Improving Depth Perception in Immersive Media Devices by Addressing Vergence-Accommodation Conflict

: Recently, immersive media devices have seen a boost in popularity. However, many problems still remain. Depth perception is a crucial part of how humans behave and interact with their environment. Convergence and accommodation are two physiological mechanisms that provide important depth cues. However, when humans are immersed in virtual environments, they experience a mismatch between these cues. This mismatch causes users to feel discomfort while also hindering their ability to fully perceive object distances. To address the conflict, we have developed a technique that encompasses inverse blurring into immersive media devices. For the inverse blurring, we utilize the classical Wiener deconvolution approach by proposing a novel technique that is applied without the need for an eye-tracker and implemented in a commercial immersive media device. The technique's ability to compensate for the vergence-accommodation conflict was verified through two user studies aimed at reaching and spatial awareness, respectively. The two studies yielded a statistically significant 36% and 48% error reduction in user performance to estimate distances, respectively. Overall, the work done demonstrates how visual stimuli can be modified to allow users to achieve a more natural perception and interaction with the virtual environment

Virtual Reality to Simulate Visual Tasks for Robotic Systems

Virtual reality (VR) can be used as a tool to analyze the interactions between the visual system of a robotic agent and the environment, with the aim of designing the algorithms to solve the visual tasks necessary to properly behave into the 3D world. The novelty of our approach lies in the use of the VR as a tool to simulate the behavior of vision systems. The visual system of a robot (e.g., an autonomous vehicle, an active vision system, or a driving assistance system) and its interplay with the environment can be modeled through the geometrical relationships between the virtual stereo cameras and the virtual 3D world. Differently from conventional applications, where VR is used for the perceptual rendering of the visual information to a human observer, in the proposed approach, a virtual world is rendered to simulate the actual projections on the cameras of a robotic system. In this way, machine vision algorithms can be quantitatively validated by using the ground truth data provided by the knowledge of both the structure of the environment and the vision system

Design strategies for direct multi-scale and multi-orientation feature extraction in the log-polar domain

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Advances in human-computer interactions: methods, algorithms, and applications

Editoria

Can Neuromorphic Computer Vision Inform Vision Science? Disparity Estimation as a Case Study

The primate visual system efficiently and effectively solves a multitude of tasks from orientation detection to motion detection. The Computer Vision community is therefore beginning to implement algorithms that mimic the processing hierarchies present in the primate visual system in the hope of achieving flexible and robust artificial vision systems. Here, we reappropriate the neuroscience “borrowed” by the Computer Vision community and ask whether neuromorphic computer vision solutions may give us insight into the functioning of the primate visual system. Specifically, we implement a neuromorphic algorithm for disparity estimation and compare its performance against that of human observers. The algorithm greatly outperforms human subjects when tuned with parameters to compete with non-neural approaches to disparity estimation on benchmarking stereo image datasets. Conversely, when the algorithm is implemented with biologically plausible receptive field sizes, spatial selectivity, phase tuning, and neural noise, its performance is directly relatable to that of human observers. The receptive field size and the number of spatial scales sensibly determine the range of spatial frequencies in which the algorithm successfully operates. The algorithm’s phase tuning and neural noise in turn determine the algorithm’s peak disparity sensitivity. When included, retino-cortical mapping strongly degrades disparity estimation in the model’s periphery, further closening human and algorithm performance. Hence, a neuromorphic computer vision algorithm can be reappropriated to model human behavior, and can provide interesting insights into which aspects of human visual perception have been or are yet to be explained by vision science

A Space-Variant Model for Motion Interpretation across the Visual Field

We implement a neural model for the estimation of the focus of radial motion (FRM) at different retinal locations and we assess the model by comparing its results with respect to the precision with which human observers can estimate the FRM in naturalistic, moving dead leaves stimuli. The proposed neural model describes the deep hierarchy of the first stages of the dorsal visual pathway [Solari et al., 2014]. Such a model is space-variant, since it takes into account the retino-cortical transformation of the primate visual system through log-polar mapping that produces a cortical representation of the visual signal to the retina. The log-polar transform of the retinal image is the input to the cortical motion estimation stage where optic flow is computed by a three-layer population of cells. A population of spatio-temporal oriented Gabor filters approximates the simple cells of area V1 (first layer), which are combined into complex cells as motion energy units (second layer). The responses of the complex cells are pooled (third layer) to encode the magnitude and direction of velocities as in the extrastriate motion pathway between area MT and MST. The sensitivity to complex motion patterns that has been found in area MST is modeled through a population of adaptive templates, and from the responses of such a population the first order description of optic flow is derived. Information about self-motion (e.g. direction of heading) is estimated by combining such first-order descriptors computed in the cortical domain

A cortical model for binocular vergence control without explicit calculation of disparity

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