scholarly journals Iterative Refinement of Uniformly Focused Image Set for Accurate Depth from Focus

2020 ◽  
Vol 10 (23) ◽  
pp. 8522
Author(s):  
Sherzod Salokhiddinov ◽  
Seungkyu Lee
Keyword(s):  
Depth Estimation ◽  
Poor Quality ◽  
Iterative Refinement ◽  
Object Boundary ◽  
Depth From Focus ◽  
3D Shape ◽  
Polynomial Curve ◽  
Frequency Domains ◽  
Image Set ◽  
Color Cameras

Estimating the 3D shape of a scene from differently focused set of images has been a practical approach for 3D reconstruction with color cameras. However, reconstructed depth with existing depth from focus (DFF) methods still suffer from poor quality with textureless and object boundary regions. In this paper, we propose an improved depth estimation based on depth from focus iteratively refining 3D shape from uniformly focused image set (UFIS). We investigated the appearance changes in spatial and frequency domains in iterative manner. In order to achieve sub-frame accuracy in depth estimation, optimal location of focused frame in DFF is estimated by fitting a polynomial curve on the dissimilarity measurements. In order to avoid wrong depth values on texture-less regions we propose to build a confidence map and use it to identify erroneous depth estimations. We evaluated our method on public and our own datasets obtained from different types of devices, such as smartphones, medical, and normal color cameras. Quantitative and qualitative evaluations on various test image sets show promising performance of the proposed method in depth estimation.

scholarly journals Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation

2021 ◽  
Vol 11 (6) ◽  
pp. 2666
Author(s):  
Hafiz Muhammad Usama Hassan Alvi ◽  
Muhammad Shahid Farid ◽  
Muhammad Hassan Khan ◽  
Marcin Grzegorzek
Keyword(s):  
Quality Assessment ◽  
Mixed Reality ◽  
Depth Estimation ◽  
Poor Quality ◽  
Depth Image ◽  
Depth Maps ◽  
Structural Distortions ◽  
Image Quality Metric ◽  
Technological Constraints

Emerging 3D-related technologies such as augmented reality, virtual reality, mixed reality, and stereoscopy have gained remarkable growth due to their numerous applications in the entertainment, gaming, and electromedical industries. In particular, the 3D television (3DTV) and free-viewpoint television (FTV) enhance viewers’ television experience by providing immersion. They need an infinite number of views to provide a full parallax to the viewer, which is not practical due to various financial and technological constraints. Therefore, novel 3D views are generated from a set of available views and their depth maps using depth-image-based rendering (DIBR) techniques. The quality of a DIBR-synthesized image may be compromised for several reasons, e.g., inaccurate depth estimation. Since depth is important in this application, inaccuracies in depth maps lead to different textural and structural distortions that degrade the quality of the generated image and result in a poor quality of experience (QoE). Therefore, quality assessment DIBR-generated images are essential to guarantee an appreciative QoE. This paper aims at estimating the quality of DIBR-synthesized images and proposes a novel 3D objective image quality metric. The proposed algorithm aims to measure both textural and structural distortions in the DIBR image by exploiting the contrast sensitivity and the Hausdorff distance, respectively. The two measures are combined to estimate an overall quality score. The experimental evaluations performed on the benchmark MCL-3D dataset show that the proposed metric is reliable and accurate, and performs better than existing 2D and 3D quality assessment metrics.

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.

A near pixel Depth from Focus architecture for video rate depth estimation

2018 ◽  
Vol 2018 (11) ◽  
pp. 447-1-447-6 ◽  
Author(s):  
Simon Emberger ◽  
Laurent Alacoque ◽  
Antoine Dupret ◽  
Gilles Sicard ◽  
Jean Louis de Bougrenet de la Tocnaye
Keyword(s):  
Depth Estimation ◽  
Depth From Focus

EFFECT OF NOISE AND SOURCE ILLUMINATION ON 3D SHAPE RECOVERY

2008 ◽  
Vol 22 (05) ◽  
pp. 945-958 ◽  
Author(s):  
AAMIR SAEED MALIK ◽  
TAE-SUN CHOI
Keyword(s):  
Shape Recovery ◽  
Noise Analysis ◽  
Depth Estimation ◽  
Optical Methods ◽  
Optical Transfer Function ◽  
Factors Affecting ◽  
3D Shape ◽  
Focus Measure ◽  
Source Illumination ◽  
3D Shape Recovery

There are many factors affecting the depth estimation for 3D shape recovery using passive optical methods. In this paper, we consider the effects of noise, source illumination and texture reflectance for shape from focus technique. We present a focus measure which shows consistent performance for varying noise levels, source illumination levels and different texture reflectance. The focus measure is based on an optical transfer function implemented in the Fourier domain and its results are compared with four other focus measures. The additive Gaussian noise is considered for noise analysis. Three illumination levels are considered for source illumination and three different textures are studied for reflectance analysis.

