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A Dimensionality Reduction Method for the Fusion of NIR and Visible image 4th

A Dimensionality Reduction Method for the Fusion of NIR and ...

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4th international conference on image processing and Capsule Networks, ICIPCN 2023

作者： Gopinath, Lokesh Ruhan Bevi, A. Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9789819970926

Fusing Near-infrared (NIR) and visible images aim to provide more detailed images for human inspection or other computer vision applications. image fusion relies heavily on two components: measuring activity levels and assigning weights. the paper employs principal component analysis network (PCANET) to fuse Near-infrared (NIR) and visible data using an image pyramid. First, utilize a PCANET, an ultra-compact neural network for deep learning, to quantify action and give relative importance to infrared and visible images. PCANET's functional level assessment is superior in expressing key concepts such as NIR object processing and detailed visual representation. Second, the image pyramid divides the weights and source images into several scales and then applies the weighted-average fusing algorithm to the combined data. In the end, reconstruction produces the fused image. More than eighty test image pairings across two datasets confirmed the efficacy of the proposed approach. the experimental results reveal that the proposed strategy beats six benchmark approaches for subjective user evaluations and objective assessment metrics. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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computer vision and Deep Learning for Precise Agriculture:A Case Study of Lemon Leaf image Classification 4

Computer Vision and Deep Learning for Precise Agriculture:A ...

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4th international conference on Computing and Data Science, CONF-CDS 2022

作者： Yang Yuan University of Calgary

Crop protection,an crucial field of precise agriculture,requires attention and improvement,as it secures sustainability and safety of crop and food *** are various threats to crops in which pest is one of the *** vision technologies based on deep learning have shown great advantages on image classification as they enable real-time pest recognition on devices with cameras,such as ***,it is promising for pest monitoring and control and many DL models have been ***,early and accurate diagnosis is need as it minimizes pest ***,traditional models are limited on speed because the massive parameters require huge computing *** this work,we investigate the capability of lightweight model based on DL for the task of leaf disease classification on uncontrolled environment and compare it with traditional DL *** models,in general,are designed to reduce computation on convolution layers with acceptable accuracy *** use an open database named LeLePhid,which contains lemon leave images,healthy or affected by *** damage caused by aphid is general as the pest makes obvious changes to leaf *** focus on two typical DL models:the traditional,DenseNet and the lightweight,MobileNet,and discuss the balance between speed and accuracy,in order to support real-time ***,we discuss the challenges and opportunities in practice.

关键词： computer vision Deep Learning Precision Agriculture

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A Graph-Based Holistic recognition of Handwritten Devanagari Words: An Approach Based on Spectral Graph Embedding 4th

A Graph-Based Holistic Recognition of Handwritten Devanagari...

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4th international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2021

作者： Bhat, Mohammad Idrees Sharada, B. Sinha, Manish Kumar University Institute of Computing Chandigarh University Mohali140413 India Department of Studies in Computer Science University of Mysore Mysore India SVIT Secunderabad India

ISBN: (纸本)9783031070044

In this paper, we make an attempt to give graph representation to handwritten Devanagari words. Each edge in resulted word graph is weighted with its corresponding stretching (or length). For transforming/embedding word graphs into feature vectors their Eigen decomposition aka spectral decomposition is carried out from their weighted associated matrices. Since, these associated matrices contain complementary information we fused their sorted Eigen values at decision level by exploiting multi-class support vector machines (SVM). In order to corroborate the experimental results, we employed Legal amount dataset. From the experimentation, we observe some interesting insights that might be useful for future investigations. © 2022, Springer Nature Switzerland AG.

关键词： Graphic methods

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Recognizing Indian Classical Dance Forms Using Transfer Learning 4th

Recognizing Indian Classical Dance Forms Using Transfer Lear...

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

作者： Reshma, M.R. Kannan, B. Jagathyraj, V.P. Sabu, M.K. Department of Computer Applications CUSAT Kerala Kochi India School of Management Studies CUSAT Kerala Kochi India

ISBN: (纸本)9789819900466

the rise of cultural tourism in India and massive digitization over the last decade has necessitated preserving Indian art forms. Recent advances in artificial intelligence (AI) have provided the tools and techniques to make these efforts more efficient. the potential research areas involved are digital image processing, computer vision, and machine intelligence. Since researchers have not much explored the scope of the digital preservation of Indian classical dance, this paper contributes a method to classify different Indian classical dance forms. computerized dance modeling offers a wide range of uses, including cultural conservation, online learning, entertainment, and even remedial treatment. Because the dataset is small, the paper uses transfer learning models for feature extraction and deep neural networks for classification. the proposed method showed a remarkable accuracy of 97.05 percent. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Fine-Grained image recognition Method Based on Input Perception Joint Probability Prediction

Fine-Grained Image Recognition Method Based on Input Percept...

