Aiming at the problem that the existing license plate location methods can't work well in locating the license plate in certain natural scenes such as low luminance, low resolution and inclination scene of vehicle...
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ISBN:
(纸本)9781728152103
Aiming at the problem that the existing license plate location methods can't work well in locating the license plate in certain natural scenes such as low luminance, low resolution and inclination scene of vehicle, a license plate location method based on cascade classifier and convolution neural network is proposed. Firstly, the cascade classifier is used for license plate coarse location, and the convolution neural network is used for license plate precise location. Experiments show that, compared with cascade classifier, the method in this paper has higher accuracy and recall rate for target license plate location, and the method in this paper has strong adaptability to different environments.
Face recognition have been widely used in different industries due to the advancement of deep convolutional neural networks. Although deep learning has greatly promoted the development of face recognition technology, ...
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ISBN:
(数字)9781728143286
ISBN:
(纸本)9781728143293
Face recognition have been widely used in different industries due to the advancement of deep convolutional neural networks. Although deep learning has greatly promoted the development of face recognition technology, its computing-intensive and memory-intensive features make it difficult to deploy the model on some embedded devices or mobile computing platforms. Many solutions which include Knowledge Distillation have been proposed to increase the calculation speed of model and reduce the storage space required for calculations, in this paper, we propose a novel Two Stage Knowledge Distillation which enhances the performance of knowledge distillation in face recognition and low resolution face recognition. After experimenting on the several major face datasets, our method turns out to have better results compared to the traditional optimization methods.
Innovative methods to interact with electronic devices are attracting researchers attentions, and many applications of Human Computer Interaction have been developed in recent years. Hand detection under different ill...
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ISBN:
(数字)9781728143286
ISBN:
(纸本)9781728143293
Innovative methods to interact with electronic devices are attracting researchers attentions, and many applications of Human Computer Interaction have been developed in recent years. Hand detection under different illuminations and backgrounds is crucial for tasks such as, actions recognition, gestures recognition and hand-based interaction with wearable devices. Many existing methods are able to detect the human hand precisely, but the task still remains challenging due to environment complexity, different invariants, processing time, and accuracy. It is required for an application to recognize the human hand precisely for proper interaction with the wearable devices. This paper proposed a robust and a compact method for hand detection by employing you only look once (YOLO) and convolutional pos machines (CPM). This paper consists of two parts, network training and testing. In the first part, Oxford hand dataset is used to train a neural network. This dataset has various hand images under different invariants, and backgrounds. Afterwards, images are captured using a digital camera in real-time and then human hands are detected using a trained neural network. In addition, detected hands, positions and orientations are calculated accordance with defined active zone parameters and a depth sensor. Our trained neural network is dubbed as HHDNet, an abbreviation for human hand detection network. The HHDNet is tested under different environment conditions, and experimental results showed that the accuracy and the frame rate of the proposed model are superior than others state of the art methods.
Digital data of patients can aid a pathologist along the diagnostic process In Medical devices generate data sets that are processed by specialized computing applications, which often run on a single computer. The res...
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ISBN:
(纸本)9781538650356
Digital data of patients can aid a pathologist along the diagnostic process In Medical devices generate data sets that are processed by specialized computing applications, which often run on a single computer. The resolution power of the devices is increasing steadily and, consequently, the volumes of the data sets are also growing and can no longer be analyzed in a reasonable amount of time. Big Data tools like Apache Spark [2] provide methods for analyzing data, however, they are not directly applicable and need considerable implementation efforts, in general. Usually, well established analysis tools for medical data are designed to run on single workstations. These tools are not designed to meet current and future challenges. Migrating processing tools from single nodes to distributed environments is nontrivial. Moreover, partitioning data sets for a parallelprocessing is a further challenge [3]. In this work, we continue our efforts for improving the speedup of a bio-medical big data application further by partitioning the images of a Whole Slide image (WSI) [4] into sub tiles and by analyzing these sub tiles on a cluster of computer nodes. The idea is to benefit from the divide and conquer strategy. However, it is shown that the score parameter is determined incorrectly, when the software package is applied to each sub tile and the score parameters of all sub tiles are combined in an apparently natural manner. The cause of this anomaly is determined and a solution suggested. The original software is based on implicit assumptions. For example, the size of the tiles is assumed to be 1024 x 1024 px(2). The anomaly shows up when this constraint is reduced.
In this paper, we propose an identification framework to determine copyright infringement in the form of illegally distributed print-scan books in a large database. The framework contains following main stages: image ...
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In this paper, we propose an identification framework to determine copyright infringement in the form of illegally distributed print-scan books in a large database. The framework contains following main stages: image pre-processing, feature vector extraction, clustering, and indexing, and hierarchical search. The image pre-processing stage provides methods for alleviating the distortions induced by a scanner or digital camera. From the preprocessed image, we propose to generate feature vectors that are robust against distortion. To enhance the clustering performance in a large database, we use a clustering method based on the parallel-distributed computing of Hadobp MapReduce. In addition, to store the clustered feature vectors efficiently and minimize the searching time, we investigate an inverted index for fedture vectors. Finally, we implement a two-step hierarchical search to achieve fast and accurate on-line identification. In a simulation, the proposed identification framework shows accurate and robust in the presence of print-scan distortions. The processing time analysis in a parallel computing environment gives extensibility of the proposed framework to massive data. In the matching performance analysis, we empirically and theoretically find that in terms of query time, the optimal number of clusters scales with O(root N) for N print-scan books. (C) 2017 Elsevier Inc. All rights reserved.
