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Optimizing image Classification Using Bag of Features and Support Vector machines 4

Optimizing Image Classification Using Bag of Features and Su...

引用

4th IEEE International conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mahantesh, K. Navyashree, K.S. Nairy, Devika S. Asha, R. Anshitha, B. Bengaluru India Sjb Institute of Technology Visvesvaraya Technological University Department of Ece Bengaluru India

ISBN: (纸本)9798350352931

image categorization is a fundamental task in computer vision, with applications in domains such as object recognition, medical imaging, and autonomous systems. Traditional approaches frequently fail to balance accuracy, computing efficiency, and scalability, particularly when dealing with big and complex datasets. This work presents a novel picture classification strategy that combines the Bag of Features (BoF) model with Support Vector machines (SVM). The BoF model describes images by extracting local visual characteristics (such as SIFT, SURF, or ORB) from image patches and quantizing them into visual words to create a histogram representation. SVM, a powerful machine learning classifier, is used to classify these histograms, utilizing its capacity to handle high-dimensional, sparse data. Experiments using common image classification datasets show that the BoF-SVM system greatly outperforms previous methods, resulting in higher classification accuracy and lower processing costs. Furthermore, it has superior generalization to previously unseen data and is more resistant to noise and picture changes. The suggested BoF-SVM system produces promising results for boosting both accuracy and efficiency in image classification tasks, with room for further optimization in more complicated and diversified applications © 2024 IEEE.

关键词： image classification

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Automated Fundus image Standardization Using a Dynamic Global Foreground Threshold Algorithm 8

Automated Fundus Image Standardization Using a Dynamic Globa...

引用

8th International conference on image, vision and Computing, ICIVC 2023

作者： Kiefer, Riley Abid, Muhammad Ardali, Mahsa Raeisi Steen, Jessica Amjadian, Ehsan Florida Polytechnic University Department of Computer Science LakelandFL United States College of Optometry Nova Southeastern University Fort LauderdaleFL United States Cheriton School of Computer Science University of Waterloo WaterlooON Canada

ISBN: (纸本)9798350335231

A generic fundus foreground extractor is required for the standardization of fundus datasets in machine-learning applications due to the vast range of retinal fundus images. Some fundus images have a large amount of non-essential background data and others have missing data because of clipping. To standardize these varied images for machine learning applications while preserving the aspect resolution, a generalized threshold algorithm is needed to separate the foreground and background. Existing threshold algorithms fail to segment images with low contrast. There is a need for a generalized algorithm to handle varied image conditions in a dynamic manner. The proposed segmentation algorithm uses shifts in histogram frequency using intensity extrema to find the ideal threshold value. The proposed post-processing algorithm crops, pads, and resizes the image to a standardized size of 512x512 pixels using the segmentation map output. To demonstrate the effectiveness of this proposed standardization approach on downstream tasks, an ablation experiment of popular standardization strategies is evaluated on a newly proposed benchmark dataset, EyePACS-light. The experimental results demonstrate the benefits of using this standardization approach for resizing fundus images. © 2023 IEEE.

关键词： Standardization

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Realistic and Visually-Pleasing 3D Generation of Indoor Scenes from a Single image 7th

Realistic and Visually-Pleasing 3D Generation of Indoor Scen...

引用

7th Chinese conference on Pattern Recognition and Computer vision

作者： Li, Jie Wang, Lei Chen, Gongbin Li, Ang Qiu, Yuhao Wu, Jiaji Cheng, Jun Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Univ Chinese Acad Sci CAS Beijing Peoples R China Shenzhen MSU BIT Univ Shenzhen Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (纸本)9789819785070;9789819785087

Artificial Intelligence Generated Content (AIGC) has experienced significant advancements, particularly in the areas of natural language processing and 2D image generation. However, the generation of three-dimensional (3D) content from a single image still poses challenges, particularly when the input image contains complex backgrounds. This limitation hinders the potential applications of AIGC in areas such as human-machine interaction, virtual reality (VR), and architectural design. Despite the progress made so far, existing methods face difficulties when dealing with single images that have intricate backgrounds. Their reconstructed 3D shapes tend to be incomplete, noisy, or lack of partial geometric structures. In this paper, we introduce a 3D generation framework for indoor scenes from a single image to generate realistic and visually-pleasing 3D geometry shapes, without the requirement of point clouds, multi-view images, depth or masks as input. The main idea of our method is clustering-based 3D shape learning and prediction, followed by a shape deformation. Since more than one objects tend to be existing in indoor scenes, our framework will simultaneously generate multi-objects and predict the layout with a camera pose, as well as 3D object bounding boxes for holistic 3D scene understanding. We have evaluated the proposed framework on benchmark datasets including ShapeNet, SUN RGB-D and Pix3D, and state-of-the-art performance has been achieved. We have also given examples to illustrate immediate applications in virtual reality.

