738 research outputs found

    On the Hardware/Software Design and Implementation of a High Definition Multiview Video Surveillance System

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    Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot

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    Recent advancements in Deep Learning have accelerated the capabilities of robotic systems in terms of visual perception, object manipulation, automated navigation, and human-robot collaboration. The capability of a robotic system to manipulate objects in unstructured environments is becoming an increasingly necessary skill. Due to the dynamic nature of these environments, traditional methods, that require expert human knowledge, fail to adapt automatically. After reviewing the relevant literature a method was proposed to utilise deep transfer learning techniques to detect object grasps from coloured depth images. A grasp describes how a robotic end-effector can be arranged to securely grasp an object and successfully lift it without slippage. In this study, a ResNet-50 convolutional neural network (CNN) model is trained on the Cornell grasp dataset. The training was completed within 30 hours using a workstation PC with accelerated GPU support via an NVIDIA Titan X. The trained grasp detection model was further evaluated with a Baxter research robot and a Microsoft Kinect-v2 and a successful grasp detection accuracy of 93.91% was achieved on a diverse set of novel objects. Physical grasping trials were conducted on a set of 8 different objects. The overall system achieves an average grasp success rate of 65.0% while performing the grasp detection in under 25 milliseconds. The results analysis concluded that the objects with reasonably straight edges and moderately pronounced heights above the table are easily detected and grasped by the system

    Scalable Real-Time Rendering for Extremely Complex 3D Environments Using Multiple GPUs

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    In 3D visualization, real-time rendering of high-quality meshes in complex 3D environments is still one of the major challenges in computer graphics. New data acquisition techniques like 3D modeling and scanning have drastically increased the requirement for more complex models and the demand for higher display resolutions in recent years. Most of the existing acceleration techniques using a single GPU for rendering suffer from the limited GPU memory budget, the time-consuming sequential executions, and the finite display resolution. Recently, people have started building commodity workstations with multiple GPUs and multiple displays. As a result, more GPU memory is available across a distributed cluster of GPUs, more computational power is provided throughout the combination of multiple GPUs, and a higher display resolution can be achieved by connecting each GPU to a display monitor (resulting in a tiled large display configuration). However, using a multi-GPU workstation may not always give the desired rendering performance due to the imbalanced rendering workloads among GPUs and overheads caused by inter-GPU communication. In this dissertation, I contribute a multi-GPU multi-display parallel rendering approach for complex 3D environments. The approach has the capability to support a high-performance and high-quality rendering of static and dynamic 3D environments. A novel parallel load balancing algorithm is developed based on a screen partitioning strategy to dynamically balance the number of vertices and triangles rendered by each GPU. The overhead of inter-GPU communication is minimized by transferring only a small amount of image pixels rather than chunks of 3D primitives with a novel frame exchanging algorithm. The state-of-the-art parallel mesh simplification and GPU out-of-core techniques are integrated into the multi-GPU multi-display system to accelerate the rendering process

    From 3D Models to 3D Prints: an Overview of the Processing Pipeline

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    Due to the wide diffusion of 3D printing technologies, geometric algorithms for Additive Manufacturing are being invented at an impressive speed. Each single step, in particular along the Process Planning pipeline, can now count on dozens of methods that prepare the 3D model for fabrication, while analysing and optimizing geometry and machine instructions for various objectives. This report provides a classification of this huge state of the art, and elicits the relation between each single algorithm and a list of desirable objectives during Process Planning. The objectives themselves are listed and discussed, along with possible needs for tradeoffs. Additive Manufacturing technologies are broadly categorized to explicitly relate classes of devices and supported features. Finally, this report offers an analysis of the state of the art while discussing open and challenging problems from both an academic and an industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and Innovation action; Grant agreement N. 68044

    Modern Optimization Algorithms and Applications: Architectural Layout Generation and Parallel Linear Programming

