OPTIMIZATION OF THE ALGORITHM FOR DETERMINING THE HAUSDORFF DISTANCE FOR CONVEX POLYGONS

The paper provides a brief historical analysis of problems that use the Hausdorff distance; provides an analysis of the existing Hausdorff distance optimization elements for convex polygons; and demonstrates an optimization approach. The existing algorithm served as the basis to propose low-level optimization with super-operative memory, ensuring the finding a precise solution by a full search of the corresponding pairs of vertices and sides of polygons with exclusion of certain pairs of vertices and sides of polygons. This approach allows a significant acceleration of the process of solving the set problem

Contextual Human Trajectory Forecasting within Indoor Environments and Its Applications

A human trajectory is the likely path a human subject would take to get to a destination. Human trajectory forecasting algorithms try to estimate or predict this path. Such algorithms have wide applications in robotics, computer vision and video surveillance. Understanding the human behavior can provide useful information towards the design of these algorithms. Human trajectory forecasting algorithm is an interesting problem because the outcome is influenced by many factors, of which we believe that the destination, geometry of the environment, and the humans in it play a significant role. In addressing this problem, we propose a model to estimate the occupancy behavior of humans based on the geometry and behavioral norms. We also develop a trajectory forecasting algorithm that understands this occupancy and leverages it for trajectory forecasting in previously unseen geometries. The algorithm can be useful in a variety of applications. In this work, we show its utility in three applications, namely person re-identification, camera placement optimization, and human tracking. Experiments were performed with real world data and compared to state-of-the-art methods to assess the quality of the forecasting algorithm and the enhancement in the quality of the applications. Results obtained suggests a significant enhancement in the accuracy of trajectory forecasting and the computer vision applications.Computer Science, Department o

Optimization of the Algorithm for Determining the Hausdorff Distance for Convex Polygons

The paper provides a brief historical analysis of problems that use the Hausdorff distance; provides an analysis of the existing Hausdorff distance optimization elements for convex polygons; and demonstrates an optimization approach. The existing algorithm served as the basis to propose low-level optimization with super-operative memory, ensuring the finding a precise solution by a full search of the corresponding pairs of vertices and sides of polygons with exclusion of certain pairs of vertices and sides of polygons. This approach allows a significant acceleration of the process of solving the set problem

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Computer vision and optimization methods applied to the measurements of in-plane deformations

fi=vertaisarvioitu|en=peerReviewed

3D SEM Surface Reconstruction: An Optimized, Adaptive, and Intelligent Approach

Structural analysis of microscopic objects is a longstanding topic in several scientific disciplines, including biological, mechanical, and material sciences. The scanning electron microscope (SEM), as a promising imaging equipment has been around to determine the surface properties (e.g., compositions or geometries) of specimens by achieving increased magnification, contrast, and resolution greater than one nanometer. Whereas SEM micrographs still remain two-dimensional (2D), many research and educational questions truly require knowledge and information about their three-dimensional (3D) surface structures. Having 3D surfaces from SEM images would provide true anatomic shapes of micro samples which would allow for quantitative measurements and informative visualization of the systems being investigated. In this research project, we novel design and develop an optimized, adaptive, and intelligent multi-view approach named 3DSEM++ for 3D surface reconstruction of SEM images, making a 3D SEM dataset publicly and freely available to the research community. The work is expected to stimulate more interest and draw attention from the computer vision and multimedia communities to the fast-growing SEM application area

Achieving illumination invariance using image filters

In this chapter we described a novel framework for automatic face recognition in the presence of varying illumination, primarily applicable to matching face sets or sequences. The framework is based on simple image processing filters that compete with unprocessed greyscale input to yield a single matching score between individuals. By performing all numerically consuming computation offline, our method both (i) retains the matching efficiency of simple image filters, but (ii) with a greatly increased robustness, as all online processing is performed in closed-form. Evaluated on a large, real-world data corpus, the proposed framework was shown to be successful in video-based recognition across a wide range of illumination, pose and face motion pattern change

Time-dependent topological snalysis for cardiovascular disease diagnosis using magnetic resonance

Treballs finals del Màster en Matemàtica Avançada, Facultat de Matemàtiques, Universitat de Barcelona: Curs: 2022-2023. Director: Carles Casacuberta i Polyxeni Gkontra[en] The present research project aims to study the topology of time varying Cardiovascular Magnetic Resonance images (CMR) for disease diagnosis. CMR is a non-invasive technique that involves the acquisition of multiple 3D images at different cardiac phases throughout the cardiac cycle. Nonetheless, conventional assessment of CMR images typically involves the quantification of parameters related to the volumes, and more recently to the shape and texture by means of radiomics (Raisi-Estabragh, 2020), of the cardiac chambers at only two static time-point points: the end-systole and the enddiastole. Therefore, potentially rich information regarding the cardiac function and structure from other phases of the cardiac cycle might be lost. To overcome this limitation, we propose to leverage Topological Data Analysis (TDA) to optimally exploit information from the entire cardiac cycle, by measuring the variation of persistence descriptors. This approach seems promising since a time series might not exhibit relevant geometrical features in its respective point cloud embedding, but it may rather display topological cyclic patterns and their respective variations that can be captured with the proposed machinery. Subsequently, the novel TDA-based CMR descriptors encompassing the entire cardiac cycle are used to feed supervised machine learning classifiers for cardiovascular disease diagnosis. A full framework from data gathering, to image processing, mathematical modelling and classifier implementation is presented for this purpose. The performance of the proposed approach based on TDA features and ML is limited. Nonetheless, the approach could be easily adapted to other diseases and scenario where the integration of ML and TDA could be more beneficial