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Temporal Convolutional and Recurrent Networks for image Captioning 14th

Temporal Convolutional and Recurrent Networks for Image Capt...

引用

14th International conference on Pattern Recognition and Information processing (PRIP)

作者： Iskra, Natalia Iskra, Vitaly Belarusian State Univ Informat & Radioelect Minsk BELARUS Omnigon Commun LLC New York NY USA

ISBN: (纸本)9783030354305;9783030354299

Recently temporal convolutional networks have shown excellent qualities in sequence modeling tasks [1]. Taking this fact into account, in this paper we investigate the possibilities of replacing recurrent networks in architectures targeted specifically at image captioning. We evaluate the solution on visual Genome dataset [2], which provides extensive set of labels and descriptions that thoroughly grounds visual concepts to natural language.

关键词： image captioning Convolutional neural networks Recurrent neural networks visual Genome Dilated convolution Weight normalization Dropout Adam optimization

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Enhancement Methods for Low Visibility and Fog Degraded images 2nd

Enhancement Methods for Low Visibility and Fog Degraded Imag...

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2nd International conference on Advanced Informatics for Computing Research (ICAICR)

作者： Singh, Gurveer Singh, Ashima Thapar Univ Dept CSE Patiala Punjab India

ISBN: (纸本)9789811331404;9789811331398

image processing methods are widely used to improvise the quality of an image to extract the hidden information in it. Phenomena of scattering and atmosphere absorption results inhaze smoke and fog. Weather conditions majorly influence the visual system as well as detection and identification of the targets and degrade the picture quality. In the previous year, researchers have been focused on the high-quality images or videos for enhancement as well as to detect objects. In this paper, we have reviewed previous papers and compare based on used techniques and performance parameters.

关键词： image processing visual system image enhancement and low visibility techniques

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DENESTO: A Tool for Video Decoding Energy Estimation and visualization

DENESTO: A Tool for Video Decoding Energy Estimation and Vis...

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IEEE visual communications and image processing (VCIP)

作者： Matthias Kränzler Christian Herglotz André Kaup Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nürnberg (FAU) Erlangen Germany

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

In previous research, it is shown that the decoding energy demand of several video codecs can be estimated accurately by using bit stream feature-based models. Therefore, we show in this paper that the visualization with the Decoding Energy Estimation Tool (DENESTO) can help to improve the understanding of the energy demand of the decoder.

关键词： Decoding visualization Transforms Tools Mathematical model Estimation Video codecs

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FishUI: Interactive Fisheye Distortion visualization

FishUI: Interactive Fisheye Distortion Visualization

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IEEE visual communications and image processing (VCIP)

作者： Andy Regensky Christian Herglotz André Kaup Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nürnberg (FAU) Erlangen Germany

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Fisheye lenses provide major benefits for many applications due to their large field of view. However, they come at the cost of strong radial distortions leading to problems in a variety of signal processing tasks which have been developed with perspective lenses in mind. As such, while state-of-the-art image and video codecs excel in reducing redundancy and irrelevance in content captured with perspective lenses, the coding gain reduces significantly when fisheye lenses are applied. To improve the understanding of distortions introduced by fisheye lenses with respect to perspective lenses, we provide an interactive user interface for the visualization of fisheye block distortions.

关键词： Lenses Distortion Cameras visualization image resolution Video surveillance Video sequences

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High-level image representation-based on Gestalt theory for image classification

High-level image representation-based on Gestalt theory for ...

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communications, Control Systems and Signal processing (CCSSP), 2020 1st International conference on

作者： Aiouez Bouzar Sabrina Souami Feryel Belattar Khadidja Computer Science Department University of Science and Technology Houari Boumediene Algiers Algeria Computer Science Department College of NTIC Constantine University 2 Constantine Algeria

ISBN: (数字)9781728158358

ISBN: (纸本)9781728158365

Feature extraction and representation are crucial stages for image classification. However, the derived features might not be robust discriminators, which radically alter image classification results. In this paper, we present an enhanced Bag of visual Words representation for better image classification. We are inspired by the Gestalt laws of grouping. So, our design goals are: (1) Introduce the spatial information into Bag of Words representation by grouping the aligned visual words (high order features), (2) Preserve the spatial relationships among the visual words groups against different geometric transformations, (3) Achieve the high-level representation of visual words by extracting visual phrases. Theses phrases convey the semantic meaning of object parts. The proposed approach has been evaluated, in terms of classification accuracy, on Caltech101 image database. Experimental results indicate that the classification performances are improved by using visual phrases.

关键词： visualization Feature extraction image classification image representation Computational modeling Histograms Vocabulary

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Geometric-visual descriptor for improved image based localization

Geometric-visual descriptor for improved image based localiz...

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IEEE visual communications and image processing (VCIP)

作者： Achref Ouni Eric Royer Marc chevaldonné Michel Dhome Institut Pascal Université Clermont Auvergne Clermont ferrand France

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

This paper addresses the problem of image based localization. The goal is to find quickly and accurately the relative pose from a query taken from a stereo camera and a map obtained using visual SLAM which contains poses and 3D points associated to descriptors. In this paper we introduce a new method that leverages the stereo vision by adding geometric information to visual descriptors. This method can be used when the vertical direction of the camera is known (for example on a wheeled robot). This new geometric visual descriptor can be used with several image based localization algorithms based on visual words. We test the approach with different datasets (indoor, outdoor) and we show experimentally that the new geometric-visual descriptor improves standard image based localization approaches.

