Computer vision.

In the past few years, violence detection has become an increasingly rele-
vant topic in computer vision with many proposed solutions by researchers. This
thesis proposes a solution called Criminal Aggression Recognition Engine (CARE),
an OpenCV based Java implementation of a violence detection system that can be
trained with video datasets to classify action in a live feed as non-violent or violent.
The algorithm extends existing work on fast ght detection by implementing violence
detection of live video, in addition to prerecorded video. The results for violence
detection in prerecorded videos are comparable to other popular detection systems
and the results for live video are also very encouraging, making the work proposed in
this thesis a solid foundation for improved live violence detection systems.

With a focus on dynamics and control, an aquatic quadcopter with optical wireless communications is modeled, designed, constructed, and tested. Optical transmitter and receiver circuitry is designed and discussed. By utilization of the small angle assumption, the nonlinear dynamics of quadcopter movement are linearized around an equilibrium state of zero motion. The set of equations are then tentatively employed beyond limit of the small angle assumption, as this work represents an initial explorative study. Specific constraints are enforced on the thrust output of all four rotors to reduce the multiple-input multiple-output quadcopter dynamics to a set of single-input single-output systems. Root locus and step response plots are used to analyze the roll and pitch rotations of the quadcopter. Ultimately a proportional integral derivative based control system is designed to control the pitch and roll. The vehicle’s yaw rate is similarly studied to develop a proportional controller. The prototype is then implemented via an I2C network of Arduino microcontrollers and supporting hardware.

Image Processing and Computer Vision solutions have become commodities
for software developers, thanks to the growing availability of Application Program-
ming Interfaces (APIs) that encapsulate rich functionality, powered by advanced al-
gorithms. To understand and create an e cient method to process faces in images
by computers, one must understand how the human visual system processes them.
Face processing by computers has been an active research area for about 50
years now. Face detection has become a commodity and is now incorporated into
simple devices such as digital cameras and smartphones.
An iOS app was implemented in Objective-C using Microsoft Cognitive Ser-
vices APIs, as a tool for human vision and face processing research. Experimental
work on image compression, upside-down orientation, the Thatcher e ect, negative
inversion, high frequency, facial artifacts, caricatures and image degradation were
completed on the Radboud and 10k US Adult Faces Databases along with other
images.