Browsing by Author "Darun Kesrarat"
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    Bidirectional Confidential with Bilateral Filter on Local Based Optical Flow for Image Reconstruction under Noisy Condition
    ( 2015) Darun Kesrarat ; Vorapoj Patanavijit
    More than a decade, Optical flow is relevant in many areas such as video coding and compression, robot vision, object tracking and segmentation, and super resolution reconstruction. By the result of the motion vector from optical flow, reduction the error stands a problem especially under noisy condition. Many models have been proposed to reduce the error and bilateral is one of the popular models. This paper introduces the model of bilateral filter in combination with bidirectional confidential over simple local based optical flow where the quality of the restored images from the returned motion vector is focal point. Several noise levels over several official sequences are observed for the noise tolerance analysis where PSNR is used as an indicator.
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    Experimental Analysis of Performance Comparison on Both Linear Filter and Bidirectional Con dential Technique for Spatial Domain Optical Flow Algorithm
    ( 2013) Darun Kesrarat ; Vorapoj Patanavijit
    Optical flow is a method for classifying the density velocity or motion vector (MV) in a degree of pixel basis for motion classification of image in video sequences. In actual situation, many unpleasant situations usually generate noises over the video sequences. These unpleasant situations corrupt the performance in efficiency of optical flow. In turn to increase the efficiency of the MV, this research work proposes the performance comparison on linear filter and bidirectional confidential technique for spatial domain optical flow algorithms. Our experiment concentrates on the 3 classical spatial based optical flow algorithms (such as spatial correlation-based optical flow (SCOF), Horn-Schunk algorithm (HS) and Lucas- Kanade algorithm (LK)). Different standard video sequences such as AKIYO, CONTAINER, COASTGUARD, and FOREMAN are comprehensively evaluated to demonstrate the effectiveness results. These video sequences have differences in aspect of action and speed in foreground and background. These video sequences are also debased by the Additive White Gaussian Noise (AWGN) at different noise degree (such as AWGN at 25 dB, 20 dB, and 15 dB consequently). Peak Signal to Noise Ratio (PSNR) is utilized as the performance index in our observation.
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    Experimental Efficiency Analysis in Robust models of Spatial Correlation Optical Flow Methods under Non Gaussian Noisy Contamination
    ( 2013-05) Darun Kesrarat ; Vorapoj Patanavijit
    In this paper, we present a performance analysis of several robust models of spatial correlation optical flow algorithms including an original spatial correlation optical flow (SCOF), bidirectional for high reliability optical flow (BHR), gradient orientation information for robust motion estimation (GOI), and robust and high reliability based on bidirectional symmetry and median motion estimation (RHR) under the non Gaussian noise conditions. The simulated results are tested on 4 different in foreground and background movement characteristics of standard sequences (AKIYO, CONTAINER, COASTGUARD, and FOREMAN) in a degree of 0.5 sub-pixel translation. In our experiment, an original sequence (no noise), and noise contaminated sequences on Salt & Pepper Noise (SPN) at density (d) = 0.025d, and 0.005d, Speckle Noise (SN) at variance (v) = 0.05v, and 0.01v, and Poisson Noise (PN) are utilized. The experiment concentrates on Peak Signal to Noise Ratio (PSNR) as an indicator in the experimental performance analysis.
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    Experimental Study on Image Reconstruction from Spatial Correlation-based Optical Flow Motion Vector over Non Gaussian Noise Contamination using Reversed Confidential with Bilateral Filter
    ( 2016) Darun Kesrarat ; Vorapoj Patanavijit ; Vincent Mary School of Engineering
    In motion estimation, noise is a verity to degrade the performance in optical flow for determining motion vector. This paper examines the performance of noise tolerance model in spatial correlation-based optical flow for image reconstruction from motion vector where the source sequences are contaminated by non Gaussian noise. There are Poisson Noise, Salt & Pepper Noise, and Speckle Noise. In the experiment, several standard sequences in different styles are used and the applied combination model of reversed confidential with bilateral filter on spatial correlation-based optical flow is mainly focused to determined the best condition to apply this model with. The result in image reconstruction from motion vector is used in performance comparison with traditional noise tolerance models by using Peak Signal to Noise Ratio (PSNR) as a primary index for studying.
