Transient electronic devices can help eliminate the growing environmental problem of "electronic pollution."However, their applications are severely limited by poor optoelectronic performance. Here, a new de...
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Transient electronic devices can help eliminate the growing environmental problem of "electronic pollution."However, their applications are severely limited by poor optoelectronic performance. Here, a new degradable polymeric dielectric material is synthesized by a one-step method for organic neuromorphic vision sensors (ONeuvSs). A high mobility of 2.74 cm 2 v - 1 s - 1 and current on/off ratio greater than 10 9 were obtained. Moreover, we achieved excellent optical figures of merit with a maximum photosensitivity of 8.7 3 10 8 and maximum detectivity of 9.42 3 10 16 Jones, which are the best values among transient electronic devices. The ONeuvS array could perform static image recognition with an accuracy of 92.7% and high-pass filtering behavior. More interestingly, both high-performance optical synapses and switching functional devices could be realized by modulating the organic semiconductors with or without alkyl chains. This study provides insights for developing a low-cost and environmentally friendly approach for constructing degradable ONeuvSs with sensing, memory, and processing in one device.
In recent years, significant progress has been achieved in medical image analysis, mainly due to the substantial advances in deep learning methods. In the past decade, Convolutional Neural Network (CNN) was the best m...
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ISBN:
(纸本)9798350354249;9798350354232
In recent years, significant progress has been achieved in medical image analysis, mainly due to the substantial advances in deep learning methods. In the past decade, Convolutional Neural Network (CNN) was the best model for image classification, demonstrating remarkable success in various medical applications. However, the advent of vision Transformers (viTs) has challenged the dominance of CNN approaches. This study aims to explore the potential of viTs in healthcare, comparing their performance with that of CNN models. The latter has traditionally excelled in image feature extraction through convolutional operations;on the other hand, viTs, relying on self-attention mechanisms, exhibit unique capabilities in capturing long-range dependencies, enabling them to effectively capture complex patterns within images. In this study, after analyzing their architectures, we assessed the behaviour of from-scratch and pre-trained models, highlighting their differences in performance and providing light on the applicability of Transfer Learning (TL) approach in the healthcare scenario.
Humans outperform object recognizers despite the fact that models perform well on current datasets, including those explicitly designed to challenge machines with debiased images or distribution shift. This problem pe...
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ISBN:
(纸本)9781713899921
Humans outperform object recognizers despite the fact that models perform well on current datasets, including those explicitly designed to challenge machines with debiased images or distribution shift. This problem persists, in part, because we have no guidance on the absolute difficulty of an image or dataset making it hard to objectively assess progress toward human-level performance, to cover the range of human abilities, and to increase the challenge posed by a dataset. We develop a dataset difficulty metric MvT, Minimum viewing Time, that addresses these three problems. Subjects view an image that flashes on screen and then classify the object in the image. images that require brief flashes to recognize are easy, those which require seconds of viewing are hard. We compute the imageNet and ObjectNet image difficulty distribution, which we find significantly undersamples hard images. Nearly 90% of current benchmark performance is derived from images that are easy for humans. Rather than hoping that we will make harder datasets, we can for the first time objectively guide dataset difficulty during development. We can also subset recognition performance as a function of difficulty: model performance drops precipitously while human performance remains stable. Difficulty provides a new lens through which to view model performance, one which uncovers new scaling laws: vision-language models stand out as being the most robust and human-like while all other techniques scale poorly. We release tools to automatically compute MvT, along with image sets which are tagged by difficulty. Objective image difficulty has practical applications - one can measure how hard a test set is before deploying a real-world system - and scientific applications such as discovering the neural correlates of image difficulty and enabling new object recognition techniques that eliminate the benchmark-vsreal-world performance gap.
This study proposes a way to detect vitamin deficiency by combining machine learning and imageprocessing. Computer vision enables the system to recognise visual symptoms of specific vitamin deficiencies. The recommen...
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This paper uses the machinevision method to identify the skirt module. We have constructed three kinds of machine recognition models of skirt profile processing, structure analysis of style drawing, and size estimati...
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image segmentation is a key task in computer vision and imageprocessing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and ...
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image segmentation is a key task in computer vision and imageprocessing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among others, and numerous segmentation algorithms are found in the literature. Against this backdrop, the broad success of deep learning (DL) has prompted the development of new image segmentation approaches leveraging DL models. We provide a comprehensive review of this recent literature, covering the spectrum of pioneering efforts in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multiscale and pyramid-based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the relationships, strengths, and challenges of these DL-based segmentation models, examine the widely used datasets, compare performances, and discuss promising research directions.
Artificial Intelligence (AI) is increasingly adopted by public sector organizations to provide better public services and to transform their internal processes. AI is now considered a key enabler for digital innovatio...
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Multi-head-self-attention (MHSA)-equipped models have achieved notable performance in computer vision. Their computational complexity is proportional to quadratic numbers of pixels in input feature maps, resulting in ...
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ISBN:
(纸本)1577358872
Multi-head-self-attention (MHSA)-equipped models have achieved notable performance in computer vision. Their computational complexity is proportional to quadratic numbers of pixels in input feature maps, resulting in slow processing, especially when dealing with high-resolution images. New types of token-mixer are proposed as an alternative to MHSA to circumvent this problem: an FFT-based token-mixer involves global operations similar to MHSA but with lower computational complexity. However, despite its attractive properties, the FFT-based token-mixer has not been carefully examined in terms of its compatibility with the rapidly evolving MetaFormer architecture. Here, we propose a novel token-mixer called Dynamic Filter and novel image recognition models, DFFormer and CDFFormer, to close the gaps above. The results of image classification and downstream tasks, analysis, and visualization show that our models are helpful. Notably, their throughput and memory efficiency when dealing with high-resolution image recognition is remarkable. Our results indicate that Dynamic Filter is one of the token-mixer options that should be seriously considered. The code is available at https://***/okojoalg/dfformer
The studies will be carried out using optical metrology methods on a Walter Helicheck inspection machine in reflected light and a number of images were stored to form a statistical sample. Established new indicators a...
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ISBN:
(纸本)9781510667877;9781510667884
The studies will be carried out using optical metrology methods on a Walter Helicheck inspection machine in reflected light and a number of images were stored to form a statistical sample. Established new indicators and criteria for grinding efficiency based on imageprocessing of the helical groove of the end mill. As a result, recommendations for the selection of optical control techniques were made for the first time at the intermediate stage of technological preparation for production, in real time, and after processing. In this work, for the first time, we prove the possibility of determining the camera displacement pith distance during continuous scanning of the profile of a helical surface in a radial section, the measurement accuracy and recreating a three-dimensional model of the object. As a result of the work of the new algorithm using the Haar-wavelet with new indicators, it was established that the actual one is located inside the focal zone, which proves the possibility of applied application of the method of monitoring the shape of helical flute of end mills using computer vision. The measurement accuracy of the helical flute increased from 4 to 12% along its profile.
Agriculture is often known as the art and science of nurturing soil. It involves preparing plants and animals for use in products. Agriculture is the process of growing crops and rearing animals for human consumption,...
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