scholarly journals Depth Map Quality Evaluation for Photographic Applications

2020 ◽  
Vol 2020 (9) ◽  
pp. 370-1-370-7
Author(s):  
Eloi Zalczer ◽  
François-Xavier Thomas ◽  
Laurent Chanas ◽  
Gabriele Facciolo ◽  
Frédéric Guichard
Keyword(s):  
Augmented Reality ◽  
Quality Evaluation ◽  
Depth Map ◽  
Depth Imaging ◽  
Depth Maps ◽  
Spatial Alignment ◽  
Need To Evaluate ◽  
New Challenges ◽  
Consumer Devices

As depth imaging is integrated into more and more consumer devices, manufacturers have to tackle new challenges. Applica- tions such as computational bokeh and augmented reality require dense and precisely segmented depth maps to achieve good re- sults. Modern devices use a multitude of different technologies to estimate depth maps, such as time-of-flight sensors, stereoscopic cameras, structured light sensors, phase-detect pixels or a com- bination thereof. Therefore, there is a need to evaluate the quality of the depth maps, regardless of the technology used to produce them. The aim of our work is to propose an end-result evalua- tion method based on a single scene, using a specifically designed chart. We consider the depth maps embedded in the photographs, which are not visible to the user but are used by specialized soft- ware, in association with the RGB pictures. Some of the aspects considered are spatial alignment between RGB and depth, depth consistency, and robustness to texture variations. This work also provides a comparison of perceptual and automatic evaluations.

scholarly journals No-Reference Depth Map Quality Evaluation Model Based on Depth Map Edge Confidence Measurement in Immersive Video Applications

2019 ◽  
Vol 11 (10) ◽  
pp. 204 ◽  
Author(s):  
Dogan ◽  
Haddad ◽  
Ekmekcioglu ◽  
Kondoz
Keyword(s):  
Quality Evaluation ◽  
Evaluation Model ◽  
Depth Map ◽  
Depth Image ◽  
Depth Maps ◽  
Model Based ◽  
Objective Quality ◽  
Quality Evaluation Model ◽  
Confidence Measurement

When it comes to evaluating perceptual quality of digital media for overall quality of experience assessment in immersive video applications, typically two main approaches stand out: Subjective and objective quality evaluation. On one hand, subjective quality evaluation offers the best representation of perceived video quality assessed by the real viewers. On the other hand, it consumes a significant amount of time and effort, due to the involvement of real users with lengthy and laborious assessment procedures. Thus, it is essential that an objective quality evaluation model is developed. The speed-up advantage offered by an objective quality evaluation model, which can predict the quality of rendered virtual views based on the depth maps used in the rendering process, allows for faster quality assessments for immersive video applications. This is particularly important given the lack of a suitable reference or ground truth for comparing the available depth maps, especially when live content services are offered in those applications. This paper presents a no-reference depth map quality evaluation model based on a proposed depth map edge confidence measurement technique to assist with accurately estimating the quality of rendered (virtual) views in immersive multi-view video content. The model is applied for depth image-based rendering in multi-view video format, providing comparable evaluation results to those existing in the literature, and often exceeding their performance.

scholarly journals High-Fidelity Depth Upsampling Using the Self-Learning Framework

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 81
Author(s):  
Inwook Shim ◽  
Tae-Hyun Oh ◽  
In Kweon
Keyword(s):  
Stereo Matching ◽  
Depth Map ◽  
The Self ◽  
Sensor Data ◽  
High Fidelity ◽  
Learning Framework ◽  
Depth Maps ◽  
Potential Applications ◽  
Self Learning

This paper presents a depth upsampling method that produces a high-fidelity dense depth map using a high-resolution RGB image and LiDAR sensor data. Our proposed method explicitly handles depth outliers and computes a depth upsampling with confidence information. Our key idea is the self-learning framework, which automatically learns to estimate the reliability of the upsampled depth map without human-labeled annotation. Thereby, our proposed method can produce a clear and high-fidelity dense depth map that preserves the shape of object structures well, which can be favored by subsequent algorithms for follow-up tasks. We qualitatively and quantitatively evaluate our proposed method by comparing other competing methods on the well-known Middlebury 2014 and KITTIbenchmark datasets. We demonstrate that our method generates accurate depth maps with smaller errors favorable against other methods while preserving a larger number of valid points, as we also show that our approach can be seamlessly applied to improve the quality of depth maps from other depth generation algorithms such as stereo matching and further discuss potential applications and limitations. Compared to previous work, our proposed method has similar depth errors on average, while retaining at least 3% more valid depth points.

scholarly journals Quality assessment for register-based statistics - Results for the Austrian census 2011

