PATTERN RECOGNITION ON HUMAN VISIONGiacomo VeneriDept. of Applied Neurological Science, University of Siena viale Banchi di Sotto 55, 53100, Siena, Italy

Recent Res. Devel. Pattern Rec., 5(2013): 19-47 ISBN: 978-81-7895-584-1

Abstract• This chapter’s book investigates some common techniques and algorithms to study human vision.

http://www.ressign.com/UserArticleDetails.aspx?arid=12023

Summary• Eye movements features extraction• Saccades identification

• Optimisations

• Saccades applications: Trajectories

• Fixations identification• Velocity threshold algorithm,

Distance Dispersion Algorithm, Covariance dispersion algorithm, Minimum spanning trees, Fixations clustering

• Microsaccades identification• Eye movements filtering

• Noise filtering, Spike removal filter

• Nystagmus identification

• Eye-movements pattern identification• Scan path• Fixations distribution on

predefined regions of interest

• Regions of interest extraction

• Other methods: Attention models

Introduction

Eye-tracking technologies

• There are several categories of eye movement measurement methodologies involving the use or measurement of: Electro Oculo Graphy (EOG), Photo Oculo Graphy (POG), Video Oculo Graphy (VOG), Scleral Contact Lens (SCL), Search Coil (SCG), and the most common video-based combined pupil/corneal reflection (VCR)

Interactive systems

• Gaze-contingent displays GC and applications, have been described by several articles and have been used in various applications, such as reading, virtual reality, images and scenes perception, computer graphics, rehabilitation and visual search studies.

• These applications change the display according to the line os sight.

Saccades identification

Fisher and Biscardi Methods

• The method (algorithm 1) is a two stage procedure; the process begins by calculating point -to-point velocities for each point of the data set.

Optimisation

• Behrens and later Behrens and MacKeben proposed an algorithm based on acceleration.

Fixation

Saccade

Saccades applications: Trajectories

• Saccade identification is the requisite to evaluate the saccade trajectory. Different methods have been used throughout the literature to quantify saccade trajectories. A recent paper [83] has compared many of these methods: some measures include all sample points on the trajectory of the saccade (area curvature and quadratic curvature), while others focus on one specific sample (saccade deviation, initial direction or saccade endpoint). Trajectory

Fixations identification

Common Methods

• The most common algorithms are based on cluster analysis, velocity based or dispersion thresholding: Distance Dispersion Algorithm, Centroid-Distance Method, Position-Variance Method and Salvucci I-DT Algorithm

Covariance dispersion algorithm

• Veneri, Piu et al. used the mutual information between the axis X and Y of the data set: in human visual search the source of variability should be due to the same system; the key principle of the proposed technique is based on supposing x and y independent with the same variance during a fixation (Algorithm 3).

Fixations identification

Microsaccades identification• Microsaccades can be detected in eye movement recordings when a participant is fixating a stationary object. While small drifts induce a rather erratic trajectory, microsaccades are ballistic movements and create small linear sequences embedded in the trajectory. Microsaccades occur at a rate of 12 per second and have a typical amplitude between 1deg and 2.5deg.

• Engbert and Kliegl developed a new algorithm for the detection of microsaccades in two-dimensional (2D) velocity space.

Eye movements filtering

Noise filtering

• Kumar et al. applied a simple FIR filter and an outlier and a saccade detectors algorithms to manage the number of the gaze points in the averaging buffer. When the threshold is exceeded, the filter buffer is cleared.

• Veneri et al. and Jimenez, et al. used a weighted averaging filter switchin on/off according to the gaze features. Savitzky-Golay filter was used by Nystrom and Holmqvist.

• Komogortsev and Khan reported about successfully applied a Kalman filter for smoothing gaze path.

Spike removal filter

• Spike removal: the algorithm removes unwanted artifact due to disease or eye tracking procedure

Nystagmus identificationNystagmus is a type of eye movement that may be induced through stimulation of the vestibular system. It is characterized by to horizontal and/or vertical motion of the eyes.Most of the developed nystagmus techniques are based on the evaluation of the direction or the velocity of fast phase components

Eye-movements pattern identification

Scan Path

• The Scan-path was one of the first methods to identify patterns of eye movement: Noton and Stark defined a number of spatial Regions of Interest (ROIs) in the scene being scanned and recoding the fixation sequence as a series of letters representing the fixated locations. Cristino et al. developed a method (ScanMatch) which consists on transition matrix among ROIs and the usage of Levenshtein distance to compare scan path.

Fixations distribution on predefined regions of interest

Method Description

ROI visiting

Count number of fixations inside the ROI

ROI revisting

Count number of fixations inside the ROI before first fixations

Time spent into ROI

Start time of first fixations inside ROI minus end time of last fixations inside ROI

Distance to nearest ROI

Euclidean distance from fixation centroid to nearest ROI or target

Regions of interest extraction

By Image Processing

• Privitera and Stark developed a set image processing algorithms (IPAs) to identify ROIs on a real image: the ten algorithms mapped the image into different domains.

• See the book for a complete reference.

WTA

• Itti and Koch proposed the Winner Take All (WTA). WTA hypothesizes that a saliency map can be built from a collection of separate features map, representing single visual features, such as colours or orientation, across the input

Fixations clustering• Ooms, Andrienko et al. used a visual analytics software package to analyze the eye movement data for usability purpose: the area is divided into a set of Voronoi polygons which reflected the density of fixations and minimized the distortion of the scanpath.

PATTERN RECOGNITION ON HUMAN VISIONGiacomo VeneriDept. of Applied Neurological Science, University of Siena viale Banchi di Sotto 55, 53100, Siena, Italy

Recent Res. Devel. Pattern Rec., 5(2013): 19-47 ISBN: 978-81-7895-584-1

13/07/2013 Giacomo Veneri - http://jugsi.blogspot.com 16

Giacomo Veneri, PhD, MCs

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