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Image Classification with Information Extraction by Evaluating the Text patterns in Bilingual Documents 1

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2nd international conference on Advanced Network Technologies and Intelligent computing (ANTIC)

作者： Puri, Shalini Manipal Univ Jaipur Jaipur Rajasthan India

ISBN: (数字)9783031281839

ISBN: (纸本)9783031281822;9783031281839

The rapid development of digital data in today's world needs highly efficient and automatic N-lingual image document classification and information extraction systems. Several text-based image processing systems have been introduced to date for different applications. However, they are limited to character recognition, word extraction, profiling, and script and language recognition. The proposed document classification and information extraction system categorizes the bilingual English-Hindi images into mutually exclusive categories by using SVM and random forest classifiers at the character-level and document-level recognitions, respectively. It used two pseudo-thesaurus, first to store the pre-defined English and Romanized Hindi characters and second to store the keywords of the pre-defined categories. It discriminates between the languages by determining the absence or presence of the shirorekha for the English or Hindi word images, respectively, and applies the minimum distance computing method to obtain the constructed words from the mapped English and Romanized Hindi characters. This system achieved promising results in extracting the information and classifying the bi-lingual image documents. It illustrates a case study to explain the working of the system.

关键词： Information extraction Bilingual system Hindi-English image documents Language pair Text pattern analysis

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Fusion of LBP and Median LBP for Dominant Region Based Multimodal recognition Using Imperfect Face and Gait Cues 3rd

Fusion of LBP and Median LBP for Dominant Region Based Multi...

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3rd international conference on Computer Vision, High Performance computing, Smart Devices, and Networks, CHSN 2022

作者： Annbuselvi, K. Santhi, N. Sivakumar, S. V.V.Vanniaperumal College for Women Madurai Kamaraj University Virudhunagar Tamilnadu Madurai India Cardamom Planters’ Association College Madurai Kamaraj University Bodinayakanur Tamilnadu Madurai India

ISBN: (纸本)9789819966899

The paper provides a novel approach to recognize imperfect face and gait cues by using only the dominant region of such cues which contain more information than that possessed by the other regions. To enhance features in such dominant region, a fusion of Local Binary pattern (LBP) and Median Local Binary pattern (Median LBP) procedure is proposed. Initially, the given imperfect face and gait probe images are divided into six overlapped half regions. After partition, the dominant overlapped half regions of face and gait are selected by using content based image retrieval process. Subsequently, the features of dominant overlapped imperfect face and gait regions are enhanced by fusing the feature vectors obtained by using fusion of LBP and Median LBP methods. Next the Eigen feature vectors followed by Fisher’s vectors are constructed using Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) dimensionality reduction algorithms. The decisions from face and gait biometric systems are obtained separately by using Euclidean distance measure. Finally, the decisions of face and gait classifiers are fused at decision level for recognition using AND/OR rule. The method is verified on freely available ORL face and CASIA B gait datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Local binary pattern

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Local Neighborhood Average pattern: A Handcrafted Feature Descriptor for Hand Gesture recognition 3

Local Neighborhood Average Pattern: A Handcrafted Feature De...

引用

3rd international conference on Secure Cyber computing and Communications, ICSCCC 2023

作者： Bahuguna, Arti Namchyo, Shalom Ben Tenzing Chaudhary, Deepak Kumar Bhaumik, Gopa Govil, Mahesh Chandra National Institute of Technology Sikkim Computer Science and Engineering Sikkim India National Institute of Technology Jamshedpur Computer Science and Engineering Jharkhand India

ISBN: (纸本)9798350300710

This paper proposes a handcrafted feature-based descriptor namely Local neighborhood average pattern (LNAP) for static hand gesture recognition. The fact, that the local descriptors are important in numerous computer vision applications and show great performance cannot be overstated. This is accentuated further in difficult environments. This is the major driving force behind continued research in this field. We developed a feature descriptor LNAP in this paper, with the aim of extracting complete microstructural features achieved by evaluating excitation in different ways and directional relevant data based on connections between pixels taken at various spatial configurations inside each 3 × 3 neighborhood. An LNAP descriptor represents the structure of hand textures in a simple and compact coding manner, resulting in its unique code in less time and memory than existing approaches. The proposed LNAP descriptor extracts the dominant features from the hand region which are further classified using SVM. The proposed LNAP's performance is assessed using three benchmark datasets: Massey University Dataset (MUGD), ASL Digit Datasets, and Ouhands Dataset in respect of accuracy and F1-score and found to achieve an accuracy of 93% (MUGD Set1), 96% (MUGD Set2), 92 % (MUGD Set 3), 89% (MUGD Set4), 71% (MUGD Set5), 99%, and 47% respectively. The experimental findings show that the suggested LNAP produces better outcomes than existing techniques. © 2023 IEEE.

关键词： Textures

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Systematic Review: Image Processing Based Dorsal Vein pattern Biometric Authentication System 9

Systematic Review: Image Processing Based Dorsal Vein Patter...

