scholarly journals Implementing bezier surface interpolation and N.N in shape reconstruction and depth estimation of a 2D image.

2019 ◽  
Vol 16 (3) ◽  
pp. 1609
Author(s):  
Mohamed Ibrahim Shujaa ◽  
Ammar Alauldeen Abdulmajeed
Keyword(s):  
Incident Light ◽  
Depth Estimation ◽  
Shape Reconstruction ◽  
Object Image ◽  
Control Points ◽  
Surface Interpolation ◽  
The Neural Network ◽  
Five Factors ◽  
Interpolation Technique ◽  
Effective Performance

<p>This paper considers a 2D image depth estimation of an object and reconstructed it into a 3D object image. The 2D image is defined by slices contains asset of points that are located along the object contours and within the object body. The depth of these slices are estimated using the neural network technique (N.N), where five factors (slice length, angle of incident light and illumination of some of point that located along the 2D object, namely control points)are used as inputs to the network the estimated depth of the slice are mapped into a 3D surface using the interpolation technique of the Bezier spleen surface. The experimental results showed an effective performance of the proposed approach.</p>

Combining Focus Measures for Three Dimensional Shape Estimation Using Genetic Programming

2011 ◽  
pp. 209-228
Author(s):  
Muhammad Tariq Mahmood ◽  
Tae-Sun Choi
Keyword(s):  
Genetic Programming ◽  
Fundamental Problem ◽  
Three Dimensional ◽  
Depth Map ◽  
Depth Estimation ◽  
Shape Reconstruction ◽  
Optical Methods ◽  
3D Shape ◽  
Focus Measure ◽  
Single Focus

Three-dimensional (3D) shape reconstruction is a fundamental problem in machine vision applications. Shape from focus (SFF) is one of the passive optical methods for 3D shape recovery, which uses degree of focus as a cue to estimate 3D shape. In this approach, usually a single focus measure operator is applied to measure the focus quality of each pixel in image sequence. However, the applicability of a single focus measure is limited to estimate accurately the depth map for diverse type of real objects. To address this problem, we introduce the development of optimal composite depth (OCD) function through genetic programming (GP) for accurate depth estimation. The OCD function is developed through optimally combining the primary information extracted using one (homogeneous features) or more focus measures (heterogeneous features). The genetically developed composite function is then used to compute the optimal depth map of objects. The performance of this function is investigated using both synthetic and real world image sequences. Experimental results demonstrate that the proposed estimator is more accurate than existing SFF methods. Further, it is found that heterogeneous function is more effective than homogeneous function.

Shape Optimization of a Camoid Follower Surface

2008 ◽  
Author(s):  
Karim A. Aguib ◽  
Keith A. Hekman ◽  
Ashraf O. Nassef
Keyword(s):  
Genetic Algorithms ◽  
Shape Optimization ◽  
Three Dimensional ◽  
Characteristic Surface ◽  
Design Constraints ◽  
B Splines ◽  
Surface Interpolation ◽  
Specific Objective ◽  
Interpolation Technique ◽  
Real Coded Genetic Algorithms

Camoids are three dimensional cams that can produce more complex follower output than plain disc cams. A camoid follower motion is described by a surface rather than a curve. The camoid profile can be directly synthesized once the follower surface is fully described. To define a camoid follower motion surface it is required that the surface pass by all predefined constraints. Constraints can be follower position, velocity and acceleration. These design constraints are scattered all along the camoid follower surface. Hence a fitting technique is needed to satisfy these constraints which include position and its derivatives (velocity and acceleration). Furthermore if the fitting function can be of a parametric nature, then it would be possible to optimize the follower surface to obtain better performance according to a specific objective. Previous research has established a method to fit camoid follower surface positions, but did not tackle the satisfaction of derivative constraints. This paper presents a method for defining a camoid follower characteristic surface B-Splines on two steps first synthesizing the sectional cam curves then using a surface interpolation technique to generate the follower characteristic surface. The fitting technique is parametric in nature which allows for its optimization. Real coded Genetic algorithms are used to optimize the parameters of the surface to meet a specified objective function. A demonstration problem to illustrate the suggested methodology is presented.

Divisional 3D shape reconstruction of object image

2001 ◽  
Author(s):  
Dong Xu ◽  
LiangZheng Xia ◽  
Shizhou Yang
Keyword(s):  
Shape Reconstruction ◽  
Object Image ◽  
3D Shape ◽  
3D Shape Reconstruction

scholarly journals An Enhancement for the Consistent Depth Estimation of Monocular Videos using Lightweight Network

2021 ◽  
Vol 8 (3) ◽  
pp. 15-27
Author(s):  
Mohamed N. Sweilam ◽  
Nikolay Tolstokulakov
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Mobile Phones ◽  
Depth Estimation ◽  
The Neural Network ◽  
Monocular Video ◽  
Great Progress ◽  
Run Time

Depth estimation has made great progress in the last few years due to its applications in robotics science and computer vision. Various methods have been implemented and enhanced to estimate the depth without flickers and missing holes. Despite this progress, it is still one of the main challenges for researchers, especially for the video applications which have more complexity of the neural network which af ects the run time. Moreover to use such input like monocular video for depth estimation is considered an attractive idea, particularly for hand-held devices such as mobile phones, they are very popular for capturing pictures and videos, in addition to having a limited amount of RAM. Here in this work, we focus on enhancing the existing consistent depth estimation for monocular videos approach to be with less usage of RAM and with using less number of parameters without having a significant reduction in the quality of the depth estimation.

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