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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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Depth-aware arbitrary style transfer using instance normalization 30

Depth-aware arbitrary style transfer using instance normaliz...

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30th international conference on computer Graphics and Machine vision, GraphiCon 2020

作者： Kitov, Victor Kozlovtsev, Konstantin Mishustina, Margarita Lomonosov Moscow State University Moscow Russia Plekhanov Russian University of Economics Moscow Russia

Style transfer is the process of rendering one image with some content in the style of another image, representing the style. Recent studies of Liu et al. (2017) show that traditional style transfer methods of Gatys et al. (2016) and Johnson et al. (2016) fail to reproduce the depth of the content image, which is critical for human perception. They suggest to preserve the depth map by additional regularizer in the optimized loss function, forcing preservation of the depth map. However these traditional methods are either computationally inefficient or require training a separate neural network for each style. AdaIN method of Huang et al. (2017) allows efficient transferring of arbitrary style without training a separate model but is not able to reproduce the depth map of the content image. We propose an extension to this method, allowing depth map preservation by applying variable stylization strength. Qualitative analysis and results of user evaluation study indicate that the proposed method provides better stylizations, compared to the original AdaIN style transfer method. © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： image processing

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A new simple, fast and robust total least square error computation in e2: Experimental comparison 5th

A new simple, fast and robust total least square error compu...

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5th international conference on Advanced Engineering Theory and Applications, AETA 2018

作者： Smolik, Michal Skala, Vaclav Majdisova, Zuzana Faculty of Applied Sciences University of West Bohemia Plzen Czech Republic

ISBN: (纸本)9783030149062

Many problems, not only in signal processing, image processing, digital imaging, computer vision and visualization, lead to the Least Square Error (LSE) problem or Total (Orthogonal) Least Square Error (TLSE) problem computation. Usually the standard least square error approximation method is used due to its simplicity, but it is not an optimal solution, as it does not optimize the orthogonal distances, but only the vertical distances. There are many problems for which the LSE is not convenient and the TLSE is to be used. Unfortunately, the TLSE is computationally much more expensive. This paper presents a new, simple, robust and fast algorithm for the total least square error computation in E2. © Springer Nature Switzerland AG 2020.

关键词： image processing

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Proceedings of international conference on computer vision and image processing 1st ed. 2017

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丛书名： Advances in Intelligent Systems and Computing

2017年

作者： Balasubramanian Raman Sanjeev Kumar Partha Pratim Roy Debashis Sen

ISBN: (数字)9789811021077

ISBN: (纸本)9789811021060

This edited volume contains technical contributions in the field of computer vision and image processing presented at the First international conference on computer vision and image processing (cvip 2016). The contributions are thematically divided based on their relation to operations at the lower, middle and higher levels of vision systems, and their applications. The technical contributions in the areas of sensors, acquisition, visualization and enhancement are classified as related to low-level operations. They discuss various modern topics reconfigurable image system architecture, Scheimpflug camera calibration, real-time autofocusing, climate visualization, tone mapping, super-resolution and image resizing. The technical contributions in the areas of segmentation and retrieval are classified as related to mid-level operations. They discuss some state-of-the-art techniques non-rigid image registration, iterative image partitioning, egocentric object detection and video shot boundary detection. The technical contributions in the areas of classification and retrieval are categorized as related to high-level operations. They discuss some state-of-the-art approaches extreme learning machines, and target, gesture and action recognition. A non-regularized state preserving extreme learning machine is presented for natural scene classification. An algorithm for human action recognition through dynamic frame warping based on depth cues is given. Target recognition in night vision through convolutional neural network is also presented. Use of convolutional neural network in detecting static hand gesture is also discussed. Finally, the technical contributions in the areas of surveillance, coding and data security, and biometrics and document processing are considered as applications of computer vision and image processing. They discuss some contemporary applications. A few of them are a system for tackling blind curves, a quick reaction target acquisition and tracking sys

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Key-Point Based Distribution Probability Optimization for Object Detection in Remote Sensing images

Key-Point Based Distribution Probability Optimization for Ob...

