A newly invented, high performance, pod-based computer architecture, called Agile Condor (patent pending), has been designed and developed. Agile Condor is supporting autonomous operations by providing a platform for ...
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ISBN:
(纸本)9781509064359
A newly invented, high performance, pod-based computer architecture, called Agile Condor (patent pending), has been designed and developed. Agile Condor is supporting autonomous operations by providing a platform for the innovative use of artificial intelligence, machine learning, and decision making algorithms upstream, near the information source, where the data is collected. In September 2016, experimental tests successfully demonstrated the ability to implement advanced neural networks and deep learning techniques on Agile Condor. We continue to use this new processing architecture, algorithms and bio-inspired computing methods to demonstrate existing, refine emerging and invent new artificial intelligence techniques that are highly applicable and needed for sensor platforms. For the first time ever, and in real-time, the system demonstrated: imageprocessing, video processing and pattern recognition through the use of deep convolutional neural networks. Because of Agile Condor's modular architecture and performance characteristics, the system is providing flexible computational resources that will continue to bring new artificial intelligence (AI) capabilities closer to sensor platforms.
Various signal and imageprocessing applications require vast acceleration in order to enable real-time processing and meet constraints in power consumption. On FPGAs these applications can be implemented as applicati...
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ISBN:
(纸本)9781538635346
Various signal and imageprocessing applications require vast acceleration in order to enable real-time processing and meet constraints in power consumption. On FPGAs these applications can be implemented as application-specific circuit. Although IP cores for various applications exist, even interfacing these usually requires experienced knowledge in hardware design. Using FPGAs or other accelerators in a heterogeneous system from a host CPU would simplify the usage of accelerator hardware for a common software developer. Recognizing this, several companies and partners from academia created the HSA Foundation (Heterogeneous System Architecture Foundation) to define a platform specification for heterogeneous system requirements as a macro-architecture for efficient and easy targeting heterogeneous processors from popular high-level languages like C/C++, Python, Java and other domain specific languages. In this paper we present an IP library (LibHSA), that greatly simplifies integration of hardware accelerator functions into existing HSA compliant systems. This allows accelerators to take advantage of the existing HSA programming model, libraries, compilers and toolchains. We will demonstrate the work of LibHSA utilizing a programmable image processor implementation on a Xilinx FPGA. The image processor supports low-level algorithms, e.g. Sobel, Median, Laplace, or Gauss. Our results show a substantial decrease integrating customized hardware accelerators using the LibHSA infrastructure. To our knowledge, our library is the first approach for integrating reconfigurable hardware into an HSA compliant system.
Recent advances in microcopy and improvements in imageprocessingalgorithms have allowed the development of computer-assisted analytical approaches in cell identification. Several applications could be mentioned in t...
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Multimedia processing with cloud is prevalent now, which the cloud server can provide abundant resources to processing various multimedia processing tasks. However, some privacy issues must be considered in cloud comp...
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ISBN:
(纸本)9783319715988;9783319715971
Multimedia processing with cloud is prevalent now, which the cloud server can provide abundant resources to processing various multimedia processing tasks. However, some privacy issues must be considered in cloud computing. For a secret image, the image content should be kept secret while conducting the multimedia processing in the cloud. Multimedia processing in the encrypted domain is essential to protect the privacy in cloud computing. Hu et al. proposed a novel framework to perform complex imageprocessingalgorithms in encrypted images with two cryptosystems: additive homomorphic encryption and privacy preserving transform. The additive homomorphic cryptosystem used in their scheme causes huge ciphertext expansion and greatly increases the cloud's computation. In this paper, we modified their framework to a two-cloud scheme, and also implemented the random nonlocal means denoising algorithm. The complexity analysis and simulation results demonstrate that our new scheme is more efficient than Hu's under the same denoising performance.
The aim of this study is twofold: first, to present a survey of the actual and most advanced methods related to the use of unmanned aerial systems (UASs) that emerged in the past few years due to the technological adv...
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The aim of this study is twofold: first, to present a survey of the actual and most advanced methods related to the use of unmanned aerial systems (UASs) that emerged in the past few years due to the technological advancements that allowed the miniaturization of components, leading to the availability of small-sized unmanned aerial vehicles (UAVs) equipped with Global Navigation Satellite systems (GNSS) and high quality and cost-effective sensors;second, to advice the target audience - mostly farmers and foresters - how to choose the appropriate UAV and imaging sensor, as well as suitable approaches to get the expected and needed results of using technological tools to extract valuable information about agroforestry systems and its dynamics, according to their parcels' size and crop's types. Following this goal, this work goes beyond a survey regarding UAS and their applications, already made by several authors. It also provides recommendations on how to choose both the best sensor and UAV, in according with the required application. Moreover, it presents what can be done with the acquired sensors' data through theuse of methods, procedures, algorithms and arithmetic operations. Finally, some recent applications in the agroforestry research area are presented, regarding the main goal of each analysed studies, the used UAV, sensors, and the data processing stage to reach conclusions.
