The increasing prevalence of mobile devices has led to significant advancements in mobile camera systems and improved image quality. Nonetheless, mobile photography still grapples with flare corruptions such as reflec...
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ISBN:
(纸本)9798350349405;9798350349399
The increasing prevalence of mobile devices has led to significant advancements in mobile camera systems and improved image quality. Nonetheless, mobile photography still grapples with flare corruptions such as reflective flare. The absence of a comprehensive real image dataset tailored for mobile phones hinders the development of effective flare mitigation techniques. To address this issue, we present a novel real image dataset specifically designed for mobile camera systems, focusing on flare removal. Capitalizing on the distinct properties of real images, this dataset serves as a solid foundation for developing advanced flare removal algorithms. The dataset comprises over 1,100 pairs of high-quality, full-resolution images for reflective flare, which generate 2,200 paired patches, ensuring broad adaptability across various imaging conditions. Experimental results demonstrate that networks trained with synthesized data struggle to cope with the complex lighting settings present in this real image dataset. Our dataset is expected to enable an array of new research in flare removal and contribute to substantial improvements in mobile image quality, benefiting mobile photographers and end-users alike.
With the continuous development of digital imageprocessingalgorithms, its application scenarios have been integrated from the simple research of a single image and a single algorithm to a multi-algorithm fusion anal...
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Cloud-based data processing latency mainly depends on the transmission delay of data to the cloud and the used data processing algorithm. To minimize the transmission delay, it is important to compress the transferred...
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ISBN:
(纸本)9798350399462
Cloud-based data processing latency mainly depends on the transmission delay of data to the cloud and the used data processing algorithm. To minimize the transmission delay, it is important to compress the transferred data without reducing the quality of the data. When using data compression algorithms, it is important to validate the impact of these algorithms on the detection quality. This work evaluates the effects of image compression and transmission over wireless interfaces on state of the art neural networks. Therefore, a modern imageprocessing platform for next generation automotive processing architectures, as used in software defined vehicles, is introduced. The impacts of different image encoders as well as data transmission parameters are investigated and discussed.
This paper presents a real-time embedded thermal imaging system architecture for compact, energy-efficient, high-quality imaging utilizing heterogeneous system-on-chip (SoC) and uncooled infrared focal plane arrays (I...
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ISBN:
(纸本)9798350387964;9798350387957
This paper presents a real-time embedded thermal imaging system architecture for compact, energy-efficient, high-quality imaging utilizing heterogeneous system-on-chip (SoC) and uncooled infrared focal plane arrays (IRFPAs). Unlike previous systems that organized separate devices for complex imageprocessing, our system provides integrated imageprocessing support for robust sensor-to-surveillance. The imageprocessing organizes two algorithm stacks: a non-uniformity correction stack to mitigate the distinctive noise vulnerabilities of uncooled IRFPAs, and an image enhancement stack including contrast enhancement and temporal noise filters. We optimized these algorithms for domain-specific factors, including asymmetric multiprocessing (AMP), cache organization, single instruction multiple data (SIMD) instructions, and very long instruction word (VLIW) architectures. The implementation on the TI TDA3x SoC demonstrates that our system can process 640x480, 60 frames per second (FPS) videos at a peak core load of 57.5% while consuming power less than 2.2 W for the entire system, denoting the possibility of processing the 1280x1024, 30 FPS videos from the cutting-edge uncooled IRFPAs. Additionally, our system improves power efficiency by 9.42% and 9.96% at 30 and 60 FPS, respectively, compared to the state-of-the-art when executing similar imageprocessingalgorithms.
This study introduces a machine vision system integrated into cyber-physical systems (CPS) for enhanced industrial control. The system employs a specialized script for real-time imageprocessing and edge detection, wi...
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ISBN:
(纸本)9798350372977;9798350372984
This study introduces a machine vision system integrated into cyber-physical systems (CPS) for enhanced industrial control. The system employs a specialized script for real-time imageprocessing and edge detection, with a focus on precision and speed. Results showcase the system's rapid processing capabilities and high-accuracy feature detection, facilitated by machine learning algorithms that enable adaptability and iterative improvement. The system distinguishes itself by not only providing rapid and accurate feature recognition but also by outputting precise coordinates, crucial for micron-level manufacturing precision. An intuitive human-machine interface ensures seamless operation within industrial workflows. This integration significantly improves automated quality control and operational efficiency, demonstrating the system's potential to advance smart manufacturing in line with Industry 4.0 standards.
