The development of computer vision based systems dedicated to help visually impaired people to perceive the environment, to orientate and navigate has been the main research subject of many works in the recent years. A significant ensemble of resources has been employed to support the development of sensory substitution devices (SSDs) and electronic travel aids for the rehabilitation of the visually impaired. The Sound of Vision (SoV) project used a comprehensive approach to develop such an SSD, tackling all the challenging aspects that so far restrained the large scale adoption of such systems by the intended audience: Wearability, real-time operation, pervasiveness, usability, cost. This article is set to present the artificial vision based component of the SoV SSD that performs the scene reconstruction and segmentation in outdoor environments. In contrast with the indoor use case, where the system acquires depth input from a structured light camera, in outdoors SoV relies on stereo vision to detect the elements of interest and provide an audio and/or haptic representation of the environment to the user. Our stereo-based method is designed to work with wearable acquisition devices and still provide a real-time, reliable description of the scene in the context of unreliable depth input from the stereo correspondence and of the complex 6 DOF motion of the head-worn camera. We quantitatively evaluate our approach on a custom benchmarking dataset acquired with SoV cameras and provide the highlights of the usability evaluation with visually impaired users.

This work presents the design aspects of TRAVEE, a neuromotor rehabilitation system. The TRAVEE system relies on innovative concepts for improving the rehabilitation process and increasing the patient recovery rate. One such concept is to present the patient an augmented feedback as part of a learning process based on neuroplasticity. Most of the rehabilitation exercises are based on visual feedback aimed at restoring the brain function for upper limbs control. This feedback is provided in a virtual reality setting by presenting the patient with a virtual model of his/her body. The movements executed by the patient are augmented in the virtual reality. Assisting the patient by a virtual therapist when executing the recovery exercises is another original feature of the proposed system. TRAVEE is a complex system, that integrates virtual reality, robotics, electrical stimulation, electromyography and brain-computer interfaces to boost the rehabilitation process. The challenges posed to the architectural design of TRAVEE reside in the complexity of its functioning in a setup that integrates a variety of devices, with real-time operation constraints and requirements of keeping the system at accessible costs, easy to install and use. In this paper, we present the proposed hardware and software architecture for TRAVEE. We analyze and discuss the advantages of our approach and the mechanisms that address the various constraints in TRAVEE.

For most visually impaired people, simple tasks such as understanding the environment or moving safely around it represent huge challenges. The Sound of Vision system was designed as a sensory substitution device, based on computer vision techniques, that encodes any environment in a naturalistic representation through audio and haptic feedback. The present paper presents a study on the usability of this system for visually impaired people in relevant environments. The aim of the study is to assess how well the system is able to help the perception and mobility of the visually impaired participants in real life environments and circumstances. The testing scenarios were devised to allow the assessment of the added value of the Sound of Vision system compared to traditional assistive instruments, such as the white cane. Various data were collected during the tests to allow for a better evaluation of the performance: system configuration, completion times, electro-dermal activity, video footage, user feedback. With minimal training, the system could be successfully used in outdoor environments to perform various perception and mobility tasks. The benefit of the Sound of Vision device compared to the white cane was confirmed by the participants and by the evaluation results to consist in: providing early feedback about static and dynamic objects, providing feedback about elevated objects, walls, negative obstacles (e.g., holes in the ground) and signs.

This paper presents a communication algorithm in which a grayscale image, shared between two parties, can be used to transmit a secret message, by applying the idea introduced in the B92 quantum protocol. The least significant qubits of the pixels’ representation in certain regions of the image are used. With the help of a server, the algorithm generates a random message, which can further act as a secret key for cryptographic algorithms in order to secure the data that two parties might want to exchange later on. The chosen representation of the image is NEQR (novel enhanced quantum representation) and the platform used for testing the described idea is IBM Quantum Experience, along with the open-source Python framework called Qiskit. This solution allows users to design, implement quantum circuits (containing various quantum gates), and simulate them using real devices and local simulators. An implementation using this platform for a sample image and the corresponding results are also presented in this paper.

Environment perception and understanding represent critical aspects in most computer vision systems and/or applications. State-of-the-art techniques to solve this vision task (e.g., semantic instance segmentation) require either dedicated hardware resources to run or a longer execution time. Generally, the main efforts were to improve the accuracy of these methods rather than make them faster. This paper presents a novel solution to speed up the semantic instance segmentation task. The solution combines two state-of-the-art methods from semantic instance segmentation and optical flow fields. To reduce the inference time, the proposed framework (i) runs the inference on every 5th frame, and (ii) for the remaining four frames, it uses the motion map computed by optical flow to warp the instance segmentation output. Using this strategy, the execution time is strongly reduced while preserving the accuracy at state-of-the-art levels. We evaluate our solution on two datasets using available benchmarks. Then, we conclude on the results obtained, highlighting the accuracy of the solution and the real-time operation capability.