CS6420 -- Presentation Schedule
- Nora Alosily
- Video Segmentation via Temporal Pattern Classification
- April 17, 2017
- Video shot boundary detection or the process of breaking up a video into
units that have coherent contents is important for further video analysis.
This method exploits low-level feature vectors to build intermediate
features, using pairwise similarity. The result is passed on to a supervised
KNN classifier that will be used subsequently to detect shot boundaries in a
given video.
- John Brandenburg
- Illumination-Aware Age Progression
- April 12, 2017
- Age progression techniques have applications beyond entertainment and can be
used to help solve missing children cases. We will present an approach that
takes a single photograph of a child and automatically produces a series of
age-progressed outputs between 1 and 80 years of age accounting for pose,
expression and illumination. Using a large dataset of individuals we will
produce averages based on clusters of age ranges, then find the differences
in shape and texture between the input photograph. Applying these
differences will yield the age progressed photo.
- Lina Deng
- Car License Plate Detection
- May 01, 2017
- Car license plate detection is an emerging area of research due to various
applications such as prevention of crime, electronic toll system,
intelligent traffic control system etc. The method is after converting the
color input image into grayscale, an adaptive thresholding is used to obtain
the binary image. Then the unwanted lines are removed through an
unwanted-line elimination algorithm. Finally to detect the license plate,
vertical edges are detected by Sobel operator.
- Tzu-An Song
- Local Binary Pattern Edge-Mapped Descriptor Using MDM Interest Points for Face Recognition
- May 03, 2017
- Present local methods such as local binary pattern (LBP), local derivative
pattern (LDP), and scale-invariant feature transform (SIFT) perform better
than holistic methods; however, their high complexity results in some
limitations for applications such as mobile devices. In addition,
SIFT-based schemes are sensitive to illumination variation. We propose an
LBP Edge-mapped descriptor by using Maxima of Gradient Magnitude (MGM)
points. It is a robust, simple and fast descriptor. LBP Edge-mapped
descriptor is a string of binary codes which record surrounding information
of illumination and edges of MGM. It can illustrate facial contours
completely and have low computational complexity simultaneously.
- Sandhya Vaidyanathan
- Enhancement of Fingerprint Images
- April 26, 2017
- Fingerprints are widely used for biometric identification due to their
consistency and uniqueness during a person's lifetime. The enhancement of
fingerprint images helps us to obtain better results during classification
of fingerprints. This enhancement method includes but not limited is
techniques like normalization, orientation estimation using Sobel operator,
Gabor filters, binarization, and thinning.
- Ameet Vishwakarma
- Image Super-Resolution Using Deep Convolutional Networks
- April 19, 2017
- This project aims at a method to recover high resolution image as output
from a single low resolution image as an input. This method uses deep
learning method for image super resolution (SR), it directly learns an
end-to-end mapping between the low- and high-resolution images with pre/post
processing beyond the optimization. It features a deep convolutional neural
network (CNN) of a lightweight structure, yet demostrates state-of-the-art
restoration quality, and achieves fast speed for practical usage over
traditional models. This could help in reducing the data traffic due to
images over the network to a great extent.
- Sherd White
- Automating Image Morphing Using Structural Similarity on a Halfway
Domain
- April 24, 2017
- The goal is to create and automate image morphing using mapping to line up
image elements such as color and geometry transitions between two images.
The map is created by analyzing for corresponding similarity of image
segments. Optimization occurs through the use of interpolation and
smoothing based on a selection of points selected by the user to identify
and aid in the image transition. The calculations can be done in parallel
on a GPU for increased speed.
- Da Zhao
- SIFT Flow: Dense Correspondence across Different Scenes
- April 10, 2017
- While image registration has been studied in different areas of computer
vision, aligning images depicting different scenes remains a challenging
problem, closer to recognition than to image matching. Analogous to optical
flow, where an image is aligned to its temporally adjacent frame, we propose
SIFT flow, a method to align an image to its neighbors in a large image
collection consisting of a variety of scenes. For a query image, histogram
intersection on a bag-of-visual-words representation is used to find the set
of nearest neighbors in the database. The SIFT flow algorithm then consists
of matching densely sampled SIFT features between the two images, while
preserving spatial discontinuities. The use of SIFT features allows robust
matching across different scene/object appearances and the
discontinuity-preserving spatial model allows matching of objects located at
different parts of the scene. Experiments show that the proposed approach is
able to robustly align complicated scenes with large spatial distortions. We
collect a large database of videos and apply the SIFT flow algorithm to two
applications: (i) motion field prediction from a single static image and
(ii) motion synthesis via transfer of moving objects.