A software tool for the semi-automatic segmentation of architectural 3D models with semantic annotation and Web fruition

The thorough documentation of Cultural Heritage artifacts is a fundamental concern for management and preservation. In this context, the semantic segmentation and annotation of 3D models of historic buildings is an important modern topic. This work describes a software tool currently under development, for interactive and semi-automatic segmentation, characterization, and annotation of 3D models produced by photogrammetric surveys. The system includes some generic and well-known segmentation approaches, such as region growing and Locally Convex Connected Patches segmentation, but it also contains original code for specific semantic segmentation of parts of buildings, in particular straight stairs and circular-section columns. Furthermore, a method for automatic wall-surface characterization is devoted to rusticated-ashlar detection, in view of masonry-unit segmentation. The software is modular, so allowing easy expandability. It also has tools for data encoding into formats ready for model fruition by Web technologies. These results were partly obtained in collaboration with Corvallis SPA (Padua-Italy, http://www.corvallis.it)

Deep learning model for doors detection a contribution for context awareness recognition of patients with Parkinson’s disease

Freezing of gait (FoG) is one of the most disabling motor symptoms in Parkinson’s disease, which is described as a symptom where walking is interrupted by a brief, episodic absence, or marked reduction, of forward progression despite the intention to continue walking. Although FoG causes are multifaceted, they often occur in response of environment triggers, as turnings and passing through narrow spaces such as a doorway. This symptom appears to be overcome using external sensory cues. The recognition of such environments has consequently become a pertinent issue for PD-affected community. This study aimed to implement a real-time DL-based door detection model to be integrated into a wearable biofeedback device for delivering on-demand proprioceptive cues. It was used transfer-learning concepts to train a MobileNet-SSD in TF environment. The model was then integrated in a RPi being converted to a faster and lighter computing power model using TensorFlow Lite settings. Model performance showed a considerable precision of 97,2%, recall of 78,9% and a good F1-score of 0,869. In real-time testing with the wearable device, DL-model showed to be temporally efficient (~2.87 fps) to detect with accuracy doors over real-life scenarios. Future work will include the integration of sensory cues with the developed model in the wearable biofeedback device aiming to validate the final solution with end-users

Stairs and Doors Recognition as Natural Landmarks Based on Clouds of 3D Edge-Points from RGB-D Sensors for Mobile Robot Localization

Natural landmarks are the main features in the next step of the research in localization of mobile robot platforms. The identification and recognition of these landmarks are crucial to better localize a robot. To help solving this problem, this work proposes an approach for the identification and recognition of natural marks included in the environment using images from RGB-D (Red, Green, Blue, Depth) sensors. In the identification step, a structural analysis of the natural landmarks that are present in the environment is performed. The extraction of edge points of these landmarks is done using the 3D point cloud obtained from the RGB-D sensor. These edge points are smoothed through the S l 0 algorithm, which minimizes the standard deviation of the normals at each point. Then, the second step of the proposed algorithm begins, which is the proper recognition of the natural landmarks. This recognition step is done as a real-time algorithm that extracts the points referring to the filtered edges and determines to which structure they belong to in the current scenario: stairs or doors. Finally, the geometrical characteristics that are intrinsic to the doors and stairs are identified. The approach proposed here has been validated with real robot experiments. The performed tests verify the efficacy of our proposed approach