572 research outputs found

    Sensor fusion in driving assistance systems

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    Mención Internacional en el título de doctorLa vida diaria en los países desarrollados y en vías de desarrollo depende en gran medida del transporte urbano y en carretera. Esta actividad supone un coste importante para sus usuarios activos y pasivos en términos de polución y accidentes, muy habitualmente debidos al factor humano. Los nuevos desarrollos en seguridad y asistencia a la conducción, llamados Advanced Driving Assistance Systems (ADAS), buscan mejorar la seguridad en el transporte, y a medio plazo, llegar a la conducción autónoma. Los ADAS, al igual que la conducción humana, están basados en sensores que proporcionan información acerca del entorno, y la fiabilidad de los sensores es crucial para las aplicaciones ADAS al igual que las capacidades sensoriales lo son para la conducción humana. Una de las formas de aumentar la fiabilidad de los sensores es el uso de la Fusión Sensorial, desarrollando nuevas estrategias para el modelado del entorno de conducción gracias al uso de diversos sensores, y obteniendo una información mejorada a partid de los datos disponibles. La presente tesis pretende ofrecer una solución novedosa para la detección y clasificación de obstáculos en aplicaciones de automoción, usando fusión vii sensorial con dos sensores ampliamente disponibles en el mercado: la cámara de espectro visible y el escáner láser. Cámaras y láseres son sensores comúnmente usados en la literatura científica, cada vez más accesibles y listos para ser empleados en aplicaciones reales. La solución propuesta permite la detección y clasificación de algunos de los obstáculos comúnmente presentes en la vía, como son ciclistas y peatones. En esta tesis se han explorado novedosos enfoques para la detección y clasificación, desde la clasificación empleando clusters de nubes de puntos obtenidas desde el escáner láser, hasta las técnicas de domain adaptation para la creación de bases de datos de imágenes sintéticas, pasando por la extracción inteligente de clusters y la detección y eliminación del suelo en nubes de puntos.Life in developed and developing countries is highly dependent on road and urban motor transport. This activity involves a high cost for its active and passive users in terms of pollution and accidents, which are largely attributable to the human factor. New developments in safety and driving assistance, called Advanced Driving Assistance Systems (ADAS), are intended to improve security in transportation, and, in the mid-term, lead to autonomous driving. ADAS, like the human driving, are based on sensors, which provide information about the environment, and sensors’ reliability is crucial for ADAS applications in the same way the sensing abilities are crucial for human driving. One of the ways to improve reliability for sensors is the use of Sensor Fusion, developing novel strategies for environment modeling with the help of several sensors and obtaining an enhanced information from the combination of the available data. The present thesis is intended to offer a novel solution for obstacle detection and classification in automotive applications using sensor fusion with two highly available sensors in the market: visible spectrum camera and laser scanner. Cameras and lasers are commonly used sensors in the scientific literature, increasingly affordable and ready to be deployed in real world applications. The solution proposed provides obstacle detection and classification for some obstacles commonly present in the road, such as pedestrians and bicycles. Novel approaches for detection and classification have been explored in this thesis, from point cloud clustering classification for laser scanner, to domain adaptation techniques for synthetic dataset creation, and including intelligent clustering extraction and ground detection and removal from point clouds.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Cristina Olaverri Monreal.- Secretario: Arturo de la Escalera Hueso.- Vocal: José Eugenio Naranjo Hernánde

