a TOF camera are projected onto the major plane representing the planar surface on which the objects are placed. Afterward, the most probable regions that an item can be placed are extracted by using kernel density estimation method and 3D points are segmented into objects. Also some well-known segmentation algorithms are tested on the 3D (depth) images.

Subject Keywords

3D sensor fusion, Segmentation, Density estimation

URI

https://hdl.handle.net/11511/52971

Conference Name

4th 3DTV Conference on the True Vision - Capture, Transmission and Display of 3D Video

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Supervised mesh segmentation for 3D objects with graph Convolutional neural networks Perek, Emir Kaan; Sahillioğlu, Yusuf; Department of Computer Engineering (2019) Mesh segmentation is a fundamental application that is primarily used for understanding and analyzing 3D shapes in a broad range of areas in Computer Science. With the increasing trend of deep learning, there have been many learning-based solutions to the mesh segmentation problem based on the classification of the individual mesh polygons. In this thesis, we cast mesh segmentation as a supervised graph labeling problem by using Graph Convolutional Neural Networks (GCNN). We treat a mesh object as a graph t...
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...
Utilization of dense depth information for monoview object detection and instance segmentation Çakırgöz, Çağlayan Can; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2022-5-10) Object detection aims for detecting objects of certain classes in an image by bounding them in rectangular boxes whereas instance segmentation tries to detect objects in pixel level. Deep learning techniques, which have shown great improvements over the last decade, are utilized in these topics as well, and a significant success is achieved against the traditional methods. Similar improvements can be observed in dense depth estimation which deals with deducing dense information of a scene from a single imag...
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...
Investigation of the effects of structural characteristics of object-oriented software on fault-proneness Gölcük, Halit; Bilgen, Semih; Department of Electrical and Electronics Engineering (2014) This study investigates the effects of structural characteristics of object-oriented software, which are observable at the model level of the software developed by means of Unified Modeling Language (UML), on software quality, assessing quality in terms of fault-proneness. In the scope of this thesis study, real-time embedded software components developed by Aselsan, a leading defense industry company in Turkey, were analyzed. The correlation between software metrics measured from the UML models of the soft...

Citation Formats

O. Akman, N. Bayramoglu, A. A. Alatan, and P. Jonker, “UTILIZATION OF SPATIAL INFORMATION FOR POINT CLOUD SEGMENTATION,” presented at the 4th 3DTV Conference on the True Vision - Capture, Transmission and Display of 3D Video, Tampere, FINLAND, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52971.