Deep clustering attempts to capture the feature representation that benefits the clustering issue for inputs. Although the existing deep clustering methods have achieved encouraging performance in many research fields...
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ISBN:
(数字)9781728143286
ISBN:
(纸本)9781728143293
Deep clustering attempts to capture the feature representation that benefits the clustering issue for inputs. Although the existing deep clustering methods have achieved encouraging performance in many research fields, there still presents some shortcomings, such as the lack of consideration of local structure preservation and sparse characteristics of input data. To this end, we propose the deep-stacked sparse embedded clustering method in this paper, which considers both the preservation of local structure and sparse property of inputs. The proposed network is trained to capture the feature representation for input data by the guidance of clustering loss and reconstruction loss, where the reconstruction loss prevents the corruption of feature space and guarantees the local structure preservation. Besides, some sparse parameters are added to the encoder to avoid learning of meaningless features. Through simultaneously minimizing the reconstruction loss and cluster loss, the proposed network can jointly learn the clustering oriented feature and optimize the assignment of cluster labels. Then we conduct amounts of comparative experiments, which consist of six clustering methods and four publicly available data sets. Eventually, the clustering accuracy, adjusted rand index and normalized mutual information are utilized as three evaluation metrics to provide a comparison. Comprehensive experiments validate the effectiveness of introducing sparse property and preserving local structure in our method.
Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral imageprocessing. Anomaly detection methods based on low-rank and spars...
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Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral imageprocessing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase of hyperspectral image repositories, such techniques consume a significant amount of time (mainly due to the massive amount of matrix computations involved). In this paper, we propose a novel distributedparallel algorithm (DPA) by redesigning key operators of LRASR in terms of MapReduce model to accelerate LRASR on cloud computing architectures. Independent computation operators are explored and executed in parallel on Spark. Specifically, we reconstitute the hyperspectral images in an appropriate format for efficient DPA processing, design the optimized storage strategy, and develop a pre-merge mechanism to reduce data transmission. Besides, a repartitioning policy is also proposed to improve DPA's efficiency. Our experimental results demonstrate that the newly developed DPA achieves very high speedups when accelerating LRASR, in addition to maintaining similar accuracies. Moreover, our proposed DPA is shown to be scalable with the number of computing nodes and capable of processing big hyperspectral images involving massive amounts of data.
A novel distributed Particle Filter Algorithm with Resampling Tree, called DART, is proposed in this paper, where particles are resampled by Branch Resampling and Root Resampling in a flexible tree-like structure. Tho...
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A novel distributed Particle Filter Algorithm with Resampling Tree, called DART, is proposed in this paper, where particles are resampled by Branch Resampling and Root Resampling in a flexible tree-like structure. Though sampling and weight calculation can be executed in parallel on a group of processing Elements, resampling is the bottleneck for distributed particle filters since it requires the knowledge of the whole particle set. Conventional approaches to accelerate resampling on distributed platforms often introduce extra procedure other than the standard processing flow and achieve acceleration limited by linear speedup. By introducing the proposed algorithm, where Branch Resampling can be executed in parallel with sampling and weight calculation, the number of particles in the final sequential implemented Root Resampling can be reduced in an exponential relationship with the depth of the tree. With the same linear speedup in sampling and weight calculation steps, the overall speedup achieved in DART surpasses linear boundary and outperforms state-of-art approaches. The corresponding implementation architecture, which possesses unique features of hardware efficiency and scalability, is also presented. The prototype of the algorithm with 8 PEs is implemented on a Xilinx Virtex-iv Pro FPGA (XC4VFX100-12FF1152) under BOT system. With 8192 particles, the input observation can achieve 63.3 kHz at a clock speed of 80 MHz.
With the rapid development of information technology, the amount of remote sensing data is increasing at an unprecedented scale. In the presence of massive remote sensing data, the traditional processingmethods have ...
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With the rapid development of information technology, the amount of remote sensing data is increasing at an unprecedented scale. In the presence of massive remote sensing data, the traditional processingmethods have the problems of low efficiency and lack of scalability, so this paper uses open source big data technology to improve it. Firstly, the storage model of remote sensing image data is designed by using the distributed storage database HBase. Then, the grid index and the Hibert curve are combined to establish the index for the image data. Finally, the method of MapReduce parallelprocessing is used to write and query remote sensing images. The experimental results show that the method can effectively improve the data writing and query speed, and has good scalability.
image features are widely used for object identification in many situations, including interpretation of data containing natural scenes captured by unmanned aerial vehicles. This paper presents a parallel framework to...
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image features are widely used for object identification in many situations, including interpretation of data containing natural scenes captured by unmanned aerial vehicles. This paper presents a parallel framework to extract additive features (such as color features and histogram of oriented gradients) using the processing power of GPUs and multicore CPUs to accelerate the algorithms with the OpenCL language. The resulting features are available in device memory and then can be fed into classifiers such as SVM, logistic regression and boosting methods for object recognition. It is possible to extract multiple features with better performance. The GPU accelerated image integral algorithm speeds up computations up to 35x when compared to the single-thread CPU implementation in a test bed hardware. The proposed framework allows real-time extraction of a very large number of image features from full-HD images (better than 30 fps) and makes them available for access in coalesced order by GPU classification algorithms. (C) 2017 Elsevier Inc. All rights reserved.
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