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computer vision, image and Deep Learning (CVIDL), international conference on

作者： Xiaoxi Yuan School of Information Wuhan Vocational College of Software and Engineering Wuhan China

A method of fine-grained image recognition in the simple background based on input perception joint probability prediction is proposed. the recognition network consists of an input perception module and a joint uncertainty estimation module. the input perception module takes the input image as the global scale image and automatically extracts the foreground scale image and key component scale image by introducing the attention mechanism and using the foreground perception mechanism and component perception mechanism. the joint uncertainty estimation module uses three dynamic probability estimation parameters to evaluate the prediction confidence of each branch and then combines three branch votes to make the final decision. In order to verify the effectiveness and rationality of the input-aware and Probabilistic Prediction Convolutional Neural Network (IAPP-CNN) algorithm, a comparative experiment was performed on the data set. Ablation experiments verified the feasibility and rationality of the algorithm. the results show that the value of each evaluation index of the algorithm is more than 99.0%, which has high recognition accuracy and robustness and meets the recognition requirements. IAPP-CNN algorithm can improve the recognition accuracy of bird images with excellent performance and can greatly improve the performance of fine-grained image recognition.

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Content-Aware Deep Feature Matching 4th

Content-Aware Deep Feature Matching

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4th international conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wang, Weice Li, Zuoyong Zheng, Xiangpan Lai, Taotao Keikhosrokiani, Pantea College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China College of Physics and Electronic Information Engineering Minjiang University Fuzhou350121 China School of Computer Sciences Universiti Sains Malaysia Penang Gelugor11800 Malaysia

ISBN: (纸本)9783031200984

Feature matching is a fundamental and critical step for many tasks of computer vision. In recent years, many methods for feature matching have been proposed. However, the image alignment algorithm used in the state-of-the-art matching method is not robust enough, and the number of the features extracted from the images are insufficient, thus the performance obtained by the state-of-the-art matching method is unsatisfactory. To address these issues, firstly, we use a more robust geometric estimation algorithm to align image pairs. Secondly, based on aligned image pairs, we design a content-aware block to more focus on effective features by using a feature extractor and a mask predictor. Experimental results show that our method achieves more superior results, and outperforms several classical and deep learning methods on the HPatches dataset. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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26th international conference on pattern recognition, ICPR 2022

26th International Conference on Pattern Recognition, ICPR 2...

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26th international conference on pattern recognition, ICPR 2022

ISBN: (纸本)9783031377419

the proceedings contain 49 papers. the special focus in this conference is on pattern recognition. the topics include: Towards Content-Aware Pixel-Wise Comic Panel Segmentation;data-Driven Transform Based Adaptive Compressive Sensing Framework for 3D Reconstruction of Forests;Land Cover Classification for Fires Using Sentinel-2 Satellite RGB images and Deep Transfer Learning;landsat 8 data for forest fire monitoring: case of Mediouna forest in Tangier, Morocco;flying Wing Wildfire Tracking Using Visual Servoing and Gimbal Control;An Overview of Tools and Algorithms Used to Classify, Detect, and Monitor Forest Area Using LiDAR Data;Towards the Automation of Wildfire Monitoring with Aerial Vehicles: the FIREFRONT Project;real-Time Georeferencing of Fire Front Aerial images Using Structure from Motion and Iterative Closest Point;forest Fires Identification Using Self-Supervised Learning;georeferencing High-Altitude Aerial imagery;Wildfires Detection and Segmentation Using Deep CNNs and vision Transformers;a Method to Annotate Who Speaks a Text Line in Manga and Speaker-Line Dataset for Manga109;towards Multi-class Forest Floor Analysis;combining Automatic Speaker Verification and Prosody Analysis for Synthetic Speech Detection;towards Unconstrained Audio Splicing Detection and Localization with Neural Networks;comprint: image Forgery Detection and Localization Using Compression Fingerprints;H4VDM: H.264 Video Device Matching;GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection;Misalignment Estimation in Non-Aligned Double JPEG Scenario Based on AC Histogram Analysis;attacking and Defending Printer Source Attribution Classifiers in the Physical Domain;descriptive Algorithmic Schemes as image Analysis Machine Programs;application of Artificial Intelligence in Ophthalmology for Coagulate Map Formation to Carry Out Laser Eye Treatment;considering Meanings and Effects of Frames Without Onomatopoeias in Japanese Comics.

