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Barrel Distortion

The Experts below are selected from a list of 441 Experts worldwide ranked by ideXlab platform

Frantz Lohier – 1st expert on this subject based on the ideXlab platform

  • Real-time, color image Barrel Distortion removal
    2012 IEEE International Symposium on Circuits and Systems (ISCAS), 2012
    Co-Authors: Henryk Blasinski, Frantz Lohier

    Abstract:

    This paper describes a new hardware architecture for Barrel Distortion correction in a color video stream. Such Distortion is omnipresent in images acquired with a large field of view optics: wide-angle or fish-eye lenses. The designed platform is composed of a standard image sensor, a USB video class ASIC chip, a low cost FPGA and a SDRAM memory chip. Image processing algorithms are implemented in the FPGA, which is inserted between the sensor and the ASIC. The FPGA is connected to an external SDRAM in which a frame buffer is implemented. Barrel Distortion is modeled using a polynominal relationship between corrected and distorted image spaces. Combinatorial logic circuit at the frame buffer output validates correct ordering of luminance and chrominance bytes in the data stream. The proposed design is capable of removing geometric Distortion from 640 × 480 pixel images at the rate of 30 frames per second. Colors in reconstructed images are within ΔE = 2 from the originals in the CIELab color space.

  • ISCAS – Real-time, color image Barrel Distortion removal
    2012 IEEE International Symposium on Circuits and Systems, 2012
    Co-Authors: Henryk Blasinski, Frantz Lohier

    Abstract:

    This paper describes a new hardware architecture for Barrel Distortion correction in a color video stream. Such Distortion is omnipresent in images acquired with a large field of view optics: wide-angle or fish-eye lenses. The designed platform is composed of a standard image sensor, a USB video class ASIC chip, a low cost FPGA and a SDRAM memory chip. Image processing algorithms are implemented in the FPGA, which is inserted between the sensor and the ASIC. The FPGA is connected to an external SDRAM in which a frame buffer is implemented. Barrel Distortion is modeled using a polynominal relationship between corrected and distorted image spaces. Combinatorial logic circuit at the frame buffer output validates correct ordering of luminance and chrominance bytes in the data stream. The proposed design is capable of removing geometric Distortion from 640 × 480 pixel images at the rate of 30 frames per second. Colors in reconstructed images are within ΔE = 2 from the originals in the CIELab color space.

  • FPGA architecture for real-time Barrel Distortion correction of colour images
    2011 IEEE International Conference on Multimedia and Expo, 2011
    Co-Authors: Henryk Blasinski, Frantz Lohier

    Abstract:

    This paper presents a hardware architecture for real time Barrel Distortion correction of YUV 4∶2∶2 encoded color images. In our solution we are presenting an alternative implementation of the correction engine which is based on multiplications rather than trigonometric transforms. The system makes minimal use of resources thus being a good candidate for embedding into a single chip. Proposed solution is implemented in a cost-effective Spartan 3 Field Programmable Gate Array (FPGA). Our architecture is capable of processing QQVGA images at the rate of 30 frames per second (fps). Qualitative and quantitative examination confirm correct preservation of color information in processed images.

Henryk Blasinski – 2nd expert on this subject based on the ideXlab platform

  • Real-time, color image Barrel Distortion removal
    2012 IEEE International Symposium on Circuits and Systems (ISCAS), 2012
    Co-Authors: Henryk Blasinski, Frantz Lohier

    Abstract:

    This paper describes a new hardware architecture for Barrel Distortion correction in a color video stream. Such Distortion is omnipresent in images acquired with a large field of view optics: wide-angle or fish-eye lenses. The designed platform is composed of a standard image sensor, a USB video class ASIC chip, a low cost FPGA and a SDRAM memory chip. Image processing algorithms are implemented in the FPGA, which is inserted between the sensor and the ASIC. The FPGA is connected to an external SDRAM in which a frame buffer is implemented. Barrel Distortion is modeled using a polynominal relationship between corrected and distorted image spaces. Combinatorial logic circuit at the frame buffer output validates correct ordering of luminance and chrominance bytes in the data stream. The proposed design is capable of removing geometric Distortion from 640 × 480 pixel images at the rate of 30 frames per second. Colors in reconstructed images are within ΔE = 2 from the originals in the CIELab color space.

