SpecSeg Network for Specular Highlight Detection and Segmentation in Real-World Images.

Specular highlights detection and removal in images is a fundamental yet non-trivial problem of interest. Most modern techniques proposed are inadequate at dealing with real-world images taken under uncontrolled conditions with the presence of complex textures, multiple objects, and bright colours, resulting in reduced accuracy and false positives. To detect specular pixels in a wide variety of real-world images independent of the number, colour, or type of illuminating source, we propose an efficient Specular Segmentation (SpecSeg) network based on the U-net architecture that is expeditious to train on nominal-sized datasets.

Segmentation-Based vs. Regression-Based Biomarker Estimation: A Case Study of Fetus Head Circumference Assessment from Ultrasound Images.

The fetus head circumference (HC) is a key biometric to monitor fetus growth during pregnancy, which is estimated from ultrasound (US) images. The standard approach to automatically measure the HC is to use a segmentation network to segment the skull, and then estimate the head contour length from the segmentation map via ellipse fitting, usually after post-processing. In this application, segmentation is just an intermediate step to the estimation of a parameter of interest.

PHROG: A Multimodal Feature for Place Recognition.

Long-term place recognition in outdoor environments remains a challenge due to high appearance changes in the environment. The problem becomes even more difficult when the matching between two scenes has to be made with information coming from different visual sources, particularly with different spectral ranges. For instance, an infrared camera is helpful for night vision in combination with a visible camera.

An Evaluation of the Pedestrian Classification in a Multi-Domain Multi-Modality Setup.

The objective of this article is to study the problem of pedestrian classification across different light spectrum domains (visible and far-infrared (FIR)) and modalities (intensity, depth and motion). In recent years, there has been a number of approaches for classifying and detecting pedestrians in both FIR and visible images, but the methods are difficult to compare, because either the datasets are not publicly available or they do not offer a comparison between the two domains. Our two primary contributions are the following: (1) we propose a public dataset, named RIFIR , containing both FIR and visible images collected in an urban environment from a moving vehicle during daytime; and (2) we compare the state-of-the-art features in a multi-modality setup: intensity, depth and flow, in far-infrared over visible domains.

Physical interpretation of polarization-encoded images by color preview.

In the framework of Stokes parameters imaging, polarizationencoded images have four channels which makes physical interpretation of such multidimensional structures hard to grasp at once. Furthermore, the information content is intricately combined in the parameters channels which involve the need for a proper tool that allows the analysis and understanding this kind of images. In this paper we address the problem of analyzing polarization-encoded images and explore the potential of this information for classification issues and propose ad hoc color displays as an aid to the interpretation of physical properties content.