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1st International Conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Datta, Anisha Si, Shukrity Biswas, Sanket Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri735102 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

ISBN: (纸本)9789811390418

The primary objective of the model applied in this work is to predict the weather of a city named Austin in Texas using supervised machine learning algorithms. In this case, artificial neural networks and gradient boosting classifier were implemented to build models to predict weather and comparisons between these two models are also made for this dataset. Here, average temperature, average dew point, average pressure of sea level, average percentage of humidity, etc. are the parameters taken into consideration which influence the weather of the place. Using these parameters, the trained models performed a classification to predict whether the weather is rainy (thunderstorm or not), not rainy, snowy, or foggy. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Supervised learning

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Spatio-temporal Attention Graph Convolutions for Skeleton-based Action recognition 23rd

Spatio-temporal Attention Graph Convolutions for Skeleton-b...

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22nd Scandinavian Conference on Image Analysis, SCIA 2023

作者： Le, Cuong Liu, Xin Computer Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping Sweden

ISBN: (纸本)9783031314346

In skeleton-based action recognition, graph convolutional networks (GCN) have been applied to extract features based on the dynamic of the human body and the method has achieved excellent results recently. However, GCN-based techniques only focus on the spatial correlations between human joints and often overlook the temporal relationships. In an action sequence, the consecutive frames in a neighborhood contain similar poses and using only temporal convolutions for extracting local features limits the flow of useful information into the calculations. In many cases, the discriminative features can present in long-range time steps and it is important to also consider them in the calculations to create stronger representations. We propose an attentional graph convolutional network, which adapts self-attention mechanisms to respectively model the correlations between human joints and between every time steps for skeleton-based action recognition. On two common datasets, the NTU-RGB+D60 and the NTU-RGB+D120, the proposed method achieved competitive classification results compared to state-of-the-art methods. The project’s GitHub page: STA-GCN. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Region-based image annotation using asymmetrical support vector machine-based multiple-instance learning

Region-based image annotation using asymmetrical support vec...

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2006 IEEE computer Society Conference on computer vision and pattern recognition, CVPR 2006

作者： Yang, Changbo Dong, Ming Hua, Jing Machine Vision and Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 Department of Computer Science Wayne State University Detroit MI 48202

ISBN: (纸本)0769525970

In region-based image annotation, keywords are usually associated with images instead of individual regions in the training data set. This poses a major challenge for any learning strategy. In this paper, we formulate image annotation as a supervised learning problem under Multiple-Instance Learning (MIL) framework. We present a novel Asymmetrical Support Vector Machine-based MIL algorithm (ASVM-MIL), which extends the conventional Support Vector Machine (SVM) to the MIL setting by introducing asymmetrical loss functions for false positives and false negatives. The proposed ASVM-MIL algorithm is evaluated on both image annotation data sets and the benchmark MUSK data sets. © 2006 IEEE.

关键词： Image processing

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ANN based traffic congestion analysis applied to parking recommendation system for electric three-wheelers

Engineering Research Express

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Engineering Research Express 2025年第2期7卷

作者： Haldar, Suman Mondal, Arindam Maity, Rajkumar Banerjee, Rajib Department of Electronics and Communication Engineering Neotia Institute of Technology Management and Science West Bengal 743368 India Department of Electrical Engineering Dr B.C Roy Engineering College West Bengal Durgapur 713206 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute West Bengal 700108 India School of Computer Science UPES Uttarakhand Dehradun 248007 India

The increasing adoption of Electric Vehicles (EVs) is transforming transportation systems with an emphasis on sustainability. In the Asia-Pacific region, Electric Three Wheelers (E3W) have gained significant market share due to their affordability and eco-friendly nature. However, the rise in E3W usage has also led to road congestion and parking challenges, particularly in suburban and rural areas. This paper presents a smart roadside parking recommendation system that utilizes a proposed Artificial Neural Network (ANN) to forecast traffic status and recommend nearby parking options. The system uses vehicle dynamics data such as voltage, current, and timestamps collected via an IoT-based data logger integrated with a cloud-based and map-based visualization interface. By analyzing the collected data, the system predicts real-time traffic status for parking recommendations, enabling an efficient parking solution that saves both time and energy. The experimental result demonstrates that the proposed ANN configuration gives the most optimal result in terms of accuracy, loss, MAE, MSE, RMSE, and R Square on purely unseen data in real-time scenarios. Moreover, the trained ANN model effectively categorizes the parking recommendation as ‘Recommended,’ ‘Waiting State,’ or ‘Not Recommended’ for specific parking areas. To validate the model performance, experiments are conducted in two distinct regions with unique routes and traffic patterns. The result is visualized through map API and demonstrates optimal parking recommendations as compared to real-world traffic scenarios, thereby improving decision-making for E3W drivers. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.

