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Eunsoo Kim - One of the best experts on this subject based on the ideXlab platform.

  • 3d image correlator using computational integral imaging reconstruction based on modified Convolution Property of periodic functions
    Journal of The Optical Society of Korea, 2014
    Co-Authors: Jaeyoung Jang, Sukpyo Hong, Donghak Shin, Byunggook Lee, Eunsoo Kim
    Abstract:

    In this paper, we propose a three-dimensional (3D) image correlator by use of computational integral imaging reconstruction based on the modified Convolution Property of periodic functions (CPPF) for recognition of partially occluded objects. In the proposed correlator, elemental images of the reference and target objects are picked up by a lenslet array, and subsequently are transformed to a sub-image array which contains different perspectives according to the viewing direction. The modified version of the CPPF is applied to the sub-images. This enables us to produce the plane sub-image arrays without the magnification and superimposition processes used in the conventional methods. With the modified CPPF and the sub-image arrays, we reconstruct the reference and target plane sub-image arrays according to the reconstruction plane. 3D object recognition is performed through cross-correlations between the reference and the target plane sub-image arrays. To show the feasibility of the proposed method, some preliminary experiments on the target objects are carried out and the results are presented. Experimental results reveal that the use of plane sub-image arrays enables us to improve the correlation performance, compared to the conventional method using the computational integral imaging reconstruction algorithm.

  • improved 3 d image reconstruction using the Convolution Property of periodic functions in curved integral imaging
    Optics and Lasers in Engineering, 2014
    Co-Authors: Jaeyoung Jang, Donghak Shin, Eunsoo Kim
    Abstract:

    Abstract In this paper, we propose a new approach for image and depth resolution-enhanced reconstruction using the Convolution Property between elemental images and the periodic δ -function array in a curved integral-imaging system. For three-dimensional (3-D) image reconstruction based on the Convolution Property of periodic δ -functions, the image resolution is proportional to the number of sampling images to be convolved, and the depth resolution is inversely related to the focal length of the elemental-image pickup system. Thus, the use of a large aperture in the curved integral-imaging system allows us to enlarge the field-of-view of the pickup system and may improve the resolution and depth of the reconstructed images. To test the feasibility of the proposed method, experiments are performed with test objects, and the results are compared with the results of the conventional method in terms of resolution and depth. The experimental results indicate that the proposed method outperforms the conventional method.

  • computational 3d image reconstruction of curved integral imaging using Convolution Property between periodic functions
    Digital Holography and Three-Dimensional Imaging (2013) paper DW2A.15, 2013
    Co-Authors: Jaeyoung Jang, Sukpyo Hong, Donghak Shin, Eunsoo Kim
    Abstract:

    An improved 3D image reconstruction using the Convolution Property between an elemental image and a periodic delta-function array in curved integral imaging is presented.

Jaeyoung Jang - One of the best experts on this subject based on the ideXlab platform.

  • depth resolution enhanced spatial filtering in integral imaging based on Convolution Property between periodic functions with interpolation method
    Optik, 2017
    Co-Authors: Kijoong Mah, Koonja Lee, Hyunsung Leem, Jungun Jang, Jaeyoung Jang
    Abstract:

    Abstract We propose a depth resolution improved target depth image extraction method in integral imaging by using the Convolution Property between periodic functions (CPPF) with image interpolation algorithm. We derive the depth resolution equation of a direct pickup system and analyze the factors related to the change of the depth resolution which are the focal length of an elemental lens and the resolution of an elemental image. We investigate the effect according to the change of these factors. The simulation results show that the key factor to change the depth resolution is the resolution of an elemental image. To show the feasibility of the proposed method, we carry out the preliminary experiments and present the results.

  • 3d image correlator using computational integral imaging reconstruction based on modified Convolution Property of periodic functions
    Journal of The Optical Society of Korea, 2014
    Co-Authors: Jaeyoung Jang, Sukpyo Hong, Donghak Shin, Byunggook Lee, Eunsoo Kim
    Abstract:

    In this paper, we propose a three-dimensional (3D) image correlator by use of computational integral imaging reconstruction based on the modified Convolution Property of periodic functions (CPPF) for recognition of partially occluded objects. In the proposed correlator, elemental images of the reference and target objects are picked up by a lenslet array, and subsequently are transformed to a sub-image array which contains different perspectives according to the viewing direction. The modified version of the CPPF is applied to the sub-images. This enables us to produce the plane sub-image arrays without the magnification and superimposition processes used in the conventional methods. With the modified CPPF and the sub-image arrays, we reconstruct the reference and target plane sub-image arrays according to the reconstruction plane. 3D object recognition is performed through cross-correlations between the reference and the target plane sub-image arrays. To show the feasibility of the proposed method, some preliminary experiments on the target objects are carried out and the results are presented. Experimental results reveal that the use of plane sub-image arrays enables us to improve the correlation performance, compared to the conventional method using the computational integral imaging reconstruction algorithm.

