3D Segmentation Method for Natural Environments based on a Geometric-Featured Voxel Map

This work proposes a new segmentation algorithm for three-dimensional dense point clouds and has been specially designed for natural environments where the ground is unstructured and may include big slopes, non-flat areas and isolated areas. This technique is based on a Geometric-Featured Voxel map (GFV) where the scene is discretized in constant size cubes or voxels which are classified in flat surface, linear or tubular structures and scattered or undefined shapes, usually corresponding to vegetation. Since this is not a point-based technique the computational cost is significantly reduced, hence it may be compatible with Real-Time applications. The ground is extracted in order to obtain more accurate results in the posterior segmentation process. The scene is split into objects and a second segmentation in regions inside each object is performed based on the voxel’s geometric class. The work here evaluates the proposed algorithm in various versions and several voxel sizes and compares the results with other methods from the literature. For the segmentation evaluation the algorithms are tested on several differently challenging hand-labeled data sets using two metrics, one of which is novel.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Utilizing Hierarchical Segmentation to Generate Water and Snow Masks to Facilitate Monitoring Change with Remotely Sensed Image Data

The hierarchical segmentation (HSEG) algorithm is a hybrid of hierarchical step-wise optimization and constrained spectral clustering that produces a hierarchical set of image segmentations. This segmentation hierarchy organizes image data in a manner that makes the image's information content more accessible for analysis by enabling region-based analysis. This paper discusses data analysis with HSEG and describes several measures of region characteristics that may be useful analyzing segmentation hierarchies for various applications. Segmentation hierarchy analysis for generating landwater and snow/ice masks from MODIS (Moderate Resolution Imaging Spectroradiometer) data was demonstrated and compared with the corresponding MODIS standard products. The masks based on HSEG segmentation hierarchies compare very favorably to the MODIS standard products. Further, the HSEG based landwater mask was specifically tailored to the MODIS data and the HSEG snow/ice mask did not require the setting of a critical threshold as required in the production of the corresponding MODIS standard product

Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation

International audienceThe Binary Partition Tree (BPT) is a hierarchical region-based representation of an image in a tree structure. BPT allows users to explore the image at different segmentation scales. Often, the tree is pruned to get a more compact representation and so the remaining nodes conform an optimal partition for some given task. Here, we propose a novel BPT construction approach and pruning strategy for hyperspectral images based on spectral unmixing concepts. Linear Spectral Unmixing (LSU) consists of finding the spectral signatures of the materials present in the image (endmembers) and their fractional abundances within each pixel. The proposed methodology exploits the local unmixing of the regions to find the partition achieving a global minimum reconstruction error. Results are presented on real hyperspectral data sets with different contexts and resolutions

MeSH indexing based on automatically generated summaries

BACKGROUND: MEDLINE citations are manually indexed at the U.S. National Library of Medicine (NLM) using as reference the Medical Subject Headings (MeSH) controlled vocabulary. For this task, the human indexers read the full text of the article. Due to the growth of MEDLINE, the NLM Indexing Initiative explores indexing methodologies that can support the task of the indexers. Medical Text Indexer (MTI) is a tool developed by the NLM Indexing Initiative to provide MeSH indexing recommendations to indexers. Currently, the input to MTI is MEDLINE citations, title and abstract only. Previous work has shown that using full text as input to MTI increases recall, but decreases precision sharply. We propose using summaries generated automatically from the full text for the input to MTI to use in the task of suggesting MeSH headings to indexers. Summaries distill the most salient information from the full text, which might increase the coverage of automatic indexing approaches based on MEDLINE. We hypothesize that if the results were good enough, manual indexers could possibly use automatic summaries instead of the full texts, along with the recommendations of MTI, to speed up the process while maintaining high quality of indexing results. RESULTS: We have generated summaries of different lengths using two different summarizers, and evaluated the MTI indexing on the summaries using different algorithms: MTI, individual MTI components, and machine learning. The results are compared to those of full text articles and MEDLINE citations. Our results show that automatically generated summaries achieve similar recall but higher precision compared to full text articles. Compared to MEDLINE citations, summaries achieve higher recall but lower precision. CONCLUSIONS: Our results show that automatic summaries produce better indexing than full text articles. Summaries produce similar recall to full text but much better precision, which seems to indicate that automatic summaries can efficiently capture the most important contents within the original articles. The combination of MEDLINE citations and automatically generated summaries could improve the recommendations suggested by MTI. On the other hand, indexing performance might be dependent on the MeSH heading being indexed. Summarization techniques could thus be considered as a feature selection algorithm that might have to be tuned individually for each MeSH heading

