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On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild

On the Real-Time Semantic Segmentation of Aphid Clusters in ...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Rahman, Raiyan Indris, Christopher Zhang, Tianxiao Li, Kaidong McCornack, Brian Flippo, Daniel Sharda, Ajay Wang, Guanghui Toronto Metropolitan University Department of Computer Science Toronto Canada University of Kansas Department of Electrical Engineering and Computer Science Lawrence United States Kansas State University Department of Entomology Manhattan United States Kansas State University Department of Biological and Agricultural Engineering Manhattan United States

ISBN: (纸本)9798350302493

Aphid infestations can cause extensive damage to wheat and sorghum fields and spread plant viruses, resulting in significant yield losses in agriculture. To address this issue, farmers often rely on chemical pesticides, which are inefficiently applied over large areas of fields. As a result, a considerable amount of pesticide is wasted on areas without pests, while inadequate amounts are applied to areas with severe infestations. The paper focuses on the urgent need for an intelligent autonomous system that can locate and spray infestations within complex crop canopies, reducing pesticide use and environmental impact. We have collected and labeled a large aphid image dataset in the field, and propose the use of real-time semantic segmentation models to segment clusters of aphids. A multiscale dataset is generated to allow for learning the clusters at different scales. We compare the segmentation speeds and accuracy of four state-of-the-art real-time semantic segmentation models on the aphid cluster dataset, benchmarking them against nonreal-time models. The study results show the effectiveness of a real-time solution, which can reduce inefficient pesticide use and increase crop yields, paving the way towards an autonomous pest detection system. © 2023 ieee.

关键词： Semantics

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Camera-based Recovery of Cardiovascular Signals from Unconstrained Face Videos using an Attention Network

Camera-based Recovery of Cardiovascular Signals from Unconst...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Deshpande, Yogesh Thapa, Surendrabikram Sarkar, Abhijit Abbott, A. Lynn Virginia Tech Bradley Department of Electrical and Computer Engineering United States Virginia Tech Department of Computer Science United States Virginia Tech Transportation Institute United States

ISBN: (纸本)9798350302493

This paper addresses the problem of recovering the shape morphology of blood volume pulse (BVP) information from a video of a person's face. Video-based remote plethysmography methods have shown promising results in estimating vital signs such as heart rate and breathing rate. However, recovering the instantaneous pulse rate signals is still a challenge for the community. This is due to the fact that most of the previous methods concentrate on capturing the temporal average of the cardiovascular signals. In contrast, we present an approach in which BVP signals are extracted with a focus on the recovery of the signal shape morphology as a generalized form for the computation of physiological metrics. We also place emphasis on allowing natural movements by the subject. Furthermore, our system is capable of extracting individual BVP instances with sufficient signal detail to facilitate candidate re-identification. These improvements have resulted in part from the incorporation of a robust skin-detection module into the overall imaging-based photoplethysmography (iPPG) framework. We present extensive experimental results using the challenging UBFC-Phys dataset and the well-known COHFACE dataset. The source code is available at https://***/yogeshd21/CVPM-2023-iPPG-Paper. © 2023 ieee.

关键词： Recovery

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Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

Unsupervised domain adaptation and super resolution on drone...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Albert, Paul Saadeldin, Mohamed Narayanan, Badri Mac Namee, Brian Hennessy, Deirdre O'Connor, Noel E. McGuinness, Kevin Dublin City Univ Sch Elect Engn Dublin Ireland Univ Coll Dublin Sch Comp Sci Dublin Ireland Insight Ctr Data Analyt Dublin Ireland TEAGASC Dublin Ireland VistaMilk Dublin Ireland

