10,754 research outputs found
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Radar-only ego-motion estimation in difficult settings via graph matching
Radar detects stable, long-range objects under variable weather and lighting
conditions, making it a reliable and versatile sensor well suited for
ego-motion estimation. In this work, we propose a radar-only odometry pipeline
that is highly robust to radar artifacts (e.g., speckle noise and false
positives) and requires only one input parameter. We demonstrate its ability to
adapt across diverse settings, from urban UK to off-road Iceland, achieving a
scan matching accuracy of approximately 5.20 cm and 0.0929 deg when using GPS
as ground truth (compared to visual odometry's 5.77 cm and 0.1032 deg). We
present algorithms for keypoint extraction and data association, framing the
latter as a graph matching optimization problem, and provide an in-depth system
analysis.Comment: 6 content pages, 1 page of references, 5 figures, 4 tables, 2019 IEEE
International Conference on Robotics and Automation (ICRA
SD-SLAM: A Semantic SLAM Approach for Dynamic Scenes Based on LiDAR Point Clouds
Point cloud maps generated via LiDAR sensors using extensive remotely sensed
data are commonly used by autonomous vehicles and robots for localization and
navigation. However, dynamic objects contained in point cloud maps not only
downgrade localization accuracy and navigation performance but also jeopardize
the map quality. In response to this challenge, we propose in this paper a
novel semantic SLAM approach for dynamic scenes based on LiDAR point clouds,
referred to as SD-SLAM hereafter. The main contributions of this work are in
three aspects: 1) introducing a semantic SLAM framework dedicatedly for dynamic
scenes based on LiDAR point clouds, 2) Employing semantics and Kalman filtering
to effectively differentiate between dynamic and semi-static landmarks, and 3)
Making full use of semi-static and pure static landmarks with semantic
information in the SD-SLAM process to improve localization and mapping
performance. To evaluate the proposed SD-SLAM, tests were conducted using the
widely adopted KITTI odometry dataset. Results demonstrate that the proposed
SD-SLAM effectively mitigates the adverse effects of dynamic objects on SLAM,
improving vehicle localization and mapping performance in dynamic scenes, and
simultaneously constructing a static semantic map with multiple semantic
classes for enhanced environment understanding
Re-engineering jake2 to work on a grid using the GridGain Middleware
With the advent of Massively Multiplayer Online Games (MMOGs), engineers and
designers of games came across with many questions that needed to be answered such
as, for example, "how to allow a large amount of clients to play simultaneously on the
same server?", "how to guarantee a good quality of service (QoS) to a great number
of clients?", "how many resources will be necessary?", "how to optimize these resources
to the maximum?". A possible answer to these questions relies on the usage of grid
computing.
Taking into account the parallel and distributed nature of grid computing, we can say
that grid computing allows for more scalability in terms of a growing number of players,
guarantees shorter communication time between clients and servers, and allows for a
better resource management and usage (e.g., memory, CPU, core balancing usage, etc.)
than the traditional serial computing model.
However, the main focus of this thesis is not about grid computing. Instead, this
thesis describes the re-engineering process of an existing multiplayer computer game,
called Jake2, by transforming it into a MMOG, which is then put to run on a grid
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