Purpose:With a retinal prosthesis connected to a head-mounted camera, subjects can perform low vision tasks using a combination of electrode discrimination and head-directed localization. The objective of the present study was to investigate the contribution of retinotopic electrode discrimination (perception corresponding to the arrangement of the implanted electrodes with respect to their position beneath the retina) to visual performance for three recipients of a 24-channel suprachoroidal retinal implant. Proficiency in retinotopic discrimination may allow good performance with smaller head movements, and identification of this ability would be useful for targeted rehabilitation.Methods:Three participants with retinitis pigmentosa performed localization and grating acuity assessments using a suprachoroidal retinal prosthesis. We compared retinotopic and nonretinotopic electrode mapping and hypothesized that participants with measurable acuity in a normal retinotopic condition would be negatively impacted by the nonretinotopic condition. We also expected that participants without measurable acuity would preferentially use head movement over retinotopic information.Results:Only one participant was able to complete the grating acuity task. In the localization task, this participant exhibited significantly greater head movements and significantly lower localization scores when using the nonretinotopic electrode mapping. There was no significant difference in localization performance or head movement for the remaining two subjects when comparing retinotopic to nonretinotopic electrode mapping.Conclusions:Successful discrimination of retinotopic information is possible with a suprachoroidal retinal prosthesis. Head movement behavior during a localization task can be modified using a nonretinotopic mapping. Behavioral comparisons using retinotopic and nonretinotopic electrode mapping may be able to highlight deficiencies in retinotopic discrimination, with a view to address these deficiencies in a rehabilitation environment. (ClinicalTrials.gov number, NCT01603576).

Objective. One strategy to improve the effectiveness of prosthetic vision devices is to process incoming images to ensure that key information can be perceived by the user. This paper presents the first comprehensive results of vision function testing for a suprachoroidal retinal prosthetic device utilizing of 20 stimulating electrodes. Further, we investigate whether using image filtering can improve results on a light localization task for implanted participants compared to minimal vision processing. No controlled implanted participant studies have yet investigated whether vision processing methods that are not task-specific can lead to improved results. Approach. Three participants with profound vision loss from retinitis pigmentosa were implanted with a suprachoroidal retinal prosthesis. All three completed multiple trials of a light localization test, and one participant completed multiple trials of acuity tests. The visual representations used were: Lanczos2 (a high quality Nyquist bandlimited downsampling filter); minimal vision processing (MVP); wide view regional averaging filtering (WV); scrambled; and, system off. Main results. Using Lanczos2, all three participants successfully completed a light localization task and obtained a significantly higher percentage of correct responses than using MVP (p <= 0.025) or with system off (p < 0.0001). Further, in a preliminary result using Lanczos2, one participant successfully completed grating acuity and Landolt C tasks, and showed significantly better performance (p = 0.004) compared to WV, scrambled and system off on the grating acuity task. Significance. Participants successfully completed vision tasks using a 20 electrode suprachoroidal retinal prosthesis. Vision processing with a Nyquist bandlimited image filter has shown an advantage for a light localization task. This result suggests that this and targeted, more advanced vision processing schemes may become important components of retinal prostheses to enhance performance. ClinicalTrials.gov Identifier: NCT01503576.

The visual prosthesis (or "bionic eye") has become a reality but provides a low resolution view of the world. Simulating prosthetic vision in normal-vision observers, previous studies report good face recognition ability using tasks that allow recognition to be achieved on the basis of information that survives low resolution well, including basic category (sex, age) and extra-face information (hairstyle, glasses). Here, we test within-category individuation for face-only information (e.g., distinguishing between multiple Caucasian young men with hair covered). Under these conditions, recognition was poor (although above chance) even for a simulated 40 × 40 array with all phosphene elements assumed functional, a resolution above the upper end of current-generation prosthetic implants. This indicates that a significant challenge is to develop methods to improve face identity recognition. Inspired by "bionic ear" improvements achieved by altering signal input to match high-level perceptual (speech) requirements, we test a high-level perceptual enhancement of face images, namely face caricaturing (exaggerating identity information away from an average face). Results show caricaturing improved identity recognition in memory and/or perception (degree by which two faces look dissimilar) down to a resolution of 32 × 32 with 30% phosphene dropout. Findings imply caricaturing may offer benefits for patients at resolutions realistic for some current-generation or in-development implants.

Objective. We evaluated a novel visual representation for current and near-term prosthetic vision. Augmented depth emphasizes ground obstacles and floor-wall boundaries in a depth-based visual representation. This is achieved by artificially increasing contrast between obstacles and the ground surface via a novel ground plane extraction algorithm specifically designed to preserve low-contrast ground-surface boundaries. Approach. The effectiveness of augmented depth was examined in human mobility trials compared against standard intensity-based (Intensity), depth-based (Depth) and random (Random) visual representations. Eight participants with normal vision used simulated prosthetic vision with 20 phosphenes and eight perceivable brightness levels to traverse a course with randomly placed small and low-contrast obstacles on the ground. Main results. The number of collisions was significantly reduced using augmented depth, compared with intensity, depth and random representations (48%, 44% and 72% less collisions, respectively). Significance. These results indicate that augmented depth may enable safe mobility in the presence of low-contrast obstacles with current and near-term implants. This is the first demonstration that an augmentation of the scene ensuring key objects are visible may provide better outcomes for prosthetic vision.