ISBN: (纸本)9781665487399

Herbage mass yield and composition estimation is an important tool for dairy farmers to ensure an adequate supply of high quality herbage for grazing and subsequently milk production. By accurately estimating herbage mass and composition, targeted nitrogen fertiliser application strategies can be deployed to improve localised regions in a herbage field, effectively reducing the negative impacts of over-fertilization on biodiversity and the environment. In this context, deep learning algorithms offer a tempting alternative to the usual means of sward composition estimation, which involves the destructive process of cutting a sample from the herbage field and sorting by hand all plant species in the herbage. The process is labour intensive and time consuming and so not utilised by farmers. Deep learning has been successfully applied in this context on images collected by high-resolution cameras on the ground. Moving the deep learning solution to drone imaging, however, has the potential to further improve the herbage mass yield and composition estimation task by extending the ground-level estimation to the large surfaces occupied by fields/paddocks. Drone images come at the cost of lower resolution views of the fields taken from a high altitude and requires further herbage ground-truth collection from the large surfaces covered by drone images. This paper proposes to transfer knowledge learned on ground-level images to raw drone images in an unsupervised manner. To do so, we use unpaired image style translation to enhance the resolution of drone images by a factor of eight and modify them to appear closer to their ground-level counterparts. We then use the enhanced drone images to train a semi-supervised algorithm that uses ground-truthed, ground-level images as the labelled data together [GRAPHICS] with a large amount of unlabeled drone images. We validate our results on a small held-out drone image test set to show the validity of our approach, which opens the way f

关键词： Deep learning computer vision Image resolution Costs Estimation Prediction algorithms Cameras

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Contactless Blood Pressure Measurement via Remote Photoplethysmography with Synthetic Data Generation Using Generative Adversarial Network

Contactless Blood Pressure Measurement via Remote Photopleth...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wu, Bing-Fei Chiu, Li-Wen Wu, Yi-Chiao Lai, Chun-Chih Chu, Pao-Hsien Natl Yang Ming Chiao Tung Univ Inst Elect & Control Engn New Taipei Taiwan Chang Gung Univ Chang Gung Mem Hosp Taoyuan Taiwan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deriving blood pressure in a non-invasive way via photoplethysmography (PPG) signals has become a familiar topic. With the knowledge of the relation between PPG and blood pressure, we expect to further make the measurement contactless for convenience reasons. An alternative signal source is remote photoplethysmography (rPPG) signals. There are mainly two kinds of approaches for exploiting blood pressure through PPG signals, one is by calculating the pulse transit time of the arterial pulse wave at two consecutive sites and the other is based on waveform feature analysis from a single signal. The calibration procedure is necessary for the former way, which leads to some limitations in general use. On the other hand, the properties of the rPPG waveform are far from PPG signals. Hence, the known waveform features in PPG signals are hard to be leveraged in the case of rPPG signals. Recently, convolutional neural networks are also applied for solving this problem. However, the lack of data is an obstacle to the training procedure and evaluation. In this study, a multichannel rPPG-based blood pressure estimator is proposed. To ease the data scarcity issue, the generative adversarial network is adopted to augment synthetic waveform data. Besides, as we know that some physiological states like age and BMI are dominant factors in blood pressure. InfoGAN is chosen in this work to generate the synthetic data with the blood pressure value fluctuating correspondingly to the controlled age and BMI combination. The proposed model outperforms the state-of-the-art methods on MIMIC III and Cuffless datasets. With the synthetic data generation, the mean absolute error (MAE) is reduced to 6.72 and 5.95 mmHg in MAP and DBP respectively. The standard deviations of the MAEs are also reduced. In the rPPG case, the MAE of SBP is 9.13 and 8.76 mmHg for DBP.

关键词： Training Interference Photoplethysmography Generative adversarial networks Blood pressure Pressure measurement pattern recognition

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Variable Few Shot Class Incremental and OpenWorld Learning

