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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

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Enhancing Text Detection in Natural Scenes: A Hybrid Approach with MSER, Connected Components, and Norm-CLAHE

Enhancing Text Detection in Natural Scenes: A Hybrid Approac...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Prabu Selvam Akshaj Nevgi C. Gunasundari Sowrish V K Natarajan B S. Sharon Jessika School of Computer Science and Engineering VIT University Chennai India School of Computer Science and Engineering VIT Vellore Chennai India Department of Computer Science and Engineering Roever Engineering College Perambalur Tamilnadu Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering VIT University Vellore India School of Computer Science and Engineering VIT Vellore Vellore India

The textual information present in natural scene images is a valuable clue for many vision-based applications. Locating text areas becomes particularly challenging due to issues like varying lighting conditions, complex backgrounds, non-uniform text sizes, and more. Detecting curved text, in particular, is a demanding task in the fields of computer vision and pattern recognition. This research work proposes a robust approach for the detection of text in natural scene images with different orientations. The proposed method is divided into three key stages: (i) Pre-processing: The original image is converted to grayscale, and this research propose a new Norm-Contrast-limited Adaptive Histogram Equalization (CLAHE) to enhance the contrast in the grayscale image by limiting noise amplification and prevents saturation in dark and bright text regions. (ii) Text Region Extraction: this research combined Otsu's thresholding, Maximally Stable Extremal Region (MSER) and Sobel edge de-tector to extract text regions and enhance edges in the image. (iii) Non-Text Region Removal: In this stage, this research combine Connected Component (CC), Morphological operations, MSER region filters, and basic text geometric properties to create a framework for removing non-text regions and drawing rectangle bounding boxes around the detected text regions. this research assessed the performance of the proposed method utilizing the ICDAR 2017 text dataset, which yielded an overall accuracy of 93.47%, demonstrating highly satisfactory results.

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Architectural implementation of high speed optical flow computation based on Lucas-Kanade algorithm

Architectural implementation of high speed optical flow comp...

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international conference on Electronic computer Technology

作者： T. R. Sathya Kalyan M Malathi Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu IN Electronics and Communication Dept. Adhiparasakthi Engineering College Melmaruvathur India

Optical flow study of visual motion has been the major area of interest among researchers for many years. It has been largely inapplicable to real-time applications, until recently, due to its computationally expensive nature. Optical flow computation in visual-based systems demands for computational power and storage area constraints. For enabling real-time processing at high resolution, the design of application-specific system for optical flow becomes essential. This paper proposes, an efficient VLSI architecture for accurate computation of the high speed Lucas-Kanade (L-K) based optical flow. The proposed architecture is simulated and verified by synthesizing onto a Model Sim 6.4a and Altera Quartus-II, which utilize less than 40% of system resources, operating at a frequency of 500MHz having low power consumption. This paper also describes the proposed design can process 1200×680 images at a high frame rate of 500-700fps in the proposed low cost FPGA-chip.

关键词： image motion analysis computer vision Optical imaging High speed optical techniques Pixel computer architecture Optical filters

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Geometric Distortion Detection in Fused Filament Fabrication Using Augmented Reality and computer vision

Geometric Distortion Detection in Fused Filament Fabrication...

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Electronic and Electrical Engineering and Intelligent System (ICE3IS), international conference on

作者： George Wu Chi-Tsun Cheng Toh Yen Pang Miro Miletic Electrical and Electronic RMIT University Melbourne Australia Manufacturing Materials and Mechatronics RMIT University Melbourne Australia Electrical and Biomedical RMIT University Melbourne Australia Aerospace MEMKO Pty. Ltd Melbourne Australia

Recent advancements in additive manufacturing, also known as 3D printing, have revolutionized the manufacturing industry by enabling the production of complex geometries and rapid prototyping. However, ensuring consistent quality in print parts remains a challenge. In this research, we address the quality monitoring issues in Fused Filament Fabrication (FFF) by proposing a system that combines computer vision and augmented reality techniques. The system measures geometric accuracy by comparing features of a 3D-printed part, captured using an ordinary camera, with the CAD model of the part that is projected in the same space. In the process, augmented reality is employed for camera calibration, pose estimation, and perspective projection, enabling accurate tracking and visualization of 3D-printed parts. image processing techniques, including image segmentation and differencing, are applied to compare the virtual and real-world images, allowing the detection of geometric distortion. To evaluate the scalability and effectiveness of the proposed system, experiments were conducted by comparing virtual and real-world images of 3D-printed parts, captured from different camera positions and orientations. The results demonstrate the capability of the proposed solution to perform geometric distortion detection in close-to-real-time, which is an essential building block for in-situ control mechanisms in FFF applications.

