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Comparative Analysis of Traditional and Deep Learning Techniques for Industrial and Wildfire Smoke Segmentation 6

Comparative Analysis of Traditional and Deep Learning Techni...

引用

6th International conference on image Information processing, ICIIP 2021

作者： Chaturvedi, Shubhangi Khanna, Pritee Ojha, Aparajita PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur India

ISBN: (纸本)9781665433617

Smoke is the first sign of ignition of fire because smoke becomes visible when the fire starts. At this stage, fire can be effectively controlled by locating the smoke at the earliest. Smoke causes several health issues such as skin allergies and breathing problems in humans and animals. One of the biggest smoke emission sources is the industrial smoke. For environmental safety, various harmful gases emitting from industrial chimneys need to be monitored constantly. Further, increasing incidents of wildfire have also resulted in severe environmental degradation in recent years. thus, detection of smoke and finding its location at early stage can help in mitigating fire hazards. Several vision based techniques have been proposed by researchers using traditional image processing techniques in the past to identify and segment smoke in images. In recent years, deep learning techniques have shown promising performance in smoke detection. In this paper, we present a comparative analysis of traditional image processing and recent deep learning based smoke segmentation techniques with focus on industrial and wildfire smoke. © 2021 IEEE.

关键词： Smoke

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Magnetic resonance image reconstruction by nullspace based finite rate of innovation framework 21

Magnetic resonance image reconstruction by nullspace based f...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Reddy, P. Sudhakar Raghavendra, B.S. Narasimhadhan, A.V. National Institute of Technology Karnataka Mangalore India

ISBN: (纸本)9781450391276

the finite rate of innovation (FRI) framework has proved that it is possible to reconstruct the analog signals which have a finite number of parameters. FRI framework is used to reconstruct the images from undersampled magnetic resonance (MR) data. the reconstruction of the MR image from the MR data is a estimation problem, which can be solved by utilizing Prony's method. However, Prony's method involves solving the polynomial roots of the annihilating filter and this fact leads to an unstable reconstruction in the high noise scenario. In this paper, we introduce a novel reconstruction approach is also based on the annihilating filter. However, it involves the use of solutions of an underdetermined linear system. the simulation results of the proposed reconstruction approach show that the peak signal to noise ratio (PSNR) and the structural similarity index measure (SSIM) are higher magnitude than that of conventional FRI methods in the high noise scenario.1 © 2021 ACM.

关键词： Magnetic resonance imaging

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Cultural Heritage Meets AI: Advanced Text-to-image Models for Digital Reconstruction and Preservation

Cultural Heritage Meets AI: Advanced Text-to-Image Models fo...

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International conference on Control and Robots (ICCR)

作者： Kunta Hsieh Tsen-Shu Sam Tsaur Min Chao I-Cheng Li Pin-Chia Huang Mei-Jou Lu The School of Big Data Fuzhou University of International Studies and Trade Fuzhou Fujian China School of Finance & Accounting Fuzhou University of International Studies and Trade Fuzhou Fujian China Department of Civil Engineering and Geomatics Cheng Shiu University Kaohsiung Taiwan Director of the Conservation and Research Center Cheng Shiu University Kaohsiung Taiwan Postdoctoral Research Fellow of Conservation and Research Center Cheng Shiu University Kaohsiung Taiwan Department of Long-term Care and Health Management Cheng Shiu University Kaohsiung Taiwan

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

this paper reviews the applications and development of web crawling and text-image matching technologies in the field of information processing, with an emphasis on the CLIP model and its adaptation for Chinese contexts. Web crawling technology utilizes automated programs to simulate browser behavior, efficiently accessing and extracting webpage information, while text-image matching technology combines computer vision and natural language processing to enable computers to understand textual descriptions and retrieve relevant images. Notably, the CLIP model, a deep learning-based text-image matching technology, has garnered attention for its high accuracy, generalization ability, and unsupervised learning, which does not require large amounts of labeled data. the paper also introduces a major application in the realm of art and cultural heritage restoration, where these technologies play a pivotal role in the digital reconstruction, preservation, and virtual presentation of artworks and artifacts. Additionally, the importance of data ethics, privacy protection, algorithmic fairness, and preventing technological misuse are emphasized. the paper concludes with future trends pointing towards more intelligent web crawlers, more precise image semantic understanding, and more user-friendly experiences.

关键词： Ethics Adaptation models Art Computational modeling Information processing Data models image restoration Cultural differences image reconstruction Unsupervised learning

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Domain Adaptation Based Technique for image Emotion Recognition Using image Captions 5th

Domain Adaptation Based Technique for Image Emotion Recognit...