引用

international conference on computer and Communications (ICCC)

作者： Xiaoke Zhu Yufei Wang Xiaopan Chen Danyang Li Jitong Zhang Yulong Cai School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781665409513

Recently, object detection in remote sensing images has been an important topic in computer vision. As a mainstream approach to detectors, single-stage detectors usually compute the important points of the bounding box to get the location of the target, and optimize them by Focal Loss. One of the development trends for one-stage detectors is to combine with Localization Quality Estimation (LQE), which can provide accurate ranking scores, and thus bring benefits to the Non-Maximum Suppression as well as the following detection results. In this paper, we propose a one-stage detector, named Key-point based Distribution Probability Optimization (KDPO), for remote sensing object detection. Specially, we first classify and locate the object through five key points. Then, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification. Next, we utilize a vector to represent arbitrary distribution of box locations. The improved representations avoid the inconsistency risk and accurately describe the flexible distribution of the data. Meanwhile, we use the improved Focal Loss for the succeed optimization. The proposed method is tested on two popular aerial image datasets, including HRSC2016 and DOTA. In all experiments, our approach achieves promising performance comparing to the state-of-the-art detectors.

关键词： Location awareness Heating systems computer vision Estimation Detectors Object detection Prediction algorithms

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Handwriting based gender classification using cold and hinge features 9th

Handwriting based gender classification using cold and hinge...

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9th international conference on image and Signal processing, ICISP 2020

作者： Gattal, Abdeljalil Djeddi, Chawki Bensefia, Ameur Ennaji, Abdellatif Department of Mathematics and Computer Science Larbi Tebessi University Tebessa Algeria LITIS Laboratory University of Rouen Rouen France Higher Colleges of Technology CIS Division Abu Dhabi United Arab Emirates

ISBN: (纸本)9783030519346

Gender Classification from handwriting is still considered to be challenging due to homogeneous vision comparing male and female handwritten documents. This paper presents a new method based on Cloud of Line Distribution (COLD) and Hinge feature for distinguishing the gender from handwriting. The SVM classifier combination decides the assigned class based on the maximum of the two decisions values resulting from COLD and Hinge feature. The proposed approach is evaluated on the standard QUWI dataset and following the framework protocol described in the ICFHR 2016 competition. Obtained results are promising regarding the classification rates announced in the literature. © Springer Nature Switzerland AG 2020.

关键词： Support vector machines

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Text Stance Detection Based on Deep Learning

Text Stance Detection Based on Deep Learning

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Symposium of image, Signal processing, and Artificial vision (STSIVA)

作者： Xu Zhang Chunyang Liu Zhongqin Gao Yue Jiang National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China Changan Communication Technology Co Ltd Beijing China

ISBN: (纸本)9781665426565

Stance analysis refers to the usage of natural language processing and data mining technology to determine the stance tendency of a specific target topic in text. The current stance detection research faces the problem of the relationship between the topic target information and the stance text information being not fully tapped, which affects the text stance analysis task performance in social media. In view of this, based on the existing deep learning framework, combined with the topic target information in stance analysis, this study proposes a stance analysis model based on a convolutional neural network under the independent encoding of the topic target information and the condition encoding of the topic target information. The SemEval2016 English Dataset and the NLPCC2016 Chinese dataset are used herein separately to conduct the experiments. The experimental results show that the model is effective in the stance detection task of a specific topic target.

关键词： Deep learning Analytical models Social networking (online) Government Encoding Natural language processing Convolutional neural networks

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Virtual to Real Unsupervised Domain Adaptation for image-Based Localization in Cultural Sites 4

Virtual to Real Unsupervised Domain Adaptation for Image-Bas...

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4th IEEE international conference on image processing, Applications and Systems, IPAS 2020

作者： Orlando, Santi Andrea Furnari, Antonino Farinella, Giovanni Maria Dmi University of Catania DWORD-XENIA Progetti S.r.l. Catania Italy Dmi University of Catania Catania Italy

ISBN: (纸本)9781728175744

The ability to localize the visitors of a cultural site from egocentric images can allow applications to understand where people go and what they pay attention to in the site. Current pipelines to tackle the problem require the collection and labeling of large amounts of images, which is challenging, especially in large-scale indoor environments. On the contrary, virtual images of a cultural site can be generated and automatically labeled using dedicated tools with minimum effort. In this paper, we investigate whether unsupervised domain adaptation techniques can be used to train localization models on labeled virtual data and unlabeled real data, and deploy them to work with real images. To perform this study, we propose a new dataset of both real and virtual images acquired in a cultural site which are labeled for room-based localization as well as for 3 DOF camera pose estimation. We hence compare two approaches to unsupervised domain adaptation: mid-level representations and image-To-image translation. Our analysis shows that both approaches can be used to reduce the domain gap arising from the different data sources and that the proposed dataset is a challenging benchmark for unsupervised domain adaptation for image-based localization. © 2020 IEEE.

