Invisible watermarks safeguard images' copyrights by embedding hidden messages only detectable by owners. They also prevent people from misusing images, especially those generated by AI models. We propose a family...
Finding test-cases that cause mission-critical behavior is crucial to increase the robustness of satellite on-board imageprocessing. Using genetic algorithms, we are able to automatically search for test cases that p...
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Detecting dengue fever using imageprocessing techniques typically involves the analysis of medical images such as blood smears or tissue samples. Dengue is a viral disease transmitted by Aedes mosquitoes, and its dia...
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Environment analysis is a critical part of autonomous vehicle for transport applications and for passenger safety. The solutions demonstrating the greatest robustness have been integrating multiple sensors used for re...
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The research presents a hybrid approach to identify and categorise nutritional deficiency syndrome in citrus leaves using imageprocessing and machine learning. The method includes processingimages, segmenting images...
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Based on human visual systems, imageprocessingalgorithms, and efficient hardware implementation methodologies are proposed to optimize the image qualities of AR displays according to the changes in ambient lights. T...
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ISBN:
(纸本)9798350327038
Based on human visual systems, imageprocessingalgorithms, and efficient hardware implementation methodologies are proposed to optimize the image qualities of AR displays according to the changes in ambient lights. To this end, methods are described to improve the image qualities perceived by humans. In addition, the delta look-up table is presented to minimize the number of additional circuits without significant changes in existing hardware. HOSA, an image quality assessment based on the human visual system is used to verify the image qualities for the extreme ambient light conditions.
Palm recognition systems play an important role in biometric authentication;however, existing systems frequently have low accuracy and resiliency due to problems such as changing lighting conditions, occlusions, and h...
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Addressing the limitations of currently rare small target detection algorithms based on Human Visual systems (HVS) that struggle with achieving satisfactory performance in complex backgrounds and lack high real-time c...
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systems known as Automatic Number Plate Recognition (ANPR), license plate recognition or LPR are now widely used in many sectors such as law enforcement, traffic control, vehicle access etc. This is a technology that ...
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Does progress on imageNet transfer to real-world datasets? We investigate this question by evaluating imageNet pre-trained models with varying accuracy (57% -83%) on six practical image classification datasets. In par...
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ISBN:
(纸本)9781713899921
Does progress on imageNet transfer to real-world datasets? We investigate this question by evaluating imageNet pre-trained models with varying accuracy (57% -83%) on six practical image classification datasets. In particular, we study datasets collected with the goal of solving real-world tasks (e.g., classifying images from camera traps or satellites), as opposed to web-scraped benchmarks collected for comparing models. On multiple datasets, models with higher imageNet accuracy do not consistently yield performance improvements. For certain tasks, interventions such as data augmentation improve performance even when architectures do not. We hope that future benchmarks will include more diverse datasets to encourage a more comprehensive approach to improving learning algorithms.
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