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11th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2014

作者： Riaz Un Nabi Jafri, Syed Ahmed, Waheed Ashraf, Zubair Chellali, Ryad Electronic Engineering Department NED UET Karachi Pakistan Pattern Analysis and Computer Vision LAB-IIT Genova Italy

ISBN: (纸本)9781479939787

This paper is presenting a multi robot simultaneous localization and mapping (SLAM) framework for environment modelling using 2D and 3D features. The proposed solution is using a team of mobile robots which are exploring unknown environment with unknown poses. Each team member is allowed to build its independent features based SLAM solution and to share the local map model among other mates. By matching the overlapping tendency of any two mates, a map merging strategy is introduced which in result is building global map. The overall approach has tested in 2D and 3D features based environment and results have shown. © 2014 IEEE.

关键词： Extended Kalman filters

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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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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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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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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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A Wide Learning Approach for Interpretable Feature Recommendation for 1-D Sensor Data in IoT Analytics

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International Journal of Automation and computing 2019年第6期16卷 800-811页

作者： Snehasis Banerjee Tanushyam Chattopadhyay Utpal Garain TCS Research&Innovation Tata Consultancy ServicesEcospaceKolkata 700160India Computer Vision&Pattern Recognition Unit Indian Statistical Institute203 B.T.RoadKolkata 700108India

This paper presents a state of the art machine learning-based approach for automation of a varied class of Internet of things(Io T) analytics problems targeted on 1-dimensional(1-D) sensor data. As feature recommendation is a major bottleneck for general Io Tbased applications, this paper shows how this step can be successfully automated based on a Wide Learning architecture without sacrificing the decision-making accuracy, and thereby reducing the development time and the cost of hiring expensive resources for specific problems. Interpretation of meaningful features is another contribution of this research. Several data sets from different real-world applications are considered to realize the proof-of-concept. Results show that the interpretable feature recommendation techniques are quite effective for the problems at hand in terms of performance and drastic reduction in development time.

关键词： Feature engineering sensor data analysis Internet of things(IoT)analytics interpretable learning automation

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Evaluation of convex optimization techniques for the weighted graph-matching problem in computer vision

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23rd German Association for pattern Recognition Symposium, DAGM 2001

作者： Schellewald, Christian Roth, Stefan Schnörr, Christoph Computer Vision Graphics and Pattern Recognition Group Dept Mathematics and Computer Science University of Mannheim MannheimD-68131 Germany

ISBN: (纸本)3540425969

We present a novel approach to the weighted graph-matching problem in computer vision, based on a convex relaxation of the underlying combinatorial optimization problem. The approach always computes a lower bound of the objective function, which is a favorable property in the context of exact search algorithms. Furthermore, no tuning parameters have to be selected by the user, due to the convexity of the relaxed problem formulation. For comparison, we implemented a recently published deterministic annealing approach and conducted numerous experiments for both established benchmark experiments from combinatorial mathematics, and for random ground-truth experiments using computer-generated graphs. Our results show similar performance for both approaches. In contrast to the convex approach, however, four parameters have to be determined by hand for the annealing algorithm to become competitive. © Springer-Verlag Berlin Heidelberg 2001.

关键词： computer vision

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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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Off-line handwritten Thai name recognition for student identification in an automated assessment system

Off-line handwritten Thai name recognition for student ident...

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2014 International Joint Conference on Neural Networks, IJCNN 2014

作者： Suwanwiwat, Hemmaphan Nguyen, Vu Blumenstein, Michael Pal, Umapada School of Information and Communication Technology Griffith University Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

ISBN: (纸本)9781479914845

In the field of pattern recognition, off-line handwriting recognition is one of the most intensive areas of study. This paper proposes an automatic off-line Thai language student name identification system which was built as a part of a completed off-line automated assessment system. There is limited work undertaken in developing off-line automatic assessment systems using handwriting recognition. To the authors' knowledge, none of the work on the proposed system has been performed on the Thai language. In addition the proposed system recognises each Thai name by using an approach for whole word recognition, which is different from the work found in the literature as most perform character-based recognition. In this proposed system, the Gaussian Grid Feature (GGF) and the Modified Direction Feature (MDF) extraction techniques are investigated on upper and lower contours, loops from full word contour images of each name sample, and artificial neural networks and support vector machine are used as classifiers. The encouraging recognition rates for both feature extraction techniques were achieved when applied on loop, upper and lower contour images (99.27% accuracy rate was achieved using MDF on artificial neural networks and 99.27% using GGF with a support vector machine classifier). © 2014 IEEE.

关键词： Students

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On machine understanding of online handwritten mathematical expressions 7

On machine understanding of online handwritten mathematical ...

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7th International Conference on Document Analysis and Recognition, ICDAR 2003

作者： Garain, Utpal Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata India

ISBN: (纸本)0769519601

This paper aims at automatic recognition of online handwritten mathematical expressions written on an electronic tablet. The proposed technique involves two major stages: symbol recognition and structural analysis. A multiple-classifier consists of both parametric and nonparametric classifier has been used for recognition of symbols. Parametric classifier is based on Hidden Markov Model (HMM), whereas, non-parametric classifier uses Nearest Neighbor classification scheme. Structural analysis uses several online and offline features to identify the spatial relationships among symbols. A context free grammar has been designed to convert input expressions into their corresponding Latex strings. Contextual information has been used to correct several errors occurring at both recognition and structural analysis stage. A new method for evaluating performance of the proposed systems has been formulated. Experiments on a dataset of considerable size show high efficiency of the proposed system. © 2003 IEEE.

关键词： Structural analysis

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