Depth Estimation Through a Generative Model of Light Field Synthesis

Light field photography captures rich structural information that may facilitate a number of traditional image processing and computer vision tasks. A crucial ingredient in such endeavors is accurate depth recovery. We present a novel framework that allows the recovery of a high quality continuous depth map from light field data. To this end we propose a generative model of a light field that is fully parametrized by its corresponding depth map. The model allows for the integration of powerful regularization techniques such as a non-local means prior, facilitating accurate depth map estimation.Comment: German Conference on Pattern Recognition (GCPR) 201

Mask-guided Style Transfer Network for Purifying Real Images

Recently, the progress of learning-by-synthesis has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. However, due to the different distribution of synthetic images compared with real images, the desired performance cannot be achieved. To solve this problem, the previous method learned a model to improve the realism of the synthetic images. Different from the previous methods, this paper try to purify real image by extracting discriminative and robust features to convert outdoor real images to indoor synthetic images. In this paper, we first introduce the segmentation masks to construct RGB-mask pairs as inputs, then we design a mask-guided style transfer network to learn style features separately from the attention and bkgd(background) regions and learn content features from full and attention region. Moreover, we propose a novel region-level task-guided loss to restrain the features learnt from style and content. Experiments were performed using mixed studies (qualitative and quantitative) methods to demonstrate the possibility of purifying real images in complex directions. We evaluate the proposed method on various public datasets, including LPW, COCO and MPIIGaze. Experimental results show that the proposed method is effective and achieves the state-of-the-art results.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0582

A New Approach to the Study of Stellar Populations in Early-Type Galaxies: K-band Spectral Indices and an Application to the Fornax Cluster

New measurements of K-band spectral features are presented for eleven early-type galaxies in the nearby Fornax galaxy cluster. Based on these measurements, the following conclusions have been reached: (1) in galaxies with no signatures of a young stellar component, the K-band Na I index is highly correlated with both the optical metallicity indicator [MgFe]' and central velocity dispersion; (2) in the same galaxies, the K-band Fe features saturate in galaxies with sigma > 150 km/s while Na I (and [MgFe]') continues to increase; (3) [Si/Fe] (and possibly [Na/Fe]) is larger in all observed Fornax galaxies than in Galactic open clusters with near-solar metallicity; (4) in various near-IR diagnostic diagrams, galaxies with signatures of a young stellar component (strong Hbeta, weak [MgFe]') are clearly separated from galaxies with purely old stellar populations; furthermore, this separation is consistent with the presence of an increased number of M-giant stars (most likely to be thermally pulsating AGB stars); (5) the near-IR diagrams discussed here seem as efficient for detecting putatively young stellar components in early-type galaxies as the more commonly used age/metallicity diagnostic plots using optical indices (e.g Hbeta vs. [MgFe]').Comment: 47 pages, 16 figures, ApJ accepte

Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy

Objective: Surgical data science is evolving into a research field that aims to observe everything occurring within and around the treatment process to provide situation-aware data-driven assistance. In the context of endoscopic video analysis, the accurate classification of organs in the field of view of the camera proffers a technical challenge. Herein, we propose a new approach to anatomical structure classification and image tagging that features an intrinsic measure of confidence to estimate its own performance with high reliability and which can be applied to both RGB and multispectral imaging (MI) data. Methods: Organ recognition is performed using a superpixel classification strategy based on textural and reflectance information. Classification confidence is estimated by analyzing the dispersion of class probabilities. Assessment of the proposed technology is performed through a comprehensive in vivo study with seven pigs. Results: When applied to image tagging, mean accuracy in our experiments increased from 65% (RGB) and 80% (MI) to 90% (RGB) and 96% (MI) with the confidence measure. Conclusion: Results showed that the confidence measure had a significant influence on the classification accuracy, and MI data are better suited for anatomical structure labeling than RGB data. Significance: This work significantly enhances the state of art in automatic labeling of endoscopic videos by introducing the use of the confidence metric, and by being the first study to use MI data for in vivo laparoscopic tissue classification. The data of our experiments will be released as the first in vivo MI dataset upon publication of this paper.Comment: 7 pages, 6 images, 2 table