Prosthetic vision provides vision which is reduced in resolution and dynamic range compared to normal human vision. This comes about both due to residual damage to the visual system from the condition that caused vision loss, and due to limitations of current technology. However, even with limitations, prosthetic vision may still be able to support functional performance which is sufficient for tasks which are key to restoring independent living and quality of life. Here vision processing can play a key role, ensuring that information which is critical to the performance of key tasks is available within the capability of the available prosthetic vision. In this paper, we frame vision processing for prosthetic vision, highlight some key areas which present problems in terms of quality of life, and present examples where vision processing can help achieve better outcomes.

Navigation and way finding including obstacle avoidance is difficult when visual perception is limited to low resolution, such as is currently available on a bionic eye. Depth visualisation may be a suitable alternative. Such an approach can be evaluated using simulated phosphenes with a wearable mobile virtual reality kit. In this paper, we present two novel approaches: (i) an implementation of depth visualisation; and, (ii) novel methods for rapid rendering of simulated phosphenes with an empirical comparison between them. Our new software-based method for simulated phosphene rendering shows large speed improvements, facilitating the display in real-time of a large number of phosphenes with size and brightness dependent on pixel intensity, and with customised output dynamic range. Further, we describe the protocol, navigation environment and system used for visual navigation experiments to evaluate the use of depth on low resolution simulations of a bionic eye perceptual experience. Results for these experiments show that a depth-based representation is effective for navigation, and shows significant advantages over intensity-based approaches when overhanging obstacles are present. The results of the experiments were reported in [1], [2].

Developing a practical multi-camera tracking solution for autonomous camera networks is a very challenging task, due to numerous constraints such as limited memory and processing power, heterogeneous visual characteristics of objects between camera views, and limited setup time and installation knowledge for camera calibration. In this paper, we propose a unified multi-camera tracking framework, which can run online in real-time and can handle both independent field of view and common field of view cases. No camera calibration, knowledge of the relative positions of cameras, or entry and exit locations of objects is required. The memory footprint of the framework is minimised by the introduction of reusing kernels. The heterogeneous visual characteristics of objects are addressed by a novel location-based kernel matching method. The proposed framework has been evaluated using real videos captured in multiple indoor settings. The framework achieves efficient memory usage without compromising tracking accuracy.

—This paper addresses the problem of mapping and exploration of an unknown space by cooperative multi-agent systems. The exploration problem is extended to jointly covering an area n times. Agents are localised and can communicate with other agents. A probability based mapping algorithm is developed. Based upon this, a potential field based exploration algorithm is proposed, with three sample charge profiles that can be used for different mission requirements. Simulation results in Webots confirm the scalability and the effectiveness of these algorithms. This research justifies and prepares for a full trial in a multi-robot testbed.

This paper addresses the problem of mapping and exploration of an unknown space by cooperative multi-agent systems. The exploration problem is extended to jointly covering an area n times. Agents are localised and can communicate with other agents. A probability based mapping algorithm is developed. Based upon this, a potential field based exploration algorithm is proposed, with three sample charge profiles that can be used for different mission requirements. Simulation results in Webots confirm the scalability and the effectiveness of these algorithms. This research justifies and prepares for a full trial in a multi-robot testbed.

This paper analyzes the effects of beamforming on the connectivity of wireless ad hoc networks. We study different beamforming techniques using the uniform circular array as the antenna model. In particular, we study centre directed beamform- ing and greedy beamforming. In centre directed beamforming each node points its main beam toward the geometric centre of the network. The greedy beamforming method allows each node to choose the beamforming direction based on knowledge of other node positions. We investigate the connectivity of each beamforming scheme and compare their performances to that of omnidirectional antennas. The percentages of connection and isolated nodes are used as metrics for connectivity. We also show that greedy beamforming is robust against errors in node position information. between any two nodes contributes to the connectivity of the entire network. Some papers have investigated the relationship between connectivity and node transmission range based on omnidirectional antennas (5), (6). It has been shown that beamforming using smart antennas can significantly improve the connectivity (7). Different beamforming techniques have also been proposed for application to ad hoc networks. The use of randomized beamforming has been studied in (7). The randomized beamforming technique allows each node in the network to direct its main beam in a direction from a uniform distribution on (0, 2�). This simple technique does not require knowledge about location of neighbouring nodes, and it is shown to give significant improvement in the connectivity of ad hoc networks. In this paper we propose two beamforming methods, one called centre directed beamforming and the other called greedy beamforming. In centre directed beamforming all nodes ori- entate their main beam towards the geometric centre of the network, assuming the location of the centre is known. Greedy beamforming allows each node to choose the direction of its main beam based on knowledge of the locations of other nodes, such that the maximum number of one hop connections for the node is achieved. In the greedy beamforming scheme, each node assumes that others are equipped with omnidirec- tional antennas with known locations and performs a simple calculation to decide the direction of the main beam which maximizes its local connectivity. We study the connectivity of an ad hoc network using both techniques. For greedy beam- forming we consider the cases of nodes having perfect and imperfect knowledge of the positions of other nodes. We show that centre directed beamforming has certain advantages over random beamforming, and greedy beamforming outperforms both random and centre directed beamforming.