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5th International conference on computer vision and image processing, CVIP 2020

作者： Kumar, Puneet Raman, Balasubramanian Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9789811610912

images are powerful tools for affective content analysis. image emotion recognition is useful for graphics, gaming, animation, entertainment, and cinematography. In this paper, a technique for recognizing the emotions in images containing facial, non-facial, and non-human components has been proposed. the emotion-labeled images are mapped to their corresponding textual captions. then the captions are used to re-train a text emotion recognition model as the domain-adaptation approach. the adapted text emotion recognition model has been used to classify the captions into discrete emotion classes. As image captions have a one-to-one mapping with the images, the emotion labels predicted for the captions have been considered the emotion labels of the images. the suitability of using the image captions for emotion classification has been evaluated using caption-evaluation metrics. the proposed approach serves as an example to address the unavailability of sufficient emotion-labeled image datasets and pre-trained models. It has demonstrated an accuracy of 59.17% for image emotion recognition. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Emotion Recognition

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Optical Mark Recognition with Object Detection and Clustering

Optical Mark Recognition with Object Detection and Clusterin...

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International conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Kaifa Tabassum Zhalok Rahman Dept. of Computer Science and Engineering Shahjalal University of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

OMR, or optical mark recognition, is the automated analysis of human-marked documents. Presently, many competitive examinations rely on multiple-choice questions, and the answers to these questions are recorded in OMR sheets. In this study, we present an automated answer detection system from an OMR sheet image using computer vision techniques. Typically, evaluating an OMR sheet from an image involves scanning, extracting data, and interpreting the results through various image processing techniques. However, traditional approaches have limitations. they cannot detect empty responses or recognize multiple choices properly without a specified layout or total number of questions stated. We took a unique approach by employing an object detection model, YOLOv8n, to detect and classify OMR marks. To address overlapping bounding boxes, we implemented the DBSCAN clustering algorithm to effectively group the columns and determine question order. Our system is designed to work with multiple layouts, as it can detect columns through clustering. Furthermore, it can identify unanswered questions as well as multiple marked options. To train and evaluate our model, we created a custom dataset with five different OMR sheet layouts. We achieved a remarkable precision of 96.5%, a recall of 99.8%, and an mAP (mean average precision) of 97.3%. these results demonstrate the efficacy of our approach to OMR sheet analysis.

关键词： Integrated optics image recognition Codes Text analysis Optical recording Layout Clustering algorithms

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Faster Training of Edge-attention Aided 6D Pose Estimation Model using Transfer Learning and Small Customized Dataset 6

Faster Training of Edge-attention Aided 6D Pose Estimation M...

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6th International conference on Wireless Communications, Signal processing and Networking (IEEE WiSPNET)

作者： Anju, S. Mishra, Deepak Rani, Sheeba J. Indian Inst Space Sci & Technol Elect & Commun Engn Av Trivandrum 695547 Kerala India

ISBN: (纸本)9781665440868

computer vision is the field of machine learning that deals with computers gaining knowledge from digital images/videos and performing tasks that human vision is capable of doing. It is widely used in the field of robotics for designing guidance systems where objects in the robot's field of view are identified and located. this research work is an application-specific project enabling a half-humanoid to find the 6D pose and bounding boxes of its hand and other objects within its field of view. We add an edge prediction head to the NOCS (Normalised Object Coordinate Space) model, which predicts the edges of each object from the predicted instance maps. An additional edge-agreement-loss found from the predicted edges is added to the total loss. this increases the attention to the edges and improves the accuracy of prediction of the instance masks. this edge-attention aided model is initialized with pre-trained weights of CAMERA and REAL dataset using transfer learning. the backbone layers of the model are frozen and the head layers alone are trained using a synthetic dataset (HAND dataset) we created using a software called blender. the model gives promising results when tested with objects kept in varying lighting conditions and at different distances from the camera. the use of transfer learning in models as large as the NOCS model allows us to train the model for a new class by only training the top few layers with a significantly small dataset.

关键词： datasets neural networks transfer learning computer vision 6D pose estimation

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Generative Adversarial Network (GAN): A general review on different variants of GAN and applications 6

Generative Adversarial Network (GAN): A general review on di...

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6th IEEE International conference on Communication and Electronics Systems, ICCES 2021

作者： Karthika, S. Durgadevi, M. Srm Institute of Science and Technology Faculty of Engneering and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665435871

Deep learning plays a very important role in the research area in the field of Artificial Intelligence (AI) and Machine Learning (ML) and many models have been developed based on GAN applications. Generative Adversarial Networks (GAN) is an unsupervised learning method which is based on a very popular logic known as zero-sum game theory for two players. the main objective of Generative Adversarial Networks is to calculate the distribution of original samples using discriminator and the work of Generator is to generate new samples from the real data samples. In current years, Generative Adversarial Networks (GAN) has made big development essentially withinside the area of computer vision, image classification, speech and language processing and so on. the paper explains about the basic introduction of GAN and different types of GAN were introduced with its applications. Original GAN version and its changed classical variations has been surveyed. the problems and limitations of GAN and the future work of GAN models were discussed. © 2021 IEEE.

