Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry

Creation of head 3D models from videos or pictures of the head by using close-range photogrammetry techniques has many applications in clinical, commercial, industrial, artistic, and entertainment areas. This work aims to create a methodology for improving 3D head reconstruction, with a focus on using selfie videos as the data source. Then, using this methodology, we seek to propose changes for the general-purpose 3D reconstruction algorithm to improve the head reconstruction process. We define the improvement of the 3D head reconstruction as an increase of reconstruction quality (which is lowering reconstruction errors of the head and amount of semantic noise) and reduction of computational load. We proposed algorithm improvements that increase reconstruction quality by removing image backgrounds and by selecting diverse and high-quality frames. Algorithm modifications were evaluated on videos of the mannequin head. Evaluation results show that baseline reconstruction is improved 12 times due to the reduction of semantic noise and reconstruction errors of the head. The reduction of computational demand was achieved by reducing the frame number needed to process, reducing the number of image matches required to perform, reducing an average number of feature points in images, and still being able to provide the highest precision of the head reconstruction.

A Novel Upsampling and Context Convolution for Image Semantic Segmentation

Semantic segmentation, which refers to pixel-wise classification of an image, is a fundamental topic in computer vision owing to its growing importance in the robot vision and autonomous driving sectors. It provides rich information about objects in the scene such as object boundary, category, and location. Recent methods for semantic segmentation often employ an encoder-decoder structure using deep convolutional neural networks. The encoder part extracts features of the image using several filters and pooling operations, whereas the decoder part gradually recovers the low-resolution feature maps of the encoder into a full input resolution feature map for pixel-wise prediction. However, the encoder-decoder variants for semantic segmentation suffer from severe spatial information loss, caused by pooling operations or stepwise convolutions, and does not consider the context in the scene. In this paper, we propose a novel dense upsampling convolution method based on a guided filter to effectively preserve the spatial information of the image in the network. We further propose a novel local context convolution method that not only covers larger-scale objects in the scene but covers them densely for precise object boundary delineation. Theoretical analyses and experimental results on several benchmark datasets verify the effectiveness of our method. Qualitatively, our approach delineates object boundaries at a level of accuracy that is beyond the current excellent methods. Quantitatively, we report a new record of 82.86% and 81.62% of pixel accuracy on ADE20K and Pascal-Context benchmark datasets, respectively. In comparison with the state-of-the-art methods, the proposed method offers promising improvements.

Thermal Remote Sensing from UAVs: A Review on Methods in Coastal Cliffs Prone to Landslides

Coastal retreat is a non-recoverable phenomenon that—together with a relevant proneness to landslides—has economic, social and environmental impacts. Quantitative data on geological and geomorphologic features of such areas can help to predict and quantify the phenomena and to propose mitigation measures to reduce their impact. Coastal areas are often inaccessible for sampling and in situ surveys, in particular where steeply sloping cliffs are present. Uses and capability of infrared thermography (IRT) were reviewed, highlighting its suitability in geological and landslides hazard applications. Thanks to the high resolution of the cameras on the market, unmanned aerial vehicle-based IRT allows to acquire large amounts of data from inaccessible steep cliffs. Coupled structure-from-motion photogrammetry and coregistration of data can improve accuracy of IRT data. According to the strengths recognized in the reviewed literature, a three-step methodological approach to produce IRTs was proposed.

Photogrammetry using visible, infrared, hyperspectral and thermal imaging of crime scenes

Photogrammetry is a method for obtaining virtual 3D models of objects and scenes. The technique is increasingly used to record the crime scene in its original, undisturbed state for mapping, analytical and reconstruction purposes. Recently, it was shown that it is possible to visualize and/or chemically analyze latent traces by using advanced cameras which either operate in wavelength ranges beyond the visible range, and/or are able to obtain spectrally resolved images. The combination of these advanced cameras and photogrammetric techniques enables the 3D registration of valuable information. We successfully explored the feasibility to obtain visible, infrared, hyperspectral and thermal 3D registrations of simulated crime scenes using photogrammetry, and demonstrate the possibilities and practical challenges for use in forensic practice.