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检索条件"任意字段=8th International Conference on Computing and Pattern Recognition, ICCPR 2019"

共 394 条记录，以下是91-100 订阅

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A multi-layer artificial intelligence and sensing based affective conversational embodied agent 8

A multi-layer artificial intelligence and sensing based affe...

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8th international conference on Affective computing and Intelligent Interaction (ACII)

作者： DiPaola, Steve Yalcin, Ozge Nilay Simon Fraser Univ Sch Interact Art & Tech Vancouver BC Canada

ISBN: (纸本)9781728138916

Building natural and conversational virtual humans is a task of formidable complexity. We believe that, especially when building agents that affectively interact with biological humans in real-time, a cognitive science-based, multilayered sensing and artificial intelligence (AI) systems approach is needed. For this demo, we show a working version (through human interaction with it) our modular system of natural, conversation 3D virtual human using AI or sensing layers. these including sensing the human user via facial emotion recognition, voice stress, semantic meaning of the words, eye gaze, heart rate, and galvanic skin response. these inputs are combined with AI sensing and recognition of the environment using deep learning natural language captioning or dense captioning. these are all processed by our AI avatar system allowing for an affective and empathetic conversation using an NLP topic-based dialogue capable of using facial expressions, gestures, breath, eye gaze and voice language-based two-way back and forth conversations with a sensed human. Our lab has been building these systems in stages over the years.

关键词： artificial intelligence conversational agent embodied agent deep learning sensing systems biosensing affective computing embodied character agents

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A Universal Way to Collect and Process Handwritten Data for Any Language 8

A Universal Way to Collect and Process Handwritten Data for ...

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8th international conference on Advances in computing & Communications (ICACC)

作者： Rabby, A. K. M. Shahariar Azad Haque, Sadeka Shahinoor, Shammi Akther Abujar, Sheikh Hossain, Syed Akhter Daffodil Int Univ Dept Comp Sci & Engn Dhaka 1205 Bangladesh

In recent years researches based on Machine learning and Deep learning have achieved much interest and one of its handwritten recognition. Handwritten recognition is very difficult due to its lack of dataset and also for collecting data from people. this research introduces a fast and comprehensive way to collect and process handwritten data to develop a way of Handwritten recognition (HWR) algorithm for any languages. In this research handwritten characters wrote on a paper and then scanned to get the data into a JPEG format. We also focused on some of the other issues and requirements while collecting handwritten data, creating form, data collection methodology, process, using software and relevant tools. We described these issues in the context of our own effort to create a handwritten database for the Bangla language. Our designed Graphical User Interface(GUI) is also able to process 100 scanned images per minute where each scanned image contains 120 characters. (C) 2018 the Authors. Published by Elsevier B.V.

关键词： Handwritten recognition pattern recognition Document Image Analysis Machine Learning Computer Vision

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Visual Transformers with Primal Object Queries for Multi-Label Image Classification 26

Visual Transformers with Primal Object Queries for Multi-Lab...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Yazici, Vacit Oguz Van De Weijer, Joost Yu, Longlong Comp Vision Ctr Wide Eyes Technol Barcelona Spain Univ Autonoma Barcelona Comp Vision Ctr Barcelona Spain Wide Eyes Technol Barcelona Spain

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Multi-label image classification is about predicting a set of class labels that can be considered as orderless sequential data. Transformers process the sequential data as a whole, therefore they are inherently good at set prediction. the first vision-based transformer model, which was proposed for the object detection task introduced the concept of object queries. Object queries are learnable positional encodings that are used by attention modules in decoder layers to decode the object classes or bounding boxes using the region of interests in an image. However, inputting the same set of object queries to different decoder layers hinders the training: it results in lower performance and delays convergence. In this paper, we propose the usage of primal object queries that are only provided at the start of the transformer decoder stack. In addition, we improve the mixup technique proposed for multi-label classification. the proposed transformer model with primal object queries improves the state-of-the-art class wise F1 metric by 2.1% and 1.8%;and speeds up the convergence by 79.0% and 38.6% on MS-COCO and NUS-WIDE datasets respectively.

