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Improved Classification Techniques Using Convolutional Neural Networks for Rice Plant Disease Detection 4th

Improved Classification Techniques Using Convolutional Neura...

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4th international conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Saxena, Swati Gohil, Bhavesh N. Sardar Vallabhbhai Natl Inst Technol SVNIT Surat Gujarat India

ISBN: (纸本)9783031837951;9783031837968

Rice, one of the most important crops globally, serves as a vital nutritional source for over 50% of the world's population. However, it is highly susceptible to various diseases that can severely impact both the quality and quantity of the harvest, sometimes causing a reduction of 20%-40% in crop yield. Timely detection and accurate identification of rice plant diseases are critical for ensuring a healthy harvest. Farmers must be able to recognize these diseases visually, but daily field surveys are labor-intensive, time-consuming and costly, ultimately driving up the price of rice for consumers. Rice leaves are prone to several bacterial, viral and fungal diseases, which significantly affect rice production. To meet the growing global demand for rice, effective disease detection is essential. However, challenges in disease recognition arise due to background noise and varying capture conditions in images. Convolutional Neural Network (CNN)-based models have significantly advanced the detection and identification of rice plant diseases. this paper aims to achieve efficient and timely detection and identification of rice plant diseases by exploring the CNN models, both with and without segmentation techniques, for diseases such as Brown Spot, Leaf Blast and Neck Blast.

关键词： Rice Disease image processing Segmentation Techniques Convolutional Neural Networks

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Robust Face Detection and recognition using image processing and OpenCV 6

Robust Face Detection and Recognition using Image Processing...

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6th international conference on Computing Methodologies and Communication, ICCMC 2022

作者： Bhat, Aruna Jha, Rahul Kumar Kedia, Vansh Delhi Technological University Department of Computer Engineering Delhi India

ISBN: (纸本)9781665410281

An enormous progress in the fields of Artificial Intelligence, Machine Learning, and computer vision is witnessed over the last decade, owing to a doubling in processing capacity per year, as well as a massive rise in data internationally. Facial identity, biometrics, and face identification have gone from a rarefied issue to a prominent topic for academics in computer science these days. Due to the basic nature of the topic, breakthroughs in computer image processing and understanding studies will give insights into how the human brain operates, according to the common belief. Face detection and recognition are often employed in national defense, CCTV surveillance, criminal investigation, personal protection, law enforcement, biometrics, and a variety of other applications. For this project, OpenCV, Haar cascade, NumPy, and Matplotlib Python packages are utilized. Haar-Cascades will be utilized for face detection, and OpenCV provides three built-in face recognizers: Face recognition algorithms such as Eigenfaces, Fisher faces, and Local binary pattern histograms are used. the approach employed in this research is literature review, which entails reading and examining numerous books or literature relevant to mathematical ideas that underpin the development of algorithms to detect a human's face, which are subsequently followed by implementation in code, particularly Python code. © 2022 IEEE.

关键词： Face recognition

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Research on the Identification of Artworks Based on image recognition 4

Research on the Identification of Artworks Based on Image Re...

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4th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2022

作者： Li, Hua Xi'an Academy of Fine Arts Department of Styling Art Xi'an710065 China

ISBN: (纸本)9781665463997

In the era of big data, digital recognition technology is particularly important, and with the continuous upgrading of relevant information technology, beyond recognition technology has been widely developed in various fields. through computer information technology as a way of information *** is the basis of digital image processing technology as well as related recognition technology. In the "metaverse"environment, a large number of outstanding paintings are made into digital images and used for "big data"recognition. In order to find such artwork images better and faster, a more scientific and effective recognition method needs to be explored. In this paper, we compare the advantages and disadvantages of feature extraction and classification design with existing methods, and improve and optimize the feature extraction and classification recognition algorithm after completing artwork pre-processing. Finally, the performance of the method is verified by experimental analysis. © 2022 IEEE.

