Multi-image optical encryption (MOE) has demonstrated promising potential in image data protection owing to its parallelprocessing capability and abundant degrees of freedom. However, existing methods suffer from eit...
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Multi-image optical encryption (MOE) has demonstrated promising potential in image data protection owing to its parallelprocessing capability and abundant degrees of freedom. However, existing methods suffer from either low compression ratios or stringent experimental conditions, such as accurate calibration of phase modulation, precise manufacturing of encryption elements, and no ambient light interference. This work introduces a lensless sparse point spread function-based multi-image optical encryption (sPSF-MOE) technique that addresses these challenges and enhances performance. In the encryption process, each plaintext image is encoded using a sparsely distributed PSF with specifically designed geometric shapes through spatial phase engineering. The resulting ciphertexts are superimposed to produce a compressed ciphertext. During decryption, an iterative algorithm recovers encrypted images with improved reconstruction quality. We show that sPSF-MOE ensures high fidelity for binary (gray-scale) images at a compression ratio of 12 (6) and resists autocorrelation-based attacks. Integrating principal component analysis (PCA) into decryption preserves image high fidelity under ambient light interference. sPSF-MOE reduces the bandwidth requirement for data transmission while ensuring data integrity.
To accurately evaluate the patient's condition, medical workers usually need to register multiple pathological images of the lesion site samples. Using computer technology to assist in registration work can effect...
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ISBN:
(纸本)9798350391961;9798350391954
To accurately evaluate the patient's condition, medical workers usually need to register multiple pathological images of the lesion site samples. Using computer technology to assist in registration work can effectively improve the efficiency of doctors analyzing pathological images. One of the most advanced methods currently is the Virtual Alignment of Pathology image Series method, which is a multi-staining digital pathology image registration method that combines global and local calculations. However, this method may encounter certain biases when processingimages with significant angle differences. Through a detailed analysis of this method, this article proposes an improvement plan which optimizes the acquisition of non-rigid registration mask images, enabling the method to obtain mask images more reasonably and achieve better registration results for images with significant angle differences. This provides more accurate judgment basis and helps doctors diagnose and develop treatment plans more accurately.
Decentralized strategies are of interest for learning from large-scale data over networks. This paper studies learning over a network of geographically distributed nodes/agents subject to quantization. Each node posse...
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Decentralized strategies are of interest for learning from large-scale data over networks. This paper studies learning over a network of geographically distributed nodes/agents subject to quantization. Each node possesses a private local cost function, collectively contributing to a global cost function, which the considered methodology aims to minimize. In contrast to many existing papers, the information exchange among nodes is log-quantized to address limited network-bandwidth in practical situations. We consider a first-order computationally efficient distributed optimization algorithm (with no extra inner consensus loop) that leverages node-level gradient correction based on local data and network-level gradient aggregation only over nearby nodes. This method only requires balanced networks with no need for stochastic weight design. It can handle log-scale quantized data exchange over possibly time-varying and switching network setups. We study convergence over both structured networks (for example, training over data-centers) and ad-hoc multi-agent networks (for example, training over dynamic robotic networks). Through experimental validation, we show that (i) structured networks generally result in a smaller optimality gap, and (ii) log-scale quantization leads to a smaller optimality gap compared to uniform quantization. Note to Practitioners-Motivated by recent developments in cloud computing, parallelprocessing, and the availability of low-cost CPUs and communication networks, this paper considers distributed and decentralized algorithms for machine learning and optimization. These algorithms are particularly relevant for decentralized data mining, where data sets are distributed across a network of computing nodes. A practical example of this is the classification of images over a networked data centre. In real-world scenarios, practical model nonlinearities such as data quantization must be addressed for information exchange among the computing nodes. T
image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignm...
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Quantum network is an emerging type of network structure that leverages the principles of quantum mechanics to transmit and process information. Compared with classical data reconstruction algorithms, quantum networks...