关键词： Generative adversarial networks

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A Comparative Study on Optimizers for Automatic image Captioning

A Comparative Study on Optimizers for Automatic Image Captio...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Eliyah Immanuel thavaraj A Sujitha Juliet Anila Sharon J Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9781665482721

In the field of artificial intelligence, computer vision and natural language processing are used to automatically generate an image's contents. the regenerative neuronal model is developed and is dependent on machine translation and computer vision. Using this technique, natural phrases are produced that finally explain the image. this architecture also includes recurrent neural networks (RNN) and convolutional neural networks (CNN). the RNN is used to create phrases, whereas the CNN is used to extract characteristics from images. the model has been taught to produce captions that, when given an input image, almost exactly describe the image. the outcome of these algorithms is determined by several factors, including feature extraction, caption generation, and optimizer selection. Our goal is to conduct a comparative analysis of several optimizers to determine the optimizer that achieves highest accuracy for a deep learning model. the deep learning model is subsequently trained with various optimizers on the Flicker dataset. the accuracy of the results of the model using optimizers are achieved as follows: RMSprop optimizer has a 92% accuracy, SGD has a 12% accuracy, Adam optimizer has 53% accuracy, and Adadelta has a 12% per cent.

关键词： Deep learning computer vision Analytical models Recurrent neural networks Computational modeling Predictive models Feature extraction

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CPU -GPU Heterogeneous Stencil Computation Algorithm Based on Dynamic Hybrid Fragmentation Partitioning

CPU -GPU Heterogeneous Stencil Computation Algorithm Based o...

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Frontiers Technology of Information and computer (ICFTIC), IEEE International conference on

作者： Jingbao Qiu Huawei Zhai Xiaodong Yuan Licheng Cui College of Information Science and Technology Dalian Maritime University Dalian China Department of Public Security Information Liaoning Police College Dalian China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Stencil computation, an algorithm that uses fixed stencils for computation, has widespread applications in fields such as image processing and computational fluid dynamics simulations. In recent years, the loop tiling strategy for parallel stencil computation has been extensively developed. Loop tiling technology accelerates computation on heterogeneous computing components by utilizing parallelism and data locality. However, existing tiling strategies have not effectively addressed the issues of cooperative computation across heterogeneous computing components and excessive computational space complexity. this paper proposes a CPU-GPU heterogeneous stencil computation algorithm based on dynamic, hybrid fragmentation partitioning. this algorithm uses a decision tree model, combined with a dynamic allocation strategy, to accurately distribute tasks across heterogeneous computing components. It optimizes instruction-level operations for CPU computation to improve vector computation speed. Additionally, leveraging tiling characteristics, the algorithm reduces space complexity during computation, enabling the concurrent execution of a broader range of data. this algorithm is implemented on the PSkel framework. Our experimental results demonstrate that the proposed algorithm can achieve speedups of up to 18 times when compared to traditional serial computation. Compared to GPU-only computation, it achieves a maximum speedup of 1.31x, and compared to traditional tiling methods, it achieves a maximum speedup of 1.22x.

关键词： Heuristic algorithms Computational modeling graphics processing units Heterogeneous networks Vectors Partitioning algorithms Complexity theory Decision trees Resource management Periodic structures

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A TinyML based Residual Binarized Neural Network for real-time image Classification

A TinyML based Residual Binarized Neural Network for real-ti...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： C Srinivasan P Sridhar V Hari Priya S Swathi Department of ECE Saveetha Engineering College Chennai Tamilnadu India Department of ECE Sri Ramakrishna Engineering College Coimbatore Tamilnadu India

ISBN: (纸本)9781665482721

image processing is an important requirement in IoT applications such as robotics, augmented reality, computer vision, Industry 4.0 etc. the capabilities of IoT devices for image processing are limited to sensing the environment, processing and communicating the results. Tiny machine learning (TinyML) is a new paradigm that takes advantage of the IoT to deploy deep learning models to perform complex tasks in resource constrained embedded devices. image classification is an important task in IoT to interpret images of a particular scene or class. Currently, this task is performed in embedded devices using Binarized Neural Networks (BNNs), which can be converted to a set of weights using a one-hot encoding process. these networks integrated with hardware accelerators can be trained to perform image processing tasks in real-time. this paper proposes a BNN for image classification based on residual learning paradigm, called Tiny-BNN which exploits the skip connections to reduce information loss, and improve the training time and accuracy. Experimental results show that the model achieves a classification accuracy of 90.1 % and 91.6% on the on CIFAR-10 and MNIST datasets respectively.

关键词： Performance evaluation Deep learning computer vision Computational modeling Neural networks Robot sensing systems Real-time systems

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