2016 ◽  
Vol 45 (2) ◽  
pp. 3-14 ◽  
Author(s):  
Eva-Maria Asamer ◽  
Franz Astleithner ◽  
Predrag Cetkovic ◽  
Stefan Humer ◽  
Manuela Lenk ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Quality ◽  
Quality Assessment ◽  
Quality Indicator ◽  
Quality Evaluation ◽  
External Source ◽  
Quality Framework ◽  
New Challenges ◽  
Available Information

In 2011, Statistics Austria carried out the first register-based census. The use of administrative data for statistical purposes is accompanied by various advantages like a reduced burden for the respondents and less costs for the NSI. However, new challenges, like the quality assessment of this kind of data, arise. Therefore, Statistics Austria developed a comprehensive standardized framework for the evaluation of the data quality for registerbased statistics.In this paper, we present the principle of the quality framework and detailed results from the quality evaluation of the 2011 Austrian census. For each attribute in the census a quality measure is derived from four hyperdimensions. The first three hyperdimensions focus on the documentation of data, the usability of the records and the comparison of data to an external source. The fourth hyperdimension assesses the quality of the imputations. In the framework all the available information on each attribute can be combined to form one final quality indicator. This procedure allows to track changes in quality during data processing and to compare the quality of different census generations.

scholarly journals Predicting the Quality of Short Narratives from Social Media

2017 ◽  
Author(s):  
Tong Wang ◽  
Ping Chen ◽  
Boyang Li
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Large Scale ◽  
Computational Models ◽  
Quality Evaluation ◽  
Future Research ◽  
Automatic Evaluation ◽  
Need To Evaluate ◽  
Difficult Challenge

An important and difficult challenge in building computational models for narratives is the automatic evaluation of narrative quality. Quality evaluation connects narrative understanding and generation as generation systems need to evaluate their own products. To circumvent difficulties in acquiring annotations, we employ upvotes in social media as an approximate measure for story quality. We collected 54,484 answers from a crowd-powered question-and-answer website, Quora, and then used active learning to build a classifier that labeled 28,320 answers as stories. To predict the number of upvotes without the use of social network features, we create neural networks that model textual regions and the interdependence among regions, which serve as strong benchmarks for future research. To our best knowledge, this is the first large-scale study for automatic evaluation of narrative quality.

scholarly journals Algorithm for correcting the image depth map based on the points brightness and their distance from the observation point

2020 ◽  
Vol 47 (3) ◽  
pp. 82-92
Author(s):  
S. I. Korotkevich ◽  
Yu. V. Minaeva
Keyword(s):  
Stereo Vision ◽  
Depth Map ◽  
Human Head ◽  
Observation Point ◽  
Mathematical Software ◽  
Depth Maps ◽  
Trained Personnel ◽  
Mathematical Algorithms ◽  
Image Contour

Objective. Modeling the human head is a significant problem that arises in a wide variety of fields of science and technology. Existing active technologies for reconstruction and modeling of the object under study require expensive equipment and trained personnel. Methods. An alternative is to use passive methods that perform image processing using special mathematical algorithms. One of these methods is the stereo vision, which is based on the use of paired images taken simultaneously with several cameras positioned and calibrated in a certain way. However, a common drawback of stereo vision methods is the possibility of obtaining erroneous depth maps due to poorquality source images or incorrect camera and lighting settings. Results. Procedures were developed that use additional parameters of image points, which can be used to correct depth maps to avoid the appearance of defects. To achieve this objective, the existing mathematical software for processing photo and video materials is analyzed; methods for suppressing noise in the image, obtaining an image contour, as well as a method for obtaining a 3D object matrix based on changing the direction of illumination are proposed; the algorithm is tested on a test example. Conclusion. The developed technique should improve the quality of the depth map of the processed image and thus make the modeling procedures more efficient. 

scholarly journals ENHANCEMENT OF DEPTH MAP BY FUSION USING ADAPTIVE AND SEMANTIC-GUIDED SPATIOTEMPORAL FILTERING

2020 ◽  
Vol V-3-2020 ◽  
pp. 227-232
Author(s):  
H. Albanwan ◽  
R. Qin
Keyword(s):  
Depth Map ◽  
Spatiotemporal Analysis ◽  
Stereo Image ◽  
Single Pair ◽  
Depth Maps ◽  
3D Model Reconstruction ◽  
Digital Elevation ◽  
Image Pairs ◽  
Spatiotemporal Filters

