Knowledge of the dynamics of pollen tube growth will provide a basis for understanding more complex cells that exhibit similar growth behavior. Current pollen tube growth models are a
collection of differential equations that represent the level of understanding that biologists have concerning apical growth. Due to their complex nature, these models are not used to
verify observed behavior in living cells as seen under a microscope. We propose biologically relevant functions based on knowledge of the growth process to explain the dynamics of model
In the field of facial emotion recognition, early research advanced with the use of Gabor filters.
However, these filters lack generalization and result in undesirably large feature vector size.
In recent work, more attention has been given to other local appearance features. Two desired
characteristics in a facial appearance feature are generalization capability, and the
compactness of representation. In this paper, we propose a novel texture feature inspired
by Gabor energy filters, called background suppressing Gabor energy filtering. The feature
has a generalization component that removes background texture. It has a reduced feature
vector size due to maximal representation and soft orientation histograms, and it is a white
box representation. We demonstrate improved performance on the non-trivial Audio/Visual Emotion
Challenge 2012 grand-challenge dataset by a factor of 7.17 over the Gabor filter on the
development set. We also demonstrate applicability of our approach beyond facial emotion
recognition which yields improved classification rate over the Gabor filter for four bioimaging
datasets by an average of 8.22%.
Pollen tube growth is an essential part of the sexual
reproductive process in plants. It is the result of a complex interaction
of cytoplasmic contents (proteins, ions, cellular structures,
etc.). Existing pollen tube models use differential equations to
represent these complex intra-cellular interactions that lead to
growth. As a result of this complex nature, these models are
not used to verify the shape and growth behavior observed
in living cells. We present a method of analyzing the growth
behavior of pollen tubes in experimental videos through affine
transformations on the detected cell tip. The method relies
on underlying biological knowledge about the growth process
and leverages these processes to determine tip morphology.
Experimental results on videos of growing pollen tube cells show
that our method is superior to the current method of treating cell
tip morphology as well as adaptive active appearance models.
In leaves of A. thaliana, there exists an intricate network of
epidermal surface layer cells responsible for anatomical stability
and vigor of flexibility to the entire leaf. Rho GTPases
direct this organization of cell polarity, but full understanding
of the underlying mechanisms demands further inquiry.
We conduct two experiments: (1) a novel procedure is proposed
that could be used in other life and plant science studies
to quantify microtubule orientation, and (2) shape analysis.
We hypothesize ARK2 as a putative interactor in cell
polarity maintenance through stabilization of microtubule ordering.
We are the first to automate pavement cell phenotype
analysis for cell polarity and microtubule orientation. Breakthroughs
in the signaling network regulating leaf cell polarity
and development will lead science into the frontier of genetically
modifying leaves to dramatically increase Earth's plant
biomass; impending food shortages in the 21st century will
be well served by such research.
ROP1, a Rho family GTPase enzyme, activates the downstream target RIC4. RIC4 is an accurate reporter
of ROP1 activity, which is periodically localized at the apex of the plasma membrane in pollen tubes. It
allows the positive feedback relationship between pollen tube growth and ROP1 activity to be established
based on its observed behavior. It regulates the growth of filamentous actin that directly affects pollen tube
growth. However, the displacement of the plasma membrane and frequency of oscillation of the localization
of RIC4 at the tip have not been quantified. Most current studies of pollen tubes are done by analyzing the
limited amount of data by hand. As a result, pollen tube growth patterns are still not thoroughly understood.
The proposed research develops computer algorithms to analyze laser microscopy videos of pollen tubes
with GFP-tagged RIC4.
A gradient relaxation method based on maximizing a criterion function was studied and compared
to the nonlinear probabilistic relaxation method for the purpose of segmentation of images having
unimodal distributions. Although both methods provided comparable segmentation results, the gradient
method had the additional advantage of providing control over the relaxation process by choosing
three parameters which could be tuned to obtain the desired segmentation results at a faster rate.