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检索条件"任意字段=6th Indian Conference on Computer Vision, Graphics and Image Processing"

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image and Video Technology 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Reinhard Klette Mariano Rivera Shin’ichi Satoh

ISBN: (数字)9783642538421

ISBN: (纸本)9783642538414

this book constitutes the thoroughly refereed post-conference proceedings of the 6th Pacific Rim Symposium on image and Video Technology, PSIVT 2013, held in Guanajuato, México in October/November 2013. the total of 43 revised papers was carefully reviewed and selected from 90 submissions. the papers are organized in topical sections on image/video processing and analysis, image/video retrieval and scene understanding, applications of image and video technology, biomedical image processing and analysis, biometrics and image forensics, computational photography and arts, computer and robot vision, pattern recognition and video surveillance.

关键词： Multimedia Information Systems image processing and computer vision computer graphics Information Storage and Retrieval computer Communication Networks Information Systems Applications (incl. Internet)

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Salient Object Detection based on Bayesian Surprise of Restricted Boltzmann Machine 11

Salient Object Detection based on Bayesian Surprise of Restr...

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Roy, Rahul Ghosh, Susmita Ghosh, Ashish Indian Stat Inst Machine Intelligence Unit Kolkata W Bengal India Jadavpur Univ Dept Comp Sci & Technol Kolkata W Bengal India

ISBN: (纸本)9781450366151

this article presents an algorithm for salient object detection by leveraging the Bayesian surprise of the Restricted Boltzmann Machine (RBM). Here an RBMis trained on patches sampled randomly from the input image. Due to this random sampling, the RBM is likely to get more exposed to background patches than that of the object. thus, the trained RBM will minimize the free energy of its hidden states with respect to the background patches as opposed to the object. this, according to the free energy principle, implies minimizing Bayesian surprise which is a measure for saliency based on Kullback Leibler divergence between the input and reconstructed patch distribution. Hence, when the trained RBM is exposed to patches from the object region, it would have high divergence and in turn a high Bayesian surprise. thus such pixels with high Bayesian surprise could be considered as salient pixels. For each pixel, a neighborhood (with the same size of training patch) is considered and is fed to the trained RBM to obtain the reconstructed patch. thereafter, the Kullback Leibler divergence between the input and reconstructed neighborhood of each pixel is computed to measure the Bayesian surprise and is stored in the corresponding position in a matrix to form the saliency map. Experiments are carried out on three datasets namelyMSRA-10K, ECSSD and DUTS. the results obtained depict promising performance by the proposed approach.

关键词： Object Detection Restricted Boltzmann Machine Bayesian Surprise Free Energy Principle

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Optimization of the Process of Parameter Adjustment: image processing as a Case Study 6

Optimization of the Process of Parameter Adjustment: Image P...

引用

6th International conference on Optimization and Applications, ICOA 2020

作者： Qaffou, Issam LISI Laboratory Faculty of Science Semlalia Cadi Ayyad University Computer Science Department Marrakech Morocco

ISBN: (纸本)9781728166544

Selecting optimal parameters' values in a computer vision task is a challenge for users because of the multitude of possible cases. this paper discusses the use of artificial intelligence methods and techniques to optimize the process of finding the best values for parameters in image processing applications. We propose an automatic method based on multi-agent system and reinforcement learning. the proposed solution is proved by an experimentation of object extraction from an image. © 2020 IEEE.

关键词： Multi agent systems

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Hierarchical Structured Learning for Indoor Autonomous Navigation of Quadcopter 16

Hierarchical Structured Learning for Indoor Autonomous Navig...