Abstract. Extracting detailed geometric information about a scene relies on the quality of the depth maps (e.g. Digital Elevation Surfaces, DSM) to enhance the performance of 3D model reconstruction. Elevation information from LiDAR is often expensive and hard to obtain. The most common approach to generate depth maps is through multi-view stereo (MVS) methods (e.g. dense stereo image matching). The quality of single depth maps, however, is often prone to noise, outliers, and missing data points due to the quality of the acquired image pairs. A reference multi-view image pair must be noise-free and clear to ensure high-quality depth maps. To avoid such a problem, current researches are headed toward fusing multiple depth maps to recover the shortcomings of single-depth maps resulted from a single pair of multi-view images. Several approaches tackled this problem by merging and fusing depth maps, using probabilistic and deterministic methods, but few discussed how these fused depth maps can be refined through adaptive spatiotemporal analysis algorithms (e.g. spatiotemporal filters). The motivation is to push towards preserving the high precision and detail level of depth maps while optimizing the performance, robustness, and efficiency of the algorithm.

scholarly journals Depth Map Renement Using Reliability Based Joint Trilateral Filter

1970 ◽  
Vol 7 (2) ◽  
pp. 107-116
Author(s):  
Takuya Matsuo ◽  
Naoki Kodera ◽  
Norishige Fukushima ◽  
Yutaka Ishibashi
Keyword(s):  
Lower Cost ◽  
Computational Cost ◽  
Depth Map ◽  
Optimization Methods ◽  
Depth Image ◽  
Depth Maps ◽  
Trilateral Filter ◽  
Depth Image Based Rendering ◽  
Depth Ranges

In this paper, we propose a renement lter for depth maps. The lter convolutes an image and a depth map with a cross computed kernel. We call the lter joint trilateral lter. Main advantages of the proposed method are that the lter ts outlines of objects in the depth map to silhouettes in the im- age, and the lter reduces Gaussian noise in other areas. The eects reduce rendering artifacts when a free viewpoint image is generated by point cloud ren- dering and depth image based rendering techniques. Additionally, their computational cost is independent of depth ranges. Thus we can obtain accurate depth maps with the lower cost than the conventional ap- proaches, which require Markov random eld based optimization methods. Experimental results show that the accuracy of the depth map in edge areas goes up and its running time decreases. In addition, the lter improves the accuracy of edges in the depth map from Kinect sensor. As results, the quality of the rendering image is improved.

scholarly journals The Design and Implementation of Postprocessing for Depth Map on Real-Time Extraction System

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiwei Tang ◽  
Bin Li ◽  
Huosheng Li ◽  
Zheng Xu
Keyword(s):  
Real Time ◽  
Stereo Matching ◽  
Depth Map ◽  
Depth Estimation ◽  
Consistency Check ◽  
Key Technology ◽  
Design And Implementation ◽  
Depth Maps ◽  
Matching Cost

Depth estimation becomes the key technology to resolve the communications of the stereo vision. We can get the real-time depth map based on hardware, which cannot implement complicated algorithm as software, because there are some restrictions in the hardware structure. Eventually, some wrong stereo matching will inevitably exist in the process of depth estimation by hardware, such as FPGA. In order to solve the problem a postprocessing function is designed in this paper. After matching cost unique test, the both left-right and right-left consistency check solutions are implemented, respectively; then, the cavities in depth maps can be filled by right depth values on the basis of right-left consistency check solution. The results in the experiments have shown that the depth map extraction and postprocessing function can be implemented in real time in the same system; what is more, the quality of the depth maps is satisfactory.

Augmented Reality Tools in Physics Training at Higher Technical Educational Institutions

2018 ◽  
Vol 51 ◽  
pp. 47-57
Author(s):  
Тетяна Грунтова ◽  
Юлія Єчкало ◽  
Андрій Стрюк ◽  
Андрій Пікільняк
Keyword(s):  
Augmented Reality ◽  
Educational Process ◽  
Educational Institutions ◽  
Training Process ◽  
Scientific Publications ◽  
Theoretical Substantiation ◽  
Research Goal ◽  
Augmented Reality Technology ◽  
Cognitive Activities

Hruntova T. V., Yechkalo YU. V., Stryuk A. M. and Pikilʹnyak A. V. Augmented Reality Tools in Physics Training at Higher Technical Educational Institutions. Research goal: the research is aimed at theoretical substantiation of applying the augmented reality technology and its peculiarities at higher technical educational institutions. Research objectives: the research is to solve the problems of determining the role and place of the technology in the educational process and its possible application to physics training. Object of research: teaching physics to students of higher technical educational institutions. Subject of research: the augmented reality technology as a component of the training process at higher educational institutions. Research methods used: theoretical methods include analysis of scientific and methodological literature; empirical methods include studying and observation of the training process. Research results: analysis of scientific publications allows defining the notion of augmented reality; application of augmented reality objects during laboratory practical works on physics is suggested. Main conclusions. introduction of the augmented reality technology in thetraining process at higher technical educational institutions increases learning efficiency, facilitates students’ training and cognitive activities, improves the quality of knowledge acquisition, provokes interest in a subject, promotesdevelopment of research skills and a future specialist’s competent personality.

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