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Research on Text Detection and Recognition Based on OCR Recognition Technology

Research on Text Detection and Recognition Based on OCR Reco...

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international conference on Information Systems and computer Aided Education (ICISCAE)

作者： Yuming He Software Engineering Shanghai Starriver Bilingual School Shanghai China

ISBN: (数字)9781728183046

ISBN: (纸本)9781728194608

Optical character recognition (OCR) is an important branch in the field of machine vision. It involves pattern recognition, image processing, digital signal processing, artificial intelligence and other disciplines. It is a comprehensive. It has important use value and theoretical significance in high-tech fields such as word information processing, office automation, machine translation and real-time monitoring system. In the 21st century, with the popularity of smart phones with high-definition cameras, OCR has a new pursuit in its development: more and more people pick up their mobile phones to photograph the things and scenes they see and obtain the text information in the pictures. Therefore, the recognition of characters in natural scenes has become a brand-new topic. In the past, text detection and text recognition algorithms were basically based on artificially designed features and traditional image processing methods. These features and algorithms were difficult to design and needed a lot of professional knowledge and experience support, so the accuracy was not high and they were not generalized. In recent years, with the rapid development of deep learning technology, breakthroughs have been made in the fields of computer vision such as image classification, object detection and semantic segmentation. Deep learning algorithm is a data-driven algorithm. The algorithm based on deep learning can automatically discover and learn the hidden feature rules in a large number of data through iterative training, without too much human intervention, so it has better generalization than traditional image processing related algorithms.

关键词： Deep learning image recognition Text recognition Signal processing algorithms Classification algorithms Optical character recognition software Character recognition

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Application of image processing and Machine Learning for Smart Restaurants

Application of Image Processing and Machine Learning for Sma...

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international conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Supreeth S. Angadi H. Rachana B.J. Sowmya S. Seema Ramaiah Institute of Technology Bangalore Bangalore India

The idea is to provide a smart package for restaurants using machine learning techniques. The techniques we have used are image processing, association rule generation and sentiment analysis . Main objective is to provide an interface for the customer where he can get complete information about the restaurant. Customer has to click and upload a picture of the restaurant. The system will perform image processing and as a response it will identify the restaurant (just by the picture). Along with identification of the restaurant it will also give the customer the previous reviews about the restaurant. These reviews will help the customer in order to decide whether the restaurant is of his choice or not. Our solution for smart restaurants also includes smart menu card for each restaurant. Here we are performing association rule mining on the previous year's transaction of each restaurant and hence obtain a list of most frequently ordered dishes according to customer's choice and then form combos of the same in order to attract the customers. In the end we have an automated mail generating system which sends an e-mail to all the customers in the restaurants database about the offers they can avail in the restaurants. This will help in getting more customers to the restaurants.

关键词： Sentiment analysis Support vector machines image processing Machine learning computer science Postal services conferences

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Detection methods for micro-cracked defects of photovoltaic modules based on machine vision

Detection methods for micro-cracked defects of photovoltaic ...

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IEEE international conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Peng Xu Wenju Zhou Minrui Fei Shanghai Key Laboratory of Power Station Automation Technology Shanghai University Shanghai China School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom

ISBN: (纸本)9781467369541

The efficiency and the service life of the photovoltaic modules are affected by the surface defects. Therefore, it is critical to detect the photovoltaic modules whether it is qualified or not before assembling into solar panels. This paper applies a method to detect micro-cracked defects in photovoltaic modules using electroluminescence (EL) technology and image processing. After applying forward bias voltage to photovoltaic modules, a large amount of non-equilibrium carriers is injected into photovoltaic modules from the diffusion region recombination to constantly composite luminescence and emit photons. Then an image is formed by a CCD camera which is used to capture these photons. As the brightness of the captured image is proportional to minority carrier diffusion length and current density, if the minority carrier diffusion length is relatively low, there may be defective, which results in a relatively dark image. Micro-cracked defects can be effectively found by analyzing the EL image. Varies of methods, including image segmentation, Gauss filtering, Hough line detection, are used to process image to judge whether the solar cell module is cracked. According to detecting results, the combination of these methods can effectively detect micro-cracked defects in photovoltaic modules.

关键词： Silicon image segmentation Loss measurement Automation Photonics Industries computers

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image-based Classification of Skin Cancer using Convolution Neural Network

Image-based Classification of Skin Cancer using Convolution ...

