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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

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Real time implementation of RGB to HSV/HSI/HSL and its reverse color space models

Real time implementation of RGB to HSV/HSI/HSL and its rever...

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international conference on Communications and Signal processing

作者： G. Saravanan G. Yamuna S. Nandhini Electrical Engineering Department Annamalai University Chidambaram India

ISBN: (纸本)9781467385497

The color space conversion employs a significant function in preprocessing phase of digital image processing. Color conversion can improve the quality of images. The color space conversion is used in various applications such as commercial, multimedia, computer vision, visual tracking systems etc. The objective is to convert one color space to another and the inverse of same. Various color space conversions are used such as RGB←→HSV, RGB←→HSI and RGB←→HSL. The conversion process can be done using color space conversion algorithm. The hardware realization of color space conversion models can be implemented by using Xilinx System Generator (XSG) is implemented on Field Programmable Gate Array (FPGA) Spartan-6 XC6SLX16 Family. Finally, the above color space conversion models are implemented in real time and then tabulated in terms of percentage resources utilization and power report of various color space models. By means of color conversion the efficiency and speed are increased.

关键词： image color analysis Hardware Real-time systems Field programmable gate arrays Transforms computer architecture Mathematical model

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Automated visual fruit detection for harvest estimation and robotic harvesting

Automated visual fruit detection for harvest estimation and ...

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Workshops on image processing Theory, Tools and Applications, IPTA

作者： Steven Puttemans Yasmin Vanbrabant Laurent Tits Toon Goedemé EAVISE KU Leuven - Campus De Nayer Sint-Katelijne-Waver Belgium Department of Earth and Environmental Sciences KU Leuven Leuven Belgium Flemisch Institute for Technological Research (VITO) Remote Sensing Unit Mol Belgium

ISBN: (纸本)9781467389112

Fully automated detection and localisation of fruit in orchards are key components in creating automated robotic harvesting systems. During recent years a lot of research on this topic has been performed, either using basic computer vision techniques, like colour based segmentation, or by resorting to other sensors, like LWIR, hyperspectral or 3D. Recent advances in computer vision present a broad range of advanced object detection techniques that could improve the quality of fruit detection from RGB images drastically. We suggest to use an object categorisation framework based on boosted cascades of weak classifiers to implement a fully automated semi-supervised fruit detector and demonstrate it on both strawberries and apples. Compared to existing techniques we improved fruit detection, mainly in the case of fruit clusters, using a supervised machine learning instead of hand crafting image filters specific to the application. Moreover we integrate application specific colour information to ensure a more stable output of our fully automated detection algorithm. Finally we make suggestions for efficient fruit cluster separation. The developed technique is validated on both strawberries and apples and is proven to have large benefits in the field of automated harvest and crop estimation.

关键词： image color analysis image segmentation Data models Robots Green products Training data Training

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DEEP-CSSR: SCENE CLASSIFICATION USING CATEGORY-SPECIFIC SALIENT REGION WITH DEEP FEATURES

DEEP-CSSR: SCENE CLASSIFICATION USING CATEGORY-SPECIFIC SALI...

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IEEE international conference on image processing

作者： Mengshi Qi Yunhong Wang Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University

ISBN: (纸本)9781467399623

Researches in neuroscience and biological vision have shown that the bio-inspired methods have excellent recognition performance, such as the salient detection, artificial neural network and the ganglion cell inspired image feature. In this paper, we introduce a novel framework towards scene classification using category-specific salient region (CSSR) with deep CNN features, called Deep-CSSR. Firstly, by using the salient region detection algorithm, we extract a set of image patches which contain the salient regions. Also we apply DERF, a novel bio-inspired image descriptor, to represent the salient patches and clustering all of them to remove the outliers. Then we learn the CSSR filters and construct the CSSR representation. Further more, we do scene image classification using CSSR representation concatenate with the deep CNN features extracted from the whole images. By using this new pipeline, we obtain better results than recent methods over MIT Indoor 67 and Sun397 databases.

