Deformable templates for plaque thickness estimation of intravascular ultrasound sequences

Comunicación presentada en el VII Symposium Nacional de Reconocimiento de Formas y Análisis de Imágenes, SNRFAI, Barcelona, abril 1997.

Deformable templates for tracking and analysis of intravascular ultrasound sequences

Deformable Template models are first applied to track the inner wall of coronary arteries in intravascular ultrasound sequences, mainly in the assistance to angioplasty surgery. A circular template is used for initializing an elliptical deformable model to track wall deformation when inflating a balloon placed at the tip of the catheter. We define a new energy function for driving the behavior of the template and we test its robustness both in real and synthetic images. Finally we introduce a framework for learning and recognizing spatio-temporal geometric constraints based on Principal Component Analysis (eigenconstraints).

Optimal synthesis of thermally coupled distillation sequences using a novel MILP approach

This paper introduces a novel MILP approach for the design of distillation columns sequences of zeotropic mixtures that explicitly include from conventional to fully thermally coupled sequences and divided wall columns with a single wall. The model is based on the use of two superstructure levels. In the upper level a superstructure that includes all the basic sequences of separation tasks is postulated. The lower level is an extended tree that explicitly includes different thermal states and compositions of the feed to a given separation task. In that way, it is possible to a priori optimize all the possible separation tasks involved in the superstructure. A set of logical relationships relates the feasible sequences with the optimized tasks in the extended tree resulting in a MILP to select the optimal sequence. The performance of the model in terms of robustness and computational time is illustrated with several examples.

A Sequential Algorithm for the Rigorous Design of Thermally Coupled Distillation Sequences

A sequential design method is presented for the design of thermally coupled distillation sequences. The algorithm starts by selecting a set of sequences in the space of basic configurations in which the internal structure of condensers and reboilers is explicitly taken into account and extended with the possibility of including divided wall columns (DWC). This first stage is based on separation tasks (except by the DWCs) and therefore it does not provide an actual sequence of columns. In the second stage the best arrangement in N-1 actual columns is performed taking into account operability and mechanical constraints. Finally, for a set of candidate sequences the algorithm try to reduce the number of total columns by considering Kaibel columns, elimination of transfer blocks or columns with vertical partitions. An example illustrate the different steps of the sequential algorithm.

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.