Optics, Photonics and Digital Technologies for Imaging Applications VI最新文献

{"title":"The capabilities of developing eye tracking for AR systems on the base of a microcontroller Raspberry Pi","authors":"Elena Shlyamova, K. Ezhova, Dmitriy Fedorenko","doi":"10.1117/12.2554951","DOIUrl":"https://doi.org/10.1117/12.2554951","url":null,"abstract":"This article describes eye tracking technology as a prospective application for AR systems, opening opportunities for dynamical focus, attention mapping and the interplay between a user and a device. Described eye tracking system is an integration of a microcontroller and a camera module. The software uses algorithms for detection a pupil, determination of its position and calculation of the gaze vector for each eye. Gaze tracking is a basis for such technologies as dynamical focus and attention maps, which use the sight vectors as initial data. AR system with basic tracking functionality is developed on the base of multifunctional microcontroller Raspberry Pi which makes project available for a wide community of students and developers by minimal cost.","PeriodicalId":235141,"journal":{"name":"Optics, Photonics and Digital Technologies for Imaging Applications VI","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114915463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Front Matter: Volume 11353","authors":"","doi":"10.1117/12.2571286","DOIUrl":"https://doi.org/10.1117/12.2571286","url":null,"abstract":"","PeriodicalId":235141,"journal":{"name":"Optics, Photonics and Digital Technologies for Imaging Applications VI","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129453839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beam profiler network (BPNet): a deep learning approach to mode demultiplexing of Laguerre-Gaussian optical beams (Conference Presentation)","authors":"Amit Bekerman, Sahar Froim, Barak Hadad, A. Bahabad","doi":"10.1117/12.2547463","DOIUrl":"https://doi.org/10.1117/12.2547463","url":null,"abstract":"The possibility of employing the spatial degree of photons for communications is gaining interest in recent years due to its unbounded dimensionality. A natural basis to span the transverse profile of photons is comprised of Laguerre-Gaussian (LG) modes which are characterized with two topological numbers: l, the orbital index, describing the orbital-angular-momentum (OAM) in units of “h-bar” per photon in the beam and p, which is the radial index or radial quantum number. One of the main challenges for utilizing LG modes in communications is the ability to perform mode-sorting and demultiplexing of the incoming physical data-flow. Nowadays, there are two leading approaches to mode demultiplexing. The first approach uses intricate optical setups in which the l and p degrees of freedom are coupled to other degrees of freedom such as the angle of propagation or the polarization of the beam. Most of these methods address either the OAM or the radial index degrees of freedom. The second approach, which emerged recently, suggests using just a camera to detect the intensity of the incoming light beam and to utilize a deep neural network (DNN) to classify the beam. To date, demonstrated DNN-based demultiplexers addressed solely the OAM degree of light. We report on an experimental demonstration of state-of-the-art mode demultiplexing of Laguerre-Gaussian beams according to both their orbital angular momentum and radial topological numbers using a flow of two concatenated deep neural networks. The first network serves as a transfer function from experimentally-generated to ideal numerically-generated data, while using a unique \"Histogram Weighted Loss\" function that solves the problem of images with limited significant information. The second network acts as a spatial-modes classifier. Our method uses only the intensity profile of modes or their superposition with no need for any phase information.","PeriodicalId":235141,"journal":{"name":"Optics, Photonics and Digital Technologies for Imaging Applications VI","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141219224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assisting classical paintings restoration: efficient paint loss detection and descriptor-based inpainting using shared pretraining","authors":"Laurens Meeus, Shaoguang Huang, Nina Žižakić, Xianghui Xie, Bart Devolder, Hélène Dubois, M. Martens, A. Pižurica","doi":"10.1117/12.2556000","DOIUrl":"https://doi.org/10.1117/12.2556000","url":null,"abstract":"In the restoration process of classical paintings, one of the tasks is to map paint loss for documentation and analysing purposes. Because this is such a sizable and tedious job automatic techniques are highly on demand. The currently available tools allow only rough mapping of the paint loss areas while still requiring considerable manual work. We develop here a learning method for paint loss detection that makes use of multimodal image acquisitions and we apply it within the current restoration of the Ghent Altarpiece. Our neural network architecture is inspired by a multiscale convolutional neural network known as U-Net. In our proposed model, the downsampling of the pooling layers is omitted to enforce translation invariance and the convolutional layers are replaced with dilated convolutions. The dilated convolutions lead to denser computations and improved classification accuracy. Moreover, the proposed method is designed such to make use of multimodal data, which are nowadays routinely acquired during the restoration of master paintings, and which allow more accurate detection of features of interest, including paint losses. Our focus is on developing a robust approach with minimal user-interference. Adequate transfer learning is here crucial in order to extend the applicability of pre-trained models to the paintings that were not included in the training set, with only modest additional re-training. We introduce a pre-training strategy based on a multimodal, convolutional autoencoder and we fine-tune the model when applying it to other paintings. We evaluate the results by comparing the detected paint loss maps to manual expert annotations and also by running virtual inpainting based on the detected paint losses and comparing the virtually inpainted results with the actual physical restorations. The results indicate clearly the efficacy of the proposed method and its potential to assist in the art conservation and restoration processes.","PeriodicalId":235141,"journal":{"name":"Optics, Photonics and Digital Technologies for Imaging Applications VI","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128289079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D point cloud reconstruction from a single 4D light field image","authors":"Helia Farhood, S. Perry, Eva Cheng, Juno Kim","doi":"10.1117/12.2555292","DOIUrl":"https://doi.org/10.1117/12.2555292","url":null,"abstract":"Obtaining accurate and noise-free three-dimensional (3D) reconstructions from real world scenes has grown in importance in recent decades. In this paper, we propose a novel strategy for the reconstruction of a 3D point cloud of an object from a single 4D light field (LF) image based on the transformation of point-plane correspondences. Considering a 4D LF image as an input, we first estimate the depth map using point correspondences between sub-aperture images. We then apply histogram equalization and histogram stretching to enhance the separation between depth planes. The main aim of this step is to increase the distance between adjacent depth layers and to enhance the depth map. We then detect edge contours of the original image using fast canny edge detection, and combine linearly the result with that of the previous steps. Following this combination, by transforming the point-plane correspondence, we can obtain the 3D structure of the point cloud. The proposed method avoids feature extraction, segmentation and the extraction of occlusion masks required by other methods, and due to this, our method can reliably mitigate noise. We tested our method with synthetic and real world image databases. To verify the accuracy of our method, we compared our results with two different state-of-the-art algorithms. In this way, we used the LOD (Level of Detail) to compare the number of points needed to describe an object. The results showed that our method had the highest level of detail compared to other existing methods.","PeriodicalId":235141,"journal":{"name":"Optics, Photonics and Digital Technologies for Imaging Applications VI","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125645818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}