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Large-field Gesture Tracking and recognition for Augmented Reality Interaction

Large-field Gesture Tracking and Recognition for Augmented R...

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2023 asia-Pacific conference on image processing, Electronics and computers, IPEC 2023

作者： Zhao, Ruiyi Ge, Yangshi Duan, Ye Jiang, Quanhong Beihang University Beijing China Australian National University Canberra Australia

In recent years, with the continuous development of computer vision and artificial intelligence technology, gesture recognition is widely used in many fields, such as virtual reality, augmented reality and so on. However, the traditional binocular camera architecture is limited by its limited field of view Angle and depth perception range. Fisheye camera is gradually applied in gesture recognition field because of its advantage of larger field of view Angle. Fisheye cameras offer a wider field of vision than previous binocular cameras, allowing for a greater range of gesture recognition. This gives fisheye cameras a distinct advantage in situations that require a wide field of view. However, because the imaging mode of fisheye camera is different from traditional camera, the image of fisheye camera has a certain degree of distortion, which makes the calculation of gesture recognition more complicated. Our goal is to design a distortion correction processing strategy suitable for fisheye cameras in order to extend the range of gesture recognition and achieve large field of view gesture recognition. Combined with binocular technology, we can use the acquired hand depth information to enrich the means of interaction. By taking advantage of the large viewing Angle of the fisheye camera to expand the range of gesture recognition, make it more extensive and accurate. This will help improve the real-time and precision of gesture recognition, which has important implications for artificial intelligence, virtual reality and augmented reality. © Published under licence by IOP Publishing Ltd.

关键词： Human computer interaction

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Dilated Convolutional Transformer for High-Quality image Deraining

Dilated Convolutional Transformer for High-Quality Image Der...

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

作者： Li, Yufeng Lu, Jiyang Chen, Hongming Wu, Xianhao Chen, Xiang Shenyang Aerospace University College of Electronic and Information Engineering China Nanjing University of Science and Technology School of Computer Science and Engineering China

ISBN: (纸本)9798350302493

Convolutional neural networks (CNNs) and Transformers have achieved significant success in image signal processing. However, little effort has been made to effectively combine the properties of these two architectures to satisfy image deraining. In this paper, we propose an effective de-raining method, dilated convolutional Transformer (DCT), which can enlarge the receptive fields of the network to aggregate global information. The fundamental building unit of our approach is the dilformer block containing multi-dilconv sparse attention (MDSA) and multi-dilconv feed-forward network (MDFN). The MDSA calculates the multi-scale query to generate accurate similarity map so that rich multi-scale information can be better utilized for the high-quality image reconstruction. In addition, we adopt ReLU to replace the original softmax to enforce sparsity in the Transformer for better feature aggregation. The MDFN is further established to better integrate the rain information of different scales in the feature transformation. Extensive experiments on the benchmarks show the favorable performance against state-of-the-art approaches. © 2023 IEEE.

关键词： image reconstruction

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Are Local Features All You Need for Cross-Domain Visual Place recognition?

Are Local Features All You Need for Cross-Domain Visual Plac...

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

作者： Barbarani, Giovanni Mostafa, Mohamad Bayramov, Hajali Trivigno, Gabriele Berton, Gabriele Masone, Carlo Caputo, Barbara Politecnico di Torino Italy

ISBN: (纸本)9798350302493

Visual Place recognition is a task that aims to predict the coordinates of an image (called query) based solely on visual clues. Most commonly, a retrieval approach is adopted, where the query is matched to the most similar images from a large database of geotagged photos, using learned global descriptors. Despite recent advances, recognizing the same place when the query comes from a significantly different distribution is still a major hurdle for state of the art retrieval methods. Examples are heavy illumination changes (e.g. night-time images) or substantial occlusions (e.g. transient objects). In this work we explore whether re-ranking methods based on spatial verification can tackle these challenges, following the intuition that local descriptors are inherently more robust than global features to domain shifts. To this end, we provide a new, comprehensive benchmark on current state of the art models. We also introduce two new demanding datasets with night and occluded queries, to be matched against a citywide database. Code and datasets are available at https://***/gbarbarani/re-ranking-for-VPR. © 2023 IEEE.

