| 1. |
- Bylow, Erik, et al.
(author)
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Combining Depth Fusion and Photometric Stereo for Fine-Detailed 3D Models
- 2019
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In: Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. - 9783030202040 ; 11482 LNCS, s. 261-274
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Conference paper (peer-reviewed)abstract
- In recent years, great progress has been made on the problem of 3D scene reconstruction using depth sensors. On a large scale, these reconstructions look impressive, but often many fine details are lacking due to limitations in the sensor resolution. In this paper we combine two well-known principles for recovery of 3D models, namely fusion of depth images with photometric stereo to enhance the details of the reconstructions. We derive a simple and transparent objective functional that takes both the observed intensity images and depth information into account. The experimental results show that many details are captured that are not present in the input depth images. Moreover, we provide a quantitative evaluation that confirms the improvement of the resulting 3D reconstruction using a 3D printed model.
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| 2. |
- Flood, Gabrielle, et al.
(author)
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Efficient Merging of Maps and Detection of Changes
- 2019
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In: Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. - 9783030202040 ; 11482 LNCS, s. 348-360
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Conference paper (peer-reviewed)abstract
- With the advent of cheap sensors and computing capabilities as well as better algorithms it is now possible to do structure from motion using crowd sourced data. Individual estimates of a map can be obtained using structure from motion (SfM) or simultaneous localization and mapping (SLAM) using e.g. images, sound or radio. However the problem of map merging as used for collaborative SLAM needs further attention. In this paper we study the basic principles behind map merging and collaborative SLAM. We develop a method for merging maps – based on a small memory footprint representation of individual maps – in a way that is computationally efficient. We also demonstrate how the same framework can be used to detect changes in the map. This makes it possible to remove inconsistent parts before merging the maps. The methods are tested on both simulated and real data, using both sensor data from radio sensors and from cameras.
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| 3. |
- Gu, Xuan, 1988-, et al.
(author)
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Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks
- 2019
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In: Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783030202040 - 9783030202057 ; 11482 LNCS, s. 489-498
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Conference paper (peer-reviewed)abstract
- Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI.
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| 4. |
- Li, Zhongguo, et al.
(author)
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Parametric Model-Based 3D Human Shape and Pose Estimation from Multiple Views
- 2019
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In: Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. - 9783030202040 ; 11482 LNCS, s. 336-347
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Conference paper (peer-reviewed)abstract
- Human body pose and shape estimation is an important and challenging task in computer vision. This paper presents a novel method for estimating 3D human body pose and shape from several RGB images, using detected joint positions in the images and based on a parametric human body model. Firstly, the 2D joint points of the RGB images are estimated using a deep neural network, which provides a strong prior on the pose. Then, an energy function is constructed based on the 2D joint points in the RGB images and a parametric human body model. By minimizing the energy function, the pose, shape and camera parameters are obtained. The main contribution of the method over previous work, is that the optimization is based on several images simultaneously using only estimated joint positions in the images. We have performed experiments on both synthetic and real image data-sets, that demonstrate that our method can reconstruct 3D human bodies with better accuracy than previous single view methods.
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| 5. |
- Majtner, Tomáš, et al.
(author)
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On the Effectiveness of Generative Adversarial Networks as HEp-2 Image Augmentation Tool
- 2019
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In: Scandinavian Conference on Image Analysis. - Cham : Springer International Publishing. - 9783030202040 ; , s. 439-451
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Conference paper (peer-reviewed)abstract
- One of the big challenges in the recognition of biomedical samples is the lack of large annotated datasets. Their relatively small size, when compared to datasets like ImageNet, typically leads to problems with efficient training of current machine learning algorithms. However, the recent development of generative adversarial networks (GANs) appears to be a step towards addressing this issue. In this study, we focus on one instance of GANs, which is known as deep convolutio nal generative adversarial network (DCGAN). It gained a lot of attention recently because of its stability in generating realistic artificial images. Our article explores the possibilities of using DCGANs for generating HEp-2 images. We trained multiple DCGANs and generated several datasets of HEp-2 images. Subsequently, we combined them with traditional augmentation and evaluated over three different deep learning configurations. Our article demonstrates high visual quality of generated images, which is also supported by state-of-the-art classification results.
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| 6. |
- Persson, Patrik, et al.
(author)
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Global Trifocal Adjustment
- 2019
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In: Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. - 9783030202040 ; 11482 LNCS, s. 287-298
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Conference paper (peer-reviewed)abstract
- In this paper we introduce a fast and robust structure-less alternative to full bundle adjustment. The method is based on optimizing algebraic errors for trilinear constraints from triplets of views. It is shown that the error generated by a triplet of views can be described by a fixed triangular matrix regardless of the number of feature correspondences between the views. The method has been evaluated on various real and synthetic datasets and shows good convergence properties with a large convergence basin and solutions that are close to the optimal solution. The method has been compared to Global Epipolar Adjustment, GEA, which is based on the bilinear constraint. It will be shown that the method can handle the degenerate configurations of GEA.
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