This paper deals with the design and implementation of a parallel software system based on Differential Evolution for the registration of images, and with its testing on two bidimensional remotely sensed images on mos...
详细信息
ISBN:
(纸本)9780769527840
This paper deals with the design and implementation of a parallel software system based on Differential Evolution for the registration of images, and with its testing on two bidimensional remotely sensed images on mosaicking problem. Registration is carried out by finding the most suitable affine transformation in terms of maximization of the mutual information between the first image and the transformation of the second one, without any need for setting control points. A coarse-grained distributed version is implemented on a cluster of personal computers.
Particle-In-Cell (PIC) methods have been widely used for plasma physics simulations in the past three decades. To ensure an acceptable level of statistical accuracy relatively large numbers of particles are needed. St...
详细信息
Particle-In-Cell (PIC) methods have been widely used for plasma physics simulations in the past three decades. To ensure an acceptable level of statistical accuracy relatively large numbers of particles are needed. State-of-the-art Graphics processing Units (GPUs), with their high memory bandwidth. hundreds of SPMD processors, and half-a-teraflop performance potential, offer a viable alternative to distributed memory parallel computers for running medium-scale PICplasma simulations on inexpensive commodity hardware. In this paper, we present an overview of a typical plasma PIC code and discuss its GPU implementation. In particular we focus on fast algorithms for the performance bottleneck operation of Particle-To-Grid interpolation. (c) 2008 Elsevier Inc. All rights reserved.
Streaming environments and similar parallel platforms are widely used in image, signal, or general data processing as a means of achieving high performance. Unfortunately, they are often associated with specific progr...
详细信息
ISBN:
(纸本)9781509060580
Streaming environments and similar parallel platforms are widely used in image, signal, or general data processing as a means of achieving high performance. Unfortunately, they are often associated with specific programming languages and, thus, hardly accessible for non-experts. In this paper, we present a framework for transformation of a C# procedural code to a Hybrid Flow Graph - a novel intermediate code which employs the streaming paradigm and can be further converted into a streaming application. This approach will allow creating streaming applications or their parts using a widely known imperative language instead of an intricate language specific to streaming. In this paper, we focus on the transformation of control flow which represents the main difference between procedural code, driven by control flow constructs, and streaming environments, driven by data. Since the use of a streaming platform automatically enables parallelism and vectorization, we were able to demonstrate that the streaming applications generated by our method may outperform their original C# implementation.
In recent years, methods based on deep learning have become a hot spot and trend in the field of remote sensing object detection, and a series of encouraging results have been achieved. With the development of science...
详细信息
ISBN:
(纸本)9781665441957
In recent years, methods based on deep learning have become a hot spot and trend in the field of remote sensing object detection, and a series of encouraging results have been achieved. With the development of science and technology, people's technology and ability to acquire remote sensing data have been comprehensively improved, and high-resolution large-scale remote sensing image data has increased dramatically. However, the current mainstream object detection models cannot directly input large-scale high-resolution images for prediction. This paper proposes a remote sensing object parallel detection algorithm, which uses the mpi4py module to realize multi-CPU+GPU distributedparallelprocessing. Based on the yolov4 object detection model, it can detect remote sensing images of any scale without reducing the prediction accuracy. And shorten the object detection time according to the number of distributed nodes. The experimental results show that the parallel algorithm has a high speedup ratio, and the parallel detection technology has a good development prospect in the field of remote sensing image object detection.
Programs that operate over recursive data structures may contain potential parallel computations. Writing parallel programs, even when aided by parallel skeletons, is very challenging, requires intricate analysis of t...
详细信息
ISBN:
(纸本)9781467387767
Programs that operate over recursive data structures may contain potential parallel computations. Writing parallel programs, even when aided by parallel skeletons, is very challenging, requires intricate analysis of the underlying algorithm and often uses inefficient intermediate data structures. Very few automated parallelisation methods that address a wide range of programs and data types exist. In this paper, we present a transformation method for functional programs defined over any recursive data types. Our method encodes the inputs of a program so that the transformed program is more likely to contain instances of polytypic fold skeletons, and less likely to contain inefficient intermediate data structures. With parallel implementations for these skeletons, the transformed programs can potentially be evaluated on hardware such as multi-core CPUs and/or GPUs.
