Segregation of images is a critical step in processingimages, computer vision, and a variety of other disciplines. The technique involves decomposing an illustration into numerous components or components, every sing...
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As a ubiquitous manipulation tool, optical tweezers are widely used in biochemistry and applied physics, so that a wide range of microscopic and nanoscopic particles could be investigated. In recent years, digital ima...
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As a ubiquitous manipulation tool, optical tweezers are widely used in biochemistry and applied physics, so that a wide range of microscopic and nanoscopic particles could be investigated. In recent years, digital imageprocessing techniques for improving target particle observation have diversified, leading to the development of numerous automatic tasks. The technique was developed in response to the need for multi-particle manipulation and feature detection. Here we describe how digital imageprocessing can be used to enhance the capabilities of optical manipulation. In particular, cutting-edge imageprocessing techniques that rely on artificial intelligence development are making optical trapping more widely accessible and enabling automatic manipulation of microscopic and nanoscopic particles.
Traditional computer-vision technology that relies on image sensors coupled with cloud processing or on-chip Artificial Intelligence (AI) processors have encountered significant challenges in terms of power consumptio...
ISBN:
(纸本)9798350306200
Traditional computer-vision technology that relies on image sensors coupled with cloud processing or on-chip Artificial Intelligence (AI) processors have encountered significant challenges in terms of power consumption, delays arising from data transmission, and/or memory access. In-sensor and near-sensor computing have been reported to solve this issue by applying pixel or array level feature extraction [2-6]. In [2, 4], capacitors are utilized for analog-domain Haar filtering to reduce the processing power consumption, but sacrificing the Fill Factor (FF) due to the use of the capacitor array [2], or complicated pixel-level logic [4]. An image sensor in introduced in [3] with Hog feature-based object detection, achieving both low power and high accuracy for the detection of up to three object classes. However, it does not work on more complex tasks. Convolutional Neural Networks (CNNs) [5, 6] offer enhanced accuracy in practical applications, but increase the power consumption and circuit complexity. Optical-domain processing [7] is capable of parallel information processing through an optical architecture based on free space. However, in [7] this is based on diffraction and requires coherent light as input, which constrains the practicality for edge computing. This paper presents a Pulse Width Modulation (PWM) pixel-based image sensor array that integrates an optical-electronic hybrid 3-layer convolutional processing unit. The first layer of the convolution is performed in the optical domain without introducing extra power consumption. The integration of optical convolution with electrical convolution offers the potential to leverage the energy-efficient advantages of optical convolution, while simultaneously providing the essential programmability needed to address multiple use-cases through reprogramming within a single device. A dual-mode processing Element (PE) is proposed enables both acquisition mode and calculation mode. The integration of a sum/differenti
Recycling facilities are often challenged by the demanding task of waste segregation, a process traditionally requiring substantial time and manpower, and lacking efficient automated solutions. Addressing this, our st...
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The proceedings contain 51 papers. The special focus in this conference is on machinevision and Augmented Intelligence. The topics include: GAN-Based Data Generation Technique and its Evaluation for Intrusion De...
ISBN:
(纸本)9789819901883
The proceedings contain 51 papers. The special focus in this conference is on machinevision and Augmented Intelligence. The topics include: GAN-Based Data Generation Technique and its Evaluation for Intrusion Detection Systems;vSCM: Blockchain-Based COvID-19 vaccine Supply Chain Management;polyp Segmentation Using Efficient Multi-supervision Net: A Deep Learning Technique Uses Attention Unit and EfficientNet Model;traffic Analysis on videos Using Deep Learning Techniques;computer vision with the Internet of Things (IoT);single Under-Water image Enhancement Using the Modified Transmission Map and Background Light Estimation;quality Assessment of the Experimental Data of Wood Structure Using Kanpur Theorem;software Fault Prediction Using Deep Neural Networks;a Hybrid and Multi-objective Approach for Data Leak and Tamper Detection in Healthcare Cloud Data;imageprocessing Techniques on Porous Silicon to Estimate Porosity and Pore Size;information Retrieval Using Data Mining;a Novel Approach Toward Detection of Glaucoma Using machine Learning Techniques;corn Leaf Disease Detection Using ResNext50, ResNext101, and Inception v3 Deep Neural Networks;SPEG—Semiotics-Based Panel Extraction from Graphic Novel;study of various Word vectors for Sentiment Analysis;a Comparative Analysis of 2D Ear Recognition for Constrained and Unconstrained Dataset Using Deep Learning Approach;robust Sliding Mode Controller and Shunt Active Power Filter for Improvement of Power Quality Indices of an Autonomous Microgrid;an Approach for Waste Classification Using Data Augmentation and Transfer Learning Models;adaptive Ridge Edge Detection in Multitemporal Satellite images;FCM-RGM: Segmentation of Nuclei via Exact Contour Enhancement in Pap Smear images;Solar Pv System Fault Classification Using machine Learning Techniques.
