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Vorapoj Patanavijit - One of the best experts on this subject based on the ideXlab platform.

  • An alternative robust optical flow on Dynamic Smoothness Weight (α) of Horn-Schunk Algorithm using median filter with sub-Pixel Displacement
    2013 13th International Symposium on Communications and Information Technologies (ISCIT), 2013
    Co-Authors: Darun Kesrarat, Vorapoj Patanavijit
    Abstract:

    Traditionally, the smoothness weight (α) of Horn-Schunk optical flow algorithm (HS) is an important parameter of the optical flow and directly impacts to the performance of the HS algorithm. This paper presents an alternative robust optical flow on dynamic smoothness weight (α) of Horn-Schunk algorithm using median filter with sub-Pixel Displacement (RDHS). We also present an investigation analysis of sub-Pixel optical flow on original Horn-Schunk algorithm over various smoothness weights or Dynamic Smoothness Weight of Horn-Schunk Algorithm (DHS) to investigate the value of the smoothness weight that return the best in Peak Signal to Noise Ratio (PSNR) on each frame of video sequence for comparison. We investigate the performance over the Additive White Gaussian Noise (AWGN) at several noise power levels (such as AWGN at 25 dB (low noise), AWGN at 20 dB, and AWGN at 15 dB (high noise) respectively). These experiment results are comprehensively tested on several standard sequences such as AKIYO, COASTGUARD, CONTAINER, and FOREMAN that have difference foreground and background movement characteristic in which PSNR is concentrated as the performance indicator. We also investigate the relationship of the movement characteristic in the sequence and noise level with the best smoothness weight.

  • investigation of performance trade off in high reliability and robust gradient orientation on differential sub Pixel Displacement optical flow algorithms over non gaussian noisy model
    International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology, 2012
    Co-Authors: Darun Kesrarat, Paitoon Porntrakoon, Vorapoj Patanavijit
    Abstract:

    This paper presents a performance analysis of 3 classical optical flow algorithms (Spatial Correlation-Based Optical Flow (SCOF), Horn-Schunk algorithm (HS) and Lucas-Kanade algorithm (LK)) under the noisy conditions. Moreover the Confidence Based Optical Flow Algorithm for High Reliability (CHR) and Robust Motion Estimation Methods Using Gradient Orientation Information (RGOI) are applied on these 3 algorithms over different characteristic of standard sequences with several Non-Gaussian noises such as Poisson Noise (PN), Salt&Pepper Noise (SPN), and Speckle Noise (SN). For HS algorithm, we also investigate the performance on the best average of smoothness weight (β) which is an important factor for the quality of outcome. These experiment results are comprehensively tested on several standard sequences such as AKIYO, COASTGUARD, CONTAINER, and FOREMAN that have different foreground and background movement characteristic in a level of 0.5 sub-Pixel Displacements. Each standard sequence has 6 sets of sequence included an original (no noise), PN, SPN density (d) = 0.005, SPN d = 0.025, SN variance (v) = 0.01, and SN v = 0.05 respectively which concentrated on Peak Signal to Noise Ratio (PSNR) as the performance indicator in our experiment.

  • empirical study on performance comparisons of block based motion estimation on multi sub Pixel Displacement with multiples block size
    Advanced Information Networking and Applications, 2012
    Co-Authors: Darun Kesrarat, Vorapoj Patanavijit
    Abstract:

    This paper presents the impact of sub-Pixel Displacement and block/windows size on the motion estimation performance (3 different levels (1, 0.5, and 0.25) for sub-Pixel Displacement (SPD) and 2 different block/windows size (8x8/16x16 and 16x16/32x32)). Our empirical study concentrates on full search (FS), a novel four-step search algorithm (NFSS), a block-based gradient descent search algorithm (BBGDS), a new diamond search algorithm (DS) and hexagon search algorithm (HS). Peak Signal to Noise Ratio (PSNR) is referenced as the indicator on our performance comparison results. These experiment results are comprehensively tested and conclude on several standard sequences such as AKIYO, COASTGUARD, CONTAINER, and FOREMAN that have different foreground and background movement characteristic.