This paper proposes a semi-supervised learning method based on weakly-labeled data to automatically classify ultrasound (US) thyroid nodules. Key to our new approach is the unification of multi-instance learning (MIL)...
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This paper proposes a semi-supervised learning method based on weakly-labeled data to automatically classify ultrasound (US) thyroid nodules. Key to our new approach is the unification of multi-instance learning (MIL) with deep learning. Benefiting from that, our method can directly use off-the-shelf clinical data, which involves no labels to indicate nodule classes. To this end, we take the US images of a patient as a bag, and take the corresponding pathology report as the bag label. Specifically, we first propose a bag generating method, wherein the detected thyroid nodules are considered as instances corresponding to certain bag. After that, we design an effective EM algorithm to train a convolutional neural network (CNN) for nodule classification. We conduct extensive experiments and comprehensive evaluations on different datasets, and all the experiments confirm that, our method significantly outperforms state-of-the-art MIL algorithms, which exhibits great potential in clinical applications.
Binary gradient cameras extract edge and temporal information directly on the sensor, allowing for low-power, low-bandwidth, and high-dynamic-range capabilities-all critical factors for the deployment of embedded comp...
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ISBN:
(纸本)9781538607336
Binary gradient cameras extract edge and temporal information directly on the sensor, allowing for low-power, low-bandwidth, and high-dynamic-range capabilities-all critical factors for the deployment of embedded computer vision systems. However, these types of images require specialized computer vision algorithms and are not easy to interpret by a human observer. In this paper we propose to recover an intensity image from a single binary spatial gradient image with a deep autoencoder. Extensive experimental results on both simulated and real data show the effectiveness of the proposed approach.
Retinal diseases are already the most common cause of childhood blindness worldwide. Accordingly, it would be extensively beneficial to humans and health-related communities if we could automate the procedure of diagn...
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ISBN:
(纸本)9781728111957;9781728111940
Retinal diseases are already the most common cause of childhood blindness worldwide. Accordingly, it would be extensively beneficial to humans and health-related communities if we could automate the procedure of diagnosis thoroughly or at least partially by exploiting capabilities of computer-aided diagnosis (CAD). This paper proposes two segmentation methods, a supervised method and an unsupervised one, which shall expertly tackle the problem of vessel segmentation in retinal fundus images. Our unsupervised method exploits the power of multi-scale spatial filters to locate and detect different types of vessels in terms of vessel diameter. Furthermore, we proposed a novel denoising filter to overcome a challenge called “FOV's tangential ring” effectively. In our supervised algorithm, we combined the unsupervised method with a support-vector machine (SVM) classifier, in which samples' features are produced using a feature-fusion technique. Dataset used in this research is the public DRIVE (Digital Retinal images for Vessel Extraction) dataset. We have also addressed another challenging problem with a solution that is dataset independent, the challenge of generating mask for retinal coloured images. Our supervised method has achieved a higher accuracy of 94.48%, and our unsupervised method has achieved an accuracy of 94.28% with a response time of 1.65 second providing human operators or automatic systems with fast and reliable results.
This paper addresses the analysis of an experimental setup designed with the aim of calibrating important parameters involved in visual servoing. More precisely, the effects of parameters related to imageprocessing, ...
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ISBN:
(纸本)9781509060009
This paper addresses the analysis of an experimental setup designed with the aim of calibrating important parameters involved in visual servoing. More precisely, the effects of parameters related to imageprocessing, camera calibration and noise reduction are investigated. Great attention is given to the algorithms used for improving the pose accuracy of a moving target while reducing their computational burden for an online implementation. A simple test case of visual servoing on an electric linear axis is shown in order to prove the effectiveness of the study. However, the obtained results are also valid for general motion of typical robotic systems. Actually, the present work is preliminary to the visual servoing of minor and full mobility parallel kinematic machines.
image segmentation is a vital task in imageprocessing/computer vision. However, no universally accepted quality measure exists for evaluating the performance of various segmentation algorithms or even different param...
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ISBN:
(纸本)9781538618295
image segmentation is a vital task in imageprocessing/computer vision. However, no universally accepted quality measure exists for evaluating the performance of various segmentation algorithms or even different parameterizations of the same algorithm. This paper proposes a new segmentation evaluation measure, based on the fusion of HOG and Harris features, thus we call it the H2. It exploits local shape, corner and edge information to evaluate the similarity between a given segmentation and its respective ground truth, and thus belongs to the category of supervised evaluation measures. The results obtained from our experiments show accuracy of up to 95% for the H2.
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