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Intelligent Technologies (CONIT), international conference on

作者： Priotosh Mondal Aditi Bhatia Roshini Panjwani Shrey Panchamia Indu Dokare Department of Computer Engineering VES Institute of Technology Mumbai India

Skin cancer is a category or collection of cancer affecting the tissues and layers of skin. Skin cancer is classified into several types depending on the type of cell it affects. These types include melanoma, melanocytic nevus, basal cell carcinoma, benign keratosis, actinic keratosis, dermatofibroma, vascular lesion, and squamous cell carcinoma. Melanoma which affects the melanocytes and is considered to be the most fatal and deadly cancer, is growing at an alarming rate, especially in the western hemisphere and the Pacific region. The proposed system contained a web-based application where the image of an affected skin area can be uploaded and the likelihood of skin cancer is displayed. This system has used a convoluted neural network (CNN) based binary and multi-classification model making efficient use of image processing, computer vision, OpenCV, and Python to classify dermatoscopic lesion images into cancerous and non-cancerous along with their types. The implemented binary classifier achieves an accuracy of 92%. Further, the multi-class classification model is implemented based on CNN to classify dermatoscopic cancerous lesion images into nine types which achieved an accuracy of 97%. Among nine classes one of the classes is non-cancerous. The models aim to provide a means of diagnostic tool that will help in the preliminary diagnosis of skin lesions. Early detection and diagnosis are appropriate measures to combat the spread and lethality of skin cancer.

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A computer vision system for automated corn seed purity analysis 90

A computer vision system for automated corn seed purity anal...

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Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1

作者： Jack C. H. Chung M. Litt G. Leininger SDRC IN Milford Ohio and The Standard Oil Company Research and Development Center Cleveland Ohio The Standard Oil Company Research and Development Center Cleveland Ohio University of Missouri in Rolla Missouri and The Standard Oil Company Research and Development Center Cleveland Ohio

ISBN: (纸本)9780897913720

Electrophoresis gel analysis is a viable technique for determining the purity of hybrid corn seeds. Visually analyzing the electrophoretic gel images is a very tedious and time-consuming task. In this paper, a computer vision system integrating image processing and pattern recognition techniques with domain-specific structural information to automate the electrophoresis gel scoring procedure is presented. A set of image processing algorithms are developed to perform extraction of the region of interest, segmentation of samples, identification of bands within samples, and final classification of different types of *** image processing algorithms utilize the structural information and operator expertise to achieve high classification rate with fuzzy and incomplete information contained on the electrophoresis gels. The developed technique clearly demonstrates the potential of using computer vision in automating the gel scoring procedure. The developed technology may also be extended to other areas such as general one-dimensional electrophoresis and high performance thin layer chromatography.

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Animal Species image Classification

Animal Species Image Classification

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international Symposium on Artificial Intelligence and Signal processing (AISP)

作者： Likhith Prudhivi M Narayana. Ch Subrahmanyam. M Gopi Krishna. Salim Chavan Infosys Ltd Hyderabad India Electronics & Communication Engineering Anurag University Hyderabad India Electronics & Communication Engineering Vignan institute of technology and science. telangana India Electronics & Communication Engineering Vardhaman College of Engineering Shamshabad India Govindrai Wanjari College of Engineering and Technology Nagpur India

In forests, in order to protect wild animals, real-time animal species identification is done via picture recognition. Many computer vision approaches were first presented in the past, however they couldn’t meet the demands since accuracy decreased as technology evolved. Nonetheless, accuracy in the categorization, identification, and segmentation of pictures was significantly improved as a consequence of the inclusion of multiple approaches. In order to achieve a high degree of accuracy, this study intends to provide an effective approach for animal species picture categorization. Convolutional neural networks have been developed specifically for the categorization of images. To achieve high accuracy, final layer features are learned and synchronised to the pre-trained model. With the dataset, a variety of architectures for deep learning are evaluated.

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Region Proposal Approach for Human Detection on Aerial imagery

Region Proposal Approach for Human Detection on Aerial Image...

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international Multidisciplinary conference on computer and Energy Science (SpliTech)

作者： Željko Marušić Dunja Božić-Štulić Sven Gotovac Tonćo Marušić University of Mostar Mostar BIH University of Split Split Croatia

In this paper we evaluated region proposal based CNN approach in human body detection from aerial perspective. Particular emphasis is on the automation of the detection for supporting search and rescue missions. This challenging task is characterized by two important requirements. The first requirement for proposed algorithm is real-time speed of execution and the other is exceptional detection quality on complex natural environment images. Evaluation is performed on high spatial resolution images with high level of details that were collected by UAV platforms. Evaluated method based on FasterRCNN detection model showed promising preliminary results, as well as fast processing of high resolution images. Overall, detection model achieved 88.3% recall with precision of 67.3%.

关键词： image segmentation Training Proposals Feature extraction Task analysis Unmanned aerial vehicles image resolution

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