关键词： 3D mesh Reconstruction Deep neural networks

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A Flexible machine vision AI System for Edge-Oriented Deep Learning Accelerators

A Flexible Machine Vision AI System for Edge-Oriented Deep L...

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2023 International Technical conference on Circuits/Systems, Computers, and Communications, ITC-CSCC 2023

作者： Song, Joon Boum Kim, Yumi Lee, Minkyu Lee, Sang-Seol Kim, Kyungho Korea Electronics Technology Institute Korea Republic of

ISBN: (纸本)9798350326413

In recent 10 years, deep learning has successfully shown its effectiveness in various computer vision fields such as autonomous vehicles, robotics, and AI surveillance. Numerous machine vision AI systems have been accordingly developed to run those deep learning algorithms. However, existing PC-based machine vision AI systems have the disadvantage of having to modify the entire system although just a small change is required. They are also considerably high cost/power consuming systems for edge device purpose which is targeted in this study. In addition, a number of FPGA-based machine vision AI systems are not suitable for multiple applications as they are dedicatedly designed. In this work, in order to overcome those disadvantages, we have developed a flexible FPGA-based machine vision AI system where various type of accelerator can be implemented. An accelerator integrated with a post processing unit(PPU) that runs an object detection was successfully demonstrated on the system satisfying the required input image resolution/format. The proposed system can also provide multiple resolution/format of input images which is proved via Xilinx integrated logic analyzer(ILA) debugging. We therefore ensure that this system can further support more accelerators which can be deployed in diverse applications. © 2023 IEEE.

关键词： Object detection

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Read Right: Empowering the Blind to Read the World

Read Right: Empowering the Blind to Read the World

引用

2024 IEEE International conference on Signal processing and Advance Research in Computing, SPARC 2024

作者： Prasuna, Vempati Lakshmi Fathimabi, Sk. Adusumilli, Apoorva Rajesh, Bandaru Veera Venkata Velagapudi Ramakrishna Siddhartha Engineering College Information Technology Vijayawada India

ISBN: (纸本)9798350385199

In the real world, knowledge comes from books and papers. Now that information only reaches to those with clear vision. In the community there are a part of people suffering either from poor eyesight or blindness. Braille is one of the methods employed to interpret several reports or books, however obtaining all these files in braille may be prohibitively expensive as well as regularly impossible. Hence, considering these issues and flaws in existing systems this paper is designed as a simple user-friendly mobile application 'Read Right' that works on voice commands like 'Take Picture' to click an image of the document which extracts text from picture as well plays it out (audio) also providing futuristic object detection. All this is designed using Firebase ML kit which predominantly uses bitmap class for preprocessing, text-to-speech synthesizer and FirebasevisionObjectDetector, for joining machine learning capabilities into portable applications. Hence, the application stands out for its flexibility in dealing with official archives through voice. © 2024 IEEE.

关键词： Natural language processing systems

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Leaf Lens: An Intelligent vision for Plant Disease Diagnosis using Deep Learning 2

Leaf Lens: An Intelligent Vision for Plant Disease Diagnosis...

引用

2nd International conference on Artificial Intelligence and machine Learning applications, AIMLA 2024

作者： Prasad, P. Yogendra Ramu, M. Reshma, Sanjamala Priya, Rayachoty Chandana Anusha, Pasupuleti Reddy, B Likhith Dept of CSSE Tirupati India Annamacharya Institute of Technology & Sciences Dept of CSE Tirupati India

ISBN: (纸本)9798350349221

In India, where 70% of the population is involved in agriculture, accurate recognition of botanical disorders is crucial to minimize crop losses. Manual monitoring of these diseases requires significant labor, expertise in plant pathology, and time investment. This paper presents a methodology for identifying plant diseases by analyzing leaves' images. image processing extracts valuable properties and information from the images, while deep learning, [3] a part of artificial intelligence, which simplifies data preprocessing. The algorithms used can process unstructured data, including text and images, automating feature extraction and diminishing the dependance on human professionals. This automated approach facilitates informed decision-making and precise predictions based on extensive training data. The paper introduces an innovative approach to plant disease detection through deep learning, specifically using the VGG19 model, which can scrutinize intricate patterns in plant images, providing a reliable method for identifying diseases. © 2024 IEEE.

关键词： Neural network models

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Intelligent Optimization of Computer image processing Technology Analysis 3rd

Intelligent Optimization of Computer Image Processing Techno...

