Optical pattern recognition

The development of an unmanned underwater vehicle at Florida Atlantic
University with onboard optical sensors has prompted the temporal and spatial optical
characterization of Port Everglades, with in-situ measurements of the turbidity,
conductivity, and temperature. Water samples were collected for laboratory analysis
where attenuation and absorption were measured with a bench top spectrometer. All of
the measurements showed a high degree of variability within the port on a temporal and
spatial basis. Correlations were researched between the measured properties as well as
tide and current. Temporal variations showed a high correlation to tidal height but no
relation was found between turbidity and current, or salinity. Spatial variations were
primarily determined by proximity to the port inlet. Proportionality constants were
discovered to relate turbidity to scattering and absorption coefficients. These constants
along with future turbidity measurements will allow the optimization of any underwater
camera system working within these waters.

Autonomous video surveillance systems are usually built with several functional blocks
such as motion detection, foreground and background separation, object tracking, depth
estimation, feature extraction and behavioral analysis of tracked objects. Each of those
blocks is usually designed with different techniques and algorithms, which may need
significant computational and hardware resources. In this thesis we present a surveillance
system based on an optical flow concept, as a main unit on which other functional blocks
depend. Optical flow limitations, capabilities and possible problem solutions are
discussed in this thesis. Moreover, performance evaluation of various methods in
handling occlusions, rigid and non-rigid object classification, segmentation and tracking
is provided for a variety of video sequences under different ambient conditions. Finally,
processing time is measured with software that shows an optical flow hardware block can
improve system performance and increase scalability while reducing the processing time
by more than fifty percent.

People with Autism Spectrum Disorders, particularly Asperger's Syndrome, have
a set of symptoms, such as difficulty processing emotive facial expressions and making
less eye contact, that affect the individual's social interactions. The prototype developed
is geared to help children with Asperger's Syndrome recognize facial expressions,
improve eye contact, and to reinforce connections between facial and verbal components
of expressions. This application utilizes a computer generated human head, an avatar, to
interact with the children while displaying various emotions and using games to increase
the children's eye contact. The unique properties of the avatar utilized allow for a
comprehensive coverage of the children's attention spectrum to maximize their learning
potential. Knowing that about 17% of Autism Spectrum Disorders belong to the
Asperger's Syndrome subclass, the positive impact of this application on their life would
be of great importance for their families and the community.

Eye fixations of the face are normally directed towards either the eyes or the
mouth, however the proportions of gaze to either of these regions are dependent on
context. Previous studies of gaze behavior demonstrate a tendency to stare into a target’s
eyes, however no studies investigate the differences between when participants believe
they are engaging in a live interaction compared to knowingly watching a pre-recorded
video, a distinction that may contribute to studies of memory encoding. This study
examined differences in fixation behavior for when participants falsely believed they
were engaging in a real-time interaction over the internet (“Real-time stimulus”)
compared to when they knew they were watching a pre-recorded video (“Pre-recorded
stimulus”). Results indicated that participants fixated significantly longer towards the
eyes for the pre-recorded stimulus than for the real-time stimulus, suggesting that
previous studies which utilize pre-recorded videos may lack ecological validity.

In this study, we investigated what informational aspects of faces could account
for the ability to match an individual’s face to their voice, using only static images. In
each of the first six experiments, we simultaneously presented one voice recording along
with two manipulated images of faces (e.g. top half of the face, bottom half of the face,
etc.), a target face and distractor face. The participant’s task was to choose which of the
images they thought belonged to the same individual as the voice recording. The voices
remained un-manipulated. In Experiment 7 we used eye tracking in order to determine
which informational aspects of the model’s faces people are fixating while performing
the matching task, as compared to where they fixate when there are no immediate task
demands. We presented a voice recording followed by two static images, a target and
distractor face. The participant’s task was to choose which of the images they thought
belonged to the same individual as the voice recording, while we tracked their total
fixation duration. In the no-task, passive viewing condition, we presented a male’s voice
recording followed sequentially by two static images of female models, or vice versa, counterbalanced across participants. Participant’s results revealed significantly better
than chance performance in the matching task when the images presented were the
bottom half of the face, the top half of the face, the images inverted upside down, when
presented with a low pass filtered image of the face, and when the inner face was
completely blurred out. In Experiment 7 we found that when completing the matching
task, the time spent looking at the outer area of the face increased, as compared to when
the images and voice recordings were passively viewed. When the images were passively
viewed, the time spend looking at the inner area of the face increased. We concluded that
the inner facial features (i.e. eyes, nose, and mouth) are not necessary informational
aspects of the face which allow for the matching ability. The ability likely relies on global
features such as the face shape and size.

