The reconstruction of film and TV scenes is an important part of the film and TV production process, which has a decisive impact on the visual effect of the film and the audience's viewing experience. The modeling...
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This book focuses on the latest developments in the fields of visual AI, imageprocessing and computer vision. It shows research in basic techniques like image pre-processing, feature extraction, and enhancement, alon...
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ISBN:
(数字)9783110756722;9783110756821
ISBN:
(纸本)9783110756678
This book focuses on the latest developments in the fields of visual AI, imageprocessing and computer vision. It shows research in basic techniques like image pre-processing, feature extraction, and enhancement, along with applications in biometrics, healthcare, neuroscience and forensics. The book highlights algorithms, processes, novel architectures and results underlying machine intelligence with detailed execution flow of models.
Correct and robust ego-lane index estimation is crucial for autonomous driving in the absence of high-definition maps, especially in urban environments. Previous ego-lane index estimation approaches rely on feature ex...
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Correct and robust ego-lane index estimation is crucial for autonomous driving in the absence of high-definition maps, especially in urban environments. Previous ego-lane index estimation approaches rely on feature extraction, which limits the robustness. To overcome these shortages, this study proposes a robust ego-lane index estimation framework upon only the original visual image. After optimization of the processing route, the raw image was randomly cropped in the height direction and then input into a double supervised LaneLoc network to obtain the index estimations and confidences. A post-process was also proposed to achieve the global ego-lane index from the estimated left and right indexes with the total lane number. To evaluate our proposed method, we manually annotated the ego-lane index of public datasets which can work as an ego-lane index estimation baseline for the first time. The proposed algorithm achieved 96.48/95.40% (precision/recall) on the CULane dataset and 99.45/99.49% (precision/recall) on the TuSimple dataset, demonstrating the effectiveness and efficiency of lane localization in diverse driving environments. The code and dataset annotation results will be exposed publicly on https://***/haomo-ai/LaneLoc.
Common computer vision (CV) tasks include image classification, object detection, segmentation, and recognition. To handle such tasks, machine learning (ML) models for imageprocessing require a great amount of annota...
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Line detection is a classic and essential problem in imageprocessing, computer vision, and machine intelligence. Line detection has many important applications, including image vectorization (e.g., document recogniti...
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Line detection is a classic and essential problem in imageprocessing, computer vision, and machine intelligence. Line detection has many important applications, including image vectorization (e.g., document recognition and art design), indoor mapping, and important societal challenges (e.g., sea ice fracture line extraction from satellite imagery). Many line detection algorithms and methods have been developed, but robust and intuitive methods are still lacking. In this paper, we proposed and implemented a topological graph-guided algorithm, named TGGLinesPlus, for line detection. Our experiments on images from a wide range of domains have demonstrated the flexibility of our TGGLinesPlus algorithm. We benchmarked our algorithm with five classic and state-of-the-art line detection methods and evaluated the benchmark results qualitatively and quantitatively, the results demonstrate the robustness of TGGLinesPlus.
作者：
Li, ZongleiZhou, YinHu, JianqiYao, JianpingYan, LianshanSouthwest Jiaotong Univ
Ctr Informat Photon & Commun Sch Informat Sci & Technol Chengdu 610031 Sichuan Peoples R China Southwest Jiaotong Univ
Sch Informat Sci & Technol Lab Intelligent Percept & Smart Operat & Maintenan Chengdu 610031 Sichuan Peoples R China Sorbonne Univ
Coll France CNRS Lab Kastler BrosselENS Univ PSL 24 Rue Lhomond F-75005 Paris France Univ Ottawa
Sch Elect Engn & Comp Sci Microwave Photon Res Lab 25 Templeton St Ottawa ON K1N 6N5 Canada
Distributed Brillouin fiber sensing, based on the linear relationship between Brillouin frequency shift (BFS) and physical quantities applied to sensing fibers, has found numerous applications in the past few decades....
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Distributed Brillouin fiber sensing, based on the linear relationship between Brillouin frequency shift (BFS) and physical quantities applied to sensing fibers, has found numerous applications in the past few decades. Recently, various advanced image denoising methods have been used for performance enhancements in Brillouin fiber sensors. Yet, even though these methods do significantly remove noises contained in raw data, the BFS measurement uncertainty is not reduced-the newly introduced image denoising appears redundant with the conventional signal processing. Here, in order to truly make Brillouin fiber sensing benefit from image denoising, we directly map BFS from the image-denoised data via the slope-assisted analysis of the Brillouin phase-gain ratio. As such, noise reduction resulting from image denoising fully translates into measurement uncertainty reduction. In order to further optimize the performance of image-denoising-enhanced Brillouin fiber sensing, we improve the quality of the raw Brillouin gain and phase data by designing an advanced coherent detection scheme called a microwave-photonic interferometer, which converts some amplitude and phase noises into common-mode noises and further eliminates them through destructive interference. A more than 20-fold sensing speed acceleration compared to the state-of-the-art is experimentally achieved. This remarkable performance enhancement is obtained by only optimizing the signal detection and processing unit, without modifying Brillouin scattering between pump and probe waves. Our method seamlessly connects Brillouin fiber sensing with advanced image denoising methods developed for computer vision and artificial intelligence, and makes imagedenoising-enhanced Brillouin fiber sensing outperform the state-of-the art significantly. (c) 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
Psoriasis is a skin disorder that results in swollen skin cells and red, itchy areas on the skin. 40% of the world's population is currently affected by psoriasis. Nowadays, using skin image analysis technology is...