  • performance analysis on weighting factor a on spatial temporal gradient technique and high confidence reliability with sub Pixel Displacement
    International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology, 2011
    Co-Authors: Darun Kesrarat, Vorapoj Patanavijit
    Abstract:

    Traditionally, the weighting factor (α) is one of the most importance parameter of the optical flow based on the temporal gradient technique and directly impacts to the optical flow performance. This paper presents a performance analysis of sub-Pixel optical flow on Horn-Schunk algorithm (HS) [1] under the kernel model of Barron, Fleet, and Beauchemin (BFB) [2] over various weighting factor (α) concerning with the feedback in Peak Signal to Noise Ratio (PSNR) for the best performance on each frame of video sequence for comparison. We also investigate over confidence based optical flow algorithm for high reliability (CBOF) [3] under the best forward and backward optical flow in PSRN of each reconstructed frame and relationship with the different on master images sequences for evaluation. Experimental results of the maximum and minimum of the best average in PSNR for each reconstructed video sequence are demonstrated for performance evaluation [4]. These experiment results are comprehensively tested on several standard sequences such as AKIYO, COASTGUARD, CONTAINER, and FOREMAN that have difference foreground and background movement characteristic.

  • Experimental performance analysis of High Confidence Reliability based on differential optical flow algorithms over AWGN sequences with sub-Pixel Displacement
    2011 International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS), 2011
    Co-Authors: Darun Kesrarat, Vorapoj Patanavijit
    Abstract:

    This paper presents a performance analysis of 3 popular optical flow algorithms (2D optical flow block-based full search algorithm (BOF), Horn-Schunk algorithm (HS) and Lucas-Kanade algorithm (LK)) under the noise conditions. And the confidence based optical flow algorithm for high reliability (CBOF) is applied on these 3 algorithms over different characteristic of standard sequences with several dB of Additive White Gaussian Noise (AWGN). For algorithm of HS and LK, we also applied the kernel model of Barron, Fleet, and Beauchemin (BFB) on these algorithms in our experiment. Especially in HS algorithm, we also investigate the performance on the best average smoothness weight (α) which is prior evaluated by Darun K. and Vorapoj P.. These experiment results are comprehensively tested on several standard sequences such as AKIYO, COASTGUARD, CONTAINER, and FOREMAN that have different foreground and background movement characteristic in a level of 0.5 sub-Pixel Displacement. Each standard sequence has 4 sets of sequence included an original (no noise), AWGN 25 dB (low noise), AWGN 20 dB, and AWGN 15 dB (high noise) respectively which concentrated on Peak Signal to Noise Ratio (PSNR) as the performance indicator in our experiment.

Darun Kesrarat - One of the best experts on this subject based on the ideXlab platform.

  • An alternative robust optical flow on Dynamic Smoothness Weight (α) of Horn-Schunk Algorithm using median filter with sub-Pixel Displacement
    2013 13th International Symposium on Communications and Information Technologies (ISCIT), 2013
    Co-Authors: Darun Kesrarat, Vorapoj Patanavijit
    Abstract:

    Traditionally, the smoothness weight (α) of Horn-Schunk optical flow algorithm (HS) is an important parameter of the optical flow and directly impacts to the performance of the HS algorithm. This paper presents an alternative robust optical flow on dynamic smoothness weight (α) of Horn-Schunk algorithm using median filter with sub-Pixel Displacement (RDHS). We also present an investigation analysis of sub-Pixel optical flow on original Horn-Schunk algorithm over various smoothness weights or Dynamic Smoothness Weight of Horn-Schunk Algorithm (DHS) to investigate the value of the smoothness weight that return the best in Peak Signal to Noise Ratio (PSNR) on each frame of video sequence for comparison. We investigate the performance over the Additive White Gaussian Noise (AWGN) at several noise power levels (such as AWGN at 25 dB (low noise), AWGN at 20 dB, and AWGN at 15 dB (high noise) respectively). These experiment results are comprehensively tested on several standard sequences such as AKIYO, COASTGUARD, CONTAINER, and FOREMAN that have difference foreground and background movement characteristic in which PSNR is concentrated as the performance indicator. We also investigate the relationship of the movement characteristic in the sequence and noise level with the best smoothness weight.