  • improved 3 d image reconstruction using the Convolution Property of periodic functions in curved integral imaging
    Optics and Lasers in Engineering, 2014
    Co-Authors: Jaeyoung Jang, Donghak Shin, Eunsoo Kim
    Abstract:

    Abstract In this paper, we propose a new approach for image and depth resolution-enhanced reconstruction using the Convolution Property between elemental images and the periodic δ -function array in a curved integral-imaging system. For three-dimensional (3-D) image reconstruction based on the Convolution Property of periodic δ -functions, the image resolution is proportional to the number of sampling images to be convolved, and the depth resolution is inversely related to the focal length of the elemental-image pickup system. Thus, the use of a large aperture in the curved integral-imaging system allows us to enlarge the field-of-view of the pickup system and may improve the resolution and depth of the reconstructed images. To test the feasibility of the proposed method, experiments are performed with test objects, and the results are compared with the results of the conventional method in terms of resolution and depth. The experimental results indicate that the proposed method outperforms the conventional method.

  • computational 3d image reconstruction of curved integral imaging using Convolution Property between periodic functions
    Digital Holography and Three-Dimensional Imaging (2013) paper DW2A.15, 2013
    Co-Authors: Jaeyoung Jang, Sukpyo Hong, Donghak Shin, Eunsoo Kim
    Abstract:

    An improved 3D image reconstruction using the Convolution Property between an elemental image and a periodic delta-function array in curved integral imaging is presented.

Donghak Shin - One of the best experts on this subject based on the ideXlab platform.

  • 3d image correlator using computational integral imaging reconstruction based on modified Convolution Property of periodic functions
    Journal of The Optical Society of Korea, 2014
    Co-Authors: Jaeyoung Jang, Sukpyo Hong, Donghak Shin, Byunggook Lee, Eunsoo Kim
    Abstract:

    In this paper, we propose a three-dimensional (3D) image correlator by use of computational integral imaging reconstruction based on the modified Convolution Property of periodic functions (CPPF) for recognition of partially occluded objects. In the proposed correlator, elemental images of the reference and target objects are picked up by a lenslet array, and subsequently are transformed to a sub-image array which contains different perspectives according to the viewing direction. The modified version of the CPPF is applied to the sub-images. This enables us to produce the plane sub-image arrays without the magnification and superimposition processes used in the conventional methods. With the modified CPPF and the sub-image arrays, we reconstruct the reference and target plane sub-image arrays according to the reconstruction plane. 3D object recognition is performed through cross-correlations between the reference and the target plane sub-image arrays. To show the feasibility of the proposed method, some preliminary experiments on the target objects are carried out and the results are presented. Experimental results reveal that the use of plane sub-image arrays enables us to improve the correlation performance, compared to the conventional method using the computational integral imaging reconstruction algorithm.

  • improved 3 d image reconstruction using the Convolution Property of periodic functions in curved integral imaging
    Optics and Lasers in Engineering, 2014
    Co-Authors: Jaeyoung Jang, Donghak Shin, Eunsoo Kim
    Abstract:

    Abstract In this paper, we propose a new approach for image and depth resolution-enhanced reconstruction using the Convolution Property between elemental images and the periodic δ -function array in a curved integral-imaging system. For three-dimensional (3-D) image reconstruction based on the Convolution Property of periodic δ -functions, the image resolution is proportional to the number of sampling images to be convolved, and the depth resolution is inversely related to the focal length of the elemental-image pickup system. Thus, the use of a large aperture in the curved integral-imaging system allows us to enlarge the field-of-view of the pickup system and may improve the resolution and depth of the reconstructed images. To test the feasibility of the proposed method, experiments are performed with test objects, and the results are compared with the results of the conventional method in terms of resolution and depth. The experimental results indicate that the proposed method outperforms the conventional method.

  • computational 3d image reconstruction of curved integral imaging using Convolution Property between periodic functions
    Digital Holography and Three-Dimensional Imaging (2013) paper DW2A.15, 2013
    Co-Authors: Jaeyoung Jang, Sukpyo Hong, Donghak Shin, Eunsoo Kim
    Abstract:

    An improved 3D image reconstruction using the Convolution Property between an elemental image and a periodic delta-function array in curved integral imaging is presented.

