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2nd Indian International Conference on Artificial Intelligence, IICAI 2005

作者： Roy, K. Pal, U. Kimura, F. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India Faculty of Engineering Mie University 1577 Kurimamachiya-cho Mie 514-8504 Japan

ISBN: (纸本)0972741216

recognition of handwritten characters is difficult because of variability involved in the writing style of different individuals. This paper deals with recognition of off-line Bangla handwritten characters using quadratic classifier based on the features obtained from directional chain code histogram. Here the bounding box of a character is segmented into blocks and chain code histogram is computed in each of the blocks. This chain code features are fed to the quadratic classifier for recognition. We tested the result on 14879 characters and we obtained 90.53% accuracy with 0.81% rejection and 8.66% error, when 50 classes are considered. From 35 classes we obtained 93.90% accuracy with 0.91% rejection and 5.19% error rate. Copyright © IICAI 2005.

关键词： Graphic methods

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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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Handwritten bangla compound character recognition using gradient feature

Handwritten bangla compound character recognition using grad...

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10th International Conference on Information Technology, ICIT 2007

作者： Pal, U. Wakabayashi, T. Kimura, F. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India Graduate School of Engineering Mie University 1577 Kurimamachiya-cho Tsu Mie Japan

ISBN: (纸本)0769530680

recognition of handwritten characters of Indian script is difficult because of the presence of many complex shaped compound characters (cluster characters) as well as variability involved in the writing style of different individuals. This paper deals with recognition of off-line Bangla handwritten compound characters using Modified Quadratic Discriminant Function (MQDF). The features used for recognition purpose are mainly based on directional information obtained from the arc tangent of the gradient. To get the feature, at first, a 2 X 2 mean filtering is applied 4 times on the gray level image and a non-linear size normalization is done on the image. A Roberts filter is then applied on the normalized image to obtain gradient image. Next, the arc tangent of the gradient (direction of gradient) is initially quantized into 32 directions and the strength of the gradient is accumulated with each of the quantized direction. Finally, the frequencies of these directions are down sampled using Gaussian filter to get 392 dimensional feature vectors. Using 5-fold cross validation technique we obtained 85.90% accuracy from a dataset of Bangla compound characters containing 20,543 samples. © 2007 IEEE.

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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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A system for off-line oriya handwritten character recognition using curvature feature

A system for off-line oriya handwritten character recognitio...

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10th International Conference on Information Technology, ICIT 2007

ISBN: (纸本)0769530680

In this paper we present a system towards the recognition of off-line Oriya handwritten characters. Since most of the Oriya characters have curve-like stroke, we use curvature feature for the recognition purpose. To get the feature, at first, the input image is size normalized and segmented into 4949 blocks. Curvature is then computed using bi-quadratic interpolation method and quantized into 3 levels according to concave, linear and convex regions. Next direction of gradient is quantized into 32 levels with π/16 intervals, and strength of the gradient is accumulated in each of the 32 directions and in each of the 3 curvature levels of every block. A spatial resolution is made to get 77 blocks from 4949 blocks and a directional resolution is made to get 8 directions from 32 directions. Using curvature features for 3 levels we get 1176 (77 blocks × 8 directions × 3 levels) dimensional features. Finally using principal component analysis we reduce the dimension 1176 to 392 and this 392 dimensional feature vector is fed to a quadratic classifier for recognition. We tested 18190 samples of Oriya handwritten samples and obtained 94.60% accuracy from our proposed system. © 2007 IEEE.