关键词： Big data

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IOT Application-Intelligent Tracking Car SPRESENSE-Base 4

IOT Application-Intelligent Tracking Car SPRESENSE-Base

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4th international conference on Intelligent Information processing, IIP 2022

作者： Kang, Xiaona Kang, Xiqiao Wang, Shaozhen Chai, Jiguo Shen, Sai Mao, Mingju Chengdu Neusoft University Dept. School of Computer and Software ChengDu China Chengdu Neusoft University Dept. Chengdu School of Computer and Software ChengDu China

ISBN: (纸本)9781665492225

this paper mainly describe the design and implementation of the intelligent tracking car system SPRESENSE-base. the system support sending instructions through image recognition;support sending instructions through intelligent voice control;support intelligently tracking through PID algorithm;support fetching and placing goods through the mechanical arm. this article take this system as example to study how to design and implement the intelligent tracking car system. It works under Linux *** system use Sony SPRESENSE as the development board, using grayscale sensors for collecting the recognition and adopt PID algorithm to realize intelligent tracking;using the mechanical arm to realize the fetch and place objects. After one year of hard work, this system is completed successful. © 2022 IEEE.

关键词： image recognition

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Enhancing Human Action recognition in High-Resolution Videos using ConvLSTM and LRCN model 4

Enhancing Human Action Recognition in High-Resolution Videos...

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4th international conference on Communication, Computing and Industry 6.0, C216 2023

作者： Pandey, Shristi Kumar, Niraj Computer Science and Engineering CSJM University Uttar Pradesh Kanpur India Varanasi Varanasi India

ISBN: (纸本)9798350327328

the human action recognition problem and its solution is the most demanding nowadays. Using deep learning methods like CNN and RNN increases the effectiveness of systems designed for recognizing human actions. RNNs have been specifically tailored for processing sequential data, while CNNs have traditionally found their use in image analysis. LSTM networks are a subset of RNNs specially trained for data sequence analysis and forecasting which is highly proficient. In our research, we have collected video data of High Definition (HD) from multiple source. We have divided the dataset into 7 categories each categories have at least 30 videos. We have used two of the best deep-learning models here specifically ConvLSTM and LRCN. the ConvLSTM method is similar to LSTM and it incorporates convolutional operations so that data processing and calculations can be carried out simultaneously. On the other hand, LRCN integrates convolutional, recurrent, and LSTM layers into a single architecture. Based on our evaluation, the accuracy of both the ConvLSTM and LRCN models and the LRCN model has given better results with 91% accuracy. To test the performance of our model, we have used human action videos from Youtube in high resolution. © 2023 IEEE.

关键词： Convolution

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Performance Analysis on Drone-Captured Aerial images Using computer vision Techniques for Industrial Applications 4

Performance Analysis on Drone-Captured Aerial Images Using C...

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4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Kshirsagar, Tejas Kowadkar, Ameya Sabne, Om Nimbhorkar, Ninad Patil, Pratik Brahmane, Anilkumar Sanjivani College of Engineering Savitribai Phule Pune University Department of Computer Engineering Pune India

ISBN: (纸本)9798350393248

this study describes the creation of a cutting-edge computer vision system that can recognize and differentiate between individuals (people) and trees in real-world data collected by drones. this system harvests priceless insights from aerial imagery using cutting-edge machine learning algorithms and image analysis techniques, opening up new prospects for applications in forestry management, disaster response, and ecological study. In our project, specialized machine learning models are developed that are intended for accurate object recognition and classification. these models are painstakingly designed to recognize and distinguish between human forms and different tree species, even in difficult outdoor settings with variable lighting and dynamic viewpoints frequently found in drone captured video. It enables automated tree detection and classification in forestry management, making resource planning and monitoring more efficient and effective. In instances involving disaster response, it helps in the quick identification of people, potentially saving lives and speeding up rescue efforts. this system can be a valuable resource for ecologists conducting research on tree populations and human-environment interactions. By enabling drone-based data analysis and advancing cutting-edge technologies, our research helps to improve resource allocation and decision-making across a variety of situations. Our study addresses significant real-world difficulties and provides a way towards more effective and efficient data-driven solutions by improving the identification of people and trees within various environments. © 2023 IEEE.

关键词： Drones

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A Survey on Attention-Based image Captioning: Taxonomy, Challenges, and Future Perspectives 4th

A Survey on Attention-Based Image Captioning: Taxonomy, Chal...

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4th international conference on Machine Intelligence and Signal processing, MISP 2022

作者： Sharma, Himanshu Padha, Devanand Selwal, Arvind Department of Computer Science and Information Technology Central University of Jammu Samba181143 India

ISBN: (纸本)9789819900466

Generating textual descriptions for images is called image captioning (IC). IC is an innovative concept of computer vision (CV) with several applications such as visual assistance, multi-modal search, machine vision, and many more. Enabling a system to generate image captions has witnessed an excess of progress aided by various state-of-the-art deep learning techniques. Recently, the attention-based image captioning (AIC) approaches have amplified the core field of IC with their promising results. the exponential growth of AIC approaches demands a thorough examination of its literature. there have been relatively lesser contributions in extensively surveying AIC systems up to this point. We review a comprehensive and analytical survey of AIC approaches reported in the literature and propose an alternative taxonomy for diverse AIC techniques. We also highlight a list of open challenges and directions for future work. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

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A New Video Dataset for Recognizing Intake Gestures in a Cafeteria Setting 26

A New Video Dataset for Recognizing Intake Gestures in a Caf...