关键词： computer vision

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Action recognition using pose estimation with an artificial 3D coordinates and CNN

Action recognition using pose estimation with an artificial ...

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2020 3D Measurement and Data processing conference, 3DMP 2020

作者： Kim, Jisu Lee, Deokwoo Department of Computer Engineering Keimyung University Daegu Korea Republic of

Activity recognition and pose estimation are ingeneral closely related in practical applications, even though they are considered to be independent tasks. In this paper, we propose an artificial 3D coordinates and CNN that is for combining activity recognition and pose estimation with 2D and 3D static/dynamic images(dynamic images are composed of a set of video frames). In other words, We show that the proposed algorithm can be used to solve two problems, activity recognition and pose estimation. End-to-end optimization process has shown that the proposed approach is superior to the one which exploits the activity recognition and pose estimation seperately. The performance is evaluated by calculating recognition rate. The proposed approach enable us to perform learning procedures using different datasets. © 2020, Society for Imaging Science and Technology.

关键词： Gesture recognition

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Small object bird detection in infrared drone videos using mask R-CNN deep learning

Small object bird detection in infrared drone videos using m...

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2020 Imaging and Multimedia Analytics in a Web and Mobile World conference, IMAWM 2020

作者： Kassim, Yasmin M. Byrne, Michael E. Burch, Cristy Mote, Kevin Hardin, Jason Larsen, David R. Palaniappan, Kannappan Department of Electrical Engineering and Computer Science University of Missouri-Columbia ColumbiaMO65201 United States School of Natural Resources University of Missouri-Columbia ColumbiaMO65201 United States Texas Parks and Wildlife Department AustinTX78744 United States

A reliable method to estimate population sizes of wild turkeys (Meleagris gallopavo) using unmanned aerial vehicles and thermal video imaging data collected at several field sites in Texas is described. Automating the data processing of airborne survey videos provides a fast and reproducible way to count wild turkeys for wildlife management and conservation. A deep learning semantic segmentation pipeline is developed to detect and count roosting Rio-Grande wild turkeys (M.g. intermedia) which appear as small faint objects in drone-based thermal IR videos. The proposed approach to detect roosting turkeys that appear as small objects, relies on Mask R-CNN, a deep architecture semantic segmentation algorithm. This is followed by a post-processing data association and filtering (DAF) process for counting the number of roosting birds. DAF was used to eliminate false positives like rocks and other small bright objects, which often have noisy detections across temporally adjacent video frames, that can be filtered using appearance association and distance-based gating across time. Transfer learning was used to train the Mask R-CNN network by initializing using imageNet weights. Drone-based thermal IR videos are extremely challenging due to the complexity of the natural environment including weather effects, occlusion of birds, terrain, trees, complex tree shapes, rocks, water and thermal inversion. The transect videos were collected at night at several times and altitudes to optimize data collection opportunities without disturbing the roosting turkeys. Preliminary performance evaluation using 280 video frames is promising. © 2020, Society for Imaging Science and Technology.

关键词： Drones

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Dynamic 3D Environment Perception Using Monocular vision and Semantic Segmentation 15

Dynamic 3D Environment Perception Using Monocular Vision and...

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IEEE 15th international conference on Intelligent computer Communication and processing (ICCP)

作者： Danescu, Radu Itu, Razvan Borza, Diana Tech Univ Cluj Napoca Comp Sci Dept Cluj Napoca Romania

ISBN: (纸本)9781728149141

This paper presents a complete system for traffic environment perception based on a single color image source. The acquired color images are segmented into road and obstacle areas using a U-Net style Convolutional Neural Network (CNN). The segmented image is mapped into a bird's-eye view using the automatically calibrated camera parameters. Obstacle scans are extracted from the bird's-eye view image, highlighting the contact points between the obstacles and the road. The scans are postprocessed to increase the connectivity between obstacle parts. The processed scans arc used to generate the measurement likelihood values for all observable cells of a dynamic occupancy grid, taking into consideration the expected measurement errors with respect to the distance from the camera. A particle-based occupancy grid is used to track the environment at cell level, and then the occupied cells arc grouped into individual objects. The system is able to estimate stable cuboids with measured width, length, orientation and speed for the moving individual objects such as vehicles and pedestrians, and also to identify generic occupied areas for the continuous structures such as fences or barriers.

关键词： color monocular vision semantic segmentation occupancy grids measurement model obstacle detection

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