Math 290J (section 2) current literature in applied mathematics
Mathematical models for image processing and medical imaging
(joint with the activity group SIG-PDE from the Center for Computational Biology)
Organizer: Luminita Vese. E-mail: firstname.lastname@example.org
Time: Monday, 1:00pm-2:00pm.
Location: MS 5138.
Presentations, Time and Location:
Please note that some meetings will be at a different time and location.
Friday, April 16, time 1-2pm, location MS 5128 (note different day and time)
Guest Speaker: Yuxing (Erica) Han, PhD student, EE UCLA, Compressive Sensing with its Application to Still Images Using the DCT and Noiselets: A New Algorithm and Its Rate Distortion Performance
Abstract: We describe an image coding algorithm combining DCT and noiselet information. The algorithm first transmits DCT information sufficient to reproduce a "low-quality" version of the image at decoder. This image is then used both at decoder and encoder to create a mutually known list of locations of likely significant noiselet coefficients. The coefficient values themselves are then transmitted to the decoder differentially, by subtracting, at the encoder, the low-quality image from the original image, obtaining the noiselet values and subjecting them to quantization and entropy coding. There remain significant opportunities for further work combining CS-inspired information theoretic techniques with the rate-distortion considerations that are critical in practical imag communications
Monday, April 19, time 1-2pm, location MS 5138
Speaker: Pascal Getreuer, Computational aspects for MR brain image restoration in the presence of Rician noise
Monday, April 26, time 1-2pm, location MS 5138 (no meeting this week)
Monday, May 3, time 1-2pm, location MS 5138
Speaker: Miyoun Jung, on "Dual norm based iterative methods for image restoration"
Monday, May 10, time 1-2pm, location MS 5138
Guest Speaker: Matthew Herman
Monday, May 17, time 1-2pm, location MS 5138
Speaker: Chao Chen, Institute of Science and Technology, Austria
Title: Topology Control for Curve and Surface Evolution
In many applications of curve and surface evolution, the curve or
surface is required to have a particular topology. We present a new
method for controlling the topology of a curve or surface within the
level set framework. We use the concept of the total robustness of the
level set function, which is based on ideas from persistent homology,
and we show how to minimize this total robustness by computing its
functional derivative with respect to the level set function. This
method differs from existing methods in that it is inherently continuous
and not digital; and in the way that total robustness directly relates
to the topology of the underlying curve or surface, versus existing
knot-based measures which are related in a more indirect fashion. The
proposed method is validated empirically on segmentation examples.
Monday, May 24, time 1-2pm, location MS 5138
Speaker: Laura Smith. Title: Geographic Data Fusion: Combining
Real-World Event Data with Spatial Information.
Monday, May 31st: No meeting (holiday).