    Gland Instance Segmentation in Colon Histology Images

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    This thesis looks at approaches to gland instance segmentation in histology images. The aim is to find suitable local image representations to describe the gland structures in images with benign tissue and those with malignant tissue and subsequently use them for design of accurate, scalable and flexible gland instance segmentation methods. The gland instance segmentation is a clinically important and technically challenging problem as the morphological structure and visual appearance of gland tissue is highly variable and complex. Glands are one of the most common organs in the human body. The glandular features are present in many cancer types and histopathologists use these features to predict tumour grade. Accurate tumour grading is critical for prescribing suitable cancer treatment resulting in improved outcome and survival rate. Different cancer grades are reflected by differences in glands morphology and structure. It is therefore important to accurately segment glands in histology images in order to get a valid prediction of tumour grade. Several segmentation methods, including segmentation with and without pre-classification, have been proposed and investigated as part of the research reported in this thesis. A number of feature spaces, including hand-crafted and deep features, have been investigated and experimentally validated to find a suitable set of image attributes for representation of benign and malignant gland tissue for the segmentation task. Furthermore, an exhaustive experimental examination of different combinations of features and classification methods have been carried out using both qualitative and quantitative assessments, including detection, shape and area fidelity metrics. It has been shown that the proposed hybrid method combining image level classification, to identify images with benign and malignant tissue, and pixel level classification, to perform gland segmentation, achieved the best results. It has been further shown that modelling benign glands using a three-class model, i.e. inside, outside and gland boundary, and malignant tissue using a two-class model is the best combination for achieving accurate and robust gland instance segmentation results. The deep learning features have been shown to overall outperform handcrafted features, however proposed ring-histogram features still performed adequately, particularly for segmentation of benign glands. The adopted transfer-learning model with proposed image augmentation has proven very successful with 100% image classification accuracy on the available test dataset. It has been shown that the modified object- level Boundary Jaccard metric is more suitable for measuring shape similarity than the previously used object-level Hausdorff distance, as it is not sensitive to outliers and could be easily integrated with region- based metrics such as the object-level Dice index, as contrary to the Hausdorff distance it is bounded between 0 and 1. Dissimilar to most of the other reported research, this study provides comprehensive comparative results for gland segmentation, with a large collection of diverse types of image features, including hand-crafted and deep features. The novel contributions include hybrid segmentation model superimposing image and pixel level classification, data augmentation for re-training deep learning models for the proposed image level classification, and the object- level Boundary Jaccard metric adopted for evaluation of instance segmentation methods

    An Image-Based Real-Time Georeferencing Scheme for a UAV Based on a New Angular Parametrization

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    Simultaneous localization and mapping (SLAM) of a monocular projective camera installed on an unmanned aerial vehicle (UAV) is a challenging task in photogrammetry, computer vision, and robotics. This paper presents a novel real-time monocular SLAM solution for UAV applications. It is based on two steps: consecutive construction of the UAV path, and adjacent strip connection. Consecutive construction rapidly estimates the UAV path by sequentially connecting incoming images to a network of connected images. A multilevel pyramid matching is proposed for this step that contains a sub-window matching using high-resolution images. The sub-window matching increases the frequency of tie points by propagating locations of matched sub-windows that leads to a list of high-frequency tie points while keeping the execution time relatively low. A sparse bundle block adjustment (BBA) is employed to optimize the initial path by considering nuisance parameters. System calibration parameters with respect to global navigation satellite system (GNSS) and inertial navigation system (INS) are optionally considered in the BBA model for direct georeferencing. Ground control points and checkpoints are optionally included in the model for georeferencing and quality control. Adjacent strip connection is enabled by an overlap analysis to further improve connectivity of local networks. A novel angular parametrization based on spherical rotation coordinate system is presented to address the gimbal lock singularity of BBA. Our results suggest that the proposed scheme is a precise real-time monocular SLAM solution for a UAV.Peer reviewe

    Characterizing Objects in Images using Human Context

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    Humans have an unmatched capability of interpreting detailed information about existent objects by just looking at an image. Particularly, they can effortlessly perform the following tasks: 1) Localizing various objects in the image and 2) Assigning functionalities to the parts of localized objects. This dissertation addresses the problem of aiding vision systems accomplish these two goals. The first part of the dissertation concerns object detection in a Hough-based framework. To this end, the independence assumption between features is addressed by grouping them in a local neighborhood. We study the complementary nature of individual and grouped features and combine them to achieve improved performance. Further, we consider the challenging case of detecting small and medium sized household objects under human-object interactions. We first evaluate appearance based star and tree models. While the tree model is slightly better, appearance based methods continue to suffer due to deficiencies caused by human interactions. To this end, we successfully incorporate automatically extracted human pose as a form of context for object detection. The second part of the dissertation addresses the tedious process of manually annotating objects to train fully supervised detectors. We observe that videos of human-object interactions with activity labels can serve as weakly annotated examples of household objects. Since such objects cannot be localized only through appearance or motion, we propose a framework that includes human centric functionality to retrieve the common object. Designed to maximize data utility by detecting multiple instances of an object per video, the framework achieves performance comparable to its fully supervised counterpart. The final part of the dissertation concerns localizing functional regions or affordances within objects by casting the problem as that of semantic image segmentation. To this end, we introduce a dataset involving human-object interactions with strong i.e. pixel level and weak i.e. clickpoint and image level affordance annotations. We propose a framework that utilizes both forms of weak labels and demonstrate that efforts for weak annotation can be further optimized using human context