关键词： Query processing

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A vision-Based Counter System for Children’s Dribble 23

A Vision-Based Counter System for Children’s Dribble

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9th International conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Jingxin, Liang Chengqi, Liang Xufeng, Ling Jie, Yang Shanghai Normal University Tianhua College AI School No. 1661 North Sheng Xin Road *** China Gaozhou Middle School Guangdong Province Gaozhou China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University 800 Dongchuan Road 200240 China

ISBN: (纸本)9781450399029

In this paper, a vision-based counter system for children’s dribble based on Google MediaPipe’s human posture recognition algorithm and YOLOv5 object recognition algorithm is introduced. Firstly, the hands’ coordinates of the child is located using MediaPipe, and the coordinates of the ball is found using YOLOv5;by the relative positions of the hands and the ball in the multi-frame images of the video, an action sequence of ball holding, touching and ball bouncing is recognized and used to count the number of dribble. The experimental results show that the system can count a single child’s dribble accurately. © 2023 Copyright held by the owner/author(s).

关键词： computer vision

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A Systematic Review: How computer vision is Transforming Agriculture in Economic Growth 3rd

A Systematic Review: How Computer Vision is Transforming Agr...

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3rd International conference on Artificial Intelligence and Knowledge processing (AIKP)

作者： Karanam, Santoshachandra Rao Kumar, A. B. Pradeep Yandrapati, Prakash Babu Tangudu, Naresh Peddada, Nagamani Bollipelly, PruthviRaj Goud GITAM Deemed Univ CSE Dept Hyderabad India Aditya Inst Technol & Management CSD & MCA Dept Tekkali Srikakulam India Anurag Univ IT Dept Hyderabad India

ISBN: (纸本)9783031686160;9783031686177

Recent years have seen the emergence of computer vision, a subfield of artificial intelligence (AI), as a technology that has the potential to revolutionize agricultural practices. This might have an impact on many agricultural practices and crop management techniques. This is due to the fact that computer vision can examine pictures and identify patterns of data. This article provides a summary of the uses of computer vision in agriculture as well as the consequences such applications have had. The issues of precision agriculture, disease diagnosis, crop monitoring, and yield computation may be overcome with the use of computer vision technologies such as image recognition, object detection, and pattern analysis. Specifically, it investigates the ways in which these strategies are useful. In addition to this, it investigates the benefits, drawbacks, and possible future applications of computer vision in agriculture, with a particular emphasis on the potential for the sector to improve its levels of productivity, sustainability, and profitability. According to this in-depth analysis, computer vision has revolutionized the agricultural industry and contributed significantly to economic growth. In order to evaluate the financial impacts that computer vision has had on the agricultural industry, this study looks at a wide range of academic papers, publications, and reports. The findings highlight the advancements, benefits, challenges, and future opportunities presented by computer vision technology in the areas of crop monitoring, precision farming, animal management, and harvesting. According to the evaluation, computer vision has the potential to improve farming in terms of productivity, resource allocation, cost reduction, and sustainable practices.

关键词： computer vision Artificial Intelligence Economic Growth Agriculture Management Agricultural Automation Machine Learning

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Exploring the Capability of NASNet Model for Plant Classification on GRASP-125 Dataset 4

Exploring the Capability of NASNet Model for Plant Classific...

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4th International conference for Emerging Technology, INCET 2023

作者： Pushpa, B.R. Hemanth Kumar, M. Shobha Rani, N. Mysuru Campus Amrita Vishwa Vidyapeetham Dept. of Computer Science School of Computing India

ISBN: (纸本)9798350335750

Plant classification and identification, which has strong ties to both computer science and taxonomy, is becoming an increasingly dynamic area of research within the field of computer recognition. Researchers have utilized different techniques including image processing, pattern recognition, and machine learning to develop systems capable of accurately identifying and classifying plants. Access to large image databases and advances in image processing and artificial intelligence have led to the development of highly accurate plant identification systems. These systems have the potential to greatly reduce the amount of time and proficiency required for plant identification, which are only achievable by skilled taxonomists. In this study, we experimented with the Greek vAScular Plants (GRASP) dataset, consisting of images across 125 different plant species native to the mountains belonging Oiti and Parnassus in central Greece, to perform automatic plant identification using a deep learning approach namely NASNet. These plant species, which include both rare and aesthetically pleasing varieties, are often found along popular hiking routes in the region. Our method for plant classification utilizes deep learning techniques on the GRASP-125 dataset, with the goal of improving the accuracy and efficiency of plant identification. © 2023 IEEE.

关键词： computer vision

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Speed Is All You Need: On-Device Acceleration of Large Diffusion Models via GPU-Aware Optimizations

Speed Is All You Need: On-Device Acceleration of Large Diffu...