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    This thesis examines two topics from the field of computational optimization; architectural layout generation and parallel linear programming. The first topic, a modern problem in heuristic optimization, focuses on deriving a general form of the optimization problem and solving it with the proposed Evolutionary Treemap algorithm. Tests of the algorithm\u27s implementation within a highly scalable web application developed with Scala and the web service framework Play reveal the algorithm is effective at generated layouts in multiple styles. The second topic, a classical problem in operations research, focuses on methodologies for implementing the Simplex Algorithm on a parallel computer for solving large-scale linear programming problems. Implementations of the algorithm\u27s data-parallel and task parallel forms illuminate the ideal method for accelerating a solver. The proposed Multi-Path Simplex Algorithm shows an average speed up of over two times that of a popular open-source solver, showing it is an effective methodology for solving linear programming problems

    Adaptation of Images and Videos for Different Screen Sizes

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    With the increasing popularity of smartphones and similar mobile devices, the demand for media to consume on the go rises. As most images and videos today are captured with HD or even higher resolutions, there is a need to adapt them in a content-aware fashion before they can be watched comfortably on screens with small sizes and varying aspect ratios. This process is called retargeting. Most distortions during this process are caused by a change of the aspect ratio. Thus, retargeting mainly focuses on adapting the aspect ratio of a video while the rest can be scaled uniformly. The main objective of this dissertation is to contribute to the modern image and video retargeting, especially regarding the potential of the seam carving operator. There are still unsolved problems in this research field that should be addressed in order to improve the quality of the results or speed up the performance of the retargeting process. This dissertation presents novel algorithms that are able to retarget images, videos and stereoscopic videos while dealing with problems like the preservation of straight lines or the reduction of the required memory space and computation time. Additionally, a GPU implementation is used to achieve the retargeting of videos in real-time. Furthermore, an enhancement of face detection is presented which is able to distinguish between faces that are important for the retargeting and faces that are not. Results show that the developed techniques are suitable for the desired scenarios