El vocabulario del cómputo en el De temporibus liber de Beda

The discipline devoted to the measurement of time and the confection of calendars was known as computus in the Middle Ages. The first treatises on the computus were written by Bede, namely De Temporibus Liber (703) and De temporum ratione (729). The aim of this paper is to analyse the lexicon of the computus –as it had its own specific vocabulary– in the first of these teatrises.Bajo la denominación de computus se conoció en la Edad Media la ciencia dedicada a la medida del tiempo y a la elaboración de calendarios. Los primeros tratados sobre el asunto se debieron a Beda: concretamente, el De temporibus liber (703) y el De temporum ratione (729). En este trabajo analizamos el léxico específico del computus en el primero de esos tratados

Hyperspectral image segmentation using a new spectral mixture-based binary partition tree representation

International audienceThe Binary Partition Tree (BPT) is a hierarchical region-based representation of an image in a tree structure. BPT allows users to explore the image at different segmentation scales, from fine partitions close to the leaves to coarser partitions close to the root. Often, the tree is pruned so the leaves of the resulting pruned tree conform an optimal partition given some optimality criterion. Here, we propose a novel BPT construction approach and pruning strategy for hyperspectral images based on spectral unmixing concepts. The proposed methodology exploits the local unmixing of the regions to find the partition achieving a global minimum reconstruction error. We successfully tested the proposed approach on the well-known Cuprite hyperspectral image collected by NASA Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This scene is considered as a standard benchmark to validate spectral unmixing algorithms

Concept of finite limit of a function at a point: meanings and specific terms

In this paper, we present some results of an exploratory study performed with students aged 16-17. We investigate the different uses that these students make of terms such as ‘to approach’, ‘to tend’, ‘to reach’, ‘to exceed’ and ‘limit’ that describe the basic notions related to the concept of the finite limit of a function at a point. We use the interpretive framework of conceptual analysis to infer the meanings that students associate with these specific terms in connection with the effective use of terms in their answers.This study was performed with aid and financing from Fellowship FPU AP2010-0906 (MEC-FEDER), Projects EDU2009-11337 and EDU2012-33030 of the National Plan for R&D&R (MICIN), Subprogram EDUC and Group FQM-193 of the 3rd Andalusian Research Plan (PAIDI).This chapter is derived in part from an article published in International Journal of Mathematical Education in Science and Technology 05 Jul 2013, available online: http://www.tandfonline.com/10.1080/0020739X.2013.80588

Nextmed: Automatic Imaging Segmentation, 3D Reconstruction, and 3D Model Visualization Platform Using Augmented and Virtual Reality

The visualization of medical images with advanced techniques, such as augmented reality and virtual reality, represent a breakthrough for medical professionals. In contrast to more traditional visualization tools lacking 3D capabilities, these systems use the three available dimensions. To visualize medical images in 3D, the anatomical areas of interest must be segmented. Currently, manual segmentation, which is the most commonly used technique, and semi-automatic approaches can be time consuming because a doctor is required, making segmentation for each individual case unfeasible. Using new technologies, such as computer vision and artificial intelligence for segmentation algorithms and augmented and virtual reality for visualization techniques implementation, we designed a complete platform to solve this problem and allow medical professionals to work more frequently with anatomical 3D models obtained from medical imaging. As a result, the Nextmed project, due to the different implemented software applications, permits the importation of digital imaging and communication on medicine (dicom) images on a secure cloud platform and the automatic segmentation of certain anatomical structures with new algorithms that improve upon the current research results. A 3D mesh of the segmented structure is then automatically generated that can be printed in 3D or visualized using both augmented and virtual reality, with the designed software systems. The Nextmed project is unique, as it covers the whole process from uploading dicom images to automatic segmentation, 3D reconstruction, 3D visualization, and manipulation using augmented and virtual reality. There are many researches about application of augmented and virtual reality for medical image 3D visualization; however, they are not automated platforms. Although some other anatomical structures can be studied, we focused on one case: a lung study. Analyzing the application of the platform to more than 1000 dicom images and studying the results with medical specialists, we concluded that the installation of this system in hospitals would provide a considerable improvement as a tool for medical image visualization