  • ISCAS – Real-time, color image Barrel Distortion removal
    2012 IEEE International Symposium on Circuits and Systems, 2012
    Co-Authors: Henryk Blasinski, Frantz Lohier

    Abstract:

    This paper describes a new hardware architecture for Barrel Distortion correction in a color video stream. Such Distortion is omnipresent in images acquired with a large field of view optics: wide-angle or fish-eye lenses. The designed platform is composed of a standard image sensor, a USB video class ASIC chip, a low cost FPGA and a SDRAM memory chip. Image processing algorithms are implemented in the FPGA, which is inserted between the sensor and the ASIC. The FPGA is connected to an external SDRAM in which a frame buffer is implemented. Barrel Distortion is modeled using a polynominal relationship between corrected and distorted image spaces. Combinatorial logic circuit at the frame buffer output validates correct ordering of luminance and chrominance bytes in the data stream. The proposed design is capable of removing geometric Distortion from 640 × 480 pixel images at the rate of 30 frames per second. Colors in reconstructed images are within ΔE = 2 from the originals in the CIELab color space.

  • FPGA architecture for real-time Barrel Distortion correction of colour images
    2011 IEEE International Conference on Multimedia and Expo, 2011
    Co-Authors: Henryk Blasinski, Frantz Lohier

    Abstract:

    This paper presents a hardware architecture for real time Barrel Distortion correction of YUV 4∶2∶2 encoded color images. In our solution we are presenting an alternative implementation of the correction engine which is based on multiplications rather than trigonometric transforms. The system makes minimal use of resources thus being a good candidate for embedding into a single chip. Proposed solution is implemented in a cost-effective Spartan 3 Field Programmable Gate Array (FPGA). Our architecture is capable of processing QQVGA images at the rate of 30 frames per second (fps). Qualitative and quantitative examination confirm correct preservation of color information in processed images.

Joonki Paik – 3rd expert on this subject based on the ideXlab platform

  • Correction of Barrel Distortion in Fisheye Lens Images Using Image-Based Estimation of Distortion Parameters
    IEEE Access, 2019
    Co-Authors: Joonki Paik

    Abstract:

    Images acquired by a fisheye lens camera contain geometric Distortion that results in deformation of the object’s shape. To correct the lens Distortion, existing methods use prior information, such as calibration patterns or lens design specifications. However, the use of a calibration pattern works only when an input scene is a 2-D plane at a prespecified position. On the other hand, the lens design specifications can be understood only by optical experts. To solve these problems, we present a novel image-based algorithm that corrects the geometric Distortion. The proposed algorithm consists of three stages: i) feature detection, ii) Distortion parameter estimation, and iii) selection of the optimally corrected image out of multiple corrected candidates. The proposed method can automatically select the optimal amount of correction for a fisheye lens Distortion by analyzing characteristics of the distorted image using neither prespecified lens design parameters nor calibration patterns. Furthermore, our method performs not only on-line correction by using facial landmark points, but also off-line correction described in subsection III-C. As a result, the proposed method can be applied to a virtual reality (VR) or augmented reality (AR) camera with two fisheye lenses in a field-of-view (FOV) of 195°, autonomous vehicle vision systems, wide-area visual surveillance systems, and unmanned aerial vehicle (UAV) cameras.

  • Non-dyadic fisheye lens correction model for image enhancement
    Journal of The Optical Society of America A-optics Image Science and Vision, 2015
    Co-Authors: Jinho Park, Joonki Paik

    Abstract:

    This paper presents a non-dyadic framework to improve example-based enhancement of radially distorted images acquired by a very wide-angle lens. In order to remove both jagging and blurring artifacts in the correction process of the fisheye lens’ Barrel Distortion, the proposed method first performs non-dyadic or multiple-step geometric correction based on the parabolic equation-based lens Distortion model. At each correction step, an example-based image enhancement method removes undesired geometric correction artifacts such as jagging and blurring. Experimental results demonstrate that the proposed method outperforms existing fisheye lens image enhancement methods in the sense of both subjective and objective measures. Based on both theoretical advancement and experimental results, the proposed method can be used for various wide-view imaging applications including vehicle front- and rear-view cameras and wide-angle video surveillance systems.

  • ICCE – Fisheye lens-based surveillance camera for wide field-of-view monitoring
    2015 IEEE International Conference on Consumer Electronics (ICCE), 2015
    Co-Authors: Eunjung Chae, Gwanghyun Jo, Joonki Paik

    Abstract:

    This paper presents a single fisheye lens camera-based visual surveillance system for monitoring a wide area. A fisheye lens has a wider field-of-view (FOV) than normal lenses at the cost of a Barrel Distortion in the acquired image. After correcting the Barrel Distortion, the proposed algorithm detects objects, and performs tracking using a histogram-based Gaussian mixture model (GMM). Experimental results show that the proposed algorithm can efficiently detect objects by reducing the geometric Distortion of the input image. For this reason it is suitable for not only surveillance cameras but also consumer applications of video object detection and recognition.