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Feature Extraction from Lunar images

Feature Extraction from Lunar Images

引用

1st international conference on Digital image processing and pattern recognition (DPPR 2011)

作者： Tamililakkiya, V. Vani, K. Anna Univ Coll Engn Madras 600025 Tamil Nadu India

ISBN: (纸本)9783642240546

Automatic feature identification from orbital imagery would be of wide use in planetary science. For geo scientific applications, automatic shape-based feature detection offers a fast and non-subjective means of identifying geological structures within data. Most previously published examples of circular feature detection for geo scientific applications aimed to identify impact craters from optical or topographic data. Various techniques used include the texture analysis, template matching, and machine learning. In this paper, we propose a new method for the extraction of features from the planetary surface, based on the combination of several image processing techniques, including a shadow removal, watershed segmentation and the Circular Hough Transform (CUT). The original edge map of craters is detected by canny operator. In most literatures Hough transform is generally used for crater detection but we have added a shadow removal which includes a novel color image fusion method, based on the multi-scale Retinex (MSR) and discrete wavelet transform (DWT), is proposed. This proposed method is capable of detecting partially visible craters, and overlapping craters.

关键词： feature extraction shadow removal multi-scale Retinex discrete wavelet transform image segmentation circular hough transform

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1st international conference on Pan-African Intelligence and Smart Systems, PAAISS 2021

1st International Conference on Pan-African Intelligence and...

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1st international conference on Pan-African Intelligence and Smart Systems, PAAISS 2021

ISBN: (纸本)9783030933135

The proceedings contain 17 papers. The special focus in this conference is on Pan-African Intelligence and Smart Systems. The topics include: Hybridised Loss Functions for Improved Neural Network Generalisation;diverging Hybrid and Deep learning Models into Predicting students’ Performance in Smart learning Environments – A Review;combining Multi-Layer Perceptron and Local Binary patterns for Thermite Weld Defects Classification;an Elliptic Curve Biometric Based User Authentication Protocol for Smart Homes Using Smartphone;Efficient Subchannel and Power Allocation in Multi-cell Indoor VLC Systems;autonomic IoT: Towards Smart System Components with Cognitive IoT;study of Customer Sentiment Towards Smart Lockers;A Patch-Based Convolutional Neural Network for Localized MRI Brain Segmentation;facial recognition Through Localized Siamese Convolutional Neural Networks;face recognition in Databases of images with Hidden Markov’s Models;Brain MRI Segmentation Using Autoencoders;effective Feature Selection for Improved Prediction of Heart Disease;Convolutional Neural Network Feature Extraction for EEG Signal Classification;race recognition Using Enhanced Local Binary pattern;detection and Classification of Coffee Plant Diseases by image processing and machine learning.

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Extreme learning machine (ELM) Algorithm for the Detection of Diabetic Retinopathy 1

Extreme Learning Machine (ELM) Algorithm for the Detection o...

引用

1st IEEE international conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Hariobulesu, Peddapullaiahgari Shaik, Fahimuddin Karimullah, Shaik Bhanu, Lakshmi Madhavi, B. Kiran, G. Mohammad Sadiq, S. AITS Dept. of ECE A.P Rajampet India

ISBN: (纸本)9798350371802

Blood vessel recognition is crucial for many medical image analysis applications, such as diagnosis and screening for diabetic retinopathy. However, manual de-tection can take a while. To address this, re-searchers have developed a method that uti-lizes an Extreme learning machine (ELM) in a supervised manner to segment blood vessels in the retina. The process involves several stages, including pre-processing images, unsupervised and supervised learning, and post-processing techniques. During the pre-processing stage, various techniques such as image cropping, color modification, enhancement of contrast, Gabor processing, and rectification using half-waves are used to pre-process the images. The pre-processed images are utilized to create a feature vector and used for Principal Component Analysis (PCA). Pixels are separated into non-vessel and vessel clusters using clustering based on K-means on the PCA result. The ensemble classification process is then applied to the non-vessel region using root-guided decision trees with bagging. Finally, the outcomes of the ensemble classification and clustering methods are combined to produce the final segmentation image. Post-processing techniques, such as morphological approaches, are used to improve the vessel-segmented output. The proposed method is verified using publicly available photos from the DRIVE database and achieves an accuracy of 95.36% with higher sensitivity, indicating that more vessel pixels are detected effectively. © 2024 IEEE.

关键词： Blood vessels

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A Survey on Decentralization and Virtualization of Medical Trials: An approach through Ensemble learning models and Convolutional Neural Networks 1

A Survey on Decentralization and Virtualization of Medical T...