关键词： visualization Three-dimensional displays Cameras Feature extraction Robot vision systems Simultaneous localization and mapping Two dimensional displays

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Stereo image quality assessment based on sparse binocular fusion convolution neural network 34

Stereo image quality assessment based on sparse binocular fu...

引用

34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Li, Sumei Han, Xu Zubair, Muhammad Ma, Shuai Tianjin University School of Electrical and Information Engineering Tianjin China Tianjin University School of Electrical and Information Engineering Pakhtunkhwa Pakistan

ISBN: (纸本)9781728137230

In this paper, a sparse binocular fusion convolution neural network is proposed to evaluate the quality of stereo image. In order to simulate the long-Term fusion and processing of the left and right views in the brain visual pathway, the network combines the two views of the stereo image four times, and the information processing is carried out through convolution together with the fusion operation. In addition, in order to overcome the computational-intensive and memory-intensive problems of convolution neural networks, a structural sparsity learning (SSL) method is used to regularize the proposed convolution neural network. The experimental results demonstrate that our proposed method performs effectively and efficiently. And the proposed method can achieve 2.0× speedups on LIVE I database and 2.3× speedup on LIVE II database on the basis of improved performance. © 2019 IEEE.

关键词： Binoculars

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No-Reference Stereoscopic image Quality Assessment Based on Convolutional Neural Network with A Long-Term Feature Fusion

No-Reference Stereoscopic Image Quality Assessment Based on ...

引用

IEEE visual communications and image processing (VCIP)

作者： Sumei Li Mingyi Wang School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

With the rapid development of three-dimensional (3D) technology, the effective stereoscopic image quality assessment (SIQA) methods are in great demand. Stereoscopic image contains depth information, making it much more challenging in exploring a reliable SIQA model that fits human visual system. In this paper, a no-reference SIQA method is proposed, which better simulates binocular fusion and binocular rivalry. The proposed method applies convolutional neural network to build a dual-channel model and achieve a long-term process of feature extraction, fusion, and processing. What's more, both high and low frequency information are used effectively. Experimental results demonstrate that the proposed model outperforms the state-of-the-art no-reference SIQA methods and has a promising generalization ability.

关键词： Feature extraction Stereo image processing Solid modeling Three-dimensional displays Fuses Transform coding image quality

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Learning From Paired and Unpaired Data: Alternately Trained CycleGAN for Near Infrared image Colorization

Learning From Paired and Unpaired Data: Alternately Trained ...

引用

IEEE visual communications and image processing (VCIP)

作者： Zaifeng Yang Zhenghua Chen Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore Institute for Infocomm Research Agency for Science Technology and Research (A*STAR) Singapore

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

This paper presents a novel near infrared (NIR) image colorization approach for the Grand Challenge held by 2020 IEEE International conference on visual communications and image processing (VCIP). A Cycle-Consistent Generative Adversarial Network (CycleGAN) with cross-scale dense connections is developed to learn the color translation from the NIR domain to the RGB domain based on both paired and unpaired data. Due to the limited number of paired NIR-RGB images, data augmentation via cropping, scaling, contrast and mirroring operations have been adopted to increase the variations of the NIR domain. An alternating training strategy has been designed, such that CycleGAN can efficiently and alternately learn the explicit pixel-level mappings from the paired NIR-RGB data, as well as the implicit domain mappings from the unpaired ones. Based on the validation data, we have evaluated our method and compared it with conventional CycleGAN method in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and angular error (AE). The experimental results validate the proposed colorization framework.

关键词： Training image color analysis Generators Gallium nitride Decoding image edge detection Gray-scale

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Neural network and SVM based kidney stone based medical image classification 4th

Neural network and SVM based kidney stone based medical imag...

引用

4th International conference on Computer Vision and image processing, CVIP 2019

作者： Chak, Priyanka Navadiya, Payal Parikh, Bhavya Pathak, Ketki C. Electronics and Communication Department SCET Surat India

ISBN: (纸本)9789811540141

In recent medical science, automatic diagnosis system is highly demanded Our research is designed to detect the kidney stones by using digital signal processing techniques. In this proposed method we have applied Neural network based feature extraction method along with Support Vector Machine (SVM) based linear classifier to classify normal CT- scan images and kidney stone images. All the CT scan kidney images are initially preprocessed to filter out speckle noise to enhance visual quality. In later part of the algorithm region of interest (stone part) is extracted using segmentation algorithm. Among these segmented images, the Gray level Co-occurrence Matrix (GLCM) algorithm is introduced to extract the features which are used to train the network. In medical diagnosis for early stage detection of diseases, one of the reliable classifier is Artificial Neural Network (ANN). In our proposed algorithm we have design ANN system for 12 input nodes, 10 hidden nodes and 2 output nodes. The network is trained until it targets its desired output. Only ANN based classification reduces diagnosis time and increase the efficiency with accuracy 85%. To classify the images into two class (kidney stone or normal kidney) Support Vector Machine (SVM) is used. Artificial Neural Network (ANN) with SVM based classification gives accuracy of 95% with lesser features and up to 99% with all features. © Springer Nature Singapore Pte Ltd 2020.

关键词： Support vector machines

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