Remote Sensing image Captioning (RSIC) is crucial for many researchers since it has many applications in environmental monitoring, disaster management, urban planning, image retrieval, performance of building planes, ...
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We propose a CNN-based framework for "real-time object detection and tracking using deep learning" in this paper, which includes a spatial–temporal mechanism. The impact of efficient data on performance ben...
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Low-light image enhancement is an effective solution for improving image recognition by both humans and machines. Due to low illuminance, images captured in such conditions possess less color information compared to t...
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Low-light image enhancement is an effective solution for improving image recognition by both humans and machines. Due to low illuminance, images captured in such conditions possess less color information compared to those taken in daylight, resulting in occluded images characterized by distortion, low contrast, low brightness, a narrow gray range, and noise. Low-light image enhancement techniques play a crucial role in enhancing the effectiveness of object detection. This paper reviews state-of-the-art low-light image enhancement techniques and their developments in recent years. Techniques such as gray transformation, histogram equalization, defogging, Retinex, image fusion, and wavelet transformation are examined, focusing on their working principles and assessing their ability to improve image quality. Further discussion addresses the contributions of deep learning and cognitive approaches, including attention mechanisms and adversarial methods, to image enhancement.
In the rapidly evolving landscape of computer vision and artificial intelligence, transfer learning has emerged as a powerful tool for efficiently applying pre-trained models to new tasks. This article delves into the...
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Light propagating through a nonuniform medium scatters as it interacts with particles with different refractive properties such as cells in the tissue. In this work we aim to utilize this scattering process to learn a...
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Light propagating through a nonuniform medium scatters as it interacts with particles with different refractive properties such as cells in the tissue. In this work we aim to utilize this scattering process to learn a volumetric reconstruction of scattering parameters, in particular particle densities. We target microscopy applications where coherent speckle effects are an integral part of the imaging process. We argue that the key for successful learning is modeling realistic speckles in the training process. To this end, we build on the development of recent physically accurate speckle simulators. We also explore how to incorporate speckle statistics, such as the memory effect, in the learning framework. Overall, this paper contributes an analysis of multiple aspects of the network design including the learning architecture, the training data and the desired input features. We hope this study will pave the road for future design of learning based imaging systems in this challenging domain. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
Nonlinear estimation in robotics and vision is typically plagued with outliers due to wrong data association or incorrect detections from signal processing and machine learning methods. This article introduces two uni...
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Nonlinear estimation in robotics and vision is typically plagued with outliers due to wrong data association or incorrect detections from signal processing and machine learning methods. This article introduces two unifying formulations for outlier-robust estimation, generalized maximum consensus ($\textrm{G}$-$\textrm{MC}$) and generalized truncated least squares ($\textrm{G-TLS}$), and investigates fundamental limits, practical algorithms, and applications. Our first contribution is a proof that outlier-robust estimation is inapproximable: In the worst case, it is impossible to (even approximately) find the set of outliers, even with slower-than-polynomial-time algorithms (particularly, algorithms running in quasi-polynomial time). As a second contribution, we review and extend two general-purpose algorithms. The first, adaptive trimming ($\textrm{ADAPT}$), is combinatorial and is suitable for $\textrm{G}$-$\textrm{MC}$;the second, graduated nonconvexity ($\textrm{GNC}$), is based on homotopy methods and is suitable for $\textrm{G-TLS}$. We extend $\textrm{ADAPT}$ and $\textrm{GNC}$ to the case where the user does not have prior knowledge of the inlier-noise statistics (or the statistics may vary over time) and is unable to guess a reasonable threshold to separate inliers from outliers (as the one commonly used in RANdom SAmple Consensus $(\textrm{RANSAC})$. We propose the first minimally tuned algorithms for outlier rejection, which dynamically decide how to separate inliers from outliers. Our third contribution is an evaluation of the proposed algorithms on robot perception problems: mesh registration, image-based object detection (shape alignment), and pose graph optimization. $\textrm{ADAPT}$ and $\textrm{GNC}$ execute in real time, are deterministic, outperform $\textrm{RANSAC}$, and are robust up to 80-90% outliers. Their minimally tuned versions also compare favorably with the state of the art, even though they do not rely on a noise bound for the inliers.
CNN is inspired from Primary Visual (V1) neurons. It is a typical deep learning technique and can help teach machine how to see and identify objects. In the most recent decade, deep learning develops rapidly and has b...
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CNN is inspired from Primary Visual (V1) neurons. It is a typical deep learning technique and can help teach machine how to see and identify objects. In the most recent decade, deep learning develops rapidly and has been well used in various fields of expertise such as computer vision and natural language processing. As the representative algorithm of deep learning, Convolution Neural Network (CNN) has been regarded as a breakthrough of historic significance in imageprocessing and visual recognition tasks since the astonishing results achieved on imageNet Large Scale Visual Recognition Competition (ILSVRC) Unlike methods based on handcrafted features, CNN models can build high-level features from low-level ones in a data-driven fashion and have displayed great potential in medical image analysis among the aspects of segmentation of histological images identification, lesion detection, tissue classification, etc. This paper provides a review on CNN from the perspectives of its basic mechanism introduction, structure, typical architecture and main application in medical image analysis through analyzing over 100 references from Google Scholar, PubMed, Web of Science and various sources published from 1958 to 2020.
In high-energy physics, the capability to accurately and efficiently track charged particles is essential for effective data analysis. This article introduces an innovative density-based clustering pipeline intended f...
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ISBN:
(数字)9798350355413
ISBN:
(纸本)9798350355420
In high-energy physics, the capability to accurately and efficiently track charged particles is essential for effective data analysis. This article introduces an innovative density-based clustering pipeline intended for the track reconstruction task, incorporating Density-Based Spatial Clustering of applications with Noise (DBSCAN) algorithm and Ordering Points To Identify the Clustering Structure (OPTICS) algorithm. Results on simulated data suggest that the proposed method offers improvements in both effectiveness and robustness compared to traditional techniques, with performance on par with state-of-the-art neural network-based approaches. Furthermore, this pipeline demonstrates significant potential for real-time applications in high-energy physics experiments, offering a scalable and robust solution.
Automatic target tracking in emerging remote sensing video-generating tools based on microwave imaging technology and radars has been investigated in this paper. A moving target tracking system is proposed to be low c...
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Automatic target tracking in emerging remote sensing video-generating tools based on microwave imaging technology and radars has been investigated in this paper. A moving target tracking system is proposed to be low complexity and fast for implementation through edge nodes in a mini-satellite or drone network enabling machine intelligence into large-scale vision systems, in particular, for marine transportation systems. The system uses a group of imageprocessing tools for video pre-processing, and Kalman filtering to do the main task. For testing the system performance, two measures of accuracy and false alarms probability are computed for real vision data. Two types of scenes are analyzed including the scene with single target, and the scene with multiple targets that is more complicated for automatic target detection and tracking systems. The proposed system has achieved a high performance in our tests.
In this work, we propose Asynchronous Perception machine (APM), a computationally-efficient architecture for test-time-training (TTT). APM can process patches of an image one at a time in any order asymmetrically, and...
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