The paper describes a distributed spectral-screening PCT algorithm for fusing hyper-spectral images in remote sensing applications. The algorithm provides intrusion tolerance from information warfare attacks using the...
详细信息
ISBN:
(纸本)0769507719
The paper describes a distributed spectral-screening PCT algorithm for fusing hyper-spectral images in remote sensing applications. The algorithm provides intrusion tolerance from information warfare attacks using the notion of computational resiliency. This concept uses replication to achieve fault tolerance, bur goes further to dynamically regenerate replication in response to an attack or failure. The concepts of resiliency are incorporated through library technology that is application independent. This library hides the details of communication protocols required to achieve dynamic replication and reconfiguration in distributed applications. The paper provides a status report on our progress in del,eloping the concept and applying it to image fusion. In particular we examine the performance of the PCT algorithm and compare the results with and without resiliency to assess the associated overheads.
Various methods have been proposed for enhancing the images. Some of those perform well in some specific application areas but most of the techniques suffer from artifacts due to over enhancement. To overcome this pro...
详细信息
ISBN:
(纸本)9781509022397
Various methods have been proposed for enhancing the images. Some of those perform well in some specific application areas but most of the techniques suffer from artifacts due to over enhancement. To overcome this problem, we have introduced a new image enhancement technique namely Bilateral Histogram Equalization with Pre-processing (BHEP) which uses Harmonic mean to divide the histogram of the image. We have performed both qualitative and quantitative measurements for experiments and the results show that BHEP creates less artifacts in several standard images than the existing state-of-the-art image enhancement techniques.
This paper presents a novel concept for resource management in cluster-based image retrieval systems. First, the paper describes image retrieval using static and dynamic feature extraction. The complexity of dynamic f...
详细信息
ISBN:
(纸本)9781932415582
This paper presents a novel concept for resource management in cluster-based image retrieval systems. First, the paper describes image retrieval using static and dynamic feature extraction. The complexity of dynamic feature extraction requires the utilization of powerful parallel architectures and in order to provide the user with reasonable response times. Most existing methods for resource management in parallelimage retrieval systems are based on sinlge query execution and do not take quality of service (QoS) aspects into account. This appears not to be practical in large-scale and commercial applications of image databases having a large number of users at any time. In order to allow an efficient utilization of the parallel system and to meet user-defined QoS demands associated with queries, we need to develop a new concept and a novel resource management architecture. Interesting aspects of the model include utility theory, flexible computations, QoS levels, and a hierarchical resource management architecture. Finally, an approach for algorithmic solution is described.
In this paper, a Hadoop MapReduce framework is presented in order to perform distributedprocessing used for CBIR system. Moreover, Hadoop MapReduce framework is used with the intention of increasing the performance o...
详细信息
ISBN:
(纸本)9781479922574
In this paper, a Hadoop MapReduce framework is presented in order to perform distributedprocessing used for CBIR system. Moreover, Hadoop MapReduce framework is used with the intention of increasing the performance of two main functionalities of data insertion and query processing. Therefore, the main objective of the study is distribution of the image data over a large number of nodes. Some of the techniques used in the paper includes: image indexing and retrieval, parallelprocessing of indexing, and comparing the similarity of retrieved images
image clustering is one of the challenging tasks in machine learning, and has been extensively used in various applications. Recently, various deep clustering methods has been proposed. These methods take a two-stage ...
详细信息
ISBN:
(纸本)9781479970612
image clustering is one of the challenging tasks in machine learning, and has been extensively used in various applications. Recently, various deep clustering methods has been proposed. These methods take a two-stage approach, feature learning and clustering, sequentially or jointly. We observe that these works usually focus on the combination of reconstruction loss and clustering loss, relatively little work has focused on improving the learning representation of the neural network for clustering. In this paper, we propose a deep convolutional embedded clustering algorithm with inception-like block (DCECI). Specifically, an inception-like block with different type of convolution filters are introduced in the symmetric deep convolutional network to preserve the local structure of convolution layers. We simultaneously minimize the reconstruction loss of the convolutional autoencoders with inception-like block and the clustering loss. Experimental results on multiple image datasets exhibit the promising performance of our proposed algorithm compared with other competitive methods.
暂无评论