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Psoriasis is a skin disorder that results in swollen skin cells and red, itchy areas on the skin. 40% of the world's population is currently affected by psoriasis. Nowadays, using skin image analysis technology is the main way for detecting psoriasis. Additionally, a number of academics have identified potential machine learning methods for categorising the psoriasis illness. However, the accuracy and computational efficiency of the model still need to be improved. Thus, in this paper, we present an optimized vision transformer for autonomous psoriasis disease detection. Following pre-processing, feature optimized image is attained using convolutional neural network (CNN) which embeds full image and concatenates to each vision transformer encoder layer. It leads the network to always "retain" the full image at the end of each transformer block output. In parallel, the pre-processed images are cropped into patches and these patches along with its positional encoded information are given as input to the optimized transformer encoder. To enhance the performance of transformer, the hyper-parameters of it are optimized using adaptive rabbit optimization algorithm (AROA). Results of this article confirm that the proposed optimized vision transformer model achieved better classification accuracy of 97.7% and F-Score of 96.5%.
Quantum computing is emerging as a transformative force in computer science, offering significant advantages in speed and efficiency over classical computing methods. Despite this promise, the practical application of...
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Quantum computing is emerging as a transformative force in computer science, offering significant advantages in speed and efficiency over classical computing methods. Despite this promise, the practical application of quantum computing to visual computing faces numerous challenges, including the complexity of quantum algorithms and the limitations of current quantum hardware. These challenges underscore the necessity for focused research and collaboration in this interdisciplinary area. This Special Issue of IEEE Computer Graphics and applications, "Quantum Visual Computing," aims at drawing attention to these challenges and bringing together pioneering research at the intersection of quantum and visual computing. By fostering dialogue and innovation between these fields, we hope to inspire new solutions and advance the state of the art in both domains.
machine Learning applications Practical resource on the importance of machine Learning and Deep Learning applications in various technologies and real-world situations machine Learning applications discusses methodolo...
ISBN:
(数字)9781394173341;9781394173334
ISBN:
(纸本)9781394173327
machine Learning applications Practical resource on the importance of machine Learning and Deep Learning applications in various technologies and real-world situations machine Learning applications discusses methodological advancements of machine learning and deep learning, presents applications in imageprocessing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader’s active learning. Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space exploration. The book describes the importance of each subject area and detail why they are so important to us from a societal and human perspective. Edited by two highly qualified academics and contributed to by established thought leaders in their respective fields, machine Learning applications includes information on:
Content based medical image retrieval (CBMIR), covering face and vehicle detection, multi-resolution and multisource analysis, manifold and imageprocessing, and morphological processing
Smart medicine, including machine learning and artificial intelligence in medicine, risk identification, tailored interventions, and association rules
AI and robotics application for transportation and infrastructure (e.g., autonomous cars and smart cities), along with global warming and climate change
Identifying diseases and diagnosis, drug discovery and manufacturing, medical imaging diagnosis, personalized medicine, and smart health records
Increasing consumer quality awareness and increase in consumer wealth drives the market demand for high quality leather and leather products. Reliable and effective detection and classification of leather surface defe...
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Increasing consumer quality awareness and increase in consumer wealth drives the market demand for high quality leather and leather products. Reliable and effective detection and classification of leather surface defects is of profound significance to tanneries and industries where leather is a major raw material for leather accessories and leather parts manufacturers. This paper presents a methodical and a detailed review of the leather surface defects detection methods starting from leather image acquisition, leather imageprocessing, feature extraction and classification for defect detection. Firstly, we introduce the fundamentals of leather image acquisition and various related imageprocessing methods, feature extraction and classification for the defect inspection. Next, the existing datasets and summary of the recent methodologies used in this field are discussed. Finally, the challenges and suggested improvements to further the development of the application of advanced machine Learning and Deep Learning in this field are discussed. Deep learning algorithms are shown to have a great potential for leather surface defect detection and can help prepare a robust system that would greatly guarantee quality leather and provide monetary wealth from such leather products. Finally, research guidelines are presented to fellow researchers regarding data augmentation, leather defect detection models which need to be investigated in the future to make progress in this crucial area of research.
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