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1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2022) 1

1st International Virtual Conference on Visual Pattern Extra...

引用

1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2022

作者： Amelio, Alessia Montelpare, Sergio Ursino, Domenico InGeo University "G. d’Annunzio" Chieti-Pescara Viale Pindaro 42 Pescara65127 Italy DII Marche Polytechnic University Via Brecce Bianche 12 Ancona60131 Italy

This document introduces the 1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2022), a premier forum for presenting the state-of-the-art, new research, ongoing work, academic and project reports in advanced statistics and machine learning, 3D modelling and simulation, knowledge representation, intelligent systems, information retrieval and software engineering, for visual pattern extraction, analysis and recognition to preserve the cultural heritage. © 2022 Copyright for this paper by its authors.

关键词： 3D modeling

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image Generation Algorithm of Text Description Based on styleGAN 24

Image Generation Algorithm of Text Description Based on Styl...

引用

1st international conference on image processing machine learning and pattern recognition

作者： Dai, Hui Hao, Pan Wuhan Univ Technol Wuhan Peoples R China

ISBN: (纸本)9798400707032

The training of the text-to-image task relies on a large number of image-text pairs, and the problems of insufficient sample size of the dataset and inprominent image subject content will greatly affect the overall performance of the model. In addition, most of the existing methods are stacked multi-stage generation structure, and the quality of generated images is difficult to meet the actual application requirements. Aiming at the above problems, this paper enhances the dataset and designs a single-stage text generation image model based on styleGAN. Based on the analysis of global semantic features and semantic diversity expressions, this paper carries out adaptive perturbation text data enhancement, and designs an image cropping method that fuses boundary information, and adopts random cropping and rotation to complete image data enhancement. In the generator, the conditional style construction network fuses global semantic information and hidden space vectors to obtain conditional style control vectors that control different visual features of the image. The generation network adopts a progressive generation structure, accepts the control of conditional style in the way of AdaIN, and adds random noise to each layer at the same time, which improves the authenticity of the image and enriches the details. At the same time, the semantic inner product is added to the discriminator, and the loss function of cyclic consistency loss and visual semantic matching loss are introduced to further improve the semantic consistency of graphics and texts. The IS, FID and R-precision values of this model on the CUB-200-2011 dataset reached 4.96, 14.11 and 75.20%, respectively.

关键词： Cross-modal Generative adversarial network image generation Text-generated images Perceived loss

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An Algorithm study of Personalized Recommendation for Electric Vehicle Charging Piles Based on Deep learning Framework 24

An Algorithm Study of Personalized Recommendation for Electr...

引用

1st international conference on image processing machine learning and pattern recognition

作者： Li, Ling Xinyu Univ Coll Informat Engn Xinyu Peoples R China

ISBN: (纸本)9798400707032

China is vigorously developing electric vehicles, and the penetration rate of new energy vehicles in China has exceeded 10%. The penetration rates in the United states, Europe, and other regions are also growing, leading to a high-speed increase in the demand for charging piles. Although the number of public charging piles has been increasing year by year, big data shows that the utilization rate of public charging piles is less than 15%. Therefore, how to guide users to avoid peak electricity consumption periods without compromising the convenience of charging for electric vehicle users is key to solving the problem. Finding an appropriate scheduling strategy is the core of solving this dilemma. For personalized recommendation algorithms for charging piles, we propose a Top-N recommendation algorithm for charging piles based on a neural collaborative filtering framework that combines multiple feature fusion methods. This algorithm uses concatenation and outer product operations to construct interaction relationships between users and items, respectively. Then, two learners are used to learn their interactive features, and finally, the learned prediction vectors are merged through concatenation. This approach not only fully excavates the potential information of embedded vectors but also has nonlinear feature fitting capabilities, thereby better improving the model's recommendation performance.

关键词： Deep learning Electric Vehicle Charging station Top-N Recommendation Algorithm

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Generalizable features-based method for breech face comparisons 24

Generalizable features-based method for breech face comparis...

