Low light image enhancement is an important task in computer vision. The Low-light images can adversely affect more advanced computer vision tasks such as target tracking, image segmentation, and target recognition du...
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ISBN:
(数字)9798350368888
ISBN:
(纸本)9798350368895
Low light image enhancement is an important task in computer vision. The Low-light images can adversely affect more advanced computer vision tasks such as target tracking, image segmentation, and target recognition due to the presence of large amounts of noise and low contrast. Therefore, research on low-light image enhancement is crucial. To address these issues, this paper delves into the comparison between traditional and non-traditional low-light image enhancement algorithms and has achieved the following results:We abandon the traditional approach based on a priori knowledge of images and instead redesign a new network structure for low-light image enhancement with the existing theoretical foundation for Transformer. The network performs information extraction in the channel dimension of the image through self-attention mechanism and then extracts the shallow feature information of the image through Shallow layer. After fusing these two types of information, more advanced information is extracted to guide the final image enhancement. Experimental results show that the redesigned network structure not only achieves better results but also has improved execution efficiency.
In this paper, the following hypothesis is validated—"Alternate image representations, of the same image, that preserve visual perception do not degrade the classification accuracy". The underlying phenomen...
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The imaging for small unmanned aerial vehicles (UAVs) in infrared imaging systems is easily disturbed when they shuttle between different surrounding regions in a large background, resulting in target contrast inversi...
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Travelling by roads is the most common and oldest way to reach the destination. The fast speed vehicles are growing each day. Many automobile industries are working on development of fast speed vehicle with various se...
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The proceedings contain 36 papers. The special focus in this conference is on Discrete Geometry and Mathematical Morphology. The topics include: Topological Analysis of Simple Segmentation Maps;on the Decomp...
ISBN:
(纸本)9783031198960
The proceedings contain 36 papers. The special focus in this conference is on Discrete Geometry and Mathematical Morphology. The topics include: Topological Analysis of Simple Segmentation Maps;on the Decomposability of Homogeneous Binary Planar Configurations with Respect to a Given Exact Polyomino;Properties of SAT Formulas Characterizing Convex Sets with Given Projections;Morphological Counterpart of Ornstein–Uhlenbeck Semigroups and PDEs;a Novel Approach for Computation of Morphological Operations Using the Number Theoretic Transform;Equivariance-Based Analysis of PDE Evolutions Related to Multivariate Medians;morphological Adjunctions Represented by Matrices in Max-Plus Algebra for signal andimageprocessing;a Topological Tree of Shapes;component-Tree Simplification Through Fast Alpha Cuts;hybrid Artificial Intelligence for Knowledge Representation and Model-Based Medical image Understanding - Towards Explainability;component Tree Loss Function: Definition and Optimization;fast and Effective Superpixel Segmentation Using Accurate Saliency Estimation;join, Select, and Insert: Efficient Out-of-core algorithms for Hierarchical Segmentation Trees;graph-Based image Segmentation with Shape Priors and Band Constraints;differential Oriented image Foresting Transform Segmentation by Seed Competition;tangential Cover for 3D Irregular Noisy Digital Curves;a Curious Invariance Property of Certain Perfect Legendre Arrays: Stirring Without Mixing;a Simple Discrete Calculus for Digital Surfaces;distance-Driven Curve-Thinning on the Face-Centered Cubic Grid;a New Lattice-Based Plane-Probing Algorithm;digital Geometry, Mathematical Morphology, and Discrete Optimization: A Survey;exact and Optimal Conversion of a Hole-free 2d Digital Object into a Union of Balls in Polynomial Time;density Functions of Periodic Sequences;approximation of Digital Surfaces by a Hierarchical Set of Planar Patches;introduction to Discrete Soft Transforms;on the Validity of the Two Raster Sequences D
The proliferation of deepfakes-also termed artificial intelligence-generated synthetic media poses unprecedented challenges to the digital authenticity of media, for media integrity, and to societies’ trust in its cr...
