Intra Prediction of Depth Picture with Plane Modeling

Dong-seok Lee; Soon-kak Kwon

doi:10.3390/sym10120715

Intra Prediction of Depth Picture with Plane Modeling

Symmetry ◽

10.3390/sym10120715 ◽

2018 ◽

Vol 10 (12) ◽

pp. 715 ◽

Cited By ~ 1

Author(s):

Dong-seok Lee ◽

Soon-kak Kwon

Keyword(s):

Coordinate System ◽

Mean Squared Error ◽

Plane Surface ◽

Prediction Method ◽

Block Size ◽

Intra Prediction ◽

Variable Size ◽

Depth Prediction ◽

Object Form ◽

Camera Coordinate System

In this paper, an intra prediction method is proposed for coding of depth pictures using plane modelling. Each pixel in the depth picture is related to the distance from a camera to an object surface, and pixels corresponding to a flat surface of an object form a relationship with the 2D plane surface. The plane surface can be represented by a simple equation in the 3D camera coordinate system in such a way that the coordinate system of depth pixels can be transformed to the camera coordinate system. This paper finds the parameters which define the plane surface closest to given depth pixels. The plane model is then used to predict the depth pixels on the plane surface. A depth prediction method is also devised for efficient intra prediction of depth pictures, using variable-size blocks. For prediction with variable-size blocks, the plane surface that occupies a large part of the picture can be predicted using a large block size. The simulation results of the proposed method show that the mean squared error is reduced by up to 96.6% for a block size of 4 × 4 pixels and reduced by up to 98% for a block size of 16 × 16, compared with the intra prediction modes of H.264/AVC and H.265/HEVC.

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Modelling growing stock volume of forest stands with various ALS area-based approaches

Forestry An International Journal of Forest Research ◽

10.1093/forestry/cpab011 ◽

2021 ◽

Author(s):

Karolina Parkitna ◽

Grzegorz Krok ◽

Stanisław Miścicki ◽

Krzysztof Ukalski ◽

Marek Lisańczuk ◽

...

Keyword(s):

Laser Scanning ◽

Mean Squared Error ◽

Prediction Method ◽

Forest Stand ◽

Boosted Regression Trees ◽

Forest Stands ◽

Individual Tree ◽

Growing Stock ◽

Modelling Techniques ◽

Stock Volume

Abstract Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.

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A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

Applied Sciences ◽

10.3390/app11041492 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1492

Author(s):

Hanita Daud ◽

Muhammad Naeim Mohd Aris ◽

Khairul Arifin Mohd Noh ◽

Sarat Chandra Dass

Keyword(s):

Gaussian Process ◽

Inverse Modeling ◽

Mean Squared Error ◽

Computational Effort ◽

Percentage Error ◽

Depth Prediction ◽

Receiver System ◽

Realistic Representation ◽

And Performance ◽

Gp Model

Seabed logging (SBL) is an application of electromagnetic (EM) waves for detecting potential marine hydrocarbon-saturated reservoirs reliant on a source–receiver system. One of the concerns in modeling and inversion of the EM data is associated with the need for realistic representation of complex geo-electrical models. Concurrently, the corresponding algorithms of forward modeling should be robustly efficient with low computational effort for repeated use of the inversion. This work proposes a new inversion methodology which consists of two frameworks, namely Gaussian process (GP), which allows a greater flexibility in modeling a variety of EM responses, and gradient descent (GD) for finding the best minimizer (i.e., hydrocarbon depth). Computer simulation technology (CST), which uses finite element (FE), was exploited to generate prior EM responses for the GP to evaluate EM profiles at “untried” depths. Then, GD was used to minimize the mean squared error (MSE) where GP acts as its forward model. Acquiring EM responses using mesh-based algorithms is a time-consuming task. Thus, this work compared the time taken by the CST and GP in evaluating the EM profiles. For the accuracy and performance, the GP model was compared with EM responses modeled by the FE, and percentage error between the estimate and “untried” computer input was calculated. The results indicate that GP-based inverse modeling can efficiently predict the hydrocarbon depth in the SBL.

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An Efficient Scheme for Intra Prediction Block Size and Mode Selection in Advanced Video Coding

2015 13th International Conference on Frontiers of Information Technology (FIT) ◽

10.1109/fit.2015.45 ◽

2015 ◽

Cited By ~ 1

Author(s):

Muhammad Asif ◽

Imtiaz A. Taj ◽

S.M. Ziauddin ◽

Maaz Bin Ahmad ◽

Muhammad Tahir

Keyword(s):

Video Coding ◽

Mode Selection ◽

Block Size ◽

Intra Prediction ◽

Efficient Scheme ◽

Advanced Video Coding

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Research on depth prediction method of igneous rock underlying strata

10.1190/segam2021-3582506.1 ◽

2021 ◽

Author(s):

Jiyan Cai ◽

Junting Song ◽

Tao Tian ◽

Min Gong

Keyword(s):

Igneous Rock ◽

Prediction Method ◽

Depth Prediction

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Research on Pose Estimation for Electric Vehicle’s Fast-Swap Battery Box Based on Visual Information

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.253-255.2252 ◽

2012 ◽

Vol 253-255 ◽

pp. 2252-2257

Author(s):

Yu Ming Wu ◽

Shuo Liu ◽

Gao Yang Zhang ◽

Xiao Yan Yin ◽

Ming Yu Zhao ◽

...

Keyword(s):

Coordinate System ◽

Pose Estimation ◽

Visual Information ◽

Experimental Results ◽

Noise Effects ◽

Simulation Results ◽

Camera Coordinate System

For the reason of difficult to get battery box pose information, we research the battery box pose measure method based on visual information. We get the coplanar four points at the lines constraints which extracted form image. We get the pose relationship between the battery box coordinate system and camera coordinate system, and then calculate the average of the measure results to reduce noise effects for measure precision. Simulation results show that the method through calculate average of the measure results can effectively reduce noise effects for measure accuracy. The actual experimental results show that the pose estimate accuracy is meet robot requirements for battery swap.

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