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We present a hybrid neural network architecture that supports the estimation of binocular disparity in a cyclopean, head-centric coordinate system without explicitly establishing retinal correspondences. Instead the responses of binocular energy neurons are gain-modulated by oculomotor signals. The network can handle the full six degrees of freedom of binocular gaze and operates directly on image pairs of possibly varying contrast. Furthermore, we show that in the absence of an oculomotor signal the same architecture is capable of estimating the epipolar geometry directly from the population response. The increased complexity of the scenarios considered in this work provides an important step towards the application of computational models centered on gain modulation mechanisms in real-world robotic applications. The proposed network is shown to outperform a standard computer vision technique on a disparity estimation task involving real-world stereo images.

This paper proposes an efficient and fast iris localization method. It uses support vector machine learning of iris features that represent closed outer and inner iris boundaries encompassing a low-intensity region. In addition, depending on the location of the iris in an eye image, an iris detection method is proposed based on three sub-datasets of eye images (middle, right, and left sub-datasets) with different iris features. The proposed method is implemented using fast sliding window and fast computation of the iris detection score with binary features. Compared with state-of-the-art methods, experimental results show that the proposed method is twice as fast and has comparable accuracy, even when factoring in head rotation, glasses, and highlights.

The blind spots brought by a car’s A-pillar are main reasons of most of accidents. In this work, a vision system that focuses on eliminating A-pillar blind spot of a car without any affection of driver’s operation is investigated. The driver’s facial features are captured by a binocular vision system that is mounted on A-pillar, head poses and gaze line directions are reconstructed. The generated blind spot by A-pillar is then simultaneously calculated according to the position of driver’s gaze. A field-of-view of the blind spots is displayed in a screen system mounted on the A-pillar. The screen conjointly with front and side windows thus provides a full view field for the driver, which can effectively reduce the occurrence of accidents.

In recent years, multi-stream gaze estimation methods have become mainstream, which estimate gaze point by eye picture or combine with facial appearance, have achieved considerable accuracy. However, these methods based on a single camera fail to obtain accurate eye spatial position information. To address this issue, we propose a multi-stream gaze estimation model that incorporates spatial position information. We acquire eye spatial position information using a stereo camera and fuse eye image features with eye spatial position information using a ResNet network with a fused attention mechanism. Additionally, we perform calibration of eye image features using the computed eye spatial position information. Our model demonstrates superior performance on our experimental dataset.

Robust and accurate eye gaze tracking can advance medical telerobotics by providing complementary data for surgical training, interactive instrument control, and augmented human–robot interactions. However, current gaze tracking solutions for systems such as the da Vinci Surgical System (dVSS) are limited to complex hardware installations. Additionally, existing methods do not account for operator head movement inside the surgeon console, invalidating the original calibration. This work provides an initial solution to these challenges that can seamlessly integrate into console devices beyond the dVSS. Our approach relies on simple and unobtrusive wearable eye tracking glasses and provides calibration routines that can contend with operator-head movements. An external camera measures movement of the glasses through trackers mounted on the glasses to detect invalidation of the prior calibration from head movement and slippage. Movements beyond a threshold of 5 cm or 9ˆ∘ prompt another calibration sequence. In a study where users moved freely in the surgeon console after an initial calibration procedure, we show that our system tracks the eye tracking glasses to initiate recalibration procedures. Recalibration can reduce the mean tracking error up to 89% compared to the current prevailing approach which relies on the initial calibration only. This work is an important first step towards incorporating user movement into gaze-based applications for the dVSS.