    Blunt Force Trauma to the Ribs: Creating Predictive Models

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    Forensic anthropologists receive more requests for trauma analysis than any other aspect of the biological profile. Blunt force trauma to the ribs is some of the most common trauma recorded in a medical examiner’s setting, however the structural complexity of ribs make it difficult to move beyond descriptive documentation of injuries. The purpose of this study is to identify common rib fracture patterns, influential variables, and provide probabilistic statements to guide rib fracture interpretations. A sample of 1,415 deceased individuals with known blunt force trauma to the torso were collected from four geographically diverse medical examiner offices. Demographic data and fracture variables were recorded per individual. Frequency distributions, chi-squared tests, Kruskal-Wallis tests of independence, Dunn’s tests, and multiple correspondence analysis were employed to understand variable relationships. Conditional probabilities were calculated to provide probabilistic statements. Additionally, random forest analysis was conducted to classify location and type of fracture based on covariates. A total of 24, 853 fractures were recorded. The most common fractures were displaced and simple fractures on ribs three through seven in the anterolateral and posterolateral locations. The less common fracture patterns revealed significant relationships with demographic data and provided empirical evidence for previously untested statements. BMI had a significant relationship with location, such that fractures were more frequently recorded in lower ribs in individuals with a BMI category of obese. Age had a significant relationship with fracture type and fracture location in all analyses; younger individuals were more likely to have incomplete fractures and incur fractures anteriorly, and older individuals were more likely to have multi-fragmentary fractures. The current study indicates that rib fracture types and location are dependent on the demographics of the individual. Demographics, such as age and health of the individual inform the material properties and structural geometry of bone, which is how bone biomechanics are recommended to be incorporated into trauma analysis. Furthermore, the results from this research can be applied to motor vehicle safety research, experimental research avenues, and bioarcheological trauma analysis. Future rib fracture research should focus on including a more holistic view of an individual during the interpretation of fracture patterns

    Vulnerable road users and connected autonomous vehicles interaction: a survey

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    There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.This work was partially funded by the Ministry of Economy, Industry, and Competitiveness of Spain under Grant: Supervision of drone fleet and optimization of commercial operations flight plans, PID2020-116377RB-C21.Peer ReviewedPostprint (published version

    Remote Sensing in Applications of Geoinformation

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    Remote sensing, especially from satellites, is a source of invaluable data which can be used to generate synoptic information for virtually all parts of the Earth, including the atmosphere, land, and ocean. In the last few decades, such data have evolved as a basis for accurate information about the Earth, leading to a wealth of geoscientific analysis focusing on diverse applications. Geoinformation systems based on remote sensing are increasingly becoming an integral part of the current information and communication society. The integration of remote sensing and geoinformation essentially involves combining data provided from both, in a consistent and sensible manner. This process has been accelerated by technologically advanced tools and methods for remote sensing data access and integration, paving the way for scientific advances in a broadening range of remote sensing exploitations in applications of geoinformation. This volume hosts original research focusing on the exploitation of remote sensing in applications of geoinformation. The emphasis is on a wide range of applications, such as the mapping of soil nutrients, detection of plastic litter in oceans, urban microclimate, seafloor morphology, urban forest ecosystems, real estate appraisal, inundation mapping, and solar potential analysis

    Proceedings of the 2018 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

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    The Proceeding of the annual joint workshop of the Fraunhofer IOSB and the Vision and Fusion Laboratory (IES) 2018 of the KIT contain technical reports of the PhD-stundents on the status of their research. The discussed topics ranging from computer vision and optical metrology to network security and machine learning. This volume provides a comprehensive and up-to-date overview of the research program of the IES Laboratory and the Fraunhofer IOSB
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