Carranza Cesar - One of the best experts on this subject based on the ideXlab platform.

  • Fast and Scalable Architectures and Algorithms for the Computation of the Forward and Inverse Discrete Periodic Radon Transform with Applications to 2D Convolutions and Cross-Correlations
    UNM Digital Repository, 2016
    Co-Authors: Carranza Cesar
    Abstract:

    The Discrete Radon Transform (DRT) is an essential component of a wide range of applications in image processing, e.g. image denoising, image restoration, texture analysis, line detection, encryption, compressive sensing and reconstructing objects from projections in computed tomography and magnetic resonance imaging. A popular method to obtain the DRT, or its inverse, involves the use of the Fast Fourier Transform, with the inherent approximation/rounding errors and increased hardware complexity due the need for floating point arithmetic implementations. An alternative implementation of the DRT is through the use of the Discrete Periodic Radon Transform (DPRT). The DPRT also exhibits discrete properties of the continuous-space Radon Transform, including the Fourier Slice Theorem and the Convolution Property. Unfortunately, the use of the DPRT has been limited by the need to compute a large number of additions O(N^3) and the need for a large number of memory accesses. This PhD dissertation introduces a fast and scalable approach for computing the forward and inverse DPRT that is based on the use of: (i) a parallel array of fixed-point adder trees, (ii) circular shift registers to remove the need for accessing external memory components when selecting the input data for the adder trees, and (iii) an image block-based approach to DPRT computation that can fit the proposed architecture to available resources, and as a result, for an NxN image (N prime), the proposed approach can compute up to N^2 additions per clock cycle. Compared to previous approaches, the scalable approach provides the fastest known implementations for different amounts of computational resources. For the fastest case, I introduce optimized architectures that can compute the DPRT and its inverse in just 2N +ceil(log2 N)+1 and 2N +3(log2 N)+B+2 clock cycles respectively, where B is the number of bits used to represent each input pixel. In comparison, the prior state of the art method required N^2 +N +1 clock cycles for computing the forward DPRT. For systems with limited resources, the resource usage can be reduced to O(N) with a running time of ceil(N/2)(N + 9) + N + 2 for the forward DPRT and ceil(N/2)(N + 2) + 3ceil(log2 N) + B + 4 for the inverse. The results also have important applications in the computation of fast Convolutions and cross-correlations for large and non-separable kernels. For this purpose, I introduce fast algorithms and scalable architectures to compute 2-D Linear Convolutions/cross-correlations using the Convolution Property of the DPRT and fixed point arithmetic to simplify the 2-D problem into a 1-D problem. Also an alternative system is proposed for non-separable kernels with low rank using the LU decomposition. As a result, for implementations with enough resources, for a an image and Convolution kernel of size PxP, linear Convolutions/cross correlations can be computed in just 6N + 4 log2 N + 17 clock cycles for N = 2P-1. Finally, I also propose parallel algorithms to compute the forward and inverse DPRT using Graphic Processing Units (GPUs) and CPUs with multiple cores. The proposed algorithms are implemented in a GPU Nvidia Maxwell GM204 with 2048 cores@1367MHz, 348KB L1 cache (24KB per multiprocessor), 2048KB L2 cache (512KB per memory controller), 4GB device memory, and compared against a serial implementation on a CPU Intel Xeon E5-2630 with 8 physical cores (16 logical processors via hyper-threading)@3.2GHz, L1 cache 512K (32KB Instruction cache, 32KB data cache, per core), L2 cache 2MB (256KB per core), L3 cache 20MB (Shared among all cores), 32GB of system memory. For the CPU, there is a tenfold speedup using 16 logical cores versus a single-core serial implementation. For the GPU, there is a 715-fold speedup compared to the serial implementation. For real-time applications, for an 1021x1021 image, the forward DPRT takes 11.5ms and 11.4ms for the inverse

  • Fast and Scalable Architectures and Algorithms for the Computation of the Forward and Inverse Discrete Periodic Radon Transform with Applications to 2D Convolutions and Cross-Correlations
    2016
    Co-Authors: Carranza Cesar
    Abstract:

