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Air Writing: Recognizing Multi-Digit Numeral String Traced in Air Using RNN-LSTM Architecture

SN computer Science 2021年第1期2卷 1-13页

作者： Rahman, Adil Roy, Prasun Pal, Umapada Department of Information Technology Heritage Institute of Technology Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Air writing provides a more natural and immersive way of interacting with devices, with the potential of having significant application in fields like augmented reality and education. However, such systems often rely on expensive hardware, making them less accessible for general purposes. In this study, we propose a robust and inexpensive system for the recognition of multi-digit numerals traced in an air-writing environment which uses only a generic device camera for input. We employ a sliding window-based algorithm to isolate a small segment of the input for processing. A dual network configuration consisting of RNN-LSTM networks are used for noise elimination and digit recognition. We conduct our experiments on English numerals using the MNIST dataset as the baseline model to allow easy adaptability of our method. Our results are further improved by the use of Pendigits and ISI-Air online datasets. We observed a drop in accuracy with increase in the number of digits owing to the accumulation of transition noise. However, bi-directional scanning considerably reduces the impact of such noise on the recognition accuracy. Under standard conditions, our system produced an accuracy of 98.75% and 85.27% for single and multi-digit English numerals, respectively. Incorporation of selective frame skipping in the sliding window algorithm resulted in a 60% reduction in computational time, significantly improving the system performance. We provide a link to the source code of our system at the end of this paper. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Air writing Handwritten character recognition Human–computer interaction Long short-term memory Recurrent neural network

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Re-identification of Saimaa Ringed Seals from Image Sequences 23rd

Re-identification of Saimaa Ringed Seals from Image Sequen...

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22nd Scandinavian Conference on Image Analysis, SCIA 2023

作者： Nepovinnykh, Ekaterina Vilkman, Antti Eerola, Tuomas Kälviäinen, Heikki Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland

ISBN: (纸本)9783031314346

Automatic game cameras are commonly used for monitoring wildlife as they allow to document of the activity of animals in a non-invasive manner. By utilizing a large number of cameras and identifying individual animals from the images, it is possible to, for example, estimate the population size and study the migration patterns of the animals. Large image volumes produced by the cameras call for automated methods for the analysis. Re-identification of animals has commonly been implemented through one-to-one matching, where images are processed individually and the best match is searched from the database of known individuals one by one. Game cameras can be configured to produce a sequence of images that allows capturing the animal from multiple angles potentially improving the re-identification accuracy. In this work, the re-identification of the endangered Saimaa ringed seal (pusa hispida saimensis) from image sequences is studied. The individual identification is realized through Saimaa ringed seal’s unique pelage pattern. The proposed one-to-many and many-to-many matching methods aggregate the pelage pattern features over the whole sequence providing better embeddings for the re-identification tasks. We show that the proposed aggregation method outperforms traditional one-to-one matching based re-identification by a large margin. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Animals

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On minimizing errors in 3D reconstruction for stereo camera systems

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pattern recognition and Image Analysis 2007年第2期17卷 337-348页

作者： Wenhardt, S. Denzler, J. Niemann, H. Department of Pattern Recognition University of Erlangen-Nurnberg Martensstr. 3 91058 Erlangen Germany Department of Computer Vision Friedrich Schiller University of Jena Ernst-Abbe-Platz 2 07743 Jena Germany

Active reconstruction of 3D surfaces deals with the control of camer a viewpoints to minimize error and uncertainty in the reconstructed shape of an object. In this paper we develop a mathematical relationship between the setup and focal lengths of a stereo camera system and the corresponding error in 3D reconstruction of a given surface. We explicitly model the noise in the image plane, which can be interpreted as pixel noise or as uncertainty in the localization of corresponding point features. The results can be used to plan sensor positioning, e.g., using information theoretic concepts for optimal sensor data selection. © Nauka/Interperiodica 2007.

