scholarly journals Continuous and Diverse Image-to-Image Translation via Signed Attribute Vectors

2022 ◽  
Author(s):  
Qi Mao ◽  
Hung-Yu Tseng ◽  
Hsin-Ying Lee ◽  
Jia-Bin Huang ◽  
Siwei Ma ◽  
...  
Keyword(s):  
Image Translation

Design and calibration of an IVEM for general 3-dimensional imaging of non-diffracting materials

1992 ◽  
Vol 50 (2) ◽  
pp. 1040-1041
Author(s):  
Neil Rowlands ◽  
Jeff Price ◽  
Michael Kersker ◽  
Seichi Suzuki ◽  
Steve Young ◽  
...  
Keyword(s):  
3D Imaging ◽  
Tomographic Reconstruction ◽  
Three Dimensional ◽  
3 Dimensional ◽  
3D Microstructure ◽  
Image Translation ◽  
Digital Correlation ◽  
On Line ◽  
Mechanical Stage ◽  
Projection Angle

Three-dimensional (3D) microstructure visualization on the electron microscope requires that the sample be tilted to different positions to collect a series of projections. This tilting should be performed rapidly for on-line stereo viewing and precisely for off-line tomographic reconstruction. Usually a projection series is collected using mechanical stage tilt alone. The stereo pairs must be viewed off-line and the 60 to 120 tomographic projections must be aligned with fiduciary markers or digital correlation methods. The delay in viewing stereo pairs and the alignment problems in tomographic reconstruction could be eliminated or improved by tilting the beam if such tilt could be accomplished without image translation.A microscope capable of beam tilt with simultaneous image shift to eliminate tilt-induced translation has been investigated for 3D imaging of thick (1 μm) biologic specimens. By tilting the beam above and through the specimen and bringing it back below the specimen, a brightfield image with a projection angle corresponding to the beam tilt angle can be recorded (Fig. 1a).

Equivariant Adversarial Network for Image-to-image Translation

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-14
Author(s):  
Masoumeh Zareapoor ◽  
Jie Yang
Keyword(s):  
State Of The Art ◽  
Generative Models ◽  
Generative Model ◽  
Target Domain ◽  
Adversarial Network ◽  
Proposed Model ◽  
Image Translation ◽  
Great Performance ◽  
Representative Model ◽  
The Ideal

Image-to-Image translation aims to learn an image from a source domain to a target domain. However, there are three main challenges, such as lack of paired datasets, multimodality, and diversity, that are associated with these problems and need to be dealt with. Convolutional neural networks (CNNs), despite of having great performance in many computer vision tasks, they fail to detect the hierarchy of spatial relationships between different parts of an object and thus do not form the ideal representative model we look for. This article presents a new variation of generative models that aims to remedy this problem. We use a trainable transformer, which explicitly allows the spatial manipulation of data within training. This differentiable module can be augmented into the convolutional layers in the generative model, and it allows to freely alter the generated distributions for image-to-image translation. To reap the benefits of proposed module into generative model, our architecture incorporates a new loss function to facilitate an effective end-to-end generative learning for image-to-image translation. The proposed model is evaluated through comprehensive experiments on image synthesizing and image-to-image translation, along with comparisons with several state-of-the-art algorithms.

scholarly journals A GAN-based temporally stable shading model for fast animation of photorealistic hair

2021 ◽  
Author(s):  
Zhi Qiao ◽  
Takashi Kanai
Keyword(s):  
Main Idea ◽  
Temporal Coherence ◽  
Image Translation ◽  
Translation Methods

AbstractWe introduce an unsupervised GAN-based model for shading photorealistic hair animations. Our model is much faster than previous rendering algorithms and produces fewer artifacts than other neural image translation methods. The main idea is to extend the Cycle-GAN structure to avoid semitransparent hair appearance and to exactly reproduce the interaction of the lights with the scene. We use two constraints to ensure temporal coherence and highlight stability. Our approach outperforms and is computationally more efficient than previous methods.

scholarly journals Convolutional Neural Networks for Semantic Segmentation as a Tool for Multiclass Face Analysis in Thermal Infrared

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
David Müller ◽  
Andreas Ehlen ◽  
Bernd Valeske
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Thermal Infrared ◽  
Relative Temperature ◽  
Face Analysis ◽  
Image Translation ◽  
The Face ◽  
Live Image ◽  
Quantitative Thermography

AbstractConvolutional neural networks were used for multiclass segmentation in thermal infrared face analysis. The principle is based on existing image-to-image translation approaches, where each pixel in an image is assigned to a class label. We show that established networks architectures can be trained for the task of multiclass face analysis in thermal infrared. Created class annotations consisted of pixel-accurate locations of different face classes. Subsequently, the trained network can segment an acquired unknown infrared face image into the defined classes. Furthermore, face classification in live image acquisition is shown, in order to be able to display the relative temperature in real-time from the learned areas. This allows a pixel-accurate temperature face analysis e.g. for infection detection like Covid-19. At the same time our approach offers the advantage of concentrating on the relevant areas of the face. Areas of the face irrelevant for the relative temperature calculation or accessories such as glasses, masks and jewelry are not considered. A custom database was created to train the network. The results were quantitatively evaluated with the intersection over union (IoU) metric. The methodology shown can be transferred to similar problems for more quantitative thermography tasks like in materials characterization or quality control in production.

scholarly journals A Novel Building Temperature Simulation Approach Driven by Expanding Semantic Segmentation Training Datasets with Synthetic Aerial Thermal Images

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 353
Author(s):  
Yu Hou ◽  
Rebekka Volk ◽  
Lucio Soibelman
Keyword(s):  
Semantic Segmentation ◽  
Sensor Data ◽  
University Campus ◽  
City Center ◽  
Thermal Images ◽  
Complex Envelope ◽  
Virtual Camera ◽  
Image Translation ◽  
Rgb Images ◽  
Synthetic Datasets

Multi-sensor imagery data has been used by researchers for the image semantic segmentation of buildings and outdoor scenes. Due to multi-sensor data hunger, researchers have implemented many simulation approaches to create synthetic datasets, and they have also synthesized thermal images because such thermal information can potentially improve segmentation accuracy. However, current approaches are mostly based on the laws of physics and are limited to geometric models’ level of detail (LOD), which describes the overall planning or modeling state. Another issue in current physics-based approaches is that thermal images cannot be aligned to RGB images because the configurations of a virtual camera used for rendering thermal images are difficult to synchronize with the configurations of a real camera used for capturing RGB images, which is important for segmentation. In this study, we propose an image translation approach to directly convert RGB images to simulated thermal images for expanding segmentation datasets. We aim to investigate the benefits of using an image translation approach for generating synthetic aerial thermal images and compare those approaches with physics-based approaches. Our datasets for generating thermal images are from a city center and a university campus in Karlsruhe, Germany. We found that using the generating model established by the city center to generate thermal images for campus datasets performed better than using the latter to generate thermal images for the former. We also found that using a generating model established by one building style to generate thermal images for datasets with the same building styles performed well. Therefore, we suggest using training datasets with richer and more diverse building architectural information, more complex envelope structures, and similar building styles to testing datasets for an image translation approach.

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