Deep Spatial–focal Network for Depth from Focus

2021 ◽  
Author(s):  
Sherzod Salokhiddinov ◽  
Seungkyu Lee
Keyword(s):  
Neural Network ◽  
Dimensional Space ◽  
Large Body ◽  
Three Dimensional ◽  
Depth Estimation ◽  
Depth Image ◽  
Data Sets ◽  
Depth From Focus ◽  
Training Samples ◽  
Public Data

Traditional depth from focus (DFF) methods obtain depth image from a set of differently focused color images. They detect in-focus region at each image by measuring the sharpness of observed color textures. However, estimating sharpness of arbitrary color texture is not a trivial task especially when there are limited color or intensity variations in an image. Recent deep learning based DFF approaches have shown that the collective estimation of sharpness in a set of focus images based on large body of training samples outperforms traditional DFF with challenging target objects with textureless or glaring surfaces. In this article, we propose a deep spatial–focal convolutional neural network that encodes the correlations between consecutive focused images that are fed to the network in order. In this way, our neural network understands the pattern of blur changes of each image pixel from a volumetric input of spatial–focal three-dimensional space. Extensive quantitative and qualitative evaluations on existing three public data sets show that our proposed method outperforms prior methods in depth estimation.

scholarly journals Pixel-wise parallel calculation for depth from focus with adaptive focus measure

2021 ◽  
Author(s):  
Yoichi Matsubara ◽  
Keiichiro Shirai ◽  
Yuya Ito ◽  
Kiyoshi Tanaka
Keyword(s):  
Computation Time ◽  
Window Size ◽  
Depth Estimation ◽  
Mean Value ◽  
Process Window ◽  
Depth From Focus ◽  
Focus Measure ◽  
The Mean ◽  
Relationship Of ◽  
The Relationship

AbstractDepth-from-focus methods can estimate the depth from a set of images taken with different focus settings. We recently proposed a method that uses the relationship of the ratio between the luminance value of a target pixel and the mean value of the neighboring pixels. This relationship has a Poisson distribution. Despite its good performance, the method requires a large amount of memory and computation time because it needs to store focus measurement values for each depth and each window radius on a pixel-wise basis, and filtering to compute the mean value, which is performed twice, makes the relationship among neighboring pixels too strong to parallelize the pixel-wise processing. In this paper, we propose an approximate calculation method that can give almost the same results with a single time filtering operation and enables pixel-wise parallelization. This pixel-wise processing does not require the aforementioned focus measure values to be stored, which reduces the amount of memory. Additionally, utilizing the pixel-wise processing, we propose a method of determining the process window size that can improve noise tolerance and in depth estimation in texture-less regions. Through experiments, we show that our new method can better estimate depth values in a much shorter time.

Practical electron tomography

1995 ◽  
Vol 53 ◽  
pp. 742-743
Author(s):  
C.L. Woodcock
Keyword(s):  
Electron Tomography ◽  
Tomographic Reconstruction ◽  
Three Dimensional ◽  
3D Shape ◽  
Computational Resources ◽  
Shape Of Particles

Despite the potential of the technique, electron tomography has yet to be widely used by biologists. This is in part related to the rather daunting list of equipment and expertise that are required. Thanks to continuing advances in theory and instrumentation, tomography is now more feasible for the non-specialist. One barrier that has essentially disappeared is the expense of computational resources. In view of this progress, it is time to give more attention to practical issues that need to be considered when embarking on a tomographic project. The following recommendations and comments are derived from experience gained during two long-term collaborative projects.Tomographic reconstruction results in a three dimensional description of an individual EM specimen, most commonly a section, and is therefore applicable to problems in which ultrastructural details within the thickness of the specimen are obscured in single micrographs. Information that can be recovered using tomography includes the 3D shape of particles, and the arrangement and dispostion of overlapping fibrous and membranous structures.

Parasite defense mechanisms in bees: behavior, immunity, antimicrobials, and symbionts

2019 ◽  
Vol 4 (1) ◽  
pp. 59-76 ◽  
Author(s):  
Alison E. Fowler ◽  
Rebecca E. Irwin ◽  
Lynn S. Adler
Keyword(s):  
Defense Mechanisms ◽  
Poor Quality ◽  
Future Research ◽  
Immune Priming ◽  
Social Bees ◽  
Bee Health ◽  
Landscape Scales ◽  
And Function ◽  
Solitary Species

Parasites are linked to the decline of some bee populations; thus, understanding defense mechanisms has important implications for bee health. Recent advances have improved our understanding of factors mediating bee health ranging from molecular to landscape scales, but often as disparate literatures. Here, we bring together these fields and summarize our current understanding of bee defense mechanisms including immunity, immunization, and transgenerational immune priming in social and solitary species. Additionally, the characterization of microbial diversity and function in some bee taxa has shed light on the importance of microbes for bee health, but we lack information that links microbial communities to parasite infection in most bee species. Studies are beginning to identify how bee defense mechanisms are affected by stressors such as poor-quality diets and pesticides, but further research on this topic is needed. We discuss how integrating research on host traits, microbial partners, and nutrition, as well as improving our knowledge base on wild and semi-social bees, will help inform future research, conservation efforts, and management.

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