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ISBN:
(纸本)9798350364613;9798350364606
Quantum network is an emerging type of network structure that leverages the principles of quantum mechanics to transmit and process information. Compared with classical data reconstruction algorithms, quantum networks make image reconstruction more efficient and accurate. They can also process more complex image information using fewer bits and faster parallel computing capabilities. Therefore, this paper will discuss image reconstruction methods based on our quantum network and explore their potential applications in imageprocessing. We will introduce the basic structure of the quantum network, the process of image compression and reconstruction, and the specific parameter training method. Through this study, we can achieve a classical image reconstruction accuracy of 97.57%. Our quantum network design will introduce novel ideas and methods for image reconstruction in the future.
Visual Question Answering (VQA) is a challenging task that bridges the computer vision and natural language processing communities. It provide natural language answers to questions related to an associated image. Most...
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Visual Question Answering (VQA) is a challenging task that bridges the computer vision and natural language processing communities. It provide natural language answers to questions related to an associated image. Most existing VQA methods focus on the fusion and inference of visual features with the textual question. However, visual features often lack the necessary semantic information required to answer the questions accurately. To address this limitation, we propose a novel approach called Question-Guided parallel Attention (QGPA), which effectively leverages the semantic information provided by an embedded image captioning model to answer related questions. First, we introduce an Attention-Aware (AA) mechanism that extends the traditional attention mechanism, helping to filter out incorrect or irrelevant information during answer prediction. Second, QGPA incorporates AA, which simultaneously utilizes visual features and semantic information from the embedded image captioning model to answer questions. Experiments results demonstrate that the accuracy of "Overall" of our proposed model delivers 72.57% and 72.55% on the test-dev and test-std split set of VQA-v2.0 dataset, respectively, which outperforms most existing VQA methods. The experiment results and ablation studies demonstrate that the proposed method has good performance.
Research in the autonomous underwater detection system has become rapidly increasing in Ocean Technology. In a recent object detection research study, there a need to enhance the quality, which needs to handle submerg...
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Research in the autonomous underwater detection system has become rapidly increasing in Ocean Technology. In a recent object detection research study, there a need to enhance the quality, which needs to handle submerged object imageprocessing techniques and a lot of demand to develop an intelligent vision system to improve the Blurred images and low-quality illumination. Manual research in undersea water leads to more significant pressures and complex environments in cost and workforce. It is necessary to develop a high acceptable autonomous image quality system to upgrade image quality. This paper proposed two approaches: (i) Gray shade and Max-RGB filter techniques to improve image quality. (ii) For optimization and low illumination problem modified Convolution Neural Technique (CNN) incorporated for classification and detection. Moreover, our proposed model has compared with Single-shot Detector (SDD), You Only Look Once (Yolo), Fast RCNN, Faster RCNN to uphold the quality detection found objects. This research article aids to found real-time underwater objects classification and detection. It helps to incorporate an Autonomous operation Vehicle (AOV) underwater research. Our experiment results show detection runs speed as 30 FPs (Frame per second).
Hand Gesture Recognition (HGR) plays a crucial role in user-friendly interactions between humans and computers. In recent years, using the Convolutional Neural Network (CNN) has improved the accuracy of image processi...
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Hand Gesture Recognition (HGR) plays a crucial role in user-friendly interactions between humans and computers. In recent years, using the Convolutional Neural Network (CNN) has improved the accuracy of imageprocessing problems. Inspired by the high recognition rate of CNN and its efficiency, we propose a model for hand gesture recognition based on CNN and evaluate the results using images with plain and complex backgrounds. Recognizing different hand signs by Two-Dimensional parallel Spatio-Temporal Pyramid Pooling (2DPSTPP) features with deep learning methods reduces the size of the map, minimizes training complexity, and by paying attention to more details, improves detection performance. The effectiveness of the proposed method is evaluated using regular cross-validation tests on six datasets, namely American Sign Language (ASL), the NUS hand posture dataset I, the NUS hand posture dataset ii, the digits dataset, the hand gesture dataset, and the leap gesture recognition dataset.
Few-shot image classification is a challenging task that aims to recognize image classes based on only a few training images. However, existing methods face the following two main challenges: (1) Ignoring the frequenc...
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The exponential growth of technological advancements in satellite and airborne remote sensing is giving rise to large volumes of high-dimensional hyperspectral image data. Apache Spark is one of the most popular, exte...
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