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Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in computer vision Tasks for Robotic Grasping on the Edge

Fast GraspNeXt: A Fast Self-Attention Neural Network Archite...

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2023 IEEE/CVF conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Wong, Alexander Wu, Yifan Abbasi, Saad Nair, Saeejith Chen, Yuhao Shafiee, Mohammad Javad Vision and Image Processing Research Group University of Waterloo Canada Waterloo Artificial Intelligence Institute WaterlooON Canada DarwinAI Corp. WaterlooON Canada

ISBN: (纸本)9798350302493

Multi-task learning has shown considerable promise for improving the performance of deep learning-driven vision systems for the purpose of robotic grasping. However, high architectural and computational complexity can result in poor suitability for deployment on embedded devices that are typically leveraged in robotic arms for real-world manufacturing and warehouse environments. As such, the design of highly efficient multi-task deep neural network architectures tailored for computer vision tasks for robotic grasping on the edge is highly desired for widespread adoption in manufacturing environments. Motivated by this, we propose Fast GraspNeXt, a fast self-attention neural network architecture tailored for embedded multi-task learning in computer vision tasks for robotic grasping. To build Fast GraspNeXt, we leverage a generative network architecture search strategy with a set of architectural constraints customized to achieve a strong balance between multitask learning performance and embedded inference efficiency. Experimental results on the MetaGraspNet benchmark dataset show that the Fast GraspNeXt network design achieves the highest performance (average precision (AP), accuracy, and mean squared error (MSE)) across multiple computer vision tasks when compared to other efficient multi-task network architecture designs, while having only 17.8M parameters (about >5× smaller), 259 GFLOPs (as much as >5× lower) and as much as >3.15× faster on a NVIDIA Jetson TX2 embedded processor. © 2023 IEEE.

关键词： computer vision

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Systematic Architectural Design of Scale Transformed Attention Condenser DNNs via Multi-Scale Class Representational Response Similarity Analysis

Systematic Architectural Design of Scale Transformed Attenti...

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2023 IEEE/CVF conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Hryniowski, Andrew Wong, Alexander Vision and Image Processing Research Group University of Waterloo Canada Waterloo Artificial Intelligence Institute WaterlooON Canada DarwinAI Corp. WaterlooON Canada

ISBN: (纸本)9798350302493

Self-attention mechanisms are commonly included in a convolutional neural networks to achieve an improved efficiency performance balance. However, adding self-attention mechanisms adds additional hyperparameters to tune for the application at hand. In this work we propose a novel type of DNN analysis called Multi-Scale Class Representational Response Similarity Analysis (ClassRep-Sim) which can be used to identify specific design interventions that lead to more efficient self-attention convolutional neural network architectures. Using insights grained from ClassRepSim we propose the Spatial Transformed Attention Condenser (STAC) module, a novel attention-condenser based self-attention module. We show that adding STAC modules to ResNet style architectures can result in up to a 1.6% increase in top-1 accuracy compared to vanilla ResNet models and up to a 0.5% increase in top-1 accuracy compared to SENet models on the imageNet64x64 dataset, at the cost of up to 1.7% increase in FLOPs and 2x the number of parameters. In addition, we demonstrate that results from ClassRepSim analysis can be used to select an effective parameterization of the STAC module resulting in competitive performance compared to an extensive parameter search. © 2023 IEEE.

关键词： Convolutional neural networks

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Estimation of Varietal Classification and Quality Analysis in Rice Seeds Using Digital image processing - A Survey

Estimation of Varietal Classification and Quality Analysis i...

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2023 IEEE International conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Sandhiya, M. Christy, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350305708

Agriculture plays a major role in today's world. 70% of rice production is cultivated in south India. Thailand, the US, India, Vietnam, etc., are exporting rice all over the world. But they are facing one of the most difficult tasks: assuring the rice seed quality. Good seeds can increase production, so selecting seeds is very important. A maximum of the work was done physically or by using huge, expensive machines. For choosing good seeds, agronomists be conscious of two factors, such as the variety which is determined by some quality criteria, and next by the skilled one who is going to analyze the rice seed physically. Both tasks take a long time and the result of accuracy is also not as expected. Nowadays computer vision technology with Digital image processing plays a major role in many fields, namely agronomy, healthcare, machine vision, pattern recognition, remote sensing, Video processing, etc. Our research work mainly focuses on the agricultural arena, going to provide good quality seeds and identify dissimilar varieties of rice seeds by digital image processing techniques such as image capturing, segmenting the rice seeds, extracting the features from the rice seeds and finally feeding into the machine learning algorithm to find out the accuracy too. © 2023 IEEE.