Variable Few Shot Class Incremental and OpenWorld Learning

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ahmad, Touqeer Dhamija, Akshay Raj Jafarzadeh, Mohsen Cruz, Steve Rabinowitz, Ryan Li, Chunchun Boult, Terrance E. Univ Colorado Vis & Secur Technol Lab Colorado Springs CO 80907 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Prior work on few-shot class incremental learning has operated with an unnatural assumption: the number of ways and number of shots are assumed to be known and fixed e.g., 10-ways 5-shots, 5-ways 5-shots, etc. Hence, we refer to this setting as Fixed-Few-Shot Class Incremental Learning (FFSCIL). In practice, the pre-specified fixed number of classes and examples per class may not be available, meaning one cannot update the model. Evaluation of FSCIL approaches in such unnatural settings renders their applicability questionable for practical scenarios where such assumptions do not hold. To mitigate the limitation of FFSCIL, we propose Variable-Few-Shot Class Incremental Learning (VFSCIL) and demonstrate it with Up-to N-Ways, Up-to K-Shots class incremental learning;wherein each incremental session, a learner may have up to N classes and up to K samples per class. Consequently, conventional FFSCIL is a special case of herein introduced VFSCIL. Further, we extend VFSCIL to a more practical problem of Variable-Few-Shot OpenWorld Learning (VFSOWL), where an agent is not only required to perform incremental learning, but must detect unknown samples and enroll only those that it detects correctly. We formulate and study VFSCIL and VFSOWL on two benchmark datasets conventionally employed for FFSCIL i.e., Caltech-UCSD Birds-200-2011 (CUB200) and miniImageNet. First, to serve as a strong baseline, we extend the state-of-the-art FSCIL approach to operate in Up-to N-Ways, Up-to K-Shots class incremental and open-world settings. Then, we propose a novel but simple approach for VFSCIL/VFSOWL where we leverage the current advancements in self-supervised feature learning. Utilizing both benchmark datasets, our proposed approach outperforms the strong baseline on the conventional FFSCIL setting and newly introduced VFSCIL/VFSOWL settings.

关键词： Representation learning computer vision Codes conferences Benchmark testing Feature extraction Power capacitors

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Attack-Agnostic Deep Face Anti-Spoofing

Attack-Agnostic Deep Face Anti-Spoofing

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Liu, Ajian Tan, Zichang Liang, Yanyan Wan, Jun Institute of Automation Chinese Academy of Sciences Mais Beijing China Institute of Deep Learning Baidu Research Beijing China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350302493

The task of face anti-spoofing (FAS) is to determine whether the captured face from a face recognition system is live or fake. Current methods which are trained with existing fake faces ignore the generalization and perform poorly on unseen attacks. To tackle this problem, a novel Attack-agnostic Face Anti-spoofing framework is proposed. Different from previous methods that can be treated as a defense system, we regard face anti-spoofing as a unified framework with the attack and defense systems and optimize the defense system against unseen attacks via adversarial training with the attack system. Concretely, the attack system consists of two modules: an Adversarial learning-based Attack pattern Generation (Adv-APG) module and a Supervised learning-based Attack pattern Drift (Sup-APD) module. The Adv-APG module generates a series of spoofing samples by recombining a live face with known attack patterns in a generative way. The Sup-APD module pulls the generated spoofing samples in a supervised way to an unknown domain that makes the defense system ineffective. The defense system is free to choose and compatible with our attack system. Extensive experiments are conducted by using three different defense architectures to verify that the proposed attack system can improve the performance on both seen- and unseen attacks on multiple datasets. © 2023 ieee.

关键词： Face recognition

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Flow cytometry with event-based vision and spiking neuromorphic hardware

Flow cytometry with event-based vision and spiking neuromorp...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Abreu, Steven Gouda, Muhammed Lugnan, Alessio Bienstman, Peter University of Groningen Groningen Netherlands Ghent University - Imec Ghent Belgium

ISBN: (纸本)9798350302493

Imaging flow cytometry systems play a critical role in the identification and characterization of large populations of cells or micro-particles. Such systems typically leverage deep artificial neural networks to classify samples. Here we show that an event-based camera and neuromorphic processor can be used in a flow cytometry setup to solve a binary particle classification task with less memory usage, and promising improvements in latency and energy scaling. To reduce the complexity of the spiking neural network, we combine the event-based camera with a free-space optical setup which acts as a non-linear high-dimensional feature map that is computed at the speed of light before the event-based camera receives the signal. We demonstrate, for the first time, a spiking neural network running on neuromorphic hardware for a fully event-based flow cytometry pipeline with 98.45% testing accuracy. Our best artificial neural network on frames of the same data reaches only 97.51%, establishing a neuromorphic advantage also in classification accuracy. We further show that our system will scale favorably to more complex classification tasks. We pave the way for real-time classification with throughput of up to 1,000 samples per second and open up new possibilities for online and on-chip learning in flow cytometry applications. © 2023 ieee.

