Hyperspectral (HS) imaging systems are useful in a diverse range of applications that involve detection and classification tasks. However, the low spatial resolution of hyperspectral images may limit the performance o...
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ISBN:
(纸本)9789082797039
Hyperspectral (HS) imaging systems are useful in a diverse range of applications that involve detection and classification tasks. However, the low spatial resolution of hyperspectral images may limit the performance of the involved tasks in such applications. In the last years, fusing the information of a HS image with high spatial resolution multispectral (MS) or panchromatic (PAN) images has been widely studied to enhance the spatial resolution. image fusion has been formulated as an inverse problem whose solution is a HS image which assumed to be sparse in an analytic or learned dictionary. This work proposes a non-local centralized sparse representation model on a set of learned dictionaries in order to regularize the conventional fusion problem. The dictionaries are learned from the observed data taking advantage of the high spectral correlation within the HS image and the non-local self-similarity over the spatial domain of the MS image. Then, conditionally on these dictionaries, the fusion problem is solved by an alternating iterative numerical algorithm. Experimental results with real data show that the proposed method outperforms the state-of-the-art methods under different quantitative assessments.
Aiming at the problem that it is difficult to realize the seamless positioning in the complex indoor and outdoor positioning environment, a fusion positioning algorithm based on convolutional neural network is propose...
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ISBN:
(数字)9781728157337
ISBN:
(纸本)9781728157344
Aiming at the problem that it is difficult to realize the seamless positioning in the complex indoor and outdoor positioning environment, a fusion positioning algorithm based on convolutional neural network is proposed. Using its strong ability of imageprocessing, the target positioning is obtained by transforming the positioning data fusion processing into solving the image classification, and the algorithm is applied to the UWB/BLE/BDS seamless positioning system. In the transition region, the positioning points obtained from a single subsystem are filtered first to form a coordinate map, which is input into the convolution neural network with supervised learning for training, subsequently, the test data is processed and input into the trained network to obtain the fusion weights based on convolution neural network in the actual measurement stage, computing out the positioning coordinates finally. The experimental results show that the average positioning accuracy of the system in the transition region processed by the weighted fusion algorithm based on convolution neural network is improved by 42.9% than that of the average weighted fusion algorithm.
The article presents the concept of how to use convolutional networks as a method for processing digital images acquired in visible region of light for the needs of smoke detection in large open area. The meaning and ...
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ISBN:
(数字)9781510627864
ISBN:
(纸本)9781510627864
The article presents the concept of how to use convolutional networks as a method for processing digital images acquired in visible region of light for the needs of smoke detection in large open area. The meaning and consequences of massive blaze were underlined on the basis of statistical data concerning the forest fires. The proposal to overcome the difficulties in using traditional methods for detection of fire threat by imageprocessing techniques was discussed. The idea, inner structure and properties of a convolutional neural network as a tool for automatic feature generation and image recognition were presented. The algorithms of data processing used in vision systems for fire detection were analyzed including the solutions implementing the networks. On the basis of the analysis the proposal to develop a neural network for smoke detection with the use of the strategy called transfer learning was presented. Using the image base of fires available on the web, the quantified assessment of the proposed approach was conducted. In the research the AlexNet framework was adopted to recognize smoke in images. The processing of the net was illustrated with examples of activations of selected layers when fed with images containing smoke. The 99% sensitivity reached by the proposed processing together with the 1% of false alarm rate seems to be very promising for the system of fire surveillance based on watchtowers or air vessels monitoring large open areas.
Per-image performance of a certain visual perception algorithm is the combination of per-task performances in the image. Based on the black box test, we address to discover and explain the potential shortness of the e...
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ISBN:
(数字)9781728176840
ISBN:
(纸本)9781728176857
Per-image performance of a certain visual perception algorithm is the combination of per-task performances in the image. Based on the black box test, we address to discover and explain the potential shortness of the evaluated intelligent algorithms/systems at the fine-grained task-level by human knowledge. By assuming the domain knowledge in visual tasks could be represented by a latent vector which is a sparse embedding of the catenated object-level and image-level features, we propose a latent dictionary learning framework for joint latent knowledge representation and knowledge-output regression at task level. In this way, so we can use semantic concepts to explain the relationship between test cases and test results. The experiments validate the idea of task-level explainable AI evaluation initially as well as the effectiveness of proposed method.
In the literature, many chaotic systems have been used in the design of image encryption algorithms. In this study, an application of fractional order chaotic systems is investigated. The aim of the study is to improv...
