Experimental analysis on noise tolerance of bidirectional confidential with bilateral filter in local based optical flow for image reconstruction
Noise is the main issue causing defects in the optical flow for motion prediction where the result in the motion vector (MV) is directly impacted. In this paper, we perform an experimental analysis on numerous noise tolerance models for a local based optical flow where the main model is bidirectional confidential with bilateral filter. For performance analysis, we focused on 2 main indicators. These are Structural SIMilarity (SSIM) and Error Vector Magnitude (EVM) where SSIM was used to indicate the quality for image reconstruction issues and EVM was used to indicate the accuracy in MV issues. In our experiments, the Additive White Gaussian Noise (AWGN) was formed at several noise levels over several standard sequences for performance evaluation.
Keywords: optical flow, bilateral filter, bidirectional confidential, SSIM, EVM, AWGN.
Experimental Study in Error Vector Magnitude of Bidirectional Confidential with Median Filter on Spatial Domain Optical Flow under Non Gaussian Noise Contamination
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.
Verification of Bidirectional Local Based Optical Flow with Bilateral Filter on Non-Gaussian Noise Contamination for Video Reconstruction
Abstract — In optical flow for motion approximation, the productive result in motion vector (MV) is an important issue for video reconstruction. Different in noisy conditions may cause the unreliable result in optical flow algorithms. A lot of robust optical flow algorithm was proposed for noise overthrown to increase the certain result under noisy conditions. This paper focuses on the efficiency of bidirectional local based optical flow with a bilateral filter on video reconstruction where the video sequence is contaminated with non-Gaussian noise (Poisson noise, Salt & Pepper noise, and Speckle noise). In our experimentation, the different set of video sequences in contamination with non-Gaussian noise are verified by the Peak Signal to Noise Ratio (PSNR) index.
Smart Matching for Car Rental
This paper presents a knowledge-based model where it can be adapted for the service of car rental system online through the website. Smart matching for car rental mainly supports the process of car matching in a search function to give the most satisfaction result for customers and avoid the rejection of unavailable car for rent by providing the alternative available cars that close to the customer’s requirement. For the search function, we analyze the problems found in general car rental system and identify the importance characteristic of the rental car. Then, we design the attributes with weighting method for matching customer’s requirement for car rental along with the available cars for avoiding the rejection to the customer as much as possible. In the experiment, we develop the smart matching system to improve and solve the problems of existing car rental system such as Avis and Hertz in comparison.
Robust Optical Flow Using Adaptive Lorentzian Filter for Image Reconstruction under Noisy Condition
In optical flow for motion allocation, the efficient result in motion vector (MV) is an important issue. Several noisy conditions may cause the unreliable result in optical flow algorithms. We discover that many classical optical flows algorithms perform better result under noisy condition when combined with modern optimized model. This paper introduces effective robust models of optical flow by using Robust high reliability spatial based optical flow algorithms using the adaptive Lorentzian norm influence function in computation on simple spatial temporal optical flows algorithm. Experiment on our proposed models confirm better noise tolerance in optical flow’s MV under noisy condition when they are applied over simple spatial temporal optical flow algorithms as a filtering model in simple frame-to-frame correlation technique. We illustrate the performance of our models by performing an experiment on several typical sequences with differences in movement speed of foreground and background where the experiment sequences are contaminated by the additive white Gaussian noise (AWGN) at different noise decibels (dB). This paper shows very high effectiveness of noise tolerance models that they are indicated by peak signal to noise ratio (PSNR).
Keywords: optical flow, motion estimation, additive white Gaussian noise, PSNR.