关键词： Training Measurement Visualization Object detection Transformers Decoding pattern recognition

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Neural Architecture Adaptation for Object Detection by Searching Channel Dimensions and Mapping Pre-trained Parameters 26

Neural Architecture Adaptation for Object Detection by Searc...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Jung, Harim Oh, Myeong-Seok Yang, Cheoljong Lee, Seong-Whan Korea Univ Dept Artificial Intelligence Seoul South Korea Korea Univ Dept Comp Engn Seoul South Korea NCSOFT AI Ctr Vis AI Lab Seongnam South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Most object detection frameworks use backbone architectures originally designed for image classification, conventionally with pre-trained parameters on ImageNet. However, image classification and object detection are essentially different tasks and there is no guarantee that the optimal backbone for classification is also optimal for object detection. Recent neural architecture search (NAS) research has demonstrated that automatically designing a backbone specifically for object detection helps improve the overall accuracy. In this paper, we introduce a neural architecture adaptation method that can optimize the given backbone for detection purposes, while still allowing the use of pre-trained parameters. We propose to adapt both the micro- and macro-architecture by searching for specific operations and the number of layers, in addition to the output channel dimensions of each block. It is important to find the optimal channel depth, as it greatly affects the feature representation capability and computation cost. We conduct experiments with our searched backbone for object detection and demonstrate that our backbone outperforms both manually designed and searched state-of-the-art backbones on the COCO dataset.

关键词： Training Costs Object detection Computer architecture Search problems pattern recognition Task analysis

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Joint Encoding of Multi-scale LBP for Infrared Face recognition 8

Joint Encoding of Multi-scale LBP for Infrared Face Recognit...

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8th international conference on Genetic and Evolutionary computing (ICGEC)

作者： Xie, Zhihua Wang, Zhengzi Jiangxi Sci & Technol Normal Univ Key Lab Opt Elect & Commun Nanchang Jiangxi Peoples R China

ISBN: (纸本)9783319122854

Due to low resolutions of infrared face image, the local feature extraction is more appreciated for infrared face feature extraction. In the current LBP (local binary pattern) feature extraction on infrared face recognition, single scale is encoded, which consider limited local discriminative information. A new infrared face recognition method based on joint encoding of multi-scale LBP (JEMLBP) is proposed in this paper. To consider correlation in different micro-structures, co-occurrence matrix of multi-scale LBP codes is used to represent the infrared face. the experimental results show the recognition rates of infrared face recognition method based on JEMLBP can reach 91.2% under variable ambient temperatures, outperforms that of the classic method based on single scale LBP histogram.

关键词： Local Binary pattern infrared face recognition multi-scale joint encoding co-occurrence matrix

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Unconstrained Face recognition using ASURF and Cloud-Forest Classifier optimized with VLAD 8

Unconstrained Face Recognition using ASURF and Cloud-Forest ...

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8th international conference on Advances in computing & Communications (ICACC)

作者： Vinay, A. Joshi, Aviral Surana, Hardik Mahipal Garg, Harsh Murthy, K. N. Balasubramanya Natarajan, S. PES Univ Ctr Pattern Recognit & Machine Intelligence Bangalore 560085 Karnataka India

the paper posits a computationally-efficient algorithm for multi-class facial image classification in which images are constrained with translation, rotation, scale, color, illumination and affine distortion. the proposed method is divided into five main building blocks including Haar-Cascade for face detection, Bilateral Filter for image preprocessing to remove unwanted noise, Affine Speeded-Up Robust Features (ASURF) for keypoint detection and description, Vector of Locally Aggregated Descriptors (VLAD) for feature quantization and Cloud Forest for image classification. the proposed method aims at improving the accuracy and the time taken for face recognition systems. the usage of the Cloud Forest algorithm as a classifier on three benchmark datasets, namely the FACES95, FACES96 and ORL facial datasets, showed promising results. the proposed methodology using Cloud Forest algorithm successfully improves the recognition model by 2-12% when differentiated against other ensemble techniques like the Random Forest classifier depending upon the dataset used. (C) 2018 the Authors. Published by Elsevier B.V.

关键词： Face recognition Haar Cascade Bilateral Filter ASURF Bag of Words VLAD Cloud Forest Random Forest

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A perturbation suppressing segmentation technique based on adaptive diffusion

A perturbation suppressing segmentation technique based on a...