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1st international conference on image processing machine learning and pattern recognition

作者： Li, Baohong Zhang, Hao Robin, Ashraf U. Z. Zaman Yu, Qianqian Nanjing Tech Univ Sch Mech & Power Engn Nanjing Peoples R China

ISBN: (纸本)9798400707032

Breech face impressions are marks left by the impact of a bullet casing against the breech face due to the reactive force of the gunpowder explosion, which is essential evidence linking firearms to fired bullet casings. Aiming at the problem that traditional methods exhibit a high error rate and cannot be applied to both granular and striated impressions, a learnable image matcher designed on the core principle of generalization (OmniGlue) is introduced for the automatic comparison of breech face impressions. The method employs an attention mechanism to propagate information across the built graphs, removes irrelevant key points and separates the local feature descriptors from the position information of key points during information propagation, thereby reducing the dependence of the feature descriptors on position information. Position information is not merged into the local descriptors used for matching, enhancing the generalization of these descriptors and improving the model's performance across different domains. The method is validated using the Fadul and Hamby datasets provided by the National Institute of standards and Technology (NIst). The results show that the method is effective for both granular and striated marks, exhibiting a lower identification error rate and superior performance compared to traditional methods.

关键词： Breech face impressions Firearm identification image feature matching

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Advantage and Drawback of Support Vector machine Functionality 1

Advantage and Drawback of Support Vector Machine Functionali...

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1st international conference on Computer, Communications, and Control Technology (I4CT)

作者： Karamizadeh, Sasan Abdullah, Shahidan M. Halimi, Mehran Shayan, Jafar Rajabi, Mohammad Javad UTM AIS Kuala Lumpur Malaysia

ISBN: (纸本)9781479945559

Support Vector machine(SVM) is one of the most efficient machine learning algorithms, which is mostly used for pattern recognition since its introduction in 1990s. SVMs vast variety of usage, such as face and speech recognition, face detection and image recognition has turned it into a very useful algorithm. This has also been applied to many pattern classification problems such as image recognition, speech recognition, text categorization, face detection, and faulty card detection. statistics was collected from journals and electronic sources published in the period of 2000 to 2013. pattern recognition aims to classify data based on either a priori knowledge or statistical information extracted from raw data, which is a powerful tool in data separation in many disciplines. The Support Vector machine ( SVM) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables.

关键词： upport Vector machine pattern recognition, classify, and face detection Biometricupport Vector machine pattern recognition, classify, and face detection BiometricS

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A Novel Inference Algorithm for Active learning Method

A Novel Inference Algorithm for Active Learning Method

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1st Iranian conference on pattern recognition and image Analysis (PRIA)

作者： Afrakoti, Iman Esmaili Paeen Shouraki, Saeed Bagheri Ghaffari, Aboozar Sharif Univ Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9781467362061;9781467362047

This paper presents a new inference algorithm for Active learning Method (ALM). ALM is a pattern-based algorithm for soft computing which uses the Ink Drop Spread (IDS) algorithm as its main engine for feature extraction. In this paper a fuzzy number is extracted from each IDS plane rather than the narrow path and spread as in previous approaches. In order to compare performance of the proposed algorithm with the original one, two functions which are widely used in literature are modeled as the benchmark. Simulation results show that the proposed algorithm is as effective as previous one in the modeling task.

关键词： Active learning method (ALM) fuzzy inference algorithm ink drop spread (IDS)

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Abnormal Target Detection Method in Multispectral Industrial images Based on machine learning 1

Abnormal Target Detection Method in Multispectral Industrial...

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

作者： Huang, Qingrong China Mobile Communications Group Fujian Co. Fujian Fuzhou China

ISBN: (纸本)9798350316469

In recent years, with the continuous development of multispectral industrial image technology, multispectral industrial image data has been greatly improved in terms of spectrum and spatial resolution, which provides convenience for the acquisition and processing of multispectral industrial image data. However, the existing automatic detection and recognition methods of ground objects often only use the spectral information of ground objects, while ignoring the spectral and spatial correlation between ground objects, and assume that ground objects and ground objects are independent of each other in the airspace, it is often neglected in practical applications. In order to solve this problem, this paper uses a machine learning model to achieve the detection and recognition of multispectral image targets by fusing spectral information and correlation characteristics through generalized likelihood ratio testing. Finally, experiments have proved that the machine learning-based multispectral industrial image abnormal target detection method performs better than other algorithms (F1 value is 0.91, Precision rate is 0.89). © 2023 IEEE.