引用

10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Duggal, Vishakh Bipin, Kumar Shah, Utsav Krishna, K. Madhava IIIT KCIS RRC Hyderabad Andhra Prades India

ISBN: (纸本)9781450347532

Autonomous navigation of generic monocular quadcopter in the indoor environment requires sophisticated approaches for perception, planning and control. this paper presents a system which enables a miniature quadcopter with a frontal monocular camera to autonomously navigate and explore the unknown indoor environment. Initially, the system estimates dense depth map of the environment from a single video frame using our proposed novel supervised Hierarchical Structured Learning (fin) technique, which yields both high accuracy levels and better generalization. the proposed HSL approach discretizes the overall depth range into multiple sets. It structures these sets hierarchically and recursively through partitioning the set of classes into two subsets with subsets representing apportioned depth range of the parent set, forming a binary tree. the binary classification method is applied to each internal node of binary tree separately using Support Vector Machine (svM). Whereas, the depth estimation of each pixel of the image starts from the root node in top-down approach, classifying repetitively till it reaches any of the leaf node representing its estimated depth. the generated depth map is provided as an input to Convolutional Neural Network (CNN), which generates flight planning commands. Finally, trajectory planning and control module employs a convex programming technique to generate collision-free minimum time trajectory which follows these flight planning commands and produces appropriate control inputs for the quadcopter. the results convey unequivocally the advantages of depth perception by HSL, while repeatable flights of successful nature in typical indoor corridors confirm the efficacy of the pipeline.

关键词： Autonomous Navigation vision Quadcopter

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Real-time fusion of multi-focus images for visual sensor networks

Real-time fusion of multi-focus images for visual sensor net...

引用

Iranian conference on Machine vision and image processing

作者： Haghighat, Mohammad Bagher Akbari Aghagolzadeh, Ali Seyedarabi, Hadi Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

ISBN: (纸本)9781424497065

the objective of image fusion is to combine information from multiple images of the same scene in order to deliver only the useful information. the discrete cosine transform (DCT) based methods of image fusion are more suitable and time-saving in real-time systems using DCT based standards of still image or video. In this paper an efficient approach for fusion of multi-focus images based on variance calculated in DCT domain is presented. the experimental results on several images show the efficiency improvement of our method both in quality and complexity reduction in comparison with several recent proposed techniques. © 2010 IEEE.

关键词： Discrete cosine transforms

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Exploring memory and time efficient neural networks for image captioning 6th

Exploring memory and time efficient neural networks for imag...

引用

6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Parameswaran, Sandeep Narayan Visualization and Perception Lab Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai600036 India

ISBN: (纸本)9789811300196

Automatically describing the contents of an image is one of the fundamental problems in artificial intelligence. Recent research has primarily focussed on improving the quality of the generated descriptions. It is possible to construct multiple architectures that achieve equivalent performance for the same task. Among these, the smaller architecture is desirable as they require less communication across servers during distributed training and less bandwidth to export a new model from one place to another through a network. Generally, a deep learning architecture for image captioning consists of a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) clubbed together within an encoder-decoder framework. We propose to combine a significantly smaller CNN architecture termed SqueezeNet and a memory and computation efficient LightRNN within a visual attention framework. Experimental evaluation of the proposed architecture on Flickr8k, Flickr30k and MS-COCO datasets reveal superior result when compared to the state of the art. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Recurrent neural networks

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Flower Search by image on Mobile Phone 6th

Flower Search by Image on Mobile Phone

引用

6th International conference on New Trends in Information Science, Service Science and Data Mining (ISSDM)

作者： Sunpetchniyom, Treepop Watanapa, Saowaluk Siricharoenchai, Rungkarn Thammasat Univ MaChi Lab Multimedia Anal & Comp Human Interact L Fac Sci & Technol Dept Comp Sci Bangkok Thailand NSTDA Image Technol Lab NECTEC Khlong Luang Pathum Thani Thailand

ISBN: (纸本)9781467308762

We propose an image search system for flowers on a mobile phone. Mobile phones have more limited resources than desktop computers in terms of CPU, RAM and data storage. the database that we created has 45 classes. We used 182 training images and 246 test images. We used an HSV histogram as a color feature. the accuracy rate using only color features was 44.86% with radius C=20. We use SURF as a shape feature. the accuracy rate using only shape-based feature was 47.31% with SURF vectors S=25. We combine both color and shape features to achieved accuracy 61.61%.

