A non-destructive, autoencoder-based approach to detecting defects and contamination in reusable food packaging.

Today, environmental sustainability is one of the most critical issue. Hence, the food service industry is actively seeking ways to minimize its ecological footprint. One solution to address this issue is the adoption of reusable foodware in the food service industry.

Multispectral indices for real-time and non-invasive tissue ischemia monitoring using snapshot cameras.

An adequate supply of oxygen-rich blood is vital to maintain cell homeostasis, cellular metabolism, and overall tissue health. While classical methods of measuring tissue ischemia are often invasive, localized and require skin contact or contrast agents, spectral imaging shows promise as a non-invasive, wide field, and contrast-free approach. We evaluate three novel reflectance-based spectral indices from the 460 - 840 spectral range.

Pancreatic metastasis of mesenchymal chondrosarcoma: a surgical case report and review of literature.

Introduction: Mesenchymal chondrosarcoma (MC) is a rapidly progressive sarcoma that predominantly impacts the bones. Making up only 3% of chondrosarcomas, about one-third of these tumours develop in extra-skeletal sites.

Case Presentation: The authors present a clinical case of a 42-year-old patient who was diagnosed with MC 8 years ago, now admitted to the hospital with a palpable epigastric mass.

Tensor Completion Using Bilayer Multimode Low-Rank Prior and Total Variation.

In this article, we propose a novel bilayer low-rankness measure and two models based on it to recover a low-rank (LR) tensor. The global low rankness of underlying tensor is first encoded by LR matrix factorizations (MFs) to the all-mode matricizations, which can exploit multiorientational spectral low rankness. Presumably, the factor matrices of all-mode decomposition are LR, since local low-rankness property exists in within-mode correlation.

Region-Based CNN for Anomaly Detection in PV Power Plants Using Aerial Imagery.

Today, solar energy is taking an increasing share of the total energy mix. Unfortunately, many operational photovoltaic plants suffer from a plenitude of defects resulting in non-negligible power loss. The latter highly impacts the overall performance of the PV site; therefore, operators need to regularly inspect their solar parks for anomalies in order to prevent severe performance drops.

Solar Panel Detection within Complex Backgrounds Using Thermal Images Acquired by UAVs.

The installation of solar plants everywhere in the world increases year by year. Automated diagnostic methods are needed to inspect the solar plants and to identify anomalies within these photovoltaic panels. The inspection is usually carried out by unmanned aerial vehicles (UAVs) using thermal imaging sensors.

An interactive ImageJ plugin for semi-automated image denoising in electron microscopy.

The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures. This has caused an explosion in dataset size, necessitating the development of automated workflows. Moreover, large 3D EM datasets typically require hours to days to be acquired and accelerated imaging typically results in noisy data.

The Effect of the Color Filter Array Layout Choice on State-of-the-Art Demosaicing.

Interpolation from a Color Filter Array (CFA) is the most common method for obtaining full color image data. Its success relies on the smart combination of a CFA and a demosaicing algorithm. Demosaicing on the one hand has been extensively studied.

Have I Seen This Place Before? A Fast and Robust Loop Detection and Correction Method for 3D Lidar SLAM.

In this paper, we present a complete loop detection and correction system developed for data originating from lidar scanners. Regarding detection, we propose a combination of a global point cloud matcher with a novel registration algorithm to determine loop candidates in a highly effective way. The registration method can deal with point clouds that are largely deviating in orientation while improving the efficiency over existing techniques.

Bayesian deconvolution of scanning electron microscopy images using point-spread function estimation and non-local regularization.

Microscopy is one of the most essential imaging techniques in life sciences. High-quality images are required in order to solve (potentially life-saving) biomedical research problems. Many microscopy techniques do not achieve sufficient resolution for these purposes, being limited by physical diffraction and hardware deficiencies.

3D Scene Reconstruction Using Omnidirectional Vision and LiDAR: A Hybrid Approach.

In this paper, we propose a novel approach to obtain accurate 3D reconstructions of large-scale environments by means of a mobile acquisition platform. The system incorporates a Velodyne LiDAR scanner, as well as a Point Grey Ladybug panoramic camera system. It was designed with genericity in mind, and hence, it does not make any assumption about the scene or about the sensor set-up.

Image Degradation in Microscopic Images: Avoidance, Artifacts, and Solutions.

The goal of modern microscopy is to acquire high-quality image based data sets. A typical microscopy workflow is set up in order to address a specific biological question and involves different steps. The first step is to precisely define the biological question, in order to properly come to an experimental design for sample preparation and image acquisition.

Automatic high-bandwidth calibration and reconstruction of arbitrarily sampled parallel MRI.

Today, many MRI reconstruction techniques exist for undersampled MRI data. Regularization-based techniques inspired by compressed sensing allow for the reconstruction of undersampled data that would lead to an ill-posed reconstruction problem. Parallel imaging enables the reconstruction of MRI images from undersampled multi-coil data that leads to a well-posed reconstruction problem.