This work presents a visual information fusion approach for robust probability-oriented feature matching. It is sustained by omnidirectional imaging, and it is tested in a visual localization framework, in mobile robotics. General visual localization methods have been extensively studied and optimized in terms of performance. However, one of the main threats that jeopardizes the final estimation is the presence of outliers. In this paper, we present several contributions to deal with that issue. First, 3D information data, associated with SURF (Speeded-Up Robust Feature) points detected on the images, is inferred under the Bayesian framework established by Gaussian processes (GPs). Such information represents a probability distribution for the feature points’ existence, which is successively fused and updated throughout the robot’s poses. Secondly, this distribution can be properly sampled and projected onto the next 2D image frame in $t+1$, by means of a filter-motion prediction. This strategy permits obtaining relevant areas in the image reference system, from which probable matches could be detected, in terms of the accumulated probability of feature existence. This approach entails an adaptive probability-oriented matching search, which accounts for significant areas of the image, but it also considers unseen parts of the scene, thanks to an internal modulation of the probability distribution domain, computed in terms of the current uncertainty of the system. The main outcomes confirm a robust feature matching, which permits producing consistent localization estimates, aided by the odometer’s prior to estimate the scale factor. Publicly available datasets have been used to validate the design and operation of the approach. Moreover, the proposal has been compared, firstly with a standard feature matching and secondly with a localization method, based on an inverse depth parametrization. The results confirm the validity of the approach in terms of feature matching, localization accuracy, and time consumption.

A biomarker is generally an analyte that indicates the presence and/or extent of a biological process, which is in itself usually directly linked to the clinical manifestations and outcome of a particular disease. The biomarkers in the field of lysosomal storage diseases (LSDs) have particular relevance where spectacular therapeutic initiatives have been achieved, most notably with the introduction of enzyme replacement therapy (ERT). There are two main types of biomarkers. The first group is comprised of those molecules whose accumulation is directly enhanced as a result of defective lysosomal function. These molecules represent the storage of the principal macro-molecular substrate(s) of a specific enzyme or protein, whose function is deficient in the given disease. In the second group of biomarkers, the relationship between the lysosomal defect and the biomarker is indirect. In this group, the biomarker reflects the effects of the primary lysosomal defect on cell, tissue, or organ functions. There is no “gold standard” among biomarkers used to diagnosis and/or monitor LSDs, but there are a number that exist that can be used to reasonably assess and monitor the state of certain organs or functions. A number of biomarkers have been proposed for the analysis of the most important LSDs. In this review, we will summarize the most promising biomarkers in major LSDs and discuss why these are the most promising candidates for screening systems.

This paper presents a comparison of different image fusion methods for matching visible-spectrum images with thermal-spectrum (far-infrared) images, aimed at enhancing person detection using convolutional neural networks (CNNs). While object detection with RGB images is a well-developed area, it is still greatly limited by lighting conditions. This limitation poses a significant challenge in image detection playing a larger role in everyday technology, where illumination cannot always be controlled. Far-infrared images (which are partially invariant to lighting conditions) can serve as a valuable complement to RGB images in environments where illumination cannot be controlled and robust object detection is needed. In this work, various early and middle fusion techniques are presented and compared using different multispectral datasets, with the aim of addressing these limitations and improving detection performance.