关键词： Image reconstruction

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Design and Implementation of a vision- and Grating-Sensor-Based Intelligent Unmanned Settlement System

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 254-264页

作者： Zhang, Hong-Bo Zhou, Yi-Zhong Dong, Li-Jia Lei, Qing Du, Ji-Xiang Department of Computer Science and Technology Huaqiao University Xiamen361000 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition The Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen361000 China

In this article, a new vision- and grating-sensor-based intelligent unmanned settlement (IUS) system is proposed for convenience stores to automatically recognize the shopping behavior of customers, record their identities, and generate invoices. First, we design a new IUS architecture, which includes a shelf module and exit module. To achieve automatic settlement for each customer, a shopping event detection method is proposed. In this method, a vision-based human pose estimation algorithm is used to detect a human form standing in front of a shelf. The hand actions of each customer are detected by a grating sensor, and an image recognition method based on a convolutional neural network (CNN) is applied to recognize the items in the hands of customers. To reduce the image annotation workload, we propose a semisupervised training method for the recognition network. Based on hand action detection and item recognition, a shopping event recognition method is designed for the system, and a facial image of the customer corresponding to each shopping behavior is captured. Finally, each detected shopping event is added to the invoice of the corresponding customer via a facial recognition method. To verify the effectiveness of the proposed IUS system, we have built a handheld item image dataset and a shopping event dataset for an unmanned convenience store. The experimental results show that the proposed system can accurately recognize shopping behaviors and generate invoices. © 2020 IEEE.

关键词： Sales

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Periodic Action Temporal Localization Method Based on Two-Path Architecture for Product Counting in Sewing Video 15th

Periodic Action Temporal Localization Method Based on Two-Pa...

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Huang, Jin-Long Zhang, Hong-Bo Du, Ji-Xiang Zheng, Jian-Feng Peng, Xiao-Xiao Department of Computer Science and Technology Huaqiao University Xiamen China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen China

ISBN: (纸本)9783030267650

Automatically product counting in the handmade process plays a vital role in the manufacturing industry, especially at the sewing industry. Nevertheless, there is currently a few methods to count the product number in the hand sewing process from surveillance video automatically. Due to the sewing procedure is a cyclical action, the product counting in hand sewing process is regarded as periodic action temporal localization and counting problem. In this paper, in order to solve this problem, we propose a novel two-path method, based on pose estimation and region-based convolutional neural network. The pose estimation method is used to obtain the trajectory information of human joint points, and the periodic action is located by detecting the periodic changes in joint trajectory. An effective two-threshold method is proposed to locate each action and count the number of periodic action from the trajectory information. To more accurately localization, we use a convolutional neural network to predict whether the workbench is empty or not. We fuse the results of joint trajectory and the status of the workbench to adjust the final periodic action localization and counting the number. To verify the proposed method, we built a new video database collected in the real sewing industry. The experimental results show that the proposed method is effective and constructive at periodic action localization and counting in the video for the sewing industry. © 2019, Springer Nature Switzerland AG.

关键词： Neural networks

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Text independent writer identification for Oriya script

Text independent writer identification for Oriya script

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

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

ISBN: (纸本)9780769546612

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 ensured. Lack of data threatens system reliability in such cases. We here propose a writer identification system for Oriya script which is capable of performing reasonably well even with small amount of text. Experiments with curvature feature are reported here, using Support Vector Machine (SVM) as classifier. We got promising results of 94.00% writer identification accuracy at first top choice and 99% when considering first three top choices. © 2012 IEEE.

关键词： Support vector machines

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UformPose: A U-Shaped Hierarchical Multi-Scale Keypoint-Aware Framework for Human Pose Estimation

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IEEE Transactions on Circuits and Systems for Video Technology 2023年第4期33卷 1697-1709页

作者： Wang, You-Jie Luo, Yan-Min Bai, Gui-Hu Guo, Jing-Ming Huaqiao University Key Laboratory for Computer Vision and Pattern Recognition The College of Computer Science and Technology Xiamen361021 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei10607 Taiwan