关键词： Principal component analysis

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Texton-based fabric defect detection and recognition

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Iranian Journal of Electrical and computer Engineering 2011年第2期10卷 57-69页

作者： Behravan, M. Tajeripour, F. Azimifar, Z. Boostani, R. Computer Vision and Pattern Recognition Group School of Electrical and Computer Engineering Shiraz University Shiraz Iran

Automatic fabric defect classification is an essential stage in production line of textile manufactures. Here, a novel method for simultaneous detection and classification of fabric defects is presented. Each fabric defect type produces a locally specific type of texture. In this paper for the first time textons are used to describe local structure of fabric defects textures. In the method, an image is divided into several blocks in which features based on filter banks are extracted from them. According to these features, the algorithm determines defective windows and their defect types. The employed datasets in this study contain six types of well-known defects of patterned fabrics and four types of unpatterned fabric defects. Our experiments have yielded excellent results and this shows that descriptors based on texture properties of defective regions are suitable for classification task. The proposed method leads to improved and robust detection and classification results compared to the traditional methods. © 2011 ACECR.

关键词： Feature extraction

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Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement 20th

Bootstrap Diffusion Model Curve Estimation for High Resolut...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Huang, Jiancheng Liu, Yifan Chen, Shifeng ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819970247

Learning-based methods have attracted a lot of research attention and led to significant improvements in low-light image enhancement. However, most of them still suffer from two main problems: expensive computational cost in high resolution images and unsatisfactory performance in simultaneous enhancement and denoising. To address these problems, we propose BDCE, a bootstrap diffusion model that exploits the learning of the distribution of the curve parameters instead of the normal-light image itself. Specifically, we adopt the curve estimation method to handle the high-resolution images, where the curve parameters are estimated by our bootstrap diffusion model. In addition, a denoise module is applied in each iteration of curve adjustment to denoise the intermediate enhanced result of each iteration. We evaluate BDCE on commonly used benchmark datasets, and extensive experiments show that it achieves state-of-the-art qualitative and quantitative performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Image enhancement

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A probabilistic framework for logo detection and localization in natural scene images

A probabilistic framework for logo detection and localizatio...

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International Conference on pattern recognition

作者： Ankush Roy Utpal Garain Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

ISBN: (纸本)9781467322164

This paper presents a probabilistic approach for logo detection and localization in natural scene images. Two probability distributions are computed, one considering the features extracted from the key points located inside a region and the second refers to shape geometry defined by the key points. The barycentric co-ordinates are considered to define the shape statistics. The performance of the proposed approach has been reported on two publicly available datasets: BelgaLogos and Flickr Logos27. It is shown that statistically significant improvement is achieved over a recently proposed method.

关键词： Feature extraction Shape Geometry Accuracy Probabilistic logic Databases Visualization

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Non-deterministic Behavior of Ranking-Based Metrics When Evaluating Embeddings 2nd

Non-deterministic Behavior of Ranking-Based Metrics When Eva...

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2nd International Workshop on Reproducible Research in pattern recognition, RRPR 2018

作者： Nicolaou, Anguelos Dey, Sounak Christlein, Vincent Maier, Andreas Karatzas, Dimosthenis Computer Vision Center Edificio O Campus UAB Bellaterra08193 Spain Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

ISBN: (纸本)9783030239862

Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits. © Springer Nature Switzerland AG 2019.

关键词： Embeddings

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Exploring the robustness of NMT systems to nonsensical inputs

arXiv

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arXiv 2019年

作者： Chaturvedi, Akshay Abijith, K.P. Garain, Utpal Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

Neural machine translation (NMT) systems have been shown to give undesirable translation when a small change is made in the source sentence. In this paper, we study the behaviour of NMT systems when multiple changes are made to the source sentence. In particular, we ask the following question "Is it possible for an NMT system to predict same translation even when multiple words in the source sentence have been replaced?". To this end, we propose a soft-attention based technique to make the aforementioned word replacements. The experiments are conducted on two language pairs: English- German (en-de) and English-French (en-fr) and two state-of-theart NMT systems: BLSTM-based encoder-decoder with attention and Transformer. The proposed soft-attention based technique achieves high success rate and outperforms existing methods like HotFlip by a significant margin for all the conducted experiments. The results demonstrate that state-of-the-art NMT systems are unable to capture the semantics of the source language. The proposed soft-attention based technique is an invariance-based adversarial attack on NMT systems. To better evaluate such attacks, we propose an alternate metric and argue its benefits in comparison with success rate. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural machine translation

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