Most of the human visual field falls in the periphery, and peripheral processing is
important for normal visual functioning. Yet, little is known about peripheral object
recognition in naturalistic scenes and factors that modulate this ability. We propose that
a critical function of scene and object memory is in order to facilitate visual object
recognition in the periphery. In the first experiment, participants identified objects in
scenes across different levels of familiarity and contextual information within the scene.
We found that familiarity with a scene resulted in a significant increase in the distance
that objects were recognized. Furthermore, we found that a semantically consistent scene
improved the distance that object recognition is possible, supporting the notion that
contextual facilitation is possible in the periphery. In the second experiment, the preview
duration of a scene was varied in order to examine how a scene representation is built and
how memory of that scene and the objects within it contributes to object recognition in
the periphery. We found that the closer participants fixated to the object in the preview,
the farther on average they recognized that target object in the periphery. However, only a preview duration of the scenes for 5000 ms produced significantly farther peripheral
object recognition compared to not previewing the scene. Overall, these experiments
introduce a novel research paradigm for object recognition in naturalistic scenes, and
demonstrates multiple factors that have systematic effects on peripheral object
recognition.

How does spatial organization of objects affect the perceptual processing of a scene? Surprisingly, little research has explored this topic. A few studies have reported that, when simple, homogenous stimuli (e.g., dots), are presented in a regular formation, they are judged to be more numerous than when presented in a random configuration (Ginsburg, 1976; 1978). However, these results may not apply to real-world objects. In the current study, fewer objects were believed to be on organized desks than their disorganized equivalents. Objects that are organized may be more likely to become integrated, due to classic Gestalt principles. Consequently, visual search may be more difficult. Such object integration may diminish saliency, making objects less apparent and more difficult to find. This could explain why, in the present study, objects on disorganized desks were found faster.

Affecting one in every 68 children, Autism Spectrum Disorder (ASD) is one
of the fastest growing developmental disabilities. Scientific research has proven that
early behavioral intervention can improve learning, communication, and social skills.
Similarly, studies have shown that the usage of of-the-shelf technology boosts
motivation in children diagnosed with ASD while increasing their attention span and
ability to interact socially. Embracing perspectives from different fields of study can
lead to the development of an effective tool to complement traditional treatment
of those with ASD. This thesis documents the re-engineering, extension, and evolu-
tion of Ying, an existing web application designed to aid in the learning of autistic
children. The original methodology of Ying combines expertise from other research
areas including developmental psychology, semantic learning, and computer science.
In this work, Ying is modifed to incorporate aspects of traditional treatment, such
as Applied Behavior Analysis. Using cutting-edge software technology in areas like
voice recognition and mobile device applications, this project aspires to use software
engineering approaches and audio-visual interaction with the learner to enhance social behavior and reinforce verbal communication skills in children with ASD, while
detecting and storing learning patterns for later study.

Autism Spectrum Disorders (ASD) affects one in every 110 children. Medical and educational research have demonstrated that ASD children's social skills and adaptation can be much improved, provided that interventions are early and intensive enough. The advancement of computer technologies and their ubiquitous penetration in people's life make them widely available to support intensive sociocognitive rehabilitation. Additionally, computer interactions are a natural choice for people with autism who value lawful and "systematizing" tools. A number of computer-aided approaches have been developed, showing effectiveness and generalization, but little quantitative research was conducted to identify the critical factors of engaging and improving the child's interest and performance. This thesis designs an adaptive computer interaction system, called Ying, which detects learning patterns in children with ASD and explores the computer interactive possibilities. The system tailors its content based on periodic performance assessments that offer a more effective learning path for children with ASD.

This dissertation introduces our work on face recognition using a novel approach based on creating 3D face model from 2D face images. Together with the pose angle estimation and illumination compensation, this method can be used successfully to recognize 2D faces with 3D recognition algorithms. The results reported here were obtained partially with our own face image database, which had 2D and 3D face images of 50 subjects, with 9 different pose angles. It is shown that by applying even the simple PCA algorithm, this new approach can yield successful recognition rates using 2D probing images and 3D gallery images. The insight gained from the 2D/3D face recognition study was also extended to the case of involving 2D probing and 2D gallery images, which offers a more flexible approach since it is much easier and practical to acquire 2D photos for recognition. To test the effectiveness of the proposed approach, the public AT&T face database, which had 2D only face photos of 40 subjects, with 10 different images each, was utilized in the experimental study. The results from this investigation show that with our approach, the 3D recognition algorithm can be successfully applied to 2D only images. The performance of the proposed approach was further compared with some of the existing face recognition techniques. Studies on imperfect conditions such as domain and pose/illumination variations were also carried out. Additionally, the performance of the algorithms on noisy photos was evaluated. Pros and cons of the proposed face recognition technique along with suggestions for future studies are also given in the dissertation.