machine learning is the state of the art for many recurring tasks in several heterogeneous domains. In the last decade, it has been also widely used in Precision Agriculture (PA) and Wild Flora Monitoring (WFM) to add...
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machine learning is the state of the art for many recurring tasks in several heterogeneous domains. In the last decade, it has been also widely used in Precision Agriculture (PA) and Wild Flora Monitoring (WFM) to address a set of problems with a big impact on economy, society and academia, heralding a paradigm shift across the industry and academia. Many applications in those fields involve imageprocessing and computer vision stages. Remote sensing devices are very popular choice for image acquisition in this context, and in particular, Unmanned Aerial vehicles (UAvs) offer a good tradeoff between cost and area coverage. For these reasons, research literature is rich of works that face problems in Precision Agriculture and Wild Flora Monitoring domains with machine learning/computer vision methods applied to UAvimagery. In this work, we review this literature, with a special focus on algorithms, model sizing, dataset characteristics and innovative technical solutions presented in many domain-specific models, providing the reader with an overview of the research trend in recent years.
User-generated content (UGC) is ubiquitous across the internet as a result of billions of videos and images being uploaded each day. All kinds of UGC media are affected by natural distortions, occurring both during an...
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ISBN:
(纸本)9798350349405;9798350349399
User-generated content (UGC) is ubiquitous across the internet as a result of billions of videos and images being uploaded each day. All kinds of UGC media are affected by natural distortions, occurring both during and after capture, which are inherently diverse and commingled. These distortions have different perceptual effects based on the media content. Given recent dramatic increases in the consumption of short-form content, the analysis and control of their perceptual quality has become an important problem. Regardless of the content, many UGC videos have overlaid and embedded texts in them, which are visually salient. Hence text quality has a significant impact on the global perception of video or image quality and needs to be studied. One of the most important factors in perceptual text quality in user-generated media is legibility, which has been studied very little in the context of computer vision. Predicting text legibility can also help in text recognition applications such as image search or document identification. This work aims at modeling text legibility using computer vision techniques and thus studying the relationship between text quality and legibility. We propose a modified dataset variant of COCO-Text [1] and a model for predicting text legibility for both handwritten and machine-generated texts. We also demonstrate how models trained to predict text legibility can help in the prediction of text (perceptual) quality. The dataset and models can be accessed here https://***/research/Quality/***.
In-sensor computing has revolutionized modern vision-based applications, particularly in scenarios like autonomous vehicles and robotics where real-time or near-real-time processing is crucial. By enabling data proces...
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ISBN:
(纸本)9798400706882
In-sensor computing has revolutionized modern vision-based applications, particularly in scenarios like autonomous vehicles and robotics where real-time or near-real-time processing is crucial. By enabling data processing at the sensor level, in-sensor computing eliminates the need to transmit data to cloud servers, significantly reducing latency and enhancing decision-making speed. Central to the in-sensor computing paradigm, CMOS image sensors (CISs) with edge computing, play a pivotal role in machinevisionapplications. The need for high resolution, low power, and real-time operation aligns seamlessly with the demands of modern vision-based applications. In this paper, we propose a novel approach for real-time image edge detection with an in-sensor, ADC-less sensing solution that achieves high energy efficiency and speed. The design utilizes the column-parallel architecture of existing CIS and the row-wise pixel readout scheme. Column voltages of three consecutive rows with a delay arrangement extract 4-bit edge pixels without deriving the actual digital image pixels. A time-to-digital conversion (TDC) technique using a 4-bit counter eliminates the requirement of power-hungry ADC. A 256(H) x 256(v) 2D CMOS pixel array with 10.. m pixel pitch is simulated using Spectre in TSMC 65nm low-power technology. CMOS pixels with wide dynamic range (WDR) capture the light intensity variation up to 92dB [10]. Simulation results show energy consumption of 2pWper pixel per frame, operating at a frame rate of 3.9kfps, all well-contained within a modest 0.5 mW power budget. The resultant frame rate emerges as notably superior in terms of speed, accompanied by a more than tenfold reduction in power consumption per edge frame-pixel compared to the existing prior art.
Classifying microplanktons in digital holographic images is challenging due to a multitude of factors. For instance, shifts in viewpoint can alter how microplanktons are perceived, while illumination changes can affec...
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Classifying microplanktons in digital holographic images is challenging due to a multitude of factors. For instance, shifts in viewpoint can alter how microplanktons are perceived, while illumination changes can affect the visibility of certain features. Geometric anomalies can distort the shapes of these microplanktons, and the presence of noise within the digital holographic microscope can further alter local image features. Additionally, the difficulty in data collection results in dataset imbalance leading to a biased classification problem. These class-imbalanced datasets pose a considerable hurdle in machine learning applications. Here, categorical representations tend to favor majority classes while neglecting minority classes that are equally important for a comprehensive understanding of microplankton diversity. Accordingly, this research contributes what we believe to be a novel debiasing method using channel attention blocks (DCABs) and a novel attention product. It enhances the model's ability to focus on relevant features while mitigating the effects of bias. This method was applied on six biased models, viz., vGG16, ResNet50v2, ResNet152v2, Inceptionv3, Xception, ShuffleNetv2, and ShincNet. The proposed method achieved a significant reduction in the degree of bias (DoB) and KL divergence (KL) values for all the six biased models. With just 6.68M parameters and 6.4 GFLOPs, the DCAB for ShincNet demonstrated a competitive performance in terms of DoB (0.125) and KL (0.82) compared to four state-of-the-art debiasing techniques. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.
