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Forget about the LiDAR: Self-supervised depth estimators with MED probability volumes 34

Forget about the LiDAR: Self-supervised depth estimators wit...

引用

34th conference on Neural Information processing Systems, NeurIPS 2020

作者： Gonzalez, Juan L. Kim, Munchurl School of Electrical Engineering Korea Advanced Institute of Science and Technology Korea Republic of

Self-supervised depth estimators have recently shown results comparable to the supervised methods on the challenging single image depth estimation (SIDE) task, by exploiting the geometrical relations between target and reference views in the training data. However, previous methods usually learn forward or backward image synthesis, but not depth estimation, as they cannot effectively neglect occlusions between the target and the reference images. Previous works rely on rigid photometric assumptions or on the SIDE network to infer depth and occlusions, resulting in limited performance. On the other hand, we propose a method to "Forget About the LiDAR" (FAL), with Mirrored Exponential Disparity (MED) probability volumes for the training of monocular depth estimators from stereo images. Our MED representation allows us to obtain geometrically inspired occlusion maps with our novel Mirrored Occlusion Module (MOM), which does not impose a learning burden on our FAL-net. Contrary to the previous methods that learn SIDE from stereo pairs by regressing disparity in the linear space, our FAL-net regresses disparity by binning it into the exponential space, which allows for better detection of distant and nearby objects. We define a two-step training strategy for our FAL-net: It is first trained for view synthesis and then fine-tuned for depth estimation with our MOM. Our FAL-net is remarkably light-weight and outperforms the previous state-of-the-art methods with 8× fewer parameters and 3× faster inference speeds on the challenging KITTI dataset. We present extensive experimental results on the KITTI, CityScapes, and Make3D datasets to verify our method’s effectiveness. To the authors’ best knowledge, the presented method performs the best among all the previous self-supervised methods until now. © 2020 Neural information processing systems foundation. All rights reserved.

关键词： Stereo image processing

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Identifying, Classifying and Searching Graphic Symbols in the NOTAE System 16th

Identifying, Classifying and Searching Graphic Symbols in th...

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16th Italian Research conference on Digital Libraries (IRCDL)

作者： Boccuzzi, Maria Catarci, Tiziana Deodati, Luca Fantoli, Andrea Ghignoli, Antonella Leotta, Francesco Mecella, Massimo Monte, Anna Sietis, Nina Sapienza Univ Roma Dipartimento Storia Antropol Relig Arte Spettacol Rome Italy Sapienza Univ Roma Dipartimento Ingn Informat Automat & Gest Rome Italy Sapienza Univ Roma Informat & Stat Fac Ingn Informaz Rome Italy

ISBN: (纸本)9783030399054;9783030399047

The use of graphic symbols in documentary records from the 5th to the 9th century has so far received scant attention. What we mean by graphic symbols are graphic signs (including alphabetical ones) drawn as a visual unit in a written text and representing something other or something more than a word of that text. The Project NOTAE represents the first attempt to investigate these graphic entities as a historical phenomenon from Late Antiquity to early medieval Europe in any written sources containing texts generated for pragmatic purposes (contracts, petitions, official and private letters, lists etc.). Identifying and classifying graphic symbols on such documents is a task that requires experience and knowledge of the field, but software applications may come in help by learning to recognize symbols from previously annotated documents and suggesting experts potential symbols and likely classification in newly acquired documents to be validated, thus easing the task. This contribution introduces the NOTAE system that, in addition to the aforementioned task, provides non expert users with tools to explore the documents annotated by experts.

关键词： Graphic symbols Paleography image processing Clustering

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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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DENESTO: A Tool for Video Decoding Energy Estimation and visualization

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

引用

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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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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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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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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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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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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