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ISBN:
(纸本)9781728122090
In the literature, many chaotic systems have been used in the design of image encryption algorithms. In this study, an application of fractional order chaotic systems is investigated. The aim of the study is to improve the disadvantageous aspects of existing methods based on discrete and continuous time chaotic systems by utilizing the features of fractional order chaotic systems. The most important advantage of the study compared to the literature is that the proposed encryption algorithm is designed with a provable security approach. Analyses results have been shown that the proposed method can be used successfully in many information security applications.
Neural networks have become the dominant algorithms rapidly as they achieve state-of-the-art performance in a broad range of applications such as image recognition, speech recognition, and natural language processing....
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Neural networks have become the dominant algorithms rapidly as they achieve state-of-the-art performance in a broad range of applications such as image recognition, speech recognition, and natural language processing. However, neural networks keep moving toward deeper and larger architectures, posing a great challenge to hardware systems due to the huge amount of data and computations. Although sparsity has emerged as an effective solution for reducing the intensity of computation and memory accesses directly, irregularity caused by sparsity (including sparse synapses and neurons) prevents accelerators from completely leveraging the benefits, i.e., it also introduces costly indexing module in accelerators. In this article, we propose a cooperative software/hardware approach to address the irregularity of sparse neural networks efficiently. Initially, we observe the local convergence, namely larger weights tend to gather into small clusters during training. Based on that key observation, we propose a software-based coarse-grained pruning technique to reduce the irregularity of sparse synapses drastically. The coarse-grained pruning technique, together with local quantization, significantly reduces the size of indexes and improves the network compression ratio. We further design a multi-core hardware accelerator, Cambricon-SE, to address the remaining irregularity of sparse synapses and neurons efficiently. The novel accelerator have three key features: 1) selector modules to filter unnecessary synapses and neurons, 2) compress/decompress modules for exploiting the sparsity in data transmission (which is rarely studied in previous work), and 3) a multi-core architecture with elevated throughput to meet the real-time processing requirement. Compared against a state-of-the-art sparse neural network accelerator, our accelerator is 1.20x and 2.72x better in terms of performance and energy efficiency, respectively. Moreover, for real-time video analysis tasks, Cambricon-SE
image watermarking systems are frequently used tools for copyright protection against unauthorized use of images in unsecured spaces. The conventional method is to embed a copyright mark (watermark) in the original im...
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ISBN:
(数字)9781728180410
ISBN:
(纸本)9781728180427
image watermarking systems are frequently used tools for copyright protection against unauthorized use of images in unsecured spaces. The conventional method is to embed a copyright mark (watermark) in the original image. However, this strategy is not suitable for sensitive images such as medical, satellite, texture and remote sensing images, etc., because the integrated watermark strongly affects the results of its application tasks. For copyright protection of this type of images, this paper proposes a robust blind zero-watermarking algorithm based on Krawtchouk Radial Moments and a chaotic system. This algorithm does not integrate any information into the original image and satisfactorily ensures robustness against various common imageprocessing attacks and geometric distortions. The experimental study uses different categories of images to evaluate and compare the proposed algorithm with other watermarking and zero-watermarking algorithms in terms of robustness against various image attacks.
Emotion Recognition is a critical component in the evolution of Human Computer Interaction (HCI). The advancement of Artificial Intelligent (AI) has enabled human and computer to achieve a symbiotic partnership where ...
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ISBN:
(纸本)9781538683965
Emotion Recognition is a critical component in the evolution of Human Computer Interaction (HCI). The advancement of Artificial Intelligent (AI) has enabled human and computer to achieve a symbiotic partnership where computers can understand human emotions (e.g., happy or sad) so that computer systems can adapt to human emotion reaction. Detecting and recognizing optimum human emotion is the first step towards this human and computer symbiosis. This paper first studies and compares different emotion recognition Machine Learning algorithms. It then proposes the application of personalized emotion recognition learning;where a machine learns the emotions of a specific user to achieve higher emotion recognition accuracy.
We propose small ball tracking with trajectory prediction to track them when the athletes playing with balls at sports field, as shown in Figure 1. This is a challenging task and important for intelligent physical edu...
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ISBN:
(数字)9781728168968
ISBN:
(纸本)9781728168975
We propose small ball tracking with trajectory prediction to track them when the athletes playing with balls at sports field, as shown in Figure 1. This is a challenging task and important for intelligent physical education, especially for the coaches to grasp the accuracy of actions by the players based on the pre-defined rules. The proposed method achieves a good performance on small ball tracking since the designed algorithm incorporates motion, temporal and directional information to predict the trajectory. Experimental results show that the proposed method effectively reduces the number of identity switches and decreases track fragmentation. With the integration of motion, directional information and frames storage, this framework efficiently track small balls when the athletes playing with them.
Recently, imageprocessing (IP) and Machine learning (ML) algorithms have been successfully used in a wide variety of industry sectors. In this paper, we first provide mining engineers with the state of the art about ...
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