引用

3rd EAI International conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Wei, Huayong Anhui Communications Vocational and Technical College Hefei China

ISBN: (纸本)9783031631382

Intelligent optimization algorithm is an advanced computing technology, which simulates the biological evolution process in nature or the logical thinking of human beings to find a solution to the problem. In computer image processing, intelligent optimization algorithms are widely used, mainly in image enhancement, image restoration, image segmentation, feature extraction, image recognition and so on. Intelligent optimization algorithms have developed rapidly, and many excellent algorithms with different characteristics have emerged, which have achieved good results in practical applications. image analysis is the basis for realizing machine vision, including image enhancement, image fusion, image recognition, image tracking, image retrieval and many other technologies. It has a great demand in medicine, transportation, military, aerospace and other fields. In particular, the development of many industries and fields such as intelligent robots, smart medicine, and smart cities has brought many optimization challenges to image analysis, At present, image analysis based on swarm intelligence optimization algorithm has become an important research hotspot. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： image enhancement

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Defect Detection in Metal Surfaces Using Computer vision 1

引用

4th International conference on Recent Trends in machine Learning, IoT, Smart Cities, and applications, ICMISC 2023

作者： Singh, Krishna Kumar Ghosh, Manish Symbiosis Centre for Information Technology Pune India

ISBN: (数字)9789819994427

ISBN: (纸本)9789819994410

Encoder models have shown remarkable success in various computer vision operations like object detection, image classification, and semantic segmentation. However, the results of one model showed that it was underfitting as it performed better on the validation set than on the training set. To gain insights into how the model was processing input images at different layers and which image features were being detected by different filters in the convolutional layers, an algorithm was used to visualize the activation maps for the output of convolutional layers in the model. To address the underfitting issue, the previous model was converted into a single feature-vector using global average pooling and used as a single layer in another deep learning model. As a result, the new model displayed a batch of images along with their corresponding segmentation masks and predicted segmentation masks generated by the encoder model. The output was a 3 × 1 image grid that included the original image, its ground truth segmentation mask, and the predicted segmentation mask. To optimize the model further, an algorithm was applied that trained an image encoder model on training data and evaluated it on validation data for a number of epochs. The algorithm then displayed a batch of samples with their ground truth and predicted segmentation masks. The results for this model were better than the previous model, as it was performing better with the train set as compared to the validation set. Overall, the usage of an encoder model and subsequent modifications allowed for improved performance in image segmentation tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computer vision

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IMPLICIT CHANNEL LEARNING FOR machine LEARNING applications IN 6G WIRELESS NETWORKS

IMPLICIT CHANNEL LEARNING FOR MACHINE LEARNING APPLICATIONS ...

引用

IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Elbir, Ahmet M. Shi, Wei Mishra, Kumar Vijay Papazafeiropoulos, Anastasios K. Chatzinotas, Symeon Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust Luxembourg Luxembourg Carleton Univ Sch Informat Technol Ottawa ON Canada US DEVCOM Army Res Lab Adelphi MD USA Univ Hertfordshire Hatfield Herts England

ISBN: (纸本)9798350302615

With the deployment of the fifth generation (5G) wireless systems gathering momentum across the world, possible technologies for 6G are under active research discussions. In particular, the role of machine learning (ML) in 6G is expected to enhance and aid emerging applications such as virtual and augmented reality, vehicular autonomy computer vision and internet of everything. This will result in large segments of wireless data traffic comprising image, video and speech. The ML algorithms process these for classification/recognition/estimation through the learning models located on cloud servers. This requires wireless transmission of data from edge devices to the cloud server. Channel estimation, handled separately from recognition step, is critical for accurate learning performance. Toward combining the learning for both channel and the ML data, we introduce implicit channel learning to perform the ML tasks without estimating the wireless channel. Here, the ML models are trained with channel-corrupted datasets in place of nominal data. Without channel estimation, the proposed approach exhibits approximately 60% improvement in image and speech classification tasks for diverse scenarios such as millimeter wave and IEEE 802.11p vehicular channels.

关键词： machine learning channel estimation artificial intelligence wireless communications

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Cotton Leaf Disease Based on image processing Using Deep Learning

Cotton Leaf Disease Based on Image Processing Using Deep Lea...

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2024 International conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Menaga, D. Shri Roshan, B.T. Shri Vikaas, S.R. St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350319019

One of the most important occupations in India is agriculture. Out of all the crops, cotton is the best and is crucial to the agricultural economy of the country. In India, 40-50 million people work in the cotton trade and processing, while six million farmers directly depend on the crop. The cotton leaf disease has grown in importance over the last few decades, resulting in losses to crops, farming operations, and financial resources. To achieve this aim, we first need to acquire different images of cotton plants. We can use image processing techniques to analyze dead leaf images and extract features like color, texture, and other characteristics with the Deep CNN model's assistance. In addition to being less expensive and more straightforward, automatic disease detection supports machine vision, which offers image-based automated process control and inspection. To properly train the algorithm, we will be using a dataset of approximately 1752(approximately 440 images in each class) images classified into different categories according to the diseases. This model will be developed using tools present in Anaconda such as Jupyter Notebook, Spyder etc. The results of this project will demonstrate whether using it in real-time applications is feasible and whether traditional or manual disease and pest identification could benefit from the use of IT- based solutions. © 2024 IEEE.

关键词： Cotton

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