    Hardware dedicado para sistemas empotrados de visión

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    La constante evolución de las Tecnologías de la Información y las Comunicaciones no solo ha permitido que más de la mitad de la población mundial esté actualmente interconectada a través de Internet, sino que ha sido el caldo de cultivo en el que han surgido nuevos paradigmas, como el ‘Internet de las cosas’ (IoT) o la ‘Inteligencia ambiental’ (AmI), que plantean la necesidad de interconectar objetos con distintas funcionalidades para lograr un entorno digital, sensible y adaptativo, que proporcione servicios de muy distinta índole a sus usuarios. La consecución de este entorno requiere el desarrollo de dispositivos electrónicos de bajo coste que, con tamaño y peso reducido, sean capaces de interactuar con el medio que los rodea, operar con máxima autonomía y proporcionar un elevado nivel de inteligencia. La funcionalidad de muchos de estos dispositivos incluirá la capacidad para adquirir, procesar y transmitir imágenes, extrayendo, interpretando o modificando la información visual que resulte de interés para una determinada aplicación. En el marco de este desafío surge la presente Tesis Doctoral, cuyo eje central es el desarrollo de hardware dedicado para la implementación de algoritmos de procesamiento de imágenes y secuencias de vídeo usados en sistemas empotrados de visión. El trabajo persigue una doble finalidad. Por una parte, la búsqueda de soluciones que, por sus prestaciones y rendimiento, puedan ser incorporadas en sistemas que satisfagan las estrictas exigencias de funcionalidad, tamaño, consumo de energía y velocidad de operación demandadas por las nuevas aplicaciones. Por otra, el diseño de una serie de bloques funcionales implementados como módulos de propiedad intelectual, que permitan aliviar la carga computacional de las unidades de procesado de los sistemas en los que se integren. En la Tesis se proponen soluciones específicas para la implementación de dos tipos de operaciones habitualmente presentes en muchos sistemas de visión artificial: la sustracción de fondo y el etiquetado de componentes conexos. Las distintas alternativas surgen como consecuencia de aplicar una adecuada relación de compromiso entre funcionalidad y coste, entendiendo este último criterio en términos de recursos de cómputo, velocidad de operación y potencia consumida, lo que permite cubrir un amplio espectro de aplicaciones. En algunas de las soluciones propuestas se han utilizado además, técnicas de inferencia basadas en Lógica Difusa con idea de mejorar la calidad de los sistemas de visión resultantes. Para la realización de los diferentes bloques funcionales se ha seguido una metodología de diseño basada en modelos, que ha permitido la realización de todo el ciclo de desarrollo en un único entorno de trabajo. Dicho entorno combina herramientas informáticas que facilitan las etapas de codificación algorítmica, diseño de circuitos, implementación física y verificación funcional y temporal de las distintas alternativas, acelerando con ello todas las fases del flujo de diseño y posibilitando una exploración más eficiente del espacio de posibles soluciones. Asimismo, con el objetivo de demostrar la funcionalidad de las distintas aportaciones de esta Tesis Doctoral, algunas de las soluciones propuestas han sido integradas en sistemas de vídeo reales, que emplean buses estándares de uso común. Los dispositivos seleccionados para llevar a cabo estos demostradores han sido FPGAs y SoPCs de Xilinx, ya que sus excelentes propiedades para el prototipado y la construcción de sistemas que combinan componentes software y hardware, los convierten en candidatos ideales para dar soporte a la implementación de este tipo de sistemas.The continuous evolution of the Information and Communication Technologies (ICT), not only has allowed more than half of the global population to be currently interconnected through Internet, but it has also been the breeding ground for new paradigms such as Internet of Things (ioT) or Ambient Intelligence (AmI). These paradigms expose the need of interconnecting elements with different functionalities in order to achieve a digital, sensitive, adaptive and responsive environment that provides services of distinct nature to the users. The development of low cost devices, with small size, light weight and a high level of autonomy, processing power and ability for interaction is required to obtain this environment. Attending to this last feature, many of these devices will include the capacity to acquire, process and transmit images, extracting, interpreting and modifying the visual information that could be of interest for a certain application. This PhD Thesis, focused on the development of dedicated hardware for the implementation of image and video processing algorithms used in embedded systems, attempts to response to this challenge. The work has a two-fold purpose: on one hand, the search of solutions that, for its performance and properties, could be integrated on systems with strict requirements of functionality, size, power consumption and speed of operation; on the other hand, the design of a set of blocks that, packaged and implemented as IP-modules, allow to alleviate the computational load of the processing units of the systems where they could be integrated. In this Thesis, specific solutions for the implementation of two kinds of usual operations in many computer vision systems are provided. These operations are background subtraction and connected component labelling. Different solutions are created as the result of applying a good performance/cost trade-off (approaching this last criteria in terms of area, speed and consumed power), able to cover a wide range of applications. Inference techniques based on Fuzzy Logic have been applied to some of the proposed solutions in order to improve the quality of the resulting systems. To obtain the mentioned solutions, a model based-design methodology has been applied. This fact has allowed us to carry out all the design flow from a single work environment. That environment combines CAD tools that facilitate the stages of code programming, circuit design, physical implementation and functional and temporal verification of the different algorithms, thus accelerating the overall processes and making it possible to explore the space of solutions. Moreover, aiming to demonstrate the functionality of this PhD Thesis’s contributions, some of the proposed solutions have been integrated on real video systems that employ common and standard buses. The devices selected to perform these demonstrators have been FPGA and SoPCs (manufactured by Xilinx) since, due to their excellent properties for prototyping and creating systems that combine software and hardware components, they are ideal to develop these applications