关键词： Classification of rice seeds Machine learning algorithm Machine vision Quality analysis of rice seed Rice seed varietal purity

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Real Time pattern recognition with Support Vector Machines and Local Binary patterns 1

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1st International conference on Advanced Computing Techniques in Engineering and Technology, ACTET 2023

作者： Baby, Ann Akhil Kumar, K.M. Kerala Kalamassery India

ISBN: (数字)9783031541629

ISBN: (纸本)9783031541612

This research introduces a facial recognition-based attendance system that leverages an open-source computer vision library and integrates with a real-time database system. The system comprises essential components, including a camera for image capture, a computer for image processing, and a database containing registered facial data. As individuals enter a room, the camera captures their images, which are then subjected to facial detection and recognition by the computer. The system conducts a comparison between the detected faces and the pre-registered facial data in the database, subsequently logging the attendance of individuals who are successfully recognized. Moreover, the system offers functionality for the inclusion of new facial data into the database, facilitating future attendance tracking. To gauge the system's performance, an assessment was carried out using a dataset of 100 images. The results indicate an accuracy rate of 95% in both facial detection and recognition tasks. This system holds promise for a wide range of practical applications in diverse environments, such as educational institutions, workplaces, and event management, by automating attendance management processes and alleviating the burdens associated with manual record-keeping. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cameras

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A Curated Dataset for Spinach Species Identification 1

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7th International conference on computer vision and image processing, CVIP 2022

作者： Priyadharshini, R. Ahila Arivazhagan, S. Arun, M. Centre for Image Processing and Pattern Recognition Mepco Schlenk Engineering College Sivakasi India

ISBN: (数字)9783031314179

ISBN: (纸本)9783031314162

India has a large population of spinach eaters. Despite this fact most people and young generation have difficulty in distinguishing the spinach species because of the structure similarity of many plant species. So, automated spinach recognition will support the people community to a greater extent. In this study, we present spinach dataset, a freely accessible annotated collection of images of spinach leaves in Indian scenario. We propose three different custom designed convolutional neural networks (CNN) and compare the performance of the same. Also we apply the transfer learning approach using MobileNetV2 pretrained model for this spinach species recognition. Using transfer learning approach we got an accuracy of 92.96%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Plant diseases

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Trees Detection from Aerial images Using the YOLOv5 Family 6th

Trees Detection from Aerial Images Using the YOLOv5 Family

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6th International conference on Recent Trends in image processing and pattern recognition, RTIP2R 2023

作者： Houde, Kavita V. Kamble, Parshuram M. Hegadi, Ravindra S. Department of Computer Science Central University of Karnataka Kalaburagi Kadaganchi India

ISBN: (纸本)9783031530814

Deep learning object recognition models, which are widely used in computer vision, may provide an opportunity to accurately recognize fruit trees. This is essential for fast data collection, selection and reducing human operational errors. This paper proposes a YOLOv5-based detection model for fruit tree detection in the farm plantation using UAV-collected data. This proposed model detects individual fruit trees from the agriculture field and also provide counts how many trees are detected. An image dataset was created from the publicly available UAV captured data which contains total 36 images. Among them 27 were used for training and 9 for testing the proposed model. Four different YOLOv5 scales for object recognition (YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) were selected for training, validation, and testing on image datasets. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object recognition

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26th Iberoamerican Congress on pattern recognition, CIARP 2023

26th Iberoamerican Congress on Pattern Recognition, CIARP 20...

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26th Iberoamerican Congress on pattern recognition, CIARP 2023

ISBN: (纸本)9783031490170

The proceedings contain 61 papers. The special focus in this conference is on pattern recognition. The topics include: Time Distributed Multiview Representation for Speech Emotion recognition;detection of Covid-19 in Chest X-Ray images Using Percolation Features and Hermite Polynomial Classification;abandoned Object Detection Using Persistent Homology;interactive Segmentation with Incremental Watershed Cuts;supervised Learning of Hierarchical image Segmentation;unveiling the Influence of image Super-Resolution on Aerial Scene Classification;Weeds Classification with Deep Learning: An Investigation Using CNN, vision Transformers, Pyramid vision Transformers, and Ensemble Strategy;leveraging Question Answering for Domain-Agnostic Information Extraction;towards a Robust Solution for the Supermarket Shelf Audit Problem: Obsolete Price Tags in Shelves;graph Embedding of Almost Constant Large Graphs;a Self-Organizing Map Clustering Approach to Support Territorial Zoning;spatial-Temporal Graph Transformer for Surgical Skill Assessment in Simulation Sessions;deep Learning in the Identification of Psoriatic Skin Lesions;wildFruiP: Estimating Fruit Physicochemical Parameters from images Captured in the Wild;depression Detection Using Deep Learning and Natural Language processing Techniques: A Comparative Study;impact of Synthetic images on Morphing Attack Detection Using a Siamese Network;face image Quality Estimation on Presentation Attack Detection;knowledge Distillation of vision Transformers and Convolutional Networks to Predict Inflammatory Bowel Disease;analysis and Impact of Training Set Size in Cross-Subject Human Activity recognition;efficient Brazilian Sign Language recognition: A Study on Mobile Devices;feature Importance for Clustering;presumably Correct Undersampling;leveraging Longitudinal Data for Cardiomegaly and Change Detection in Chest Radiography;self-supervised Monocular Depth Estimation on Unseen Synthetic Cameras;novelty Detection in Human-Machine Inter

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Cell-like SNP System with Several Types of Spikes and Generation of Basic Picture Languages

Cell-like SNP System with Several Types of Spikes and Genera...