关键词： Scene Classification Salient Region Detection Deep CNN features CSSR Ganglion Cell image patch image feature Taxonomy image classification Neurosciences Neurophysiology

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CAMOUFLA GE IMA GE GENERA TION SYSTEM FOR SECURITY

CAMOUFLA GE IMA GE GENERA TION SYSTEM FOR SECURITY

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international conference on Inventive Computation Technologies

作者： Mamata M Jaliminche Reshma S. Patil Mohd Mudassir M. Shaikh Prateek K. Singh Vijay A. Kotkar Department of Computer Engineering AISSMS College of Engineering Pune Maharashtra India

ISBN: (纸本)9781509012862

Camouflage image is also referred as hidden image. It is an instance of recreational art. Such an image contains one or more hidden objects. The color plays an important role in vision. Camouflaging technique is also called cryptic coloration and it is trick that is used to disguise its appearance, usually to blend with its surroundings. This technique is used to mask location, identity and movement while it is the mixture of resources, design or information which is hidden within an image. Our proposed solution is identified to apply these color extraction technique which increases high visual quality of image. Our system can be used in many fields such as military security, gaming, automotive manufactures, animation and graphic designing purposes.

关键词： image processing camouflage image generation noise removal security

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D-HAZY: A DATASET TO EVALUATE QUANTITATIVELY DEHAZING ALGORITHMS

D-HAZY: A DATASET TO EVALUATE QUANTITATIVELY DEHAZING ALGORI...

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IEEE international conference on image processing

作者： Cosmin Ancuti Codruta O. Ancuti Christophe De Vleeschouwer ICTEAM Universite Catholique de Louvain Belgium Computer Vision and Robotics Group University of Girona Spain

ISBN: (纸本)9781467399623

Dehazing is an image enhancing technique that emerged in the recent years. Despite of its importance there is no dataset to quantitatively evaluate such techniques. In this paper we introduce a dataset that contains 1400+ pairs of images with ground truth reference images and hazy images of the same scene. Since due to the variation of illumination conditions recording such images is not feasible, we built a dataset by synthesizing haze in real images of complex scenes. Our dataset, called D-HAZY, is built on the Middelbury [1] and NYU Depth [2] datasets that provide images of various scenes and their corresponding depth maps. Due to the fact that in a hazy medium the scene radiance is attenuated with the distance, based on the depth information and using the physical model of a hazy medium we are able to create a corresponding hazy scene with high fidelity. Finally, using D-HAZY dataset, we perform a comprehensive quantitative evaluation of several state of the art single-image dehazing techniques.

关键词： Atmospheric modeling Mathematical model image color analysis image restoration Computational modeling Observers Attenuation image colour analysis Dataset Atmospheric models image restoration technique Dehazing Computational modeling images Mathematical Model Observers Attenuation Attenuation state of the art

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Multi-View Inpainting for image-Based Scene Editing and Rendering

Multi-View Inpainting for Image-Based Scene Editing and Rend...

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international conference on 3D Imaging, Modeling, processing, Visualization and Transmission (3DIMPVT)

作者： Theo Thonat Eli Shechtman Sylvain Paris George Drettakis Inria Adobe Research

ISBN: (纸本)9781509054084

We propose a method to remove objects such as people and cars from multi-view urban image datasets, enabling free-viewpoint IBR in the edited scenes. Our method combines information from multi-view 3D reconstruction with image inpainting techniques, by formulating the problem as an optimization of a global patch-based objective function. We use image-Based Rendering (IBR) techniques to reproject information from neighboring views, and 3D multi-view stereo reconstruction to perform multiview coherent initialization for inpainting of pixels not filled by reprojection. Our algorithm performs multi-view consistent inpainting for color and 3D by blending reprojections with patch-based image inpainting. We run our algorithm on casually captured datasets, and Google StreetViewdata, removing objects cars, people and pillars, showing that our approach produces results of sufficient quality for free-viewpoint IBR on "cleaned up" scenes, as well as IBR scene editing, such as limited motion of real objects.

关键词： Three-dimensional displays Cameras Automobiles image color analysis Rendering (computer graphics) image reconstruction Google

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Nonconvex Second-Order Variational image Denoising Model with Adaptive Selection of Regularization Parameters

Nonconvex Second-Order Variational Image Denoising Model wit...