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

作者： Chen, Yu-Hui Sarokin, Raman Lee, Juhyun Tang, Jiuqiang Chang, Chuo-Ling Kulik, Andrei Grundmann, Matthias Google LLC 1600 Amphitheatre Parkway Mountain ViewCA94043 United States

ISBN: (纸本)9798350302493

The rapid development and application of foundation models have revolutionized the field of artificial intelligence. Large diffusion models have gained significant attention for their ability to generate photorealistic images and support various tasks. On-device deployment of these models provides benefits such as lower server costs, offline functionality, and improved user privacy. However, common large diffusion models have over 1 billion parameters and pose challenges due to restricted computational and memory resources on devices. We present a series of implementation optimizations for large diffusion models that achieve the fastest reported inference latency to-date(under 12 seconds for Stable Diffusion 1.4 without INT8 quantization for a 512 × 512 image with 20 iterations) on GPU-equipped mobile devices. These enhancements broaden the applicability of generative AI and improve the overall user experience across a wide range of devices. © 2023 IEEE.

关键词： Graphics processing unit

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Lightweight target detection method based on improved YOLOv5

Lightweight target detection method based on improved YOLOv5

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2023 International conference on image, Signal processing, and pattern recognition, ISPP 2023

作者： Feng, Jianxin Zhou, Minghao Hao, Enguang Pan, Chengsheng Liaoning Key Laboratory of Communication Network Liaoning Dalian116622 China College of Information Engineering Dalian University Liaoning Dalian116622 China

ISBN: (纸本)9781510666351

Target detection is one of the hot research issues in the field of computer vision in recent years. Large scale convolution networks can effectively improve the accuracy of target detection, but they are not suitable for application scenarios with limited computing and storage capacity. In order to solve the above problems, this paper proposes a lightweight target detection network based on improved YOLOv5 - CS-YOLOv5 network. This network uses a lightweight network ShuffleNetv2 to reconstruct backbone, effectively reducing the network complexity, and adds a CA attention module to the feature extraction network to enhance the network feature expression ability, and solves the problem of neuron deactivation through H-Swish activation function. The final model size was compressed to 1.64MB, and the mAP50 on the COCO test set reached 47.6%, reducing the model size by 13.7% and improving the detection accuracy by 1.9% compared with the original YOLOv5 model. © 2023 SPIE.

关键词： image processing

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Design of a Crop Disease Identification System Based on LabVIEW Machine vision 7

Design of a Crop Disease Identification System Based on LabV...

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7th IEEE Information Technology and Mechatronics Engineering conference, ITOEC 2023

作者： Tian, Liguo Han, Han Li, Meng Wang, Yuesong Liu, Jinqi Cui, Xudong Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin China

ISBN: (纸本)9798350334197

A crop disease detection method suitable for modern agricultural greenhouses is proposed in this paper. Based on the principle of machine vision detection technology and LabVIEW software program, the disease recognition and detection on the surface of potted crops can be quickly achieved. The system first preprocesses the image, and through processes such as system calibration and image correction, grayscale histogram analysis, threshold segmentation, particle analysis and processing, feature analysis, and pattern matching, it achieves rapid recognition of crop diseases. When performing threshold segmentation on images, this system introduces a background correction algorithm to preprocess the images. The image recognition method for crop leaf diseases studied in this article makes monitoring and formulating pest control measures more effective. © 2023 IEEE.

关键词： computer vision

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Contactless Gesture recognition Using Air Canvas 8th

Contactless Gesture Recognition Using Air Canvas

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8th International conference on ICT for Sustainable Development, ICT4SD 2023

作者： Gosavi, Janhavi Kadam, Neerajaa Shetty, Ameya Verekar, Anant Vishwakarma, Pinki Department of Computer Engineering Shah and Anchor Kutchhi College Mumbai India

ISBN: (纸本)9789819949311

The proposed work is a real-time implementation in Python for air-writing using algorithms like media pipe hands and easy OCR. Air writing has emerged as one of the most difficult study fields in image processing and pattern recognition in recent years. Air writing is the act of using one’s hands or fingers to scribble letters or words in a clear area. The proposed work aims at the implementation of a contactless gesture recognition software which uses an air canvas which sums up as providing an image input to the machine via a camera device, while the machine processes it and gives a delineated digital output of the work in real time, features such as optical character recognition, screenshot, and duplication will be an added attempt at closing the gap of limitations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： computer vision

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