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9th IEEE international conference on computing, Engineering and Design, ICCED 2023

作者： Syam, Fajar M Yusup, Rian Maulana Samsudin Yudono, Muchtar Ali Setyo Electrical Engineering Nusa Putra University Sukabumi Indonesia

ISBN: (纸本)9798350370126

Biometrics leverages unique biological or behavioral traits for person or object identification, characterized by traits such as uniqueness, indivisibility, immutability, and resilience against loss or theft. Evolving biometric systems include facial recognition, iris scanning, fingerprint recognition, palm line recognition, and dorsal hand vein recognition. Dorsal hand vein identification, concealed beneath the skin, provides a highly secure biometric modality resistant to forgery, tampering, or damage. This systematic literature review offers an overview of dorsal hand vein image processing for biometric authentication systems, covering recent advancements in image processing, including concept clarification, data acquisition, pre-processing, segmentation, feature extraction, and identification phases. The review spans research articles from 2018 to 2023, sourced from reputable outlets such as 208 MDPI journals, Google Scholar, IEEE Xplore, Google Books, and other scholarly repositories. Stringent article selection, guided by predefined keywords, ensures research quality. Findings emphasize the necessity of a camera and infrared LED for dorsal hand vein data acquisition to build an effective identification system. Pre-processing involves Region of Interest (ROI) selection, contrast enhancement, and intensity normalization. Segmentation employs morphological operations, filter banks, and adaptive thresholding. Feature extraction encompasses statistical features and the Gray Level Co-occurrence Matrix (GLCM). Convolutional Neural Networks (CNNs) play a pivotal role in identification, featuring layers such as Conv1-1, Conv1-2, batch normalization, max-pooling, and dropout layers. © 2023 IEEE.

关键词： Feature extraction

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Deep Learning-Powered Face recognition Attendance System: A Novel Approach to Automated Prediction 2

Deep Learning-Powered Face Recognition Attendance System: A ...

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2nd international conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Sornalakshmi, M. Abinash, M.J. Gopalakrishnan, S. Kasthuri, R. Nivedhitha Harini, C.M. Balamurugan, K. Department of Computer Science PG Arulmigu Kalasalingam College of Arts and Science Tamilnadu India Arulmigu Kalasalingam College of Arts and Science Tamilnadu India

ISBN: (纸本)9798350340235

Taking attendance is one of the most critical activities that must be completed on a regular basis at colleges, universities, organizations, schools, and companies. The bulk of the time, it is done manually, such as by name or roll number. The primary purpose of this work is to develop a Face recognition-based attendance system that will automate this human procedure. This research work fits the standards for modernizing the way attendance is handled, as well as the time management criteria. This gadget is put in the classroom and is programmed with student information such as name, roll number, class, sec, and photos. Open Computer Vision (CV) is used to extract the photos. The understudy can move toward the machine before the beginning of the connected example, and it will start taking photos and contrasting them with the passing dataset. The camera and processor board in this task were a Logitech C270 web camera and a NVIDIA Jetson Nano Engineer pack. The image is changed as follows: Initial, a Haar Cascade classifier is utilized to recognize faces, trailed by the LBPH (Neighborhood Twofold Example Histogram) Calculation, histogram information is considered in contrast to a preset dataset, and the gadget consequently distinguishes participation. A Succeed document is made and refreshed consistently with data from the pertinent class educator. The accompanying work summarizes the fundamental elements of a facial recognition -based attendance system. © 2023 IEEE.

关键词： Face recognition Local Binary pattern Histogram Open Computer Vision

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Oracle Bone Script recognition Based on Multi-scale Feature Fusion and Knowledge Distillation 27th

Oracle Bone Script Recognition Based on Multi-scale Feature ...

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27th international conference on pattern recognition, ICPR 2024

作者： Wang, Jiaoyan Xu, Xuebin Aysa, Alimjan Ding, Jun Ubul, Kurban School of Computer Science and Technology Xinjiang University Ürümqi830046 China Xinjiang Multilingual Information Technology Key Laboratory Xinjiang University Ürümqi830046 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Ürümqi830046 China

ISBN: (纸本)9783031783111

Oracle bone scripts, being the oldest and most developed writing system discovered in China till date, have been meticulously studied by numerous scholars. This paper presents the Multi–scale Feature Fusion Attention Net (MFFA–Net) as a solution to the issue of variant characters in the oracle bone affecting recognition accuracy. The network is based on ResNet18 and employs asymmetric convolution alongside a refined coordinate attention technique to acquire image features. It also integrates perceptual field information of varying sizes through hierarchical bilinear pooling. Furthermore, knowledge from the Wide_ResNet101 and DenseNet169 is transferred to MFFA-Net using knowledge distillation techniques. Finally, to validate the effectiveness of our proposed method, we conducted rigorous experiments on the OBC306, OBC265, and EOBC datasets, comparing our results with those of existing methods. The experimental results demonstrate that our method obtains remarkable performance in oracle character recognition, reaching state-of-the-art Top–1 accuracy with 92.42%, 94.78%, and 98.82% on the OBC306, OBC265, and EOBC datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Attention Feature Fusion Knowledge Distillation Oracle recognition

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User Activity recognition and pattern Prediction Using Call Data Mining

User Activity Recognition and Pattern Prediction Using Call ...