关键词： Industrial image machine learning Multi-Spectral Anomaly Target Detection Method RX processing Algorithm

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A pattern Analysis-based Segmentation to Localize Early and Late Blight Disease Lesions in Digital images of Plant Leaves

A Pattern Analysis-based Segmentation to Localize Early and ...

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IEEE international conference on Signal and image processing Applications (IEEE ICSIPA)

作者： Abdu, Aliyu Muhammad Mokji, Musa Mohd Sheikh, Usman Ullah Univ Technol Malaysia Sch Elect Engn Skudai 81310 Malaysia

ISBN: (纸本)9781728133775

This study reports a disease symptom classification algorithm using a proposed pattern recognition approach to individually localize early and late blight visual disease symptoms. The algorithm uses the pathological analogy hierarchy of the diseases to produce a novel homogeneous pattern localization, more informative to extract features that would be utilized for a machine learning system to classify the two diseases in digital photographs of vegetable plants. One of the most significant advantages of the proposed pattern analysis is localizing symptomatic and necrotic regions based on pathological disease analogy using soft computing, with which the pattern of each disease manifestation along the leaf surface can be tracked and quantified for characterization. In the 1st phase of the experiment, individual symptomatic (R-S), necrotic (R-N), and blurred (R-B, in-between healthy and symptomatic) regions were identified, segmented, and quantified. The 2nd phase focuses on the extraction of pattern features for classification and severity estimation with a machine learning classifier. The obtained results are encouraging, successfully localizing and quantifying individual disease lesions. This also indicates the enhanced applicability of the proposed approach discriminating the two diseases based on their dissimilarity. It is also envisaged that the algorithm can be extended to other plant disease symptoms. Moreover, it provides opportunities for early identification and detection of subtle changes in plant growth, disease stage, and severity estimation to assisting crop diagnostics in precision agriculture.

关键词： pattern analysis Computer vision Segmentation Plant disease lesion Plant disease detection

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Q(λ)-based image thresholding

Q(λ)-based image thresholding

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1st Canadian conference on Computer and Robot Vision

作者： Shokri, M Tizhoosh, HR Univ Waterloo Pattern Anal & Machine Intelligence Lab Syst Design Engn Waterloo ON N2L 3G1 Canada

ISBN: (纸本)0769521274

One of the problems in image processing is finding an appropriate threshold in order to convert an image to a binary one. In this paper we introduce a new method for image thresholding. We use reinforcement learning as an effective way to find the optimal threshold. Q (A) is implemented as a learning algorithm to achieve more accurate results. The reinforcement agent uses objective rewards to explore/exploit the solution space. It means that there is not any experienced operator involved and the reward and punishment function must be defined for the agent. The results show that this method works successfully and can be trained for any particular application.

关键词： reinforcement learning Q(lambda) image processing image thresholding

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pattern recognition and machine Intelligence 2011

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丛书名： Lecture Notes in Computer Science

2011年

作者： Sergei O. Kuznetsov Deba P. Mandal Malay K. Kundu Sankar Kumar Pal

ISBN: (数字)9783642217869

ISBN: (纸本)9783642217852

This book constitutes the refereed proceedings of the 4th international conference on pattern recognition and machine Intelligence, PReMI 2011, held in Moscow, Russia in June/July 2011. The 65 revised papers presented together with 5 invited talks were carefully reviewed and selected from 140 submissions. The papers are organized in topical sections on pattern recognition and machine learning; image analysis; image and video information retrieval; natural language processing and text and data mining; watermarking, steganography and biometrics; soft computing and applications; clustering and network analysis; bio and chemo analysis; and document image processing.

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