Geometric Algebra (GA) has proven to be an advanced language for mathematics, physics, computer science, and engineering. This review presents a comprehensive study of works on Quaternion Algebra and GA applications i...
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Geometric Algebra (GA) has proven to be an advanced language for mathematics, physics, computer science, and engineering. This review presents a comprehensive study of works on Quaternion Algebra and GA applications in computer science and engineering from 1995 to 2020. After a brief introduction of GA, the applications of GA are reviewed across many fields. We discuss the characteristics of the applications of GA to various problems of computer science and engineering. In addition, the challenges and prospects of various applications proposed by many researchers are analyzed. We analyze the developments using GA in imageprocessing, computer vision, neurocomputing, quantum computing, robot modeling, control, and tracking, as well as improvement of computer hardware performance. We believe that up to now GA has proven to be a powerful geometric language for a variety of applications. Furthermore, there is evidence that this is the appropriate geometric language to tackle a variety of existing problems and that consequently, step-by-step GA-based algorithms should continue to be further developed. We also believe that this extensive review will guide and encourage researchers to continue the advancement of geometric computing for intelligent machines.
Pixel-level sea-land segmentation on high-resolution remote sensing images is a basic task in remote sensing applications and is of great significance for coastline extraction and near-shore marine target detection. T...
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Weather conditions play a crucial role in both daily human activities and industrial operations. For example, recognizing different weather patterns is critical for outdoor automation systems. With the development of ...
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Existing generative adversarial network (GAN) based conditional image generative models typically produce fixed output for the same conditional input, which is unreasonable for highly subjective tasks, such as large-m...
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ISBN:
(纸本)1577358872
Existing generative adversarial network (GAN) based conditional image generative models typically produce fixed output for the same conditional input, which is unreasonable for highly subjective tasks, such as large-mask image inpainting or style transfer. On the other hand, GAN-based diverse image generative methods require retraining/fine-tuning the network or designing complex noise injection functions, which is computationally expensive, task-specific, or struggle to generate high-quality results. Given that many deterministic conditional image generative models have been able to produce high-quality yet fixed results, we raise an intriguing question: is it possible for pre-trained deterministic conditional image generative models to generate diverse results without changing network structures or parameters? To answer this question, we re-examine the conditional image generation tasks from the perspective of adversarial attack and propose a simple and efficient plug-in projected gradient descent (PGD) like method for diverse and controllable image generation. The key idea is attacking the pre-trained deterministic generative models by adding a micro perturbation to the input condition. In this way, diverse results can be generated without any adjustment of network structures or fine-tuning of the pre-trained models. In addition, we can also control the diverse results to be generated by specifying the attack direction according to a reference text or image. Our work opens the door to applying adversarial attack to low-level vision tasks, and experiments on various conditional image generation tasks demonstrate the effectiveness and superiority of the proposed method.
The universal transmission of pandemic COvID-19 (Coronavirus) causes an immediate need to commit in the fight across the whole human population. The emergencies for human health care are limited for this abrupt outbre...
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The universal transmission of pandemic COvID-19 (Coronavirus) causes an immediate need to commit in the fight across the whole human population. The emergencies for human health care are limited for this abrupt outbreak and abandoned environment. In this situation, inventive automation like computer vision (machine learning, deep learning, artificial intelligence), medical imaging (computed tomography, X-Ray) has developed an encouraging solution against COvID-19. In recent months, different techniques using imageprocessing are done by various researchers. In this paper, a major review on image acquisition, segmentation, diagnosis, avoidance, and management are presented. An analytical comparison of the various proposed algorithm by researchers for coronavirus has been carried out. Also, challenges and motivation for research in the future to deal with coronavirus are indicated. The clinical impact and use of computer vision and deep learning were discussed and we hope that dermatologists may have better understanding of these areas from the study.
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