关键词： Cameras

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Stereo Cross Global Learnable Attention Module for Stereo Image Super-Resolution

Stereo Cross Global Learnable Attention Module for Stereo Im...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Zhou, Yuanbo Xue, Yuyang Deng, Wei Nie, Ruofeng Zhang, Jiajun Pu, Jiaqi Gao, Qinquan Lan, Junlin Tong, Tong Fuzhou University China University of Edinburgh United Kingdom Imperial Vision Technology

ISBN: (纸本)9798350302493

Stereo super-resolution is a technique that utilizes corresponding information from multiple viewpoints to enhance the texture of low-resolution images. In recent years, numerous impressive works have advocated attention mechanisms based on epipolar constraints to boost the performance of stereo super-resolution. However, techniques that exclusively depend on epipolar constraint attention are insufficient to recover realistic and natural textures for heavily corrupted low-resolution images. We noticed that global self-similarity features within the image and across the views can proficiently fix the texture details of low-resolution images that are severely damaged. Therefore, in the current paper, we propose a stereo cross global learnable attention module (SCGLAM), aiming to improve the performance of stereo super-resolution. The experimental outcomes show that our approach outperforms others when dealing with heavily damaged low-resolution images. The relevant code is made available on this link as open source. © 2023 ieee.

关键词： Textures

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Diversified and Multi-Class Controllable Industrial Defect Synthesis for Data Augmentation and Transfer

Diversified and Multi-Class Controllable Industrial Defect S...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Wei, Jing Shen, Fei Lv, Chengkan Zhang, Zhengtao Zhang, Feng Yang, Huabin Institute of Automation Chinese Academy of Sciences China The School of Artificial Intelligence University of Chinese Academy of Sciences China Casi Vision Technology CO. China

ISBN: (纸本)9798350302493

Data augmentation is crucial to solve few-sample issues in industrial inspection based on deep learning. However, current industrial data augmentation methods have not yet demonstrated on-par ability in the synthesis of complex defects with pixel-level annotations. This paper proposes a new defect synthesis framework to fill the gap. Firstly, DCDGANc (Diversified and multi-class Controllable Defect Generation Adversarial Networks based on constant source images) is proposed to employ class labels to construct source inputs to control the category and random codes to generate diversified styles of defects. DCDGANc can generate defect content images with pure backgrounds, which avoids the influence of non-defect information and makes it easy to obtain binary masks by segmentation. Secondly, the Poisson blending is improved to avoid content loss when blending generated defect contents to the normal backgrounds. Finally, the complete defect samples and accurate pixel-level annotations are obtained by fine image processing. Experiments are conducted to verify the effectiveness of our work in wood, fabric, metal, and marble. The results show that our methods yield significant improvement in the segmentation performance of industrial products. Moreover, our work enables zero-shot inspection by facilitating defect transfer between datasets with different backgrounds but similar defects, which can greatly reduce the cost of data collection in industrial inspection. © 2023 ieee.

关键词： Defects

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Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural Network

Asynchronous Events-based Panoptic Segmentation using Graph ...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Kachole, Sanket Alkendi, Yusra Baghaei Naeini, Fariborz Makris, Dimitrios Zweiri, Yahya Kingston University Department of Computer Science London United Kingdom Abu Dhabi United Arab Emirates Khalifa University of Science and Technology Department of Aerospace Engineering Abu Dhabi United Arab Emirates Ipsotek An Eviden Company London United Kingdom

ISBN: (纸本)9798350302493

In the context of robotic grasping, object segmentation encounters several difficulties when faced with dynamic conditions such as real-time operation, occlusion, low lighting, motion blur, and object size variability. In response to these challenges, we propose the Graph Mixer Neural Network that includes a novel collaborative contextual mixing layer, applied to 3D event graphs formed on asynchronous events. The proposed layer is designed to spread spatiotemporal correlation within an event graph at four nearest neighbor levels parallelly. We evaluate the effectiveness of our proposed method on the Event-based Segmentation (ESD) Dataset, which includes five unique image degradation challenges, including occlusion, blur, brightness, trajectory, scale variance, and segmentation of known and unknown objects. The results show that our proposed approach outperforms state-of-the-art methods in terms of mean intersection over the union and pixel accuracy. Code available at: https://***/sanket0707/*** © 2023 ieee.

关键词： Image segmentation

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