Bayes Filter based Jitter Noise Removal in Shape Recovery from Image Focus

Jang

2019

Sensors

Self Cite

Three-dimensional (3D) cameras are expensive because they employ additional charged coupled device sensors and optical elements, e.g., lasers or complicated scanning mirror systems. One passive optical method, shape from focus (SFF), provides an efficient low cost solution for 3D cameras. However, mechanical vibration of the SFF imaging system causes jitter noise along the optical axis, which makes it difficult to obtain accurate shape information of objects. In traditional methods, this error cannot be removed and increases as the estimation of the shape recovery progresses. Therefore, the final 3D shape may be inaccurate. We introduce an accurate depth estimation method using an adaptive neural network (ANN) filter to remove the jitter noise effects. Jitter noise is modeled by both Gaussian distribution and non-Gaussian distribution. Then, focus curves are modeled by quadratic functions. The ANN filter is designed as an optimal estimator restoring the original position of each frame of the input image sequence in the modeled jitter noise, as a pre-processing step before the initial depth map is obtained. The proposed method was evaluated using image sequences of both synthetic and real objects. Experimental results demonstrate that it is reasonably efficient and that its accuracy is comparable with that of existing systems.

Section: Resultsmentioning

confidence: 99%

“…Therefore, the results are not very accurate [16]. In the literature, various filters can be used for removing the jitter noise [17,18,19]. Recent research has applied Kalman filter to remove the jitter noise in SFF.…”

Section: Introductionmentioning

confidence: 99%

Jitter Elimination in Shape Recovery by using Adaptive Neural Network Filter

Jang

2019

Sensors

Self Cite

“…By substituting (9), (10), (11) into (3), Gaussian focus curve without jitter (Lévy) noise is obtained, [19]. Previously modeled jitter noise is added to (3) as:…”

Section: Focus Curve Modelingmentioning

confidence: 99%

“…This jitter noise is different from image noise, and it changes focus values along the optical axis due to random vibrations, thus, degrading the performance of 3D shape recovery. Filtering methods, such as Kalman filter, [18], Bayes filter, [19], particle filter, [20], modified Kalman filter, [21], and adaptive neural network filter, [22], have been proposed for removing this type of jitter noise. Since the conventional filters use minimum mean squared error (MMSE) criterion as a cost function, they only capture the second-order statistics of the error signal.…”

Section: Introductionmentioning

confidence: 99%

Removal of Non-Gaussian Jitter Noise for Shape From Focus Through Improved Maximum Correntropy Criterion Kalman Filter

2020

Self Cite

Three-dimensional (3D) shape reconstruction from one or multiple observations is a primary problem of computer vision. Shape from Focus (SFF) is a passive optical method that uses multiple twodimensional (2D) images with different focus levels. When obtaining 2D images in each step along the optical axis, mechanical vibrations, referred as jitter noise, occur. SFF techniques are vulnerable to jitter noise that can vary focus values in 2D images. In this paper, new filtering method, which provides high accuracy of 3D shape reconstruction and low computational cost, is proposed. First, jitter noise is modeled as Lévy distribution. This assumption makes it possible to show the influence of proposed filtering method in real environment with non-Gaussian jitter noise. Second, focus curves are modeled as Gaussian function to compare the performance of proposed filtering method with those of the conventional filtering methods. Finally, improved maximum correntropy criterion Kalman filter (IMCC-KF) is designed as a post-processing step, and is applied to the modeled focus curves. The experiments are performed on real and synthetic objects and the results demonstrate the effectiveness of proposed method. INDEX TERMS Focus curve, improved maximum correntropy criterion Kalman filter (IMCC-KF), jitter noise, non-Gaussian distribution, shape from focus (SFF).

“…5 At present, 3D surface morphology reconstruction methods of microparts based on 2D images obtained by MVS can be summarised as follows: shape from shading (SFS) method, 6 shape from texture (SFT) method, 7,8 shape from motion (SFM) method, 9,10 shape from focus (SFF) and shape from defocus (SFD) method. [11][12][13][14] Among them, SFS method obtains depth information by using shadow information or changes of shade, 6 SFT method obtains depth information by using texture details existing on the surface of observed object in image, 7,8 and SFM method obtains the depth information of observed object by taking images on multiple positions. 9,10 SFF and SFD methods obtain depth information of observed object through focusing and defocusing information in image.…”

Section: Introductionmentioning

confidence: 99%

Depth estimation method of surface of micropart in microassembly space based on microscopic vision tomographic scanning images

Wang

Qiang

2021

Journal of Microscopy

Three-dimensional (3D) morphology of microparts has an important influence on performance of microassembly system that mainly assembles microparts in millimetre and micron scale. Because 3D morphology of microparts cannot be accurately obtained by conventional microscopic vision system, a depth estimation method of surface of micropart in microassembly space based on microscopic vision tomographic scanning (MVTS) images is proposed in this paper. The proposed method uses the positions of pixels with the largest focus values in MVTS image to construct the isodepth contours of surface of micropart and obtains the depth values of micropart's surface at the positions of MVTS by assigning depth values to corresponding isodepth contours. The MVTS images are obtained by MVTS and pixels with the largest focus values in MVTS image are obtained by focus measurement of MVTS images of micropart in microassembly space. On these bases, 3D spatial interpolation method is applied to map depth value of space between adjacent isodepth contours and to obtain depth values of all surface of micropart. Simulation experiments are carried out to verify the proposed method by generating simulated MVTS image array from two simulation objects, and the influence parameters of the proposed method are analysed. In established experimental setup of microassembly that can realise MVTS, experimental verification for the proposed depth estimation method are carried out by using cone cavity and end jaws of microgripper. 3D morphologies of depth maps of cone cavity and end jaws of microgripper are registered with their respective CAD models using iterative nearest point registration algorithm to quantify accuracy of depth estimation. The research results show that 3D morphology of micropart can be obtained by the proposed method and has better accuracy than those by conventional shape from focus method. This method provides a new way to obtain the morphology of microparts and lays a foundation for improving