  • investigation of performance trade off in high reliability and robust gradient orientation on differential sub Pixel Displacement optical flow algorithms over non gaussian noisy model
    International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology, 2012
    Co-Authors: Darun Kesrarat, Paitoon Porntrakoon, Vorapoj Patanavijit
    Abstract:

    This paper presents a performance analysis of 3 classical optical flow algorithms (Spatial Correlation-Based Optical Flow (SCOF), Horn-Schunk algorithm (HS) and Lucas-Kanade algorithm (LK)) under the noisy conditions. Moreover the Confidence Based Optical Flow Algorithm for High Reliability (CHR) and Robust Motion Estimation Methods Using Gradient Orientation Information (RGOI) are applied on these 3 algorithms over different characteristic of standard sequences with several Non-Gaussian noises such as Poisson Noise (PN), Salt&Pepper Noise (SPN), and Speckle Noise (SN). For HS algorithm, we also investigate the performance on the best average of smoothness weight (β) which is an important factor for the quality of outcome. These experiment results are comprehensively tested on several standard sequences such as AKIYO, COASTGUARD, CONTAINER, and FOREMAN that have different foreground and background movement characteristic in a level of 0.5 sub-Pixel Displacements. Each standard sequence has 6 sets of sequence included an original (no noise), PN, SPN density (d) = 0.005, SPN d = 0.025, SN variance (v) = 0.01, and SN v = 0.05 respectively which concentrated on Peak Signal to Noise Ratio (PSNR) as the performance indicator in our experiment.

  • empirical study on performance comparisons of block based motion estimation on multi sub Pixel Displacement with multiples block size
    Advanced Information Networking and Applications, 2012
    Co-Authors: Darun Kesrarat, Vorapoj Patanavijit
    Abstract:

    This paper presents the impact of sub-Pixel Displacement and block/windows size on the motion estimation performance (3 different levels (1, 0.5, and 0.25) for sub-Pixel Displacement (SPD) and 2 different block/windows size (8x8/16x16 and 16x16/32x32)). Our empirical study concentrates on full search (FS), a novel four-step search algorithm (NFSS), a block-based gradient descent search algorithm (BBGDS), a new diamond search algorithm (DS) and hexagon search algorithm (HS). Peak Signal to Noise Ratio (PSNR) is referenced as the indicator on our performance comparison results. These experiment results are comprehensively tested and conclude on several standard sequences such as AKIYO, COASTGUARD, CONTAINER, and FOREMAN that have different foreground and background movement characteristic.

  • performance analysis on weighting factor a on spatial temporal gradient technique and high confidence reliability with sub Pixel Displacement
    International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology, 2011
    Co-Authors: Darun Kesrarat, Vorapoj Patanavijit
    Abstract:

    Traditionally, the weighting factor (α) is one of the most importance parameter of the optical flow based on the temporal gradient technique and directly impacts to the optical flow performance. This paper presents a performance analysis of sub-Pixel optical flow on Horn-Schunk algorithm (HS) [1] under the kernel model of Barron, Fleet, and Beauchemin (BFB) [2] over various weighting factor (α) concerning with the feedback in Peak Signal to Noise Ratio (PSNR) for the best performance on each frame of video sequence for comparison. We also investigate over confidence based optical flow algorithm for high reliability (CBOF) [3] under the best forward and backward optical flow in PSRN of each reconstructed frame and relationship with the different on master images sequences for evaluation. Experimental results of the maximum and minimum of the best average in PSNR for each reconstructed video sequence are demonstrated for performance evaluation [4]. These experiment results are comprehensively tested on several standard sequences such as AKIYO, COASTGUARD, CONTAINER, and FOREMAN that have difference foreground and background movement characteristic.

  • Experimental performance analysis of High Confidence Reliability based on differential optical flow algorithms over AWGN sequences with sub-Pixel Displacement
    2011 International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS), 2011
    Co-Authors: Darun Kesrarat, Vorapoj Patanavijit
    Abstract:

    This paper presents a performance analysis of 3 popular optical flow algorithms (2D optical flow block-based full search algorithm (BOF), Horn-Schunk algorithm (HS) and Lucas-Kanade algorithm (LK)) under the noise conditions. And the confidence based optical flow algorithm for high reliability (CBOF) is applied on these 3 algorithms over different characteristic of standard sequences with several dB of Additive White Gaussian Noise (AWGN). For algorithm of HS and LK, we also applied the kernel model of Barron, Fleet, and Beauchemin (BFB) on these algorithms in our experiment. Especially in HS algorithm, we also investigate the performance on the best average smoothness weight (α) which is prior evaluated by Darun K. and Vorapoj P.. These experiment results are comprehensively tested on several standard sequences such as AKIYO, COASTGUARD, CONTAINER, and FOREMAN that have different foreground and background movement characteristic in a level of 0.5 sub-Pixel Displacement. Each standard sequence has 4 sets of sequence included an original (no noise), AWGN 25 dB (low noise), AWGN 20 dB, and AWGN 15 dB (high noise) respectively which concentrated on Peak Signal to Noise Ratio (PSNR) as the performance indicator in our experiment.