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international conference on Computational Intelligence and Applications (ICCIA)

作者： Ryan Wen Liu Yi Liu Jinming Duan Jingxian Liu Hubei Key Laboratory of Inland Shipping Technology Wuhan University of Technology Wuhan China School of Computer Science University of Nottingham Nottingham UK

image denoising is a typically ill-conditioned inverse problem, which has attracted much attention in the fields of image processing and computer vision. In order to overcome the ill-conditioned nature of this inverse problem, a nonconvex total generalized variation (NTGV)-regularized variational model was proposed in this paper for edge-preserving image denoising. The introduced NTGV regularizer is capable of restoring the degraded images while preserving the fine image details. To further improve the image quality, a local variance-based estimation method was introduced to automatically compute the spatially variant regularization parameters, which can make a good balance between noise suppression and detail preservation. A numerical optimization algorithm based on Alternating Direction Method of Multipliers was proposed to effectively solve the resulting image restoration model. The numerical experiments have been conducted to compare the proposed model with current state-of-the-art image denoising methods. The experimental results have illustrated the good performance of the proposed method.

关键词： image restoration image quality image edge detection Optimization image denoising Computational modeling TV

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Analysis of Foreground Detection in MRI images using Region Based segmentation

Analysis of Foreground Detection in MRI Images using Region ...

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international conference on Communication and Signal processing

作者： R. Kotteswari K. G. Sathiya Electronics and Communication Engineering Department Arunai College of Engineering Arunai College of Engineering

ISBN: (纸本)9781467385497

Foreground Detection is one of the critical parts in the field of computer vision that aims to identify changes in the image sequences and to separate the foreground image from their background image. It is an arrangement of systems that typically examine the video sequences progressively and are recorded with a stationary camera. To detect brain tissue at early stage, a robotized framework utilizing images caught from multispectral camera is proposed. This multispectral camera has point of interest of catching images at higher determination. This can be implemented using region based segmentation (RBS) algorithm. There are two primary stages;first stage involves segmenting the input image by using preprocessing, which is performed by Anisotropic diffusion filtering and region segmentation. Second stage includes classification process by using Support vector machine (SVM) classifier and their main contribution is sobel edge detection and feature extraction. Experimental analysis indicates that the proposed method performs on diverse dataset with specific accomplishment on testing scenes that contain unpredictable or multiple associated foregrounds. Moreover, the change in accuracy is accomplished with low computational cost and executed utilizing MATLAB.

关键词： Foreground detection Magnetic resonance imaging (MRI) image image segmentation Similarity analysis

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Facial expression recognition using depth information and spatiotemporal features

Facial expression recognition using depth information and sp...

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international conference on Advanced Communication Technology (ICACT)

作者： Md. Zia Uddin Dept. of Computer Education Sungkyunkwan University Seoul South Korea

Facial expression recognition is considered to be one of the very important topics in image processing, pattern recognition, and computer vision due to its broad applications such as human-computer interaction, behavior analysis, and image understanding. In this work, a novel approach is proposed to recognize some facial expressions from time-sequential depth videos. First of all, efficient Local Directional Pattern (LDP) features are obtained from the time-sequential depth faces that are further augmented with the optical flow features. The augmented features are classified by Generalized Discriminant Analysis (GDA) to make the features more robust and finally, the spatiotemporal features are fed into Hidden Markov Models (HMMs) to train and recognize different facial expressions successfully. The depth information-based proposed facial expression recognition approach is compared to the conventional approaches where the proposed one outperforms others.

关键词： Artificial intelligence

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SIGN LANGUAGE RECOGNITION BASED ON ADAPTIVE HMMS WITH DATA AUGMENTATION

SIGN LANGUAGE RECOGNITION BASED ON ADAPTIVE HMMS WITH DATA A...

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IEEE international conference on image processing

作者： Dan Guo Wengang Zhou Meng Wang Houqiang Li School of Computer and Information Hefei University of Technology Hefei 230009 China EEIS Department University of Science and Technology of China Hefei 230027 China

ISBN: (纸本)9781467399623

vision based sign language recognition (SLR) is a challenging task due to the complexity of signs and limited data collection. To improve the recognition precision, this paper proposes an adaptive GMM-based (Gaussian mixture model) HMMs (Hidden Markov Models) framework. We discover that inherent latent states in HMMs are not only related to the number of key gestures and body poses, but also related to the kinds of their translation relationships. We propose adaptive HMMs and obtain the hidden state number for each sign with affinity propagation clustering. Furthermore, to enrich the training dataset, we propose a data augmentation strategy by adding Gaussian random disturbances. Experiments on a vocabulary of 370 signs demonstrate the effectiveness of our proposed method over the comparison algorithms.

关键词： Hidden Markov models Adaptation models Training Assistive technology Gesture recognition Feature extraction Skeleton

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