Saliency-Based Gaze Visualization for Eye Movement Analysis

Yoo, Seong-Joon; Jeong, Seong‐Min; Kim, Seokyeon; Jang, Yun

doi:10.3390/s21155178

Cited by 4 publications

(4 citation statements)

References 56 publications

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“…Their model integrates sensory information with top-down models to predict eye movements, using an ideal observer model, like our formulation. Separately, active vision theory also considers the active component to be central component of visual perception, for example the Saliency Map Model ( Yoo et al. 2021 ), Guided Search Model ( Wolfe 2021 ), etc.…”

Section: Discussionmentioning

confidence: 99%

Bistable perception, precision and neuromodulation

Novicky,

Parr,

Friston

et al. 2023

Cerebral Cortex

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Bistable perception follows from observing a static, ambiguous, (visual) stimulus with two possible interpretations. Here, we present an active (Bayesian) inference account of bistable perception and posit that perceptual transitions between different interpretations (i.e. inferences) of the same stimulus ensue from specific eye movements that shift the focus to a different visual feature. Formally, these inferences are a consequence of precision control that determines how confident beliefs are and change the frequency with which one can perceive—and alternate between—two distinct percepts. We hypothesized that there are multiple, but distinct, ways in which precision modulation can interact to give rise to a similar frequency of bistable perception. We validated this using numerical simulations of the Necker cube paradigm and demonstrate the multiple routes that underwrite the frequency of perceptual alternation. Our results provide an (enactive) computational account of the intricate precision balance underwriting bistable perception. Importantly, these precision parameters can be considered the computational homologs of particular neurotransmitters—i.e. acetylcholine, noradrenaline, dopamine—that have been previously implicated in controlling bistable perception, providing a computational link between the neurochemistry and perception.

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Section: Discussionmentioning

confidence: 99%

Bistable perception, precision and neuromodulation

Novicky,

Parr,

Friston

et al. 2023

Cerebral Cortex

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“…The selected eye movement characteristics included pupillary response, fixation point density, scene semantics at fixation points and several saccade characteristics. Yoo et al [ 42 ] also analyzed eye movement behavior, emphasizing the salience of visual stimuli (again, attention is paid to the semantics of the scene). A low-pass filter was used to remove fixation movements that were considered noise in the system.…”

Section: Related Workmentioning

confidence: 99%

Can Microsaccades Be Used for Biometrics?

Alexiev

Vakarelski

2022

Sensors

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Human eyes are in constant motion. Even when we fix our gaze on a certain point, our eyes continue to move. When looking at a point, scientists have distinguished three different fixational eye movements (FEM)—microsaccades, drift and tremor. The main goal of this paper is to investigate one of these FEMs—microsaccades—as a source of information for biometric analysis. The paper argues why microsaccades are preferred for biometric analysis over the other two fixational eye movements. The process of microsaccades’ extraction is described. Thirteen parameters are defined for microsaccade analysis, and their derivation is given. A gradient algorithm was used to solve the biometric problem. An assessment of the weights of the different pairs of parameters in solving the biometric task was made.

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“…Figure 1d shows a sampling cycle, which is comprised of three steps: (1) pressing action that makes the adhesive tape stick on the floor with adequate pressure to extract the tiny dirt particles to its sticky surface; (2) The winding of adhesive tape by winding stepper motors to move the adhesive tape surface to the field of view (FoV) of a camera; and finally, (3) capturing of the images of the adhesive tape surface for dirt analysis. The extent of dirtiness of the sampled region is estimated using structural similarity index (SSIM) analysis [40] and saliency-based dirt detection [41,42] using the embedded computer on the BELUGA platform. The sampling audit sensor inspects a sample area (2 cm × 2 cm) and provides an audit score (in the range of 0-1) that defines the extent of dirtiness.…”

Section: System Overviewmentioning

confidence: 99%

A Reinforcement Learning Based Dirt-Exploration for Cleaning-Auditing Robot

Pathmakumar¹,

Elara²,

Gómez³

et al. 2021

Sensors

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Cleaning is one of the fundamental tasks with prime importance given in our day-to-day life. Moreover, the importance of cleaning drives the research efforts towards bringing leading edge technologies, including robotics, into the cleaning domain. However, an effective method to assess the quality of cleaning is an equally important research problem to be addressed. The primary footstep towards addressing the fundamental question of “How clean is clean” is addressed using an autonomous cleaning-auditing robot that audits the cleanliness of a given area. This research work focuses on a novel reinforcement learning-based experience-driven dirt exploration strategy for a cleaning-auditing robot. The proposed approach uses proximal policy approximation (PPO) based on-policy learning method to generate waypoints and sampling decisions to explore the probable dirt accumulation regions in a given area. The policy network is trained in multiple environments with simulated dirt patterns. Experiment trials have been conducted to validate the trained policy in both simulated and real-world environments using an in-house developed cleaning audit robot called BELUGA.

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Saliency-Based Gaze Visualization for Eye Movement Analysis

Cited by 4 publications

References 56 publications

Bistable perception, precision and neuromodulation

Bistable perception, precision and neuromodulation

Can Microsaccades Be Used for Biometrics?

A Reinforcement Learning Based Dirt-Exploration for Cleaning-Auditing Robot

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