6D Pose Estimation using an Improved Method based on Point Pair Features

The Point Pair Feature (Drost et al. 2010) has been one of the most successful 6D pose estimation method among model-based approaches as an efficient, integrated and compromise alternative to the traditional local and global pipelines. During the last years, several variations of the algorithm have been proposed. Among these extensions, the solution introduced by Hinterstoisser et al. (2016) is a major contribution. This work presents a variation of this PPF method applied to the SIXD Challenge datasets presented at the 3rd International Workshop on Recovering 6D Object Pose held at the ICCV 2017. We report an average recall of 0.77 for all datasets and overall recall of 0.82, 0.67, 0.85, 0.37, 0.97 and 0.96 for hinterstoisser, tless, tudlight, rutgers, tejani and doumanoglou datasets, respectively

FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition

Transfer learning is a promising technique for medical image classification, particularly for long-tailed datasets. However, the scarcity of data in medical imaging domains often leads to overparameterization when fine-tuning large publicly available pre-trained models. Moreover, these large models are ineffective in deployment in clinical settings due to their computational expenses. To address these challenges, we propose FoPro-KD, a novel approach that unleashes the power of frequency patterns learned from frozen publicly available pre-trained models to enhance their transferability and compression. FoPro-KD comprises three modules: Fourier prompt generator (FPG), effective knowledge distillation (EKD), and adversarial knowledge distillation (AKD). The FPG module learns to generate targeted perturbations conditional on a target dataset, exploring the representations of a frozen pre-trained model, trained on natural images. The EKD module exploits these generalizable representations through distillation to a smaller target model, while the AKD module further enhances the distillation process. Through these modules, FoPro-KD achieves significant improvements in performance on long-tailed medical image classification benchmarks, demonstrating the potential of leveraging the learned frequency patterns from pre-trained models to enhance transfer learning and compression of large pre-trained models for feasible deployment

Meridià: un portal para la difusión de la ciencia desde una óptica integral y cooperativa

The proper functioning of the Research, Development and Innovation(R&D&I) cycle requires an efficient processing of scientific information. The Institute for Catalan Studies (IEC) possesses a scientific observatory which recently presented the web portal Meridià. From a global point of view, this project aims to be a cooperative initiative to integrate and share information with other agents of the scientific system, providing a thorough understanding of their environment and the evolution of the different areas in science and technology

Aplicación web para la gestión de una base de datos pública de mamografía digital: MamoDB

Cada vez son más los hospitales que disponen de sistemas computarizados de adquisición y visualización de imágenes digitales , con las ventaj as que ello supone cu anto a acceso a la información , capacidad de diagnóstico y aprendizaje . Sin embargo, el volumen ingente de datos requiere de nuevas herramientas para su alm acenaje, gestión y recuperación . En este trabajo se propone un modelo de estructura basado en tecnol ogía web como herramienta de ayuda al diagnóstico de Cáncer de Mama. La estructura propuesta se basa en la administración de imágenes y estudios mamográfico s con el objetivo de ser un referente en la comunidad científica. Su arquitectura, metodología y aplicación en formato web se presentan en es te trabajo así como conclusiones y trabajos futurosPostprint (published version

Organic particle export, remineralization and advection in the North Atlantic mesopelagic layer

The mesopelagic layer of the oceans extends between ~200 and 1000 m depth and plays a fundamental role in global biogeochemical cycles and climate. In addition, it hosts a massive biomass of zooplankton and small fish, what is essential to regulate marine resources. However, scientific understanding and predictive capacity of biogeochemical processes in the mesopelagic zone are still underdeveloped. This lack of information has societal and economic costs because there is uncertainty in estimates of oceanic carbon storage (which inform policies for the reduction of carbon dioxide emissions), and it hampers the management of mesopelagic biological resources. In this way, this thesis would address the study of the carbon cycle and the associated biogeochemical processes. The main objective is to analyse the variability of the transport and transformation of particles that carry organic carbon from the surface to the deep sea in the North Atlantic. For this purpose, this study will focus on the analysis of simulations from dynamical (NEMO4 (Madec & NEMO team, 2008)) and biogeochemical (PISCES-v2 (Amount et al., 2015)) models, together with observations from bio-ARGO floats and satellite data