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8th international conference on Adaptive and Natural computing Algorithms (ICANNGA)

作者： Middelmann, Wolfgang Ebert, Alfons Deissler, Tobias thoennessen, Ulrich FGAN-FOM Research Institute for Optronics and Pattern Recognition Germany

ISBN: (纸本)9783540715900

Segmentation is a fundamental task in pattern recognition and basis for high level applications like scene reconstruction, change detection, or object classification. the performance of these tasks suffers from a distorted segmentation. In this contribution an adaptive diffusion-based segmentation method is proposed suppressing perturbations in the segmentation with focusing on small regions with high contrast to their surrounding. the algorithm determines in each step the diffusion tensor. It is re-weighted with respect to an assessment stage. A comparative study uses high-resolution remote sensing data.

关键词： Image segmentation

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On the relevance of using affordable tools for white spaces identification

On the relevance of using affordable tools for white spaces ...

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2012 IEEE 8th international conference on Wireless and Mobile computing, Networking and Communications, WiMob 2012

作者： Zennaro, Marco Pietrosemoli, Ermanno Bagula, Antoine Nleya, Sindiso Telecommunications/ICT for Development Laboratory Abdus Salam International Centre for Theoretical Physics Trieste Italy ISAT Laboratory Department of Computer Science University of Cape Town 18 University Avenue Rhodes Gift 7707 Cape Town South Africa

ISBN: (纸本)9781467314305

It is widely recognized that white spaces identification is an important milestone for the wide deployment of next generation cognitive wireless networks. However, spectrum holes detection tools used for white spaces discovery are still either in the infancy stage or too expensive to enable massive white spaces exploitation. Building upon cheap hardware equipment, this paper presents experiments conducted in the town of Trieste in Italy to sense the environment and find out which frequencies are not being used in a particular place and time-of-the-day. As a a step towards white spaces exploitation, we believe that our experimental frequency exploration is an important milestone upon which white spaces patterns recognition will be built with the aim of using these patterns in wireless network planning and management. © 2012 IEEE.

关键词： pattern recognition

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Primitive Contrastive Learning for Handwritten Mathematical Expression recognition 26

Primitive Contrastive Learning for Handwritten Mathematical ...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Guo, Hong-Yu Wang, Chuang Yin, Fei Liu, Heng-Ye Wu, Jin-Wen Liu, Cheng-Lin Inst Automat Chinese Acad Sci Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Contrastive learning has gained significant attention recently as it can learn a representation from a large amount of unlabeled training data to improve downstream tasks. While the existing approaches mainly focus on standard tasks of image classification and object detection, they are not easily applied to structured prediction problems. In this paper, we propose an unsupervised pre-trained model, called PrimCLR, for handwritten mathematical expression recognition. For a formula recognition model of encoder-decoder architecture, a pre-trained representation is obtained by PrimCLR, where the contrastive loss is computed from pairs of patches so as to better discriminate primitives. the pre-trained representation is transferred to downstream formula recognition with supervised fine-tuning. Experiments show that pre-training by PrimCLR can significantly improve the formula recognition performance, and PrimCLR shows superiority to conventional contrastive learning methods. Our model achieves state-of-the-art performance on standard datasets CROHME 2016 and CROHME 2019.

关键词： Representation learning Handwriting recognition Visualization Computational modeling Training data Self-supervised learning Object detection

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Two dimensional pattern formation of prey-predator system

Two dimensional pattern formation of prey-predator system

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SNPD 2007: 8th ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing

作者： Hongxia, Shen Zhen, Jin Department of Mathematics North University of China Taiyuan 030051 China

ISBN: (纸本)0769529097

We investigate the reaction-diffusion system of the classical Bazykin model in spatial two dimensional domain. In this paper, we derive the conditions for turing instability in detail and obtain the turing space, in which the spatial system can emerge turing pattern. Furthermore, we simulate the pattern formation using the periodical boundary condition. Our results show that the Bazykin system stabilizes to a striplike pattern structure when diffusion is present. © 2007 IEEE.

关键词： pattern recognition

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