3,154 research outputs found
Mean value coordinates–based caricature and expression synthesis
We present a novel method for caricature synthesis based on mean value coordinates (MVC). Our method can be applied to any single frontal face image to learn a specified caricature face pair for frontal and 3D caricature synthesis. This technique only requires one or a small number of exemplar pairs and a natural frontal face image training set, while the system can transfer the style of the exemplar pair across individuals. Further exaggeration can be fulfilled in a controllable way. Our method is further applied to facial expression transfer, interpolation, and exaggeration, which are applications of expression editing. Additionally, we have extended our approach to 3D caricature synthesis based on the 3D version of MVC. With experiments we demonstrate that the transferred expressions are credible and the resulting caricatures can be characterized and recognized
Carried baggage detection and recognition in video surveillance with foreground segmentation
Security cameras installed in public spaces or in private organizations continuously
record video data with the aim of detecting and preventing crime. For that reason,
video content analysis applications, either for real time (i.e. analytic) or post-event
(i.e. forensic) analysis, have gained high interest in recent years. In this thesis,
the primary focus is on two key aspects of video analysis, reliable moving object
segmentation and carried object detection & identification.
A novel moving object segmentation scheme by background subtraction is presented
in this thesis. The scheme relies on background modelling which is based
on multi-directional gradient and phase congruency. As a post processing step,
the detected foreground contours are refined by classifying the edge segments as
either belonging to the foreground or background. Further contour completion
technique by anisotropic diffusion is first introduced in this area. The proposed
method targets cast shadow removal, gradual illumination change invariance, and
closed contour extraction.
A state of the art carried object detection method is employed as a benchmark
algorithm. This method includes silhouette analysis by comparing human temporal
templates with unencumbered human models. The implementation aspects of
the algorithm are improved by automatically estimating the viewing direction of
the pedestrian and are extended by a carried luggage identification module. As
the temporal template is a frequency template and the information that it provides
is not sufficient, a colour temporal template is introduced. The standard
steps followed by the state of the art algorithm are approached from a different
extended (by colour information) perspective, resulting in more accurate carried
object segmentation.
The experiments conducted in this research show that the proposed closed
foreground segmentation technique attains all the aforementioned goals. The incremental
improvements applied to the state of the art carried object detection
algorithm revealed the full potential of the scheme. The experiments demonstrate
the ability of the proposed carried object detection algorithm to supersede the
state of the art method
Adaptive Resonance Theory
SyNAPSE program of the Defense Advanced Projects Research Agency (Hewlett-Packard Company, subcontract under DARPA prime contract HR0011-09-3-0001, and HRL Laboratories LLC, subcontract #801881-BS under DARPA prime contract HR0011-09-C-0001); CELEST, an NSF Science of Learning Center (SBE-0354378
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