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Features Fusion-Based Gait recognition with Covariate Conditions 4th

Features Fusion-Based Gait Recognition with Covariate Condit...

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4th international conference on Advanced Computational and Communication Paradigms, ICACCP 2023

作者： Kathing, Margaret Brahma, Rishang Kumar Saharia, Sarat Computer Science and Engineering Tezpur University Assam Tezpur India

ISBN: (纸本)9789819942831

Identification of a person based on their walking pattern is affected by factors such as camera viewing angle, apparel with the subject, holding a bag, walking surface, and complex situations. these are the covariate conditions in human gait analysis. the covariate conditions change an individual's gait pattern, making it difficult to implement gait recognition in a realistic environment. this work addresses the issue of covariate conditions in gait identification through a fusion of features. Using a pre-trained VGG16 model with four fully connected layers, the dynamic features are extracted and merged with HoG (Histogram of Oriented Gradients) features extracted from the raw GEI (Gait Energy image) gait templates. PCA (Principal Component Analysis) is then used to lower the dimension of the combined features in order to select the discriminant feature vectors. the CASIA-B dataset is used to examine the efficacy of the suggested technique, which employs an MLP (Multi-layer Perceptron) classifier. the findings show that the proposed technique outperforms other existing approaches in terms of accuracy while walking normally, wearing a coat, and carrying a bag under identical viewing conditions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Gait analysis

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Skin Cancer Prediction Using Deep Learning Techniques 4

Skin Cancer Prediction Using Deep Learning Techniques

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4th international conference on Signal processing and Communication, ICSPC 2023

作者： Sekar, R Raja Reddy, Y Jagan Mohan Nani, K. Reddy, C Sai Prathap Chiranjeevi, K. Vikram, K. Kalasalingam Academy of Research and EducationKrishnankoil Department of Computer Science and Engineering Srivilliputhur India

ISBN: (纸本)9798350300772

Skin cancer is the one of ten most common cancer types in the world. the abnormal growth of skin cells most often develops on the skin exposed to the sun. Skin cancer occurs when errors occur in the DNA of skin cells, it begins at the top of the skin. there are three types of skin cancer they are 1. Melanoma 2. Basal cell Carcinoma 3. Squamous cell skin cancer. the mutation causes the cells to grow out of control and form a mass of cancer cells. Melanoma is the most dangerous skin form of skin cancer when compared to the other types. the main symptom of skin cancer is a mole are other growth on our skin. Skin cancer symptoms include darker looking skin yellowish and eyes reddened skin, itching and excessive hair growth. there is proper technique followed to reduce the skin cancer by limiting or avoiding exposure to ultraviolet (UV) radiation. Detection of skin cancer at an earlier stage can increase the survival rate. When compared to the ancient days the rate of skin cancer peoples is increased significantly, due to modern techniques and lifestyles, direct exposure to harmful UV radiation from sun. It was estimated that in the year 2022 around 57,180 men's and 42,600 women's would develop Melanoma of *** the case of positive skin cancer, determining the type is very important because it helps to identify the most suitable treatment. In recent times the usage of image processing and mission vision in the field of health care and medical applications is increasing at a great face. In this paper we are using the convolution neural network (CNN) to detect and classified the clause of cancer based on historical data of clinical images usingRESNET. © 2023 IEEE.

关键词： Cells

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image Domain Translation for Few-Shot Learning 27th

Image Domain Translation for Few-Shot Learning

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27th international conference on pattern recognition, ICPR 2024

作者： Chen, Liangyuan He, Zhenan Zhang, Hai College of Computer Science Sichuan University Chengdu China

ISBN: (纸本)9783031781827

Few-shot learning is crucial in machine learning and computer vision. It enables models to recognize new objects with limited labeled data, addressing the challenge of data scarcity and expanding the application of machine learning to domains with scarce data. Previous methods built metric space using labeled data from the base set and then classified queried images from the novel set by finding the nearest class prototype. However, due to the presence of poor-quality data in the novel set, the class prototype often exhibits instability. In response to this challenge, this paper proposes a Semantic Conditional Translation Network for reconstructing stable class prototypes. Specifically, images are first divided into edge domain (i.e., images at the cluster edge) and prototype domain (i.e., images at the cluster center). then, an Enhanced Generative Adversarial Network is introduced to learn the translation from edge toward prototype, where a Non-parametric Classification Regularizer is designed to enlarge the discriminability of the translated samples. Meanwhile, class definitions are exploited as semantics providing precise descriptions and enhancing translation performance. Experimental results demonstrate that the proposed method obtains competitive results on four benchmark datasets. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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