This paper presents a wide angle holographic display system with extended viewing angle in both horizontal and vertical directions. The display is constructed from six spatial light modulators (SLM) arranged on a circle and an additional SLM used for spatiotemporal multiplexing and a viewing angle extension in two perpendicular directions. The additional SLM, that is synchronized with the SLMs on the circle is placed in the image space. This method increases effective space bandwidth product of display system data from 12.4 to 50 megapixels. The software solution based on three Nvidia graphic cards is developed and implemented in order to achieve fast and synchronized displaying. The experiments presented for both synthetic and real 3D data prove the possibility to view binocularly having good quality images reconstructed in full FoV of the display.

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Skin lesion segmentation plays a key role in the diagnosis of skin cancer; it can be a component in both traditional algorithms and end-to-end approaches. The quality of segmentation directly impacts the accuracy of classification; however, attaining optimal segmentation necessitates a substantial amount of labeled data. Semi-supervised learning allows for employing unlabeled data to enhance the results of the machine learning model. In the case of medical image segmentation, acquiring detailed annotation is time-consuming and costly and requires skilled individuals so the utilization of unlabeled data allows for a significant mitigation of manual segmentation efforts. This study proposes a novel approach to semi-supervised skin lesion segmentation using self-training with a Noisy Student. This approach allows for utilizing large amounts of available unlabeled images. It consists of four steps—first, training the teacher model on labeled data only, then generating pseudo-labels with the teacher model, training the student model on both labeled and pseudo-labeled data, and lastly, training the student* model on pseudo-labels generated with the student model. In this work, we implemented DeepLabV3 architecture as both teacher and student models. As a final result, we achieved a mIoU of 88.0% on the ISIC 2018 dataset and a mIoU of 87.54% on the PH2 dataset. The evaluation of the proposed approach shows that Noisy Student training improves the segmentation performance of neural networks in a skin lesion segmentation task while using only small amounts of labeled data.

We present HoloFace, an open-source framework for face alignment, head pose estimation and facial attribute retrieval for Microsoft HoloLens. HoloFace implements two state-of-the-art face alignment methods which can be used interchangeably: one running locally and one running on a remote backend. Head pose estimation is accomplished by fitting a deformable 3D model to the landmarks localized using face alignment. The head pose provides both the rotation of the head and a position in the world space. The parameters of the fitted 3D face model provide estimates of facial attributes such as mouth opening or smile. Together the above information can be used to augment the faces of people seen by the HoloLens user, and thus their interaction. Potential usage scenarios include facial recognition, emotion recognition, eye gaze tracking and many others. We demonstrate the capabilities of our framework by augmenting the faces of people seen through the HoloLens with various objects and animations.

Structured light camera which is being designed with the joined effort of Institute of Radioelectronics and Institute of Optoelectronics (both being large units of the Warsaw University of Technology within the Faculty of Electronics and Information Technology) combines various hardware and software contemporary technologies. In hardware it is integration of a high speed stripe projector and a stripe camera together with a standard high definition video camera. In software it is supported by sophisticated calibration techniques which enable development of advanced application such as real time 3D viewer of moving objects with the free viewpoint or 3D modeller for still objects.