Browsing by Author "Costa, Paulo"
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- Obstacle detection and avoidance module for the blindPublication . Costa, Paulo; Fernandes, Hugo; Barroso, João; Paredes, Hugo; Hadjileontiadis, Leontios J.; CostaAssistive technology enables people to achieve independence when performing daily tasks and it enhances their overall quality of life. Visual information is the basis for most navigational tasks, so visually impaired individuals are at disadvantage due to the lack of sufficient information about their surrounding environment. With recent advances in inclusive technology it is possible to extend the support given to people with visual disabilities in terms of their mobility. In this context we present and describe a wearable system (Blavigator project), whose global objective is to assist visually impaired people in their navigation on indoor and outdoor environments. This paper is focused mainly on the Computer Vision module of the Blavigator prototype. We propose an object collision detection algorithm based on stereo vision. The proposed algorithm uses Peano-Hilbert Ensemble Empirical Mode Decomposition (PH-EEMD) for disparity image processing and a two layer disparity image segmentation to detect nearby objects. Using the adaptive ensemble empirical mode decomposition (EEMD) image analysis real time is not achieved, with PH-EEMD results on a fast implementation suitable for real time applications.
- Prototype to Increase Crosswalk Safety by Integrating Computer Vision with ITS-G5 TechnologiesPublication . Gaspar, Francisco; Guerreiro, Vitor; Loureiro, Paulo; Costa, Paulo; Mendes, Sílvio; Rabadão, CarlosHuman errors are probably the main cause of car accidents, and this type of vehicle is one of the most dangerous forms of transport for people. The danger comes from the fact that on public roads there are simultaneously different types of actors (drivers, pedestrians or cyclists) and many objects that change their position over time, making difficult to predict their immediate movements. The intelligent transport system (ITS-G5) standard specifies the European communication technologies and protocols to assist public road users, providing them with relevant information. The scientific community is developing ITS-G5 applications for various purposes, among which is the increasing of pedestrian safety. This paper describes the developed work to implement an ITS-G5 prototype that aims at the increasing of pedestrian and driver safety in the vicinity of a pedestrian crosswalk by sending ITS-G5 decentralized environmental notification messages (DENM) to the vehicles. These messages are analyzed, and if they are relevant, they are presented to the driver through a car’s onboard infotainment system. This alert allows the driver to take safety precautions to prevent accidents. The implemented prototype was tested in a controlled environment pedestrian crosswalk. The results showed the capacity of the prototype for detecting pedestrians, suitable message sending, the reception and processing on a vehicle onboard unit (OBU) module and its presentation on the car onboard infotainment system.
- Synthetic image generation for effective deep learning model training for ceramic industry applicationsPublication . Gaspar, Fábio; Daniel Carreira; Rodrigues, Nuno; Miragaia, Rolando; Ribeiro, José; Costa, Paulo; Pereira, AntónioIn the rapidly evolving field of machine learning engineering, access to large, high-quality, and well-balanced labeled datasets is indispensable for accurate product classification. This necessity holds particular significance in sectors such as the ceramics industry, in which effective production line activities are paramount and deep learning classification mechanisms are particularly relevant for streamlining processes; but real-world image samples are scarce and difficult to obtain, hindering dataset building and consequently model training and deployment. This paper presents a novel approach for dataset building in the context of the ceramic industry, which involves employing synthetic images for building or complementing datasets for image classification problems. The proposed methodology was implemented in CeramicFlow, an innovative computer graphics rendering pipeline designed to create synthetic images by employing computer-aided design models of ceramic objects and incorporating domain randomization techniques. As a result, a fully synthetic image dataset named Synthetic CeramicNet was created and validated in real-world ceramic classification problems. The results demonstrate that synthetic images provide an adequate basis for datasets and can significantly reduce reliance on real-world data when developing deep learning approaches for image classification problems in the ceramic industry. Furthermore, the proposed approach can potentially be applied to other industrial fields.