Side information averaging method for PML emission tomography

ICIP, Chicago, October, 1998
Side information averaging method for PML emission tomography
Robinson Piramuthu, Alfred O Hero III
eBay Authors

The authors previously presented a methodology for incorporating perfect extracted MRI anatomical boundary estimates to improve the performance of penalized likelihood (PL) emission computed tomography (ECT) image reconstruction and ECT tracer uptake estimation. This technique used a spatially variant quadratic Gibbs penalty which enforced smoothness everywhere in the ECT image except across the MRI-extracted boundary of the ROI.

When high quality estimates of the anatomical boundary are available and MRI and ECT images are perfectly registered, the performance of this Gibbs penalty method is very close to that attainable using perfect side information, i.e., an errorless anatomical boundary estimate. However when the variance of the MRI-extracted boundary estimate becomes significant this method performs poorly. Here we present a modified Gibbs penalty function which accounts for errors in side information based on an asymptotic min-max robustness approach.

The resulting penalty is implemented with a set of averaged Gibbs weights where the averaging is performed with respect to a limiting form of the min-max induced posterior distribution of the MRI boundary parameters. Examples are presented for tracer uptake estimation using the SAGE version of the EM algorithm and various parameterizations of the anatomical boundaries.

Another publication from the same author: Robinson Piramuthu

WACV, March, 2016

Fashion Apparel Detection: The Role of Deep Convolutional Neural Network and Pose-dependent Priors

Kota Hara, Vignesh Jagadeesh, Robinson Piramuthu

In this work, we propose and address a new computer vision task, which we call fashion item detection, where the aim is to detect various fashion items a person in the image is wearing or carrying. The types of fashion items we consider in this work include hat, glasses, bag, pants, shoes and so on.

The detection of fashion items can be an important first step of various e-commerce applications for fashion industry. Our method is based on state-of-the-art object detection method which combines object proposal methods with a Deep Convolutional Neural Network.

Since the locations of fashion items are in strong correlation with the locations of body joints positions, we incorporate contextual information from body poses in order to improve the detection performance. Through the experiments, we demonstrate the effectiveness of the proposed method.

Another publication from the same category: Computer Vision

Mathematics in Image Formation and Processing, July 2000

Statistical proximal point methods for image reconstruction

A.O. Hero, S. Crétien and Robinson Piramuthu