Bing Pan - One of the best experts on this subject based on the ideXlab platform.

  • bias error reduction of digital image correlation using gaussian pre filtering
    Optics and Lasers in Engineering, 2013
    Co-Authors: Bing Pan
    Abstract:

    Abstract In digital image correlation (DIC), the iterative spatial domain cross-correlation algorithm using high-order B-spline interpolation algorithms has been strongly recommended for accurate sub-Pixel Displacement measurement. However, the magnitude of the position-dependent bias error increases with the increase of noise level, which dramatically reduces the registration accuracy of DIC for real experimental images. In this paper, a simple method, based on pre-smoothing the speckle images with a 5×5 Pixels Gaussian low-pass filter prior to correlation analysis, is proposed for reducing the bias error in measured Displacements. Both numerical simulations and real experiments reveal that the proposed technique is capable of reducing the bias error in measured Displacement to a negligible degree for both noisy and noiseless images, even though a simple bicubic interpolation is used.

  • a fast digital image correlation method for deformation measurement
    Optics and Lasers in Engineering, 2011
    Co-Authors: Bing Pan
    Abstract:

    Abstract Fast and high-accuracy deformation analysis using digital image correlation (DIC) has been increasingly important and highly demanded in recent years. In literature, the DIC method using the Newton–Rapshon (NR) algorithm has been considered as a gold standard for accurate sub-Pixel Displacement tracking, as it is insensitive to the relative deformation and rotation of the target subset and thus provides highest sub-Pixel registration accuracy and widest applicability. A significant drawback of conventional NR-algorithm-based DIC method, however, is its extremely huge computational expense. In this paper, a fast DIC method is proposed deformation measurement by effectively eliminating the repeating redundant calculations involved in the conventional NR-algorithm-based DIC method. Specifically, a reliability-guided Displacement scanning strategy is employed to avoid time-consuming integer–Pixel Displacement searching for each calculation point, and a pre-computed global interpolation coefficient look-up table is utilized to entirely eliminate repetitive interpolation calculation at sub-Pixel locations. With these two approaches, the proposed fast DIC method substantially increases the calculation efficiency of the traditional NR-algorithm-based DIC method. The performance of proposed fast DIC method is carefully tested on real experimental images using various calculation parameters. Results reveal that the computational speed of the present fast DIC is about 120–200 times faster than that of the traditional method, without any loss of its measurement accuracy

Mohammed Ali Hussain - One of the best experts on this subject based on the ideXlab platform.

  • a novel image encryption technique using rgb Pixel Displacement for color images
    International Conference on Advanced Computing, 2016
    Co-Authors: Shrija Somaraj, Mohammed Ali Hussain
    Abstract:

    In the present scenario when all data is in network, cloud or some data center, security and protection of data is a major concern. Encryption is one of the techniques used for this purpose. Image encryption is applied for protecting images from different kinds of attacks. Image Encryption is possible by a kind of transposition in color images or 3D images by displacing the rgb components of the color image. This paper presents a method for encryption and decryption of Color images using RGB Pixel Displacement. In the proposed method the original plain image is split into its basic three components, that is the RGB components and the key image is also split into RGB Components. Further by application of XOR operation and scrambling of the three components the cipher image is generated. This method is suitable for encrypting 3D images. The algorithm is implemented in MATLAB environment and tested on various color images.

Shrija Somaraj - One of the best experts on this subject based on the ideXlab platform.

  • a novel image encryption technique using rgb Pixel Displacement for color images
    International Conference on Advanced Computing, 2016
    Co-Authors: Shrija Somaraj, Mohammed Ali Hussain
    Abstract:

    In the present scenario when all data is in network, cloud or some data center, security and protection of data is a major concern. Encryption is one of the techniques used for this purpose. Image encryption is applied for protecting images from different kinds of attacks. Image Encryption is possible by a kind of transposition in color images or 3D images by displacing the rgb components of the color image. This paper presents a method for encryption and decryption of Color images using RGB Pixel Displacement. In the proposed method the original plain image is split into its basic three components, that is the RGB components and the key image is also split into RGB Components. Further by application of XOR operation and scrambling of the three components the cipher image is generated. This method is suitable for encrypting 3D images. The algorithm is implemented in MATLAB environment and tested on various color images.