Superpixel-based segmentation is an important preprocessing step for the simplification of imageprocessing. The subjective nature behind the determination of optimal cluster numbers in segmentation algorithms can res...
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ISBN:
(纸本)9798350366235;9798350366242
Superpixel-based segmentation is an important preprocessing step for the simplification of imageprocessing. The subjective nature behind the determination of optimal cluster numbers in segmentation algorithms can result in either under-or over-segmentation burdens, depending on the image type. Insect wings, with their intricate color patterns, pose significant challenges for the accurate capture of color diversity in clustering algorithms, assuming a spherical and isotropic cluster distribution is used. This paper introduces a hybrid approach for color clustering in insect wings, integrating the Simple Linear Iterative Clustering (SLIC) method to generate the initial superpixels, and a DeltaE 2000 function the precisely discriminated merging of superpixels. Color differences between superpixels serve to measure homogeneity during the merging process. The proposed new algorithm demonstrates enhanced segmentation as it overcomes the issue of over-segmentation and under-segmentation, as evidenced by the results derived from the Boundary Recall, Rand index, Under-segmentation Error, and Bhattacharyya distance using ground truth data. The Silhouette score and Dunn Index are also used to quantitatively evaluate the efficacy of our new proposed clustering technique.
In order to comply with the trend of intelligent visual communication, this study proposed an innovative visual communication scenario based on imageprocessingalgorithms. The framework aims to optimize traditional k...
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At present, the application of industrial robots combined with visual systems to achieve dynamic grasping materials of the belt is becoming increasingly widespread. Unlike the behavior of industrial robots grabbing af...
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At present, the application of industrial robots combined with visual systems to achieve dynamic grasping materials of the belt is becoming increasingly widespread. Unlike the behavior of industrial robots grabbing after the belt stops, industrial robots dynamically tracking and grabbing materials on the belt can greatly improve production efficiency. For vision, processing distorted material images during high-speed movement and to obtain accurate coordinate points of materials is a key task to improve the accuracy of the belt tracking applications with industrial robot. Developing imageprocessingalgorithms based on MATLAB can, on the one hand, utilize existing software and hardware interface functions to improve development efficiency;On the other hand, autonomous and controllable imageprocessingalgorithms can be developed based on application requirements to maximize system accuracy.
Crack is an important factor to consider when assessing the quality of concrete structures since it impacts the structure's longevity, application, and safety. Convolutional neural networks are increasingly the be...
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ISBN:
(纸本)9798350350470;9798350350487
Crack is an important factor to consider when assessing the quality of concrete structures since it impacts the structure's longevity, application, and safety. Convolutional neural networks are increasingly the best option to replace manual crack detection because of the advancement of methods for deep learning. Machine learning algorithms known as artificial neural networks (ANNs) imitate how the human brain functions. These Neural Networks can be implemented in software. However, these neural networks require large computations. Hardware implementation of these neural networks has higher processing speeds than their software implementations. CNN is a particular kind of artificial neural network that is used to interpret pixel data and is utilised in image detection and processing. Computer Vision applications including object identification, image segmentation, and image classification work well with convolutional neural networks. employed for categorization The proposed method uses a configurable convolution neural network system for crack detection. An accuracy of 97.5% is achieved over 200 images. By detecting the crack effectively using the method, the quality of the concrete structures will be ensured using dedicated hardware shortly.
Recently, providing real-time navigation of unmanned aerial vehicles independent of global positioning systems has become of great importance. The state-of-the-art methods based on deep learning, which give good resul...
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ISBN:
(纸本)9798350388978;9798350388961
Recently, providing real-time navigation of unmanned aerial vehicles independent of global positioning systems has become of great importance. The state-of-the-art methods based on deep learning, which give good results in certain datasets, and the existing methods can not provide real-time and good solutions on images with dynamic and fast moving. Moreover, the methods, were developed so far, were focused on object-based tracking algorithms. In this paper, the tracking of the points belonging to the target pattern, found by image matching, was performed with the machine learning model we developed for 10 sequential video images. The features extracted for the machine learning model are: (i) the change between the points of the previous image and the image before that, (ii) the points of interest in the previous image, (iii) the changes found with the homography matrix between sequential images. It was experimentally shown that, point tracking can be achieved with the least error, on avarage about 23 pixels for a 2 mega-pixel resolution image, among the algorithms in the literature that can process more than 30 images per second in a CPU environment of 2 GHz or above.
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