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International conference on computer vision and Internet of Things 2023, ICCVIoT 2023

作者： Ceon, Y. Preethi Christinal, A. Hepzibah Jebasingh, S. Chandy, D. Abraham Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9781839539848

A basic picture language consists of basic pictures (patterns) described using strings over finite alphabets and generated using the Chomsky form of grammars. Spiking Neural P Systems (SN P Systems) is a nature inspired computing in which the transformation of collections of elements occurs through the absorption of an existing elements within a membrane -like structure and concurrently producing a new multiset of alphabets. It was found to compute string language and functions. pattern recognition and image processing are practical applications stemming from the scientific exploration of picture languages and the grammatical structures used to generate them. In this paper, we compare the family of basic picture languages generated by regular, linear, and context free grammars with the family of languages generated by cell-like spiking neural P system with several types of Spikes. © The Institution of Engineering & Technology 2023.

关键词： pattern recognition

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SC-NAFSSR: Perceptual-Oriented Stereo image Super-Resolution Using Stereo Consistency Guided NAFSSR

SC-NAFSSR: Perceptual-Oriented Stereo Image Super-Resolution...

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2023 IEEE/CVF conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Qiu, Zidian He, Zongyao Zhan, Zhihao Pan, Zilin Xian, Xingyuan Jin, Zhi Sun Yat-sen University China Guangdong Provincial Key Laboratory of Fire Science and Technology China

ISBN: (纸本)9798350302493

Stereo image Super-Resolution (SR) has made significant progress since binocular systems are widely accepted in recent years. Most stereo SR methods focus on improving the PSNR performance, while their visual quality is over-smoothing and lack of detail. Perceptual-oriented SR methods are mainly designed for single-view images, thereby their performance decreases on stereo SR due to stereo inconsistency. We propose a perceptual-oriented stereo SR framework that considers both single-view and cross-view information, noted as SC-NAFSSR. With NAF-SSR [3] as our backbone, we combine LPIPS-based perceptual loss and VGG-based perceptual loss for perceptual training. To improve stereo consistency, we perform supervision on each Stereo Cross-Attention Module (SCAM) with stereo consistency loss [27], which calculates photometric loss, smoothness loss, and cycle loss using the cycle-attention maps and valid masks of SCAM. Furthermore, we propose training strategies to fully exploit the performance on perceptual-oriented stereo SR. Both extensive experiments and ablation studies demonstrate the effectiveness of our proposed method. In particular, SC-NAFSSR outperforms the SOTA methods on Flickr1024 dataset [30]. In the NTIRE 2023 Stereo image Super-Resolution Challenge Track 2 Perceptual & Bicubic [26], SC-NAFSSR ranked 2nd place on the leaderboard. Our source code is available at https://***/FVL2020/SC-NAFSSR. © 2023 IEEE.

关键词： Stereo image processing

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A Multi-modality Driven Promptable Transformer for Automated Parapneumonic Effusion Staging 6th

A Multi-modality Driven Promptable Transformer for Automated...

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6th Chinese conference on pattern recognition and computer vision (PRCV)

作者： Chen, Yan Liu, Qing Xiang, Yao Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China Ningbo 2 Hosp Ningbo Peoples R China Ningbo Univ Elect Engn & Comp Sci Ningbo Peoples R China

ISBN: (纸本)9789819985579;9789819985586

Pneumonia is a prevalent disease, and some pneumonia patients develop parapneumonic effusion. Among patients with pneumonia, the mortality rate of patients with parapneumonic effusion is higher than that of patients without parapneumonic effusions. Parapneumonic effusion is classified as the uncomplicated stage or complicated stage. Patients with complicated parapneumonic effusion necessitate pleural drainage, while patients with uncomplicated parapneumonic effusion only require antibiotic treatment. Consequently, staging parapneumonic effusion plays a crucial role in reducing mortality among pneumonia patients. The previous method employs convolutional neural networks to extract features from CT slice and graph neural networks for classifying the slice-level feature sequence in parapneumonic effusion staging. However, we argue that transformers outperform graph neural networks in feature sequence classification. Thus, we integrate transformers into our method. Distinguishing between uncomplicated and complicated parapneumonic effusions based solely on CT slices is challenging due to their similar appearance. Therefore, we incorporate additional information in the form of a prompt to aid in the staging process. This allows us to propose a promptable model for parapneumonic effusion staging that incorporates multimodal information. Our method surpasses previous approaches, achieving impressive results with an F1-score of 92.72 and an AUC of 96.67.

关键词： Deep learning Medical image processing Pneumonia effusion staging

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