关键词： artificial neural network battery monitoring data logger deep learning electric three-wheeler internet of things

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Two-stage recognition of handwritten bangla alphanumeric characters using neural classifiers

Two-stage recognition of handwritten bangla alphanumeric cha...

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2nd Indian International Conference on Artificial Intelligence, IICAI 2005

作者： Bhattacharya, U. Parui, S.K. Sridhar, M. Kimura, F. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Electrical and Computer Engineering University of Michigan-Dearborn United States Faculty of Engineering Mie University Japan

ISBN: (纸本)0972741216

No significant research work towards recognition of handwritten Bangla characters has yet been done. Only a few works in this area are found in the literature which are based on small databases col-lected in laboratory environments. Recently, a large and representative database of Bangla character images [1] has been developed. The first part of this article is devoted to shape analysis of samples of this database vis-a-vis shapes of the respective printed characters. The next part describes a two-stage scheme for recognition of handwritten Bangla alphanumeric characters. This is a 57 class problem (shapes of 3 numerals are not distinguishable from shapes of 3 basic characters among 10 numerals and 50 basic characters). In the first stage of this recognition scheme, an input character is identified in one of 11 smaller groups of characters. In the second stage, classification is performed within the particular smaller group as determined by the first stage. In both the stages of the proposed approach, local chain code histograms of character skeleton are used as the feature vector and multilayer perceptron (MLP) as the classifier. recognition accuracies obtained by the proposed approach on the training and test sets of the present handwritten Bangla alphanumeric character database are respectively 91.45% and 84.65%. Copyright © IICAI 2005.

关键词： Database systems

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Pectoral muscle detection in digital breast tomosynthesis and mammography

Pectoral muscle detection in digital breast tomosynthesis an...

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3rd International MICCAI Workshop on Medical computer vision, MCV 2013

作者： Ghesu, Florin C. Wels, Michael Jerebko, Anna Sühling, Michael Hornegger, Joachim Kelm, B. Michael Siemens Corporate Technology Imaging and Computer Vision Erlangen Germany Pattern Recognition Department Friedrich Alexander University Erlangen Germany Siemens AG Healthcare Erlangen Germany

ISBN: (纸本)9783319055299

Screening and diagnosis of breast cancer with Digital Breast Tomosynthesis (DBT) and Mammography are increasingly supported by algorithms for automatic post-processing. The pectoral muscle, which dorsally delineates the breast tissue towards the chest wall, is an important anatomical structure for navigation. Along with the nipple and the skin, the pectoral muscle boundary is often used for reporting the location of breast lesions. It is visible in mediolateral oblique (MLO) views where it is well approximated by a straight line. Here, we propose two machine learning-based algorithms to robustly detect the pectoral muscle in MLO views from DBT and mammography. Embedded into the Marginal Space Learning framework, the algorithms involve the evaluation of multiple candidate boundaries in a hierarchical manner. To this end, we propose a novel method for candidate generation using a Hough-based approach. Experiments were performed on a set of 100 DBT volumes and 95 mammograms from different clinical cases. Our novel combined approach achieves competitive accuracy and robustness. In particular, for the DBT data, we achieve significantly lower deviation angle error and mean distance error than the standard approach. The proposed algorithms run within a few seconds. © 2014 Springer International Publishing Switzerland.