In recent years, the model of improved GAN has been widely applied in the field of machinevision. It not only covers the traditional imageprocessing, but also includes image conversion, image synthesis and so on.. F...
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In recent years, the model of improved GAN has been widely applied in the field of machinevision. It not only covers the traditional imageprocessing, but also includes image conversion, image synthesis and so on.. Firstly, this paper describes the basic principles and existing problems of GAN, then introduces several improved GAN models, including Info-GAN, DC-GAN, f-GAN, Cat-GAN and others. Secondly, several improved GAN models for different applications in the field of machinevision are described. Finally, the future trend and development of GAN are prospected.
It's widely accepted that human expressions, considering for roughly sixty percent of all daily interactions, are among the most authentic forms of communication. Numerous studies are being conducted to explore th...
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It's widely accepted that human expressions, considering for roughly sixty percent of all daily interactions, are among the most authentic forms of communication. Numerous studies are being conducted to explore the importance of facial expressions and the development of machine-assisted recognition techniques. Significant progress is being made in facial and expression recognition, largely due to the rapid growth of machine learning and computer vision. A variety of algorithmic approaches and methods exist for detecting and recognizing facial expressions and features. This study investigates various optimization algorithms used with convolutional neural networks for facial expression recognition. The primary focus is on Adam, RMSProp, stochastic gradient descent and AdaMax optimizers. A comprehensive comparison is being made, examining the key aspects of each optimizer, including its advantages and disadvantages. Furthermore, the study also incorporates findings from recent studies that used these optimizers in various applications, highlighting their performance in terms of training time and precision. The aim is to illuminate the process of selecting a suitable optimizer for specific applications, analysing the trade-offs between training speed and higher accuracy levels. Moreover, this study provides a deeper analysis of the role optimizers play in machine learning-based facial expression recognition models. The discussion of the technical challenges posed by these optimizers and future improvements for achieving much more optimal results concludes the study.
We present a new data generation method to facilitate an automatic machine interpretation of 2D engineering part drawings. While such drawings are a common medium for clients to encode design and manufacturing require...
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We present a new data generation method to facilitate an automatic machine interpretation of 2D engineering part drawings. While such drawings are a common medium for clients to encode design and manufacturing requirements, a lack of computer support to automatically interpret these drawings necessitates part manufacturers to resort to laborious manual approaches for interpretation which, in turn, severely limits processing capacity. Although recent advances in trainable computer vision methods may enable automatic machine interpretation, it remains challenging to apply such methods to engineering drawings due to a lack of labeled training data. As one step toward this challenge, we propose a constrained data synthesis method to generate an arbitrarily large set of synthetic training drawings using only a handful of labeled examples. Our method is based on the randomization of the dimension sets subject to two major constraints to ensure the validity of the synthetic drawings. The effectiveness of our method is demonstrated in the context of a binary component segmentation task with a proposed list of descriptors. An evaluation of several image segmentation methods trained on our synthetic dataset shows that our approach to new data generation can boost the segmentation accuracy and the generalizability of the machine learning models to unseen drawings.
This paper aims to explore an innovative method combining computer vision and machine learning to accurately identify and analyze various movements in badminton. This paper first summarizes the application prospect of...
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Convolutional neural networks (CNNs) have significantly contributed to recent advances in machine learning and computer vision. Although initially designed for image classification, the application of CNNs has stretch...
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Convolutional neural networks (CNNs) have significantly contributed to recent advances in machine learning and computer vision. Although initially designed for image classification, the application of CNNs has stretched far beyond the context of images alone. Some exciting applications, e.g., in natural language processing and image segmentation, implement one-dimensional CNNs, often after a pre-processing step that transforms higher-dimensional input into a suitable data format for the networks. However, local correlations within data can diminish or vanish when one converts higher-dimensional data into a one-dimensional string. The Hilbert space-filling curve can minimize this loss of locality. Here, we study this claim rigorously by comparing an analytical model that quantifies locality preservation with the performance of several neural networks trained with and without Hilbert mappings. We find that Hilbert mappings offer a consistent advantage over the traditional flatten transformation in test accuracy and training speed. The results also depend on the chosen kernel size, agreeing with our analytical model. Our findings quantify the importance of locality preservation when transforming data before training a one-dimensional CNN and show that the Hilbert space-filling curve is a preferential transformation to achieve this goal.
The significance of high-speed machinevision in scientific and technological fields is growing, especially with the era of Industry 4.0 technologies. There are several pattern-matching algorithms that have various in...
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The significance of high-speed machinevision in scientific and technological fields is growing, especially with the era of Industry 4.0 technologies. There are several pattern-matching algorithms that have various intriguing applications in ultralow-latency machinevisionprocessing. However, the low frame rate of image sensors-which usually operate at tens of hertz-fundamentally limits the processing rate. The paper will conceptualize and develop the computerized pattern recognition technique that can be applied to investigate light beam profiles and extract the desired information according to the purpose required in this case study. In the current work, the automatic detection and inspection of laser spots were designed to perform analysis and alignment for laser beam in comparison with the electron spot beam using the LabvIEW graphical programming environment, especially when the laser and electron beams overlap. This is one of the important steps for realizing the fundamental aim of test-FEL to produce short wavelengths with the second, third, and fifth harmonics at 131.5, 88, and 53 nm, respectively. The tentative version of the program achieved the elementary purpose, which fulfilled the accurate transversal alignment of the ultrashort laser pulses with the electron beam in the system of the FEL test facility at MAX-Lab, in addition to studying the beam's stability and jittering range. Copyright (C) 2024 The Authors.
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