Köksal, Ali

Profile URL
https://hdl.handle.net/11147/16362
Name Variants
Koksal, A.
A. Koksal
A. Köksal
Köksal, Ali.
Koksal, Ali.
Köksal, A.
Job Title
Email Address
Main Affiliation
01. Izmir Institute of Technology
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

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SDG data is not available
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Scholarly Output

3

Articles

1

Views / Downloads

35245/634

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

0

Scopus Citation Count

1

Patents

0

Projects

0

WoS Citations per Publication

0.00

Scopus Citations per Publication

0.33

Open Access Source

2

Supervised Theses

1

JournalCount
2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings1
IET Computer Vision1
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2015 - 20193

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Scholarly Output Search Results

Now showing 1 - 3 of 3
  • Master Thesis
    Keypoint Detection and Description on Image Curves
    (Izmir Institute of Technology, 2017) Köksal, Ali; Özuysal, Mustafa
    Image curves are one of the choices for representing interest points which also provide discriminative information about images. Boundary of regions and contour of shapes are real-time instances of image curves. In this thesis, we propose two approaches for keypoint detection and description on image curves. To extract keypoints on image curves, we compute the extrema curvature of region boundaries. This mechanism improves repeatability of keypoints on 3D data. For the description of image curves, shape contours are used. This is similar to approaches that describe the features based on shapes and image gradients. Unlike these approaches, we combine spatial and directional information of tangent directions to extract a feature vector that leads to improved matching and recognition on several standard computer vision tasks such as character and object recognition.
  • Article
    Citation - Scopus: 1
    Curve Description by Histograms of Tangent Directions
    (Institution of Engineering and Technology, 2019) Köksal, Ali; Özuysal, Mustafa
    The authors propose a novel approach for the description of objects based on contours in their images using real-valued feature vectors. The approach is particularly suitable when objects of interest have high contrast and texture-free images or when the texture variations are high so textural cues are nuisance factors for classification. The proposed descriptor is suitable for nearest neighbour classification still popular in embedded vision applications when the power considerations outweigh the performance requirements. They describe object outlines purely based on the histograms of contour tangent directions mimicking many of the design heuristics of texture-based descriptors such as scale-invariant feature transform (SIFT). However, unlike SIFT and its variants, the proposed approach is directly designed to work with contour data and it is robust to variations inside and outside the object outline as well as the sampling of the contour itself. They show that relying on tangent direction estimation as opposed to gradient computation yields a more robust description and higher nearest neighbour classification rates in a variety of classification problems.
  • Conference Object
    A Detailed Analysis of Mser and Fast Repeatibility
    (Institute of Electrical and Electronics Engineers Inc., 2015) Uzyıldırım, Furkan Eren; Köksal, Ali; Özuysal, Mustafa
    This paper investigates the relationship between the MSER and FAST repeatability and changes in various camera parameters. By employing a realistic view synthesis methodology, it is possible to observe the effect of small parameter changes on the repeatability. Furthermore, for the analysis of MSER repeatability, a convex hull approach is proposed instead of fitting ellipses to the MSER region. This yields a better approximation to the MSER region without significantly increasing computation time.