Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
CoSPAIR: Colored Histograms of Spatial Concentric Surflet-Pairs for 3D object recognition
Date
2016-01-01
Author
Logoglu, K. Berker
Kalkan, Sinan
Temizel, Alptekin
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
235
views
0
downloads
Cite This
Introduction of RGB-D sensors together with the efforts on open-source point-cloud processing tools boosted research in both computer vision and robotics. One of the key areas which have drawn particular attention is object recognition since it is one of the crucial steps for various applications. In this paper, two spatially enhanced local 3D descriptors are proposed for object recognition tasks: Histograms of Spatial Concentric Surflet-Pairs (SPAIR) and Colored SPAIR (CoSPAIR). The proposed descriptors are compared against the state-of-the-art local 3D descriptors that are available in Point Cloud Library (PCL) and their object recognition performances are evaluated on several publicly available datasets. The experiments demonstrate that the proposed CoSPAIR descriptor outperforms the state-of-the-art descriptors in both category-level and instance-level recognition tasks. The performance gains are observed to be up to 9.9 percentage points for category-level recognition and 16.49 percentage points for instance-level recognition over the second-best performing descriptor.
Subject Keywords
3D descriptors
,
3D object recognition
,
Point clouds
,
RGB-D
URI
https://hdl.handle.net/11511/30296
Journal
ROBOTICS AND AUTONOMOUS SYSTEMS
DOI
https://doi.org/10.1016/j.robot.2015.09.027
Collections
Graduate School of Informatics, Article
Suggestions
OpenMETU
Core
Spatial 3D local descriptors for object recognition in RGB-D images
Loğoğlu, K. Berker; Temizel, Alptekin; Kalkan, Sinan; Department of Information Systems (2016)
Introduction of the affordable but relatively high resolution color and depth synchronized RGB-D sensors, along with the efforts on open-source point-cloud processing tools boosted research in both computer vision and robotics. One of the key areas which have drawn particular attention is object recognition since it is one of the crucial steps for various applications. In this thesis, two spatially enhanced local 3D descriptors are proposed for object recognition tasks: Histograms of Spatial Concentric Surf...
A comparison on textured motion classification
Oztekin, Kaan; Akar, Gözde (2006-01-01)
Textured motion - generally known as dynamic or temporal texture analysis, classification, synthesis, segmentation and recognition is popular research areas in several fields such as computer vision, robotics, animation, multimedia databases etc. In the literature, several algorithms are proposed to characterize these textured motions such as stochastic and deterministic algorithms. However, there is no study which compares the performances of these algorithms. In this paper, we carry out a complete compari...
Face classification with support vector machine
Kepenekci, B; Akar, Gözde (2004-04-30)
A new approach to feature based frontal face recognition with Gabor wavelets and support vector machines is presented in this paper. The feature points are automatically extracted using the local characteristics of each individual face. A kernel that computes the similarity between two feature vectors, is used to map the face features to a space with higher dimension. To find the identity of a test face, the possible labels of each feature vector of that face is found with support vector machines, then the ...
Real-time object-oriented framework for fmi co-simulation
Çam Silik, Merve; Oğuztüzün, Mehmet Halit S.; Department of Computer Engineering (2020)
Development of models, their integration into FMI-compliant co-simulation and performing the simulation in real-time environment are crucial tasks in embedded system development. To introduce an object-oriented co-simulation environment to real-time domain, promote model reuse and minimize effort for co-simulation environment generation a Real-Time Object-Oriented Framework for FMI Co-Simulation was developed for completion of this thesis. A case study which comprises of a control actuation system and sine ...
FPGA implementation of field oriented control forpermanent magnet synchronous motor
Irmak, Gizem; Saranlı, Afşar; Department of Electrical and Electronics Engineering (2019)
The thesis study focuses on the fully operational FPGA implementation for the current/torque control of a Permanent Magnet Synchronous Motor. 3-phase synchronous motors with permanent magnets can be categorized into two categories as Permanent Magnet Synchronous Motor (PMSM) and Brushless Direct Current (BLDC) motor. The main difference between PMSM and BLDC is the shape of the induced back-EMF voltage. While BLDC motors have trapezoidal shaped back-EMF, PMSMs have a sinusoidal back-EMF. In order to take ad...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
K. B. Logoglu, S. Kalkan, and A. Temizel, “CoSPAIR: Colored Histograms of Spatial Concentric Surflet-Pairs for 3D object recognition,”
ROBOTICS AND AUTONOMOUS SYSTEMS
, pp. 558–570, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30296.