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26th international conference on pattern recognition / 8th international Workshop on image Mining - theory and Applications (IMTA)

作者： Tang, Zeyu Hoover, Adam Clemson Univ Dept Elect & Comp Engn Clemson SC 29634 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Intake gesture detection plays an important role in dietary monitoring systems to help individuals objectively quantify their eating behavior. Intake gestures include motions related to consumption, such as ingestion of a bite (food) and drink (liquid). Video provides an automatic and non-contact way to detect gestures. Previous studies have evaluated video-based detectors in laboratories where participants were invited to consume meals containing a limited number of food and beverage choices. Because of this, previous datasets have limited variability in food and beverage appearances, and types of intake tools and gestures. Cameras were positioned conspicuously close to participants. To address these limitations, we describe a new video dataset that was collected by recording the meals of 264 participants in a real restaurant. Participants consumed 374 different types of foods and beverages. the camera was installed on the ceiling of the restaurant to minimize the effect of an experimental observer. In this study, we present the details of collecting and labeling the data for video-based research. To explore the feasibility of detecting intake gestures using our dataset and establish baseline performance, we implemented three state-of-art models, namely CNN-LSTM, SlowFast and X3D-L. Results indicate that the best F1 scores were 0.899 and 0.778 on bite and drink gesture detection respectively.

关键词： Intake gesture detection computer vision video processing neural network deep learning

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Fractional Differential-interpolation Combined image Amplification Algorithm 4

Fractional Differential-interpolation Combined Image Amplifi...

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4th international conference on Robotics and computer vision, ICRCV 2022

作者： Xiao, Lifang Jin, Dacong Cheng, Yali Luo, Mingyang Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

image magnification is an image processing technique that obtains high-resolution images from low-resolution images. this paper is proposed a combined image enlargement algorithm of fractional differential and bilinear interpolation, which makes up for the defect that the texture of the image cannot be saved compared with the previous integer order. First, the original image is reduced to one-half, and then the image is magnified by two times by bilinear interpolation, and then the image magnification algorithm is constructed by using the fractional order differential operator to enlarge the image, and the image magnification algorithm of the combination of the fractional partial differential operator and the bilinear interpolation is constructed. Experimental results show that the combined image magnification algorithm can maintain the basic characteristics of the image while better maintaining the texture features of the image detail edges. © 2022 IEEE.

关键词： Interpolation

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Adaptive graph residual network for hand shape estimation in single images 4

Adaptive graph residual network for hand shape estimation in...

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4th international conference on computer vision and Information Technology, CVIT 2023

作者： Li, Rui Lu, Haopeng Cui, Chen Ma, Siwei National Engineering Research Center of Visual Technology School of Computer Science Peking University No.5 YiHeYuan Road Haidian District BeijingCN 100871 China Cooperative Medianet Innovation Center Shanghai Jiaotong University No.800 Dongchuan Road Minhang District ShanghaiCN 200030 China

ISBN: (纸本)9781510672864

A graph convolutional network (GCN) has demonstrated impressive success in hand pose and shape estimation, due to its high interpretability and powerful capability for dealing with non-Euclidean data. In traditional GCN-based hand pose and shape estimation methods, the Chebyshev spectral graph convolution is most widely-used, and it is directly introduced to a simple multilayer network. In terms of the form, this graph convolution does not resemble a standard 2D convolution on an image. In terms of the practical effect, this graph convolution equally treats a center node and its neighbors. Inspired by action recognition studies, we introduce an adaptive graph convolution to hand pose and shape estimation, which not only considers the difference between a center node and its neighbors, but also considers the edge importance. Based on the adaptive graph convolution, we design a multilayer graph residual network with a double-skip-connection architecture. Extensive ablation studies are conducted to demonstrate the improvements due to the use of the adaptive graph convolution and the advantages of the graph residual network. Our method outperforms recent baselines on the public FreiHAND hand pose and shape estimation dataset. © 2024 SPIE.

关键词： Convolution

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