Mr. Omer Faruk Adil Profile

Omer Faruk Adil

Computer Vision Research Engineer at Aselsan AS

SPIE Involvement:

Author

Publications (3)

Proceedings Article | 9 October 2018 Paper

Scene-based bad pixel detection using interframe registration

Omer Faruk Adil, Huseyin Seckin Demir

Proceedings Volume 10795, 1079505 (2018) https://doi.org/10.1117/12.2324957

KEYWORDS: Image registration, Technetium, Staring arrays, Cameras, Infrared imaging, Calibration, Image filtering, Nonuniformity corrections, Thermography, Detection and tracking algorithms

Read Abstract +

Infrared imagery has been used in many areas such as military, surveillance, medical imaging and numerous industrial branches. The recent increase in the use of infrared (IR) imaging techniques in various fields draws the attention of more studies towards this area. One of the common problems of the thermal IR detector units is the existence of bad pixels. Bad pixels may arise from various reasons such as manufacturing processes or operating conditions. This phenomenon is commonly dealt with well-known calibration methods. However, they are generally applied at factory level or they interrupt the operational use due to the need for utilizing a uniform reference scene. For those reasons recent methods employ scene based approaches without requiring a special equipment. Those types of methods commonly make some assumptions based on statistical characteristics of bad pixels. They mainly assume that bad pixels deviate at a certain level from their neighboring pixels. They rely on sufficient variation of scene content in time and the fact that possible false detections can be canceled out due to scene variation. Nevertheless, this assumption does not always hold, especially when the camera is stationary. In such cases, some distinctive parts of the underlying scene may be falsely regarded as bad pixels. To that end, we develop a method that is able to isolate the scene content from bad pixels in order to eliminate erroneous detections of scene parts. The proposed method benefits from the motion of the camera which provides responses of different pixels for the same scene region. From this information, we expect similar responses for the registered pixels, if they are not defective. On the other hand, if the pixel responses are exceedingly different, then we can deduce that the corresponding pixel may be defective. For this purpose, we first register adjacent frames using an efficient 1D projection based matching method. To ensure a more robust registration, we use edge maps rather than the intensity image. After the registration of two frames, we construct an error map for the overlapping regions of the two frames. We declare our candidate defective pixels by assessing the deviation levels of error values. Candidate pixels are accumulated across non-stationary frames to obtain temporally consistent detections. Since our inter-frame registration step provides motion information, we avoid accumulation when camera is stationary. We also prevent erroneous registrations by checking for the sufficient scene detail. The performance evaluations are carried out on an extensive dataset consisting of real thermal camera images. The dataset contains a wide variety of scene content and various scenarios featuring stationary camera conditions that causes failures in traditional statistical variation based approaches. The results of our experiments are assessed in terms of true and false bad pixel detections as compared to ground-truth bad pixel labellings. The results show that the proposed inter-frame registration based bad pixel detection method achieves successful results without any assumption about scene content and any additional reference surface.

Proceedings Article | 17 September 2018 Paper

IRSUM: inter-frame registration based non-uniformity correction using spatio-temporal update mask

Huseyin Seckin Demir, Omer Faruk Adil

Proceedings Volume 10752, 107521D (2018) https://doi.org/10.1117/12.2320435

KEYWORDS: Image registration, Nonuniformity corrections, Thermography, Staring arrays, Sensors, Infrared imaging, Cameras, Signal to noise ratio, Calibration, Error analysis

Read Abstract +

SPIE Journal Paper | 13 January 2017

Optical alignment procedure utilizing neural networks combined with Shack–Hartmann wavefront sensor

Fatime Zehra Adil, Erhan İlhan Konukseven, Tuna Balkan, Ömer Faruk Adil

OE, Vol. 56, Issue 05, 051402, (January 2017) https://doi.org/10.1117/12.10.1117/1.OE.56.5.051402

KEYWORDS: Neural networks, Optical alignment, Wavefronts, Wavefront sensors, Zernike polynomials, Zemax, Optical testing, Optical engineering, MATLAB, Objectives

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years