Jacob Barhak

Chronic disease modeling and simulation software

Computers allow describing the progress of a disease using computerized models. These models allow aggregating expert and clinical information to allow researchers and decision makers to forecast disease progression. To make this forecast reliable, good models and therefore good modeling tools are required. This paper will describe a new computer tool designed for chronic disease modeling. The modeling capabilities of this tool were used to model the Michigan model for diabetes. The modeling approach and its advantages such as simplicity, availability, and transparency are discussed.

Robust Alignment of Multi-view Range Data to CAD Model

Surface matching is a common task in computer graphics and computer vision. In this paper, we introduce a novel algorithm that aligns scanned point-based surfaces to the related 3D model without assumptions about their relative position and orientation. Our method estimates the transformation that matches two surfaces from a pair of corresponding points using their surface positions, normals and principal curvature directions. This approximate alignment is used as the input of ICP algorithm to refine the registration of the surface to the model. The algorithm is easy to implement and proved to be reliable even with coarse point clouds. Furthermore, with the help of multi-scale feature points, we can improve the reliability of our surface matching algorithm

Indirect estimation of a discrete‐state discrete‐time model using secondary data analysis of regression data

Multi-state models of chronic disease are becoming increasingly important in medical research to describe the progression of complicated diseases. However, studies seldom observe health outcomes over long time periods. Therefore, current clinical research focuses on the secondary data analysis of the published literature to estimate a single transition probability within the entire model. Unfortunately, there are many difficulties when using secondary data, especially since the states and transitions of published studies may not be consistent with the proposed multi-state model. Early approaches to reconciling published studies with the theoretical framework of a multi-state model have been limited to data available as cumulative counts of progression. This paper presents an approach that allows the use of published regression data in a multi-state model when the published study may have ignored intermediary states in the multi-state model. Colloquially, we call this approach the Lemonade Method since when study data give you lemons, make lemonade. The approach uses maximum likelihood estimation. An example is provided for the progression of heart disease in people with diabetes.

Automatic registration for inspection of complex shapes

With the increase in computing power, new ways of inspecting manufactured parts can be realized. The availability of computing power allows computational alignment between measurements and computer aided design (CAD) models using registration algorithms. The present paper proposes a new inspection approach that removes a constraint of traditional inspection processes related to part alignment. Traditional techniques require a fixture to align the part so the inspection machine can establish a common coordinate system between the measurement and the CAD model. Removing the need of a fixture aids automating the inspection process. The proposed approach employs an automated registration methodology based on two main stages. First, a part measurement in an arbitrary coordinate system is transformed to approximately fit the shape of the CAD model. Then, this approximation is iteratively refined until its convergence. To demonstrate the feasibility of the proposed method, results are demonstrated on measurements obtained from three rapid prototyped parts with complex geometry.

FEATURE-BASED ALIGNMENT OF RANGE SCAN DATA TO CAD MODEL

Surface matching is a common task in computer graphics and computer vision. In this paper, we introduce a novel algorithm that aligns scanned point-based surfaces to the related 3D model without assumptions about their relative position and orientation. Our method estimates the transformation that matches two surfaces from a pair of corresponding points using their surface positions, normals and an assistant direction on the tangent plane. This approximate alignment is used as the input of ICP algorithm to refine the registration of the surface to the model. The algorithm is easy to implement and proved to be reliable even with coarse point clouds. Furthermore, with the help of feature points, we can improve the reliability of our surface matching algorithm.

GRAPP 2006 International Conference on Computer Graphics Theory and Applications

The GRAPP conference, held in Setúbal, Portugal, 25–28 February 2006, was characterized by its multidisciplinary approach towards interactive computer graphics. The fact that it was held in parallel to the VISAPP conference (International Conference on Computer Vision Theory and Applications) contributed to this multidisciplinary approach. People were able to seamlessly participate in both conferences which contributed to the exchange of information. This synergy between both graphics and computer vision was another quality of this conference.