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Hair loss is a phenomenon known to affect people’s morale and self-confidence. Often, the awareness of the phenomenon and the possibilities of treatment is late. This paper investigates deep learning methods for detecting hairs loss levels by men from face images. In this context, a specific training dataset has been prepared with face images having varied levels of baldness. Moreover, in spite of the low visibility of hairs in such images, a matching method is proposed for automatically classifying facial images with respect to pattern classification tables of male baldness from the medical area. Experimental results show the potential and the efficiency for medical, security and commercial applications.

A vision-based and straightforward parking space surveillance system is presented. In particular, the novelty of the approach lies in the use of self-driven and direct dissimilarity computing mechanisms based on image raw brightness. Notably, the parking occupancy is estimated in real-time by applying fast computed estimators that optimize a global dissimilarity measure over slot-centered parking images. Experimental results show the operational capabilities and the efficiency of the proposed system mechanisms.

This paper presents a pipeline in progress for generating virtual 3D model of urban street facades by fusing cadastral map with terrestrial images and laser data. A Mobile Mapping System (MMS) has been employed to massively gather georeferenced images and laser raw data at street level. Firstly, we describe the context as well as our motivation that aims at enriching the quality of the virtual urban environments. Secondly, we briefly present advanced works on 3D street facades mapping by merging the cadastral map and laser data. These works addressed the street facade plane estimation, their delimitation, and their texturing. Thirdly, we present a novel extension we are working on for extracting 3D detailed facade limits by fusing the cadastral map with laser and image data. This extension is essentially focused to the detection of fine 2D skylines from acquired high resolution facade images and to the estimation of the detailed 3D facade top boundaries. The presented work deals with datasets acquired in the city of Paris under real conditions. Advances and results show the potential of the envisaged pipeline for applications such as the visualization and the large scale navigation in virtual 3D street model.

This paper presents an approach for extracting 3D outlined planar clusters of street facades. Terrestrial laser data are acquired using a Mobile Mapping System (MMS). Mapping of street facades is of great interest in various digital mapping and robotic research topics. After a filtering step of the 3D point cloud, the dominant hypothetical facade planes are detected using an adapted Progressive Probabilistic Hough Transform (PPHT). The corresponding planar clusters are extracted using a priori geometric knowledge of street. The clusters are horizontally and vertically delimited using heuristic approaches. The adapted PPHT allows the automatic extraction of georeferenced planar clusters of facades with a fine detection of dominant facade lines and a low computation time. The adopted approach has been tested on a set of point cloud acquired in the city of Paris under real conditions. Examples and experimental results show the efficiency and the potential of the proposed approach.