关键词： image processing computer vision Flower search Flower retrieval HSV RGB SIFT SURF Mobile phone Smart phone

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An Integrated Model for Text to Text, image to Text and Audio to Text Linguistic Conversion using Machine Learning Approach 6

An Integrated Model for Text to Text, Image to Text and Audi...

引用

6th International conference on Information Systems and computer Networks, ISCON 2023

作者： Singh, Aman Raj Bhardwaj, Diwakar Dixit, Mridul Kumar, Lalit GLA University Department of Cea Mathura India Amity University Noida India

ISBN: (纸本)9798350346961

this paper presents an integrated model that uses machine learning techniques to perform text-to-text, image-to-text, and audio-to-text conversions, with particularly focus on indian languages. the proposed model which can translate text, image, and voice has been tested on large datasets of various indian languages and utilizes state-of-the-art techniques such as machine learning, computer vision, and speech recognition to accurately transcribe and translate the input data. the results obtained from the experiments demonstrate the effectiveness of the model by accurately converting text, images, and audio to text, and the potential applications of our proposed model range from language learning, accessibility for non-verbal or non-hearing individuals to cross-language communication. the proposed model is intended to bridge the language gap and facilitate communication among people from different linguistic backgrounds.. © 2023 IEEE.

关键词： computer vision

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Two-dimensional variable step-size normalized least mean squares and affine projection adaptive filter algorithms

Two-dimensional variable step-size normalized least mean squ...

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Iranian conference on Machine vision and image processing

作者： Abadi, Mohammad Shams Esfand Nikbakht, Sahar Faculty of Electrical and Computer Engineering Shahid Rajaee University Tehran Iran

ISBN: (纸本)9781424497065

In this paper the new two-dimensional (TD) adaptive filter algorithms are introduced. the presented algorithms are TD variable step-size (VSS) normalized least mean squares (TD-VSS-NLMS) and TD-VSS affine projection algorithms (TD-VSS-APA). In these algorithms, the step-size changes during the adaptation which leads to the low steady-state mean square error (MSE), and fast convergence speed. We demonstrate the good performance of the derived algorithms in TD system identification and adaptive noise cancellation in digital images for image restoration. © 2010 IEEE.

关键词： Adaptive filters

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MoCoMER: A Self Supervised Representation Learning for Handwritten Mathematical Expression Recognition 24

MoCoMER: A Self Supervised Representation Learning for Handw...

引用

15th indian conference on computer vision graphics and image processing

作者： Pramanik, Sandip Mitra, Shree Das, Nibaran Jadavpur Univ Dept CSE Kolkata W Bengal India Indian Inst Informat Technol Guwahati Gauhati India

ISBN: (纸本)9798400710759

this paper presents a self-supervised learning (SSL) based framework, specifically designed for Handwritten Mathematical Expression Recognition (HMER). the proposed approach incorporates a momentum encoding technique and a non-linear projection head into the image encoder to effectively address the common issue of dimensional collapse in self-supervised learning (SSL) methods. Our approach consists of two main steps: first, we use self-supervised pre-training to train the image encoder to obtain strong feature representations from HME images. Subsequently, we fine-tune the model using a Transformer network to predict LaTeX sequences from HME images. the assessment demonstrates that our SSL framework surpasses other existing SSL frameworks as well as several supervised methods in terms of performance. the findings indicate that our approach is highly advanced, emphasizing its potential to enhance the robustness and efficiency of feature representations in HMER tasks. the integration of momentum encoding and a non-linear projection head in the image encoder is shown to enhance the durability and effectiveness of feature representations, leading to superior performance in HMER tasks. Our experiments reveal that our approaches achieve an expression recognition rate (ExpRate) of 62. 17%, 61. 03%, 64. 8% on the CROHME 2014,2016,2019 test datasets respectively. the CROHME 2019 test data achieves the highest ExpRate, which is state-of-the-art (SOTA). this success is achieved by overcoming the challenges of dimensional collapse and leveraging the advantages of both self-supervised and supervised learning.(1)

关键词： HMER SSL Contrastive Learning Momentum Contrast (MoCo) Non-linear projection head Transformer

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