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2023 international conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Ojha, Nitish Kumar Ranjan, Preetish Amity University Dept. of CSE Noida India Amity University Dept. of CSE Bihar Patna India

ISBN: (纸本)9798350333244

The story of technology is certainly as old as humanity itself. With the advancement of time, human society has been instrumental in communication technology. Technology has worked in two ways, as a supporting tool for crime as well as a helping tool to identify and investigate the e at the same time. In the last decade, India has seen rapid growth in the field of mobile communication as it is continuously increasing at an exponential rate, the availability of cheap data and availability of economical rate of calling encourages users to make calls easily and this continuous calling within a certain group creates a network considering relationship a parameter. The use of existing models of rapid data mining on mobile phone call logs / call data records (CDR)establishes a new dimension in the field of a user's social behavior. Meanwhile, it makes a possible methodology of suspect detection based on certain attributes i.e., cell phone conversation ratio, calling pattern among users, terrain-basedfacts, etc. In this paper, key areas which have been covered are 'Machine's perception towards prediction of complex social systems and strange behavior of cell phone user' based on data mining techniques applied on call details of cell phone users. © 2023 IEEE.

关键词： Forecasting

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Revisiting the Benefits of Knowledge Distillation against Adversarial Examples 6

Revisiting the Benefits of Knowledge Distillation against Ad...

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6th international conference on Data Science and Information Technology, DSIT 2023

作者： Li, Jiawen Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350304442

Deep neural networks (DNNs) have always been a popular base model in many image classification tasks. However, some recent works suggest that there are some man made images will easily lead DNNs give wrong predictions. These images are generated by adding some invisible perturbations into clean images, which are the so-called adversarial examples. Since they are almost similar to original data but can fool DNNs, adversarial examples can bring the insecurity to many life-critical applications. Adversarial knowledge distillation is a positive defense method to attach improved robustness to the target DNNs against adversarial examples. In distillation, student models are trained by soft labels coming from robust pre-trained teacher networks. However, it is hard for researchers to ensure that supervised information comes from a better teacher model. In this paper, We focus on four potential factors of the teacher that may have an effect to the distillation performance of student model, and investigate whether and when they can boost the model robustness against adversarial attacks. Multiple quantitative experiment illustrate that these factors have impact on the distillation process and these results can be considered to choose a better teacher model. Further studies can focus on the combination of adversarial knowledge distillation and other defense schemes. Empirical results in this paper may also help select proper teacher in new complex distillation tasks. © 2023 IEEE.

关键词： Distillation

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An LSTM-based Framework for Motion pattern recognition of Handling Robots 7

An LSTM-based Framework for Motion Pattern Recognition of Ha...

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7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Li, Fangyu Zheng, Jianbin Yan, Zhiqi Tang, Yong Chen, Liufei Wang, Zhuorui Gao, Yifan Wang, Yu Wuhan University of Technology School of Information Engineering Wuhan China Third Construction Co. Ltd. China Construction Third Engineering Bureau Wuhan China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China

ISBN: (纸本)9781665478571

With the development of the construction industry, construction work more and more tends to mechanization, automation, and intelligence. The appearance of handling robots greatly improves the handling efficiency and reduces the cost. Even though great efforts have been made on motion pattern recognition for handling robots, the existing methods still have some shortcomings. Previous studies are all used for motion pattern recognition, each input is processed separately, which cuts off the time continuity between the latter data and the former data. To this end, in this paper, we first emphasize the importance of temporal correlation between data in the field of motion pattern recognition and devise an LSTM based method to ease the issue above. Finally, we use softmax to effectively calculate the classification results of data potential features and temporal information. Compared with traditional machine learning models and popular neural network models, extensive experiments on real-world datasets demonstrate the effect of our framework is superior to the baselines, thus providing novel perspectives to the building automation industry. © 2022 IEEE.

关键词： Long short-term memory

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Implementing Open Source Biometric Face Authentication for Multi-factor Authentication Procedures 11th

Implementing Open Source Biometric Face Authentication for M...

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11th international conference on High-Performance computing Systems and Technologies in Scientific Research, Automation of Control and Production (HPCST)

作者： Minakova, Natalya Mansurov, Alexander Altai State Univ Lenin Ave 61 Barnaul 656049 Russia

ISBN: (纸本)9783030941413;9783030941406

This study proposes a solution that extends the capabilities of web information systems with single-factor authentication by introducing an additional authentication factor based on biometric face recognition. The proposed solution design and its main operation steps are presented and discussed. The solution utilizes the standard multimedia functionality of popular web browsers and supports available or built-in image capturing devices (photo and web cameras). Robust program algorithms from the open source computer vision library are used for face image processing and analysis. Experimental testing and validation of the algorithms for face localization and recognition are conducted with image sets produced with consideration of reality. Experimental results demonstrate high effectiveness with a success rate of 80% ... 93% for the solution based on the local binary pattern face localization algorithm with the local binary pattern histogram face recognition algorithm.

关键词： Biometrics Face authentication Face recognition Computer vision Local binary pattern

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