引用

1st international conference on Computer Science and Emerging Technologies, CSET 2023

作者： Chakravarty, Diksha Thomas, Amy Mariam Vivek, V. Bangalore India

ISBN: (纸本)9798350341737

- In sectors such as medicine where accuracy and time are the most prioritized factors the use of technology and its tools have led to huge advancements and contributed to saving millions of lives. Technology has been bifurcated into various categories to suit multiple requirements of the medical sector. The use of machine learning along with well-designed models and sister technologies such as Computer Vision has opened up a whole new dimension in medical sciences. machine learning deals with the building of algorithms that can predict using fed patterns which further specializes in Deep learning. Deep learning's success in a variety of pattern recognition applications has sparked excitement and raised expectations that deep learning, or Artificial Intelligence (AI), could revolutionize the health care that we know today. Right from diagnosis to treatment it can revolutionize the way we construct and operate the medical sector. Over the last decade, Convolutional Neural Networks have achieved breakthroughs in a range of pattern recognition domains. CNN's are generally employed to solve complex image-driven pattern recognition tasks. They have proven to be a great tool in image recognition and classification and are tuned according to the needs of the user. Ensemble learning, as a research area, strives to bring data fusion, data modeling, and data mining together in a single framework. Ensemble learning combines the informative knowledge gained from the previous outcomes to produce knowledge, discovery, and improved predictive performance using adaptive voting methods. In this study, the methodologies of numerous models of image recognition and classification, as well as ensemble learning from multiple authors, are evaluated. In addition, this study shall discuss any feasible enhancements that could be implemented to the existing models as well as how the ideologies of the models could be used for efficient diagnosis in the field of medicine. © 2023 IEEE.

关键词： learning systems

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Ceramic microscopic image processing based on fast discrete curvelet transform

Ceramic microscopic image processing based on fast discrete ...

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5th international conference on Wavelet Analysis and pattern recognition

作者： Li, Qing-Wu Liu, Guo-Gao Hohai Univ Coll Comp & Informat Engn Changzhou 213022 Peoples R China

ISBN: (纸本)9781424410651

The properties of ceramic materials are strongly dependent on their microstructure. Ceramic microscopic image processing may be divided into two main procedures: image preprocessing for noise reduction to clarify the image and image segmentation for locating and detecting the objects of interest. Curvelet transform is a new extension to wavelet transform in two dimensions. Curvelet overcomes the limitation of wavelet in analyzing signals with dimension higher than 1-D because it has the character anisotropy. The fast discrete curvelet transform theory makes it understood and implemented more easily. A novel image denoising method is proposed in this paper. The nonlinear hyperbolic tangential function is selected as curvelet thresholding function. This denoising method is used to ceramic image processing. Firstly, the noisy ceramic image is denoised by the novel curvelet transform thresholding denoising method and then watershed algorithm is applied to the segmentation. Finally the grain size distribution can be obtained from segmentation image. It has been proved that this method is effective for the ceramic microscopic image.

关键词： ceramic image curvelet transform threshold denoising segmentation

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Hand Sign Language Detection Using Deep learning and Computer Vision 1

Hand Sign Language Detection Using Deep Learning and Compute...

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1st IEEE international conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Joshi, Sujata Sharma, Bhaskar Dharmender Tripathi, Divyansh Shah, Akshat Nitte Meenakshi Institute of Technology Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350316469

This work presents an innovative system/app designed to assist individuals with visual or hearing impairments in communicating with the outside world using American Sign Language (ASL). By leveraging machine learning and image processing techniques, the proposed system captures and processes hand gestures, enabling the optimal transliteration of sign language alphabets and numbers into machine-readable English text. The system employs deep learning or random forest algorithms to classify the gestures, followed by mapping them to specific alphabets or numbers. The resulting text is presented audibly, bridging the communication gap and facilitating interactions between ASL users and those unfamiliar with sign language. Through the integration of technology, this work aims to enhance inclusivity and accessibility for individuals having disabilities, fostering greater engagement and interaction with the wider world. © 2023 IEEE.