    Vision Pipelines and Optimizations

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    This chapter explores some hypothetical computer vision pipeline designs to understand HW/SW design alternatives and optimizations. Instead of looking at isolated computer vision algorithms, this chapter ties together many concepts into complete vision pipelines. Vision pipelines are sketched out for a few example applications to illustrate the use of different methods. Example applications include object recognition using shape and color for automobiles, face detection and emotion detection using local features, image classification using global features, and augmented reality. The examples have been chosen to illustrate the use of different families of feature description metrics within the Vision Metrics Taxonomy presented in Chap. 5. Alternative optimizations at each stage of the vision pipeline are explored. For example, we consider which vision algorithms run better on a CPU versus a GPU, and discuss how data transfer time between compute units and memory affects performance. Document type: Part of book or chapter of boo

    NOVEL DENSE STEREO ALGORITHMS FOR HIGH-QUALITY DEPTH ESTIMATION FROM IMAGES

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    This dissertation addresses the problem of inferring scene depth information from a collection of calibrated images taken from different viewpoints via stereo matching. Although it has been heavily investigated for decades, depth from stereo remains a long-standing challenge and popular research topic for several reasons. First of all, in order to be of practical use for many real-time applications such as autonomous driving, accurate depth estimation in real-time is of great importance and one of the core challenges in stereo. Second, for applications such as 3D reconstruction and view synthesis, high-quality depth estimation is crucial to achieve photo realistic results. However, due to the matching ambiguities, accurate dense depth estimates are difficult to achieve. Last but not least, most stereo algorithms rely on identification of corresponding points among images and only work effectively when scenes are Lambertian. For non-Lambertian surfaces, the brightness constancy assumption is no longer valid. This dissertation contributes three novel stereo algorithms that are motivated by the specific requirements and limitations imposed by different applications. In addressing high speed depth estimation from images, we present a stereo algorithm that achieves high quality results while maintaining real-time performance. We introduce an adaptive aggregation step in a dynamic-programming framework. Matching costs are aggregated in the vertical direction using a computationally expensive weighting scheme based on color and distance proximity. We utilize the vector processing capability and parallelism in commodity graphics hardware to speed up this process over two orders of magnitude. In addressing high accuracy depth estimation, we present a stereo model that makes use of constraints from points with known depths - the Ground Control Points (GCPs) as referred to in stereo literature. Our formulation explicitly models the influences of GCPs in a Markov Random Field. A novel regularization prior is naturally integrated into a global inference framework in a principled way using the Bayes rule. Our probabilistic framework allows GCPs to be obtained from various modalities and provides a natural way to integrate information from various sensors. In addressing non-Lambertian reflectance, we introduce a new invariant for stereo correspondence which allows completely arbitrary scene reflectance (bidirectional reflectance distribution functions - BRDFs). This invariant can be used to formulate a rank constraint on stereo matching when the scene is observed by several lighting configurations in which only the lighting intensity varies

    Working With Incremental Spatial Data During Parallel (GPU) Computation

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    Central to many complex systems, spatial actors require an awareness of their local environment to enable behaviours such as communication and navigation. Complex system simulations represent this behaviour with Fixed Radius Near Neighbours (FRNN) search. This algorithm allows actors to store data at spatial locations and then query the data structure to find all data stored within a fixed radius of the search origin. The work within this thesis answers the question: What techniques can be used for improving the performance of FRNN searches during complex system simulations on Graphics Processing Units (GPUs)? It is generally agreed that Uniform Spatial Partitioning (USP) is the most suitable data structure for providing FRNN search on GPUs. However, due to the architectural complexities of GPUs, the performance is constrained such that FRNN search remains one of the most expensive common stages between complex systems models. Existing innovations to USP highlight a need to take advantage of recent GPU advances, reducing the levels of divergence and limiting redundant memory accesses as viable routes to improve the performance of FRNN search. This thesis addresses these with three separate optimisations that can be used simultaneously. Experiments have assessed the impact of optimisations to the general case of FRNN search found within complex system simulations and demonstrated their impact in practice when applied to full complex system models. Results presented show the performance of the construction and query stages of FRNN search can be improved by over 2x and 1.3x respectively. These improvements allow complex system simulations to be executed faster, enabling increases in scale and model complexity
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