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    Performance Investigation on Bilateral Filter with Confidence Based over Spatial Correlation-based Optical Flow for Image Reconstruction
    ( 2014) Darun Kesrarat ; Vorapoj Patanavijit
    Optical flow in image sequences gives essential data on motion structure. It is relevant in several areas such as robot vision, video coding, and super resolution reconstruction. Even though this realm has been concentrated for more than a decade, reduction the glitch in estimation stands a difficult trouble. Many techniques were proposed to enhance the performance and one of the most is bilateral filter. This paper presents the performance study of bilateral filter and bilateral filter together with confidence based over spatial correlationbased optical flow where the quality of the reconstructed images from the result of motion vector is highly focused. We perform an experiment over several standard sequences with several noise levels in contamination to study the robustness of each robust technique and used PSNR as an indicator.
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    Robust Block-Based Motion Estimation for Image Reconstruction Using Bi-direction Confidential
    ( 2014) Darun Kesrarat ; Vorapoj Patanavijit ; Vincent Mary School of Engineering
    In block-based motion estimation where the outcome of the motion vector (MV) is used to reconstruct the image, noise is one of the major problems that impact the quality of the performance in image reconstruction. There are several aspects to improve the quality of the reconstructed image but we focus on improvement of the accuracy in MV from existing block-based motion estimation algorithms when they applies our proposed model only without other any additional models. Because we would like to prove that our proposed model improves an accuracy of the MV that it leads to the better quality of the reconstructed image as a result. This paper presents robust block-based motion estimation where bidirection confidential model is applied over the existing blockbased motion estimation algorithm to improve the accuracy of the MV itself. In the experiment where we simulated several Additive White Gaussian Noise (AWGN) levels over several experiment sequences, we found that the proposed model improved the quality of the reconstructed image when it is applied over several existing block-based motion estimation algorithms. In our experiment, we evaluated the quality of reconstructed image by using Peak Signal to Noise Ratio (PSNR).
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    Robust Global Based Spatial Correlation Optical Flow in Bidirectional Confidential Technique with Bilateral Filter
    ( 2015-11) Darun Kesrarat ; Vorapoj Patanavijit ; Assumption University. Vincent Mary School of Engineering
    Noise is one of the main factors that impact the performance of optical flow where the result in motion vector of optical flow is degraded. Many areas in advance require a result of optical flow as a preprocessing such as super resolution image reconstruction, robot vision, motion estimation, edge detection, motion tracking and etc. Then, the accuracy in the result of motion vector from optical flow is very importance. This paper introduces the robust model of optical flow by using bidirectional confidential technique together with bilateral filter for global based spatial correlation where the efficiency of the proposed model is evaluated by Peak Signal to Noise Ratio (PSNR) under noisy condition.
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    Robust Optical Flow Algorithm Based on Spatial Domain Optical Flow
    (Assumption University, ) Darun Kesrarat
    In this paper, we focus on the robustness in noise tolerance of spatial domain optical flow. We present a performance study of bidirectional confidential with median filter on spatial domain optical flow (spatial correlation, local based optical flow, and global based optical flow) under non-Gaussian noise. Several noise tolerance models on spatial domain optical flow are used in comparison. The experimental results are investigated on robustness under noisy condition by using non-Gaussian noise (Poisson Noise, Salt & Pepper noise, and Speckle Noise) over several standard sequences. The experiment concentrates on error vector magnitude (EVM) as performance indicators for accuracy in the direction and distance of motion vector (MV). In EVM, the result in MV of each method is used to compare with the ground truth vector in the experimental performance analysis.
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    Self-similarity measurement using percentage of angle similarity on correlations of face objects
    ( 2009-07) Darun Kesrarat ; Paitoon Porntrakoon
    A 2D face image can be used to search the self-similar images in the criminal database. This self-similar search can assist the human user to make the final decision among the retrieved images. In previous self-similar search, a 2D face image comprises of objects and object correlations. The attribute values of objects and their correlations are measured and stored in the face image database. The similarity percentage is specified to retrieve the self-similar images from the database. The problem of previous self-similar search is that the percentage of the angle differentiation among the objects in different part is different although their angle differentiation is exactly the same. The proposed model is introduced to improve the stability of the similarity percentage by reducing the number of face objects, object correlations, and the degree calculation. After testing over 100 samples, the proposed method illustrated that the stability of similarity percentage is improved especially for the left side objects of the face image.
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