关键词： American Sign Language Communication Barriers Deep learning Gesture recognition Hand Gestures Hearing Impairment Random Forest Sign Language Visual Impairment

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Iranian License Plate recognition using Deep learning 1

Iranian License Plate Recognition using Deep Learning

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1st international conference on machine Vision and image processing, MVIP 2020

作者： Rashtehroudi, Atefeh Ranjkesh Shahbahrami, Asadollah Akoushideh, Alireza University of Guilan Department of Computer Engineering Rasht Iran

ISBN: (纸本)9781728168326

Automated License Plate recognition (ALPR) has many applications in intelligent transport system. The ALPR has three main steps, License Plate (LP) localization, segmentation and Optical Character recognition (OCR). Each step needs different techniques in real condition and each technique has its specific characteristics. The LP localization techniques detect the LP after that segmentation algorithms should segment and isolate each character from each other. Finally, the OCR step is applied to recognize the separated characters. The final accuracy depends on the accuracy of each step. To improve the OCR step performance, we combine both segmentation and OCR steps as a single-stage using deep learning techniques such as the You Only Look Once (YOLO) framework. Our experimental results show that this proposed approach recognizes the Iranian LP characters with accuracy 99.2% compared to previous works. © 2020 IEEE.

关键词： Optical character recognition

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Facial expression recognition using log-euclidean statistical shape models

Facial expression recognition using log-euclidean statistica...

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1st international conference on pattern recognition Applications and Methods, ICPRAM 2012

作者： Papiez, Bartlomiej W. Matuszewski, Bogdan J. Shark, Lik-Kwan Quan, Wei Applied Digital Signal and Image Processing Research Centre University of Central Lancashire PR1 2HE Preston United Kingdom

ISBN: (纸本)9789898425980

This paper presents a new method for facial expression modelling and recognition based on diffeomorphic image registration parameterised via stationary velocity fields in Log-Euclidean framework. The validation and comparison are done using different statistical shape models (SSM) built using the Point Distribution Model (PDM), velocity fields, and deformation fields. The obtained results show that the facial expression representation based on stationary velocity field can be successfully utilised in facial expression recognition, and this parameterisation produces higher recognition rate than the facial expression representation based on deformation fields.

关键词： Velocity

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Affective Visual Question Answering Network 1

Affective Visual Question Answering Network

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1st IEEE conference on Multimedia Information processing and Retrieval (MIPR)

作者： Ruwa, Nelson Mao, Qirong Wang, Liangjun Dong, Ming Jiangsu Univ Sch Comp Sci Zhenjiang Jiangsu Peoples R China Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

ISBN: (纸本)9781538618578

Visual Question Answering (VQA) has recently attracted considerable attention from researchers in the trending field of deep learning. The need to improve VQA models by focusing on local regions of images, has resulted in the development of various attention models. This paper proposes the Affective Visual Question Answering Network (AVQAN), an attention model that combines the locality of the image features, the question and the mood detected from the specific region of the image to produce an affective answer using a preprocessed image dataset. The experimental results depict that AVQAN enriches the analysis and understanding of images by adding affective information to the answer, while still managing to maintain the accuracy levels within the range of recent ordinary VQA baseline models. The proposed model significantly contributes towards the development of rapidly improving emotion-aware machines that are becoming increasingly vital in everyday life.

关键词： affective visual question answering deep learning attention model long short-term memory mood detection

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Soldering Defect Identification Using Deep learning and machine learning Approach 1

Soldering Defect Identification Using Deep Learning and Mach...

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1st international conference on Electronics, Communication and Signal processing, ICECSP 2024

作者： Shilaskar, Swati Singar, Sai Sonawane, Abhijit Shinde, Shobit Vishwakarma Institute of Technology Dept. of Electronics and Telecommunication Engineering Maharashtra Pune India

ISBN: (纸本)9798350364590

Electronic device functionality and dependability can be severely compromised by soldering flaws in printed circuit boards (PCBs). Effective quality control in manufacturing processes depends on automated flaw identification. Using a bespoke dataset of 500 photos of defective PCBs, this research proposes a novel approach for soldering defect recognition and summary creation on printed circuit boards (PCBs) using a combination of Deep learning (DL) and machine learning (ML) techniques. A framework for creating computer vision models is used to carefully classify these pictures according to six different defect categories: solder-bridge, solder-skip, overheated, offtrack-solder, overwetted, and underwetted. Total 300 images are classified into mentioned categories. To improve the model's performance and expand the dataset, the authors implemented augmentation methods, which led to the creation of a comprehensive 30% of total image dataset. The proposed system uses the Google Collab platform to train our unique dataset using the YOLOv5s model architecture, a deep learning model that helped us optimize the model to identify and categorize soldering faults properly. To ensure smooth implementation and reproducibility, we carefully recorded and downloaded the necessary dependencies for YOLOv5s. Preprocessing techniques for image enhancement were carried out on SVM algorithm to deploy the machine learning approach. To enable effective quality control measures in PCB manufacturing processes, our suggested methodology shows promising results in precisely identifying soldering faults. © 2024 IEEE.

关键词： Deep learning

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