关键词： Learning algorithms

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Text independent writer identification for Bengali script

Text independent writer identification for Bengali script

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International Conference on pattern recognition

作者： Chanda, Sukalpa Franke, Katrin Pal, Umapada Wakabayashi, Tetsushi Department of Computer Science and Media Technology Gjøvik University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Graduate School of Engineering Mie University Japan

ISBN: (纸本)9780769541099

Automatic identification of an individual based on his/her handwriting characteristics is an important forensic tool. In a computational forensic scenario, presence of huge amount of text/information in a questioned document cannot be always ensured. Also, compromising in terms of systems reliability under such situation is not desirable. We here propose a system to encounter such adverse situation in the context of Bengali script. Experiments with discrete directional feature and gradient feature are reported here, along with Support Vector Machine (SVM) as classifier. We got promising results of 95.19% writer identification accuracy at first top choice and 99.03% when considering first three top choices. © 2010 IEEE.

关键词： Support vector machines

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Lexicon reduction technique for Bangla handwritten word recognition

Lexicon reduction technique for Bangla handwritten word reco...

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10th IAPR International Workshop on Document Analysis Systems, DAS 2012

作者： Bhowmik, Tapan Kumar Roy, Utpal Parui, Swapan K. Faculty of Mathematics and Natural Sciences University of Groningen Netherlands Department of Computer and System Sciences Visva Bharati Santiniketan India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9780769546612

In this paper we introduce a stroke based lexicon reduction technique in order to reduce the search space for recognition of handwritten words. The principle of this technique involves mainly two aspects of a word image to constitute a feature vector: one is word-length and the other is shape of the word. The length of the word image is represented by the number of specific vertical strokes present in the word image and, on the other hand, the shape of a word image is realized with the combination of both horizontal and vertical strokes. The experiment has been carried out with a database of 35,700 off-line handwritten Bangla word images. Though our proposed lexicon reduction technique is developed for recognition of Bangla handwritten words, its generalization property can easily be exploited for recognition of handwriting in other scripts also. © 2012 IEEE.

关键词： Character recognition

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Multi-gradient-direction based deep learning model for arecanut disease identification

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CAAI Transactions on Intelligence Technology 2022年第2期7卷 156-166页

作者： S.B.Mallikarjuna Palaiahnakote Shivakumara Vijeta Khare M.Basavanna Umapada Pal B.Poornima Department of Computer Science and Engineering Bapuji Institute of Engineering and TechnologyDavanagereAffiliated to Visvesvaraya Theological UniversityBelagaviKarnatakaIndia Faculty of Computer Science and Information Technology University of MalayaKuala LumpurMalaysia Adani Institute of Infrastructure Engineering AhmedabadIndia Department of Computer Science Davanagere UniversityDavanagereKarnatakaIndia Computer Vision and Pattern Recognition Unit Indian Statistical InstituteKolkataWest BengalIndia

Arecanut disease identification is a challenging problem in the field of image *** this work,we present a new combination of multi-gradient-direction and deep con-volutional neural networks for arecanut disease identification,namely,rot,split and *** to the effect of the disease,there are chances of losing vital details in the *** enhance the fine details in the images affected by diseases,we explore multi-Sobel directional masks for convolving with the input image,which results in enhanced *** proposed method extracts arecanut as foreground from the enhanced images using Otsu ***,the features are extracted for foreground information for disease identification by exploring the ResNet *** advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut *** results on the dataset of four classes(healthy,rot,split and rot-split)show that the proposed model is superior in terms of classification rate.

关键词： deep learning image analysis pattern recognition

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Script identification - A Han & Roman script perspective

Script identification - A Han & Roman script perspective

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2010 20th International Conference on pattern recognition, ICPR 2010

作者： Chanda, Sukalpa Pal, Umapada Franke, Katrin Kimura, Fumitaka Department of Computer Science and Media Tech. Gjøvik University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Graduate School of Engineering Mie University Japan

ISBN: (纸本)9780769541099

All Han-based scripts (Chinese, Japanese, and Korean) possess similar visual characteristics. Hence system development for identification of Chinese, Japanese and Korean scripts from a single document page is quite challenging. It is noted that a Han-based document page might also have Roman script in them. A multi-script OCR system dealing with Chinese, Japanese, Korean, and Roman scripts, demands identification of scripts before execution of respective OCR modules. We propose a system to address this problem using directional features along with a Gaussian Kernel-based Support Vector Machine. We got promising results of 98.39% script identification accuracy at character level and 99.85% at block level, when no rejection was considered. © 2010 IEEE.

关键词： Optical character recognition

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