    The Discrete Radon Transform (DRT) is an essential component of a wide range of applications in image processing, e.g. image denoising, image restoration, texture analysis, line detection, encryption, compressive sensing and reconstructing objects from projections in computed tomography and magnetic resonance imaging. A popular method to obtain the DRT, or its inverse, involves the use of the Fast Fourier Transform, with the inherent approximation/rounding errors and increased hardware complexity due the need for floating point arithmetic implementations. An alternative implementation of the DRT is through the use of the Discrete Periodic Radon Transform (DPRT). The DPRT also exhibits discrete properties of the continuous-space Radon Transform, including the Fourier Slice Theorem and the Convolution Property. Unfortunately, the use of the DPRT has been limited by the need to compute a large number of additions O(N^3) and the need for a large number of memory accesses. This PhD dissertation introduces a fast and scalable approach for computing the forward and inverse DPRT that is based on the use of: (i) a parallel array of fixed-point adder trees, (ii) circular shift registers to remove the need for accessing external memory components when selecting the input data for the adder trees, and (iii) an image block-based approach to DPRT computation that can fit the proposed architecture to available resources, and as a result, for an NxN image (N prime), the proposed approach can compute up to N^2 additions per clock cycle. Compared to previous approaches, the scalable approach provides the fastest known implementations for different amounts of computational resources. For the fastest case, I introduce optimized architectures that can compute the DPRT and its inverse in just 2N +ceil(log2 N)+1 and 2N +3(log2 N)+B+2 clock cycles respectively, where B is the number of bits used to represent each input pixel. In comparison, the prior state of the art method required N^2 +N +1 clock cycles for computing the forward DPRT. For systems with limited resources, the resource usage can be reduced to O(N) with a running time of ceil(N/2)(N + 9) + N + 2 for the forward DPRT and ceil(N/2)(N + 2) + 3ceil(log2 N) + B + 4 for the inverse. The results also have important applications in the computation of fast Convolutions and cross-correlations for large and non-separable kernels. For this purpose, I introduce fast algorithms and scalable architectures to compute 2-D Linear Convolutions/cross-correlations using the Convolution Property of the DPRT and fixed point arithmetic to simplify the 2-D problem into a 1-D problem. Also an alternative system is proposed for non-separable kernels with low rank using the LU decomposition. As a result, for implementations with enough resources, for a an image and Convolution kernel of size PxP, linear Convolutions/cross correlations can be computed in just 6N + 4 log2 N + 17 clock cycles for N = 2P-1. Finally, I also propose parallel algorithms to compute the forward and inverse DPRT using Graphic Processing Units (GPUs) and CPUs with multiple cores. The proposed algorithms are implemented in a GPU Nvidia Maxwell GM204 with 2048 cores@1367MHz, 348KB L1 cache (24KB per multiprocessor), 2048KB L2 cache (512KB per memory controller), 4GB device memory, and compared against a serial implementation on a CPU Intel Xeon E5-2630 with 8 physical cores (16 logical processors via hyper-threading)@3.2GHz, L1 cache 512K (32KB Instruction cache, 32KB data cache, per core), L2 cache 2MB (256KB per core), L3 cache 20MB (Shared among all cores), 32GB of system memory. For the CPU, there is a tenfold speedup using 16 logical cores versus a single-core serial implementation. For the GPU, there is a 715-fold speedup compared to the serial implementation. For real-time applications, for an 1021x1021 image, the forward DPRT takes 11.5ms and 11.4ms for the inverse.Computer EngineeringDoctoralUniversity of New Mexico. Dept. of Electrical and Computer EngineeringPattichis, MariosCalhoun, VinceJordan, RamiroLlamocca, Danie

Seungkyun Oh - One of the best experts on this subject based on the ideXlab platform.

  • l m fold image resizing in block dct domain using symmetric Convolution
    IEEE Transactions on Image Processing, 2003
    Co-Authors: Hyunwook Park, Youngseo Park, Seungkyun Oh
    Abstract:

    Image resizing is to change an image size by upsampling or downsampling of a digital image. Most still images and video frames on digital media are given in a compressed domain. Image resizing of a compressed image can be performed in the spatial domain via decompression and recompression. In general, resizing of a compressed image in a compressed domain is much faster than that in the spatial domain. We propose a novel approach to resize images with L/M resizing ratio in the discrete cosine transform (DCT) domain, which exploits the multiplication-Convolution Property of DCT (multiplication in the spatial domain corresponds to symmetric Convolution in the DCT domain). When an image is given in terms of its 8/spl times/8 block-DCT coefficients, its resized image is also obtained in 8/spl times/8 block-DCT coefficients. The proposed approach is computationally fast and produces visually fine images with high PSNR.