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ISBN:
(数字)9798331530389
ISBN:
(纸本)9798331530396
The proliferation of deepfakes-also termed artificial intelligence-generated synthetic media poses unprecedented challenges to the digital authenticity of media, for media integrity, and to societies’ trust in its credibility. Despite such significant technological advancements, current methodologies for detecting deepfakes remain somewhat fragmented, reactive, and often unable to keep pace with rapidly evolving technologies in generative AI. This article considers a comprehensive multi-modal approach toward deepfake detection by integrating an advanced machine learning algorithm, novel statistical correlation techniques, and forensic image analysis that would fill in the critical gaps in the existing frameworks. By using an extensive dataset of 10,000 synthetic and authentic media samples from diverse domains, we developed a hybrid neural network architecture that attained 94.3% accuracy on identifying AI-generated content. Our methodology utilized ensemble learning by combining spatial-temporal inconsistency detection, biological signal analysis, andadvanced feature extraction algorithms. Key findings demonstrate significant vulnerabilities in current deepfake generation models, with our proposed technique successfully identifying subtle artifacts and inconsistencies across multiple generative AI platforms. This research conclusively demonstrates that proactive, adaptive detection strategies are critical to mitigating the potential risks associated with synthetic media, providing a robust framework for future technological interventions in digital forensics and media authentication.
It is possible to preserve power quality by classifying and identifying abnormalities. Prior studies focused on enhancing the PQD classification performance in one-dimensional (1D) CNNs. Recently, various image conver...
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Since the rapid development of deep learning technology, traffic picture recognition technology has been significantly enhanced. In the process of recognizing complex traffic pictures, errors are likely to occur. Our ...
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Since the rapid development of deep learning technology, traffic picture recognition technology has been significantly enhanced. In the process of recognizing complex traffic pictures, errors are likely to occur. Our objective is to improve the efficiency of the algorithm and the characteristics of the neural network, as well as to utilize the characteristics of stereo image analysis to conduct in-depth research on road conditions pictures based on multi-scale feature algorithms. The aim of this paper is to investigate in detail the process of image recognition and reconstruction using the multi-scale feature algorithm. Furthermore, we derive its loss function, which significantly improves its running characteristics.
With the rapid development of computer technology and the Internet era, machine vision is a new science and technology formed by the cross-integration of various disciplines such as imageprocessing theory, advanced i...
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ISBN:
(纸本)9781665491129
With the rapid development of computer technology and the Internet era, machine vision is a new science and technology formed by the cross-integration of various disciplines such as imageprocessing theory, advanced information science and digital signalprocessing methods based on computers. The application of computer in information processing has become an inevitable trend, it can better acquire and understandimages, and it can quickly and effectively analyze, extract useful targets and provide decision makers with the required content. This can also improve work efficiency and accuracy to achieve a more precise and automated management level, and play a huge role in life. First of all, this paper uses the ORB algorithm for target recognition for the identification and positioning of static targets, but this algorithm still has the problem of low feature matching accuracy. Therefore, an improved ORB feature matching method based on multiple constraints is proposed; Secondly, it studies the design of the machine vision image target recognition system, including the overall function design of the system, the system process design and the system experimental verification.
This research study analyzes the multidimensional landscape of steganography, examining its historical roots, theoretical background, contemporary approaches, and various applications. Beginning with a historical over...
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ISBN:
(数字)9798350379990
ISBN:
(纸本)9798350391558
This research study analyzes the multidimensional landscape of steganography, examining its historical roots, theoretical background, contemporary approaches, and various applications. Beginning with a historical overview, this study investigates the evolution of steganography from its ancient roots to its present iterations in the digital world. Next, the study progresses towards analyzing the fundamental principles and theoretical frameworks that underpin steganographic systems, such as cryptography and digital signalprocessing. Finally, this study presents a thorough evaluation of contemporary steganographic technologies, which range from simple LSB (Least Significant Bit) substitution techniques to advanced adaptive algorithms and machine learning methods by including deep-learning based steganography and coverless steganography. Notably, this study identifies key challenges, including detection resistance, payload capacity, and robustness against attacks. Overall, this study presents a thorough understanding of steganography, emphasizing its significance as a versatile tool for communication in the digital era, while also highlighting the challenges that pave way for future innovations.
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