Human pose estimation is a fundamental yet challenging task in computer vision. However, difficult scenarios such as invisible keypoints, occlusions and small-scale persons are still not well-handed. In this paper, we present a novel pose estimation framework named UformPose which targets to relieve these issues. UformPose has two core designs: Shared Feature Pyramid Stem (SFPS) and U-shaped hierarchical Multi-scale Keypoint-aware Attention Module (U-MKAM). SFPS is a feature pyramid stem with shared mechanism to learn stronger low-level features at the initial stage, and the shared mechanism can facilitate cross-resolution commonality learning. Our U-MKAM attempts to generate high-quality high-resolution representations by integrating all levels of feature representation of the backbone layer by layer. More importantly, we utilize the flexibility of attention operations for keypoint-aware modeling, which explicitly captures and trades-offs the dependencies between keypoints. We empirically demonstrate the effectiveness of our framework through the competitive pose estimation results on the COCO dataset. Extensive experiments and visual analysis on CrowdPose demonstrate the robustness of our model in crowd scenes. © 1991-2012 IEEE.

关键词： computer vision

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Human-focused computer vision applications

Human-focused computer vision applications

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International Conference on computer Graphics, Imaging and Visualisation, CGIV'06

作者： Piccardi, Massimo Department of Computer Systems Faculty of IT University of Technology Sydney Australia UTS Computer Vision Research Group IEEE IEEE Computer Society International Association for Pattern Recognition

ISBN: (纸本)0769526063

Recent years have seen an increasing number of computer vision applications focusing on humans as their objects of interest. Such applications include video surveillance, domotics, multimedia semantic annotation and indexing, human-computer interfaces, affective computing, just to cite a few. What exactly are their "objects of interest"? A broad range of human-related features: motion, gestures, actions, interactions, activities, attitudes, behaviours, identity. This keynote will offer a survey of this field and present some current work from the speaker and his collaborators in the areas of emotion recognition and people tracking within camera networks. © 2006 IEEE.

关键词： computer vision

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Efficient Audio-Visual Speaker recognition via Deep Heterogeneous Feature Fusion 12th

Efficient Audio-Visual Speaker Recognition via Deep Heteroge...

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12th Chinese Conference on Biometric recognition, CCBR 2017

作者： Liu, Yu-Hang Liu, Xin Fan, Wentao Zhong, Bineng Du, Ji-Xiang Department of Computer Science Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen361021 China

ISBN: (纸本)9783319699226

Audio-visual speaker recognition (AVSR) has long been an active research area primarily due to its complementary information for reliable access control in biometric system, and it is a challenging problem mainly attributes to its multimodal nature. In this paper, we present an efficient audio-visual speaker recognition approach via deep heterogeneous feature fusion. First, we exploit a dual-branch deep convolutional neural networks (CNN) learning framework to extract and fuse the high-level semantic features of face and audio data. Further, by considering the temporal dependency of audio-visual data, we embed the fused features into a bidirectional Long Short-Term Memory (LSTM) networks to produce the recognition result, though which the speakers acquired under different challenging conditions can be well identified. The experimental results have demonstrated the efficiency of our proposed approach in both audio-visual feature fusion and speaker recognition. © 2017, Springer International Publishing AG.

关键词： Long short-term memory

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A new dataset of Persian handwritten documents and its segmentation

A new dataset of Persian handwritten documents and its segme...

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2011 7th Iranian Conference on Machine vision and Image Processing, MVIP 2011

作者： Alaei, Alireza Nagabhushan, P. Pal, Umapada Department of Studies in Computer Science University of Mysore Mysore 570006 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India

ISBN: (纸本)9781457715358

In document image analysis and especially in handwritten document image recognition, standard datasets play vital roles for evaluating performances of algorithms and comparing results obtained by different groups of researchers. In this paper, an unconstrained Persian handwritten text dataset (PHTD) is introduced. The PHTD contains 140 handwritten documents of three different categories written by 40 individuals. Total number of text-lines and words/subwords in the dataset are 1787 and 27073, respectively. In most of the PHTD documents either an overlapping or a touching text-lines is present. The average number of text-lines in documents of the PHTD is 13. Two types of ground truths based on pixels information and content information are generated for the dataset. Providing these two types of ground truths for the PHTD, it can be utilized in many areas of document image processing such as sentence recognition/understanding, text-line segmentation, word segmentation, word recognition, and character segmentation. To provide a framework for other researches, recent text-line segmentation results on this dataset are also reported. © 2011 IEEE.

关键词： Image segmentation

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