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visual Analytics Approach to Comprehensive Meteorological Time-Series Analysis

DATA 2020年第4期5卷 94-94页

作者： Vuckovic, Milena Schmidt, Johanna VRVis Zentrum Virtual Real & Visualisierung Forsc A-1220 Vienna Austria

In some of the domain-specific sectors, such as the climate domain, the provision of publicly available present-day high-resolution meteorological time series is often quite limited or completely lacking. This repeatedly leads to excessive deployment of synthetically generated (historical) meteorological time series (TMY) to support thermal performance assessments on both building and urban scale. These datasets are generally a misrepresentation of current weather variability, which may lead to erroneous inferences drawn from modelling results. In this regard, we outline the application potential of a visual analytics approach in the context of data quality assessment and validation of TMYs. For this purpose, we deployed a standalone visual analytics tool Visplore, enriched with interlinked dashboards, customizable visualizations, and intuitive workflows, to support continuous interaction and early visual feedback. Driven by such integrated visual representations and visual interactions to enhance the analytical reasoning process, we were able to detect critical multifaceted discrepancies, on different levels of granularity, between TMY and present-day meteorological time series and synthetize them into cohesive patterns and insights. These mainly entailed diverging temporal trends and event time lags, under- and overestimation of warming and cooling regimes, respectively, and seasonal discrepancies, in particular meteorological parameters, to name a few.

关键词： visual analytics interactive visualization visual computing data quality climate data

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Grapes visual Segmentation for Harvesting Robots Using Local Texture Descriptors 12th

Grapes Visual Segmentation for Harvesting Robots Using Local...

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12th International Conference on Computer Vision Systems (ICVS)

作者： Badeka, Eftichia Kalabokas, Theofanis Tziridis, Konstantinos Nicolaou, Alexander Vrochidou, Eleni Mavridou, Efthimia Papakostas, George A. Pachidis, Theodore Int Hellen Univ Dept Comp Sci HUman MAchines INteract Lab HUMAIN Lab Kavala 65404 Greece

ISBN: (纸本)9783030349950;9783030349943

This paper investigates the performance of Local Binary Patterns variants in grape segmentation for autonomous agricultural robots, namely Agrobots, applied to viniculture and winery. Robust fruit detection is challenging and needs to be accurate to enable the Agrobot to execute demanding tasks of precise farming. Segmentation task is handled by classification with the supervised machine learning model k-Nearest Neighbor (k-NN), including extracted features from Local Binary Patterns (LBP) and their variants in combination of color components. LBP variants are tested for both varieties of red and white grapes, subject to performance measures of accuracy, recall and precision. The results for red grapes indicate an approximate intended accuracy of 94% of detection, while the results relating to white grapes confirm the concerns of complex indiscreet visual cues providing accuracies of 83%.

关键词： visual computing Computer vision Grapes detection Image segmentation Local binary patterns

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Graphics and Media Technologies for Operators in Industry 4.0

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2018年第5期38卷 119-132页

作者： Posada, Jorge Zorrilla, Mikel Dominguez, Ana Simoes, Bruno Eisert, Peter Stricker, Didier Rambach, Jason Doellner, Juergen Guevara, Miguel GraphicsMedia Net Kaiserslautern Germany Vicomtech San Sebastian Spain Fraunhofer HHI Berlin Germany DFKI Kaiserslautern Germany Hasso Plattner Inst Potsdam Germany CCG Cologne Germany

visual computing technologies have an important role in manufacturing and production, particularly in new Industry 4.0 scenarios with intelligent machines, human-robot collaboration and learning factories. In this article, we explore challenges and examples on how the fusion of graphics, vision and media technologies can enhance the role of operators in this new context.

关键词： computer graphics computer vision industry 4.0 media smart factory visual computing

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Semantic binary coding for visual recognition via joint concept-attribute modelling

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第17期77卷 22185-22198页

作者： Xu, Xing Wu, Haiping Yang, Yang Shen, Fumin Xie, Ning Ji, Yanli Guizhou Univ Guizhou Prov Key Lab Publ Big Data Guiyang Guizhou Peoples R China Univ Elect Sci & Technol China Ctr Future Media Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Sichuan Peoples R China

Recent years have witnessed the unprecedented efforts of visual representation for enabling various efficient and effective multimedia applications. In this paper, we propose a novel visual representation learning framework, which generates efficient semantic hash codes for visual samples by substantially exploring concepts, semantic attributes as well as their inter-correlations. Specifically, we construct a conceptual space, where the semantic knowledge of concepts and attributes is embedded. Then, we develop an effective on-line feature coding scheme for visual objects by leveraging the inter-concept relationships through the intermediate representative power of attributes. The code process is formulated as an overlapping group lasso problem, which can be efficiently solved. Finally, we may binarize the visual representation to generate efficient hash codes. Extensive experiments have been conducted to illustrate the superiority of our proposed framework on visual retrieval task as compared to state-of-the-art methods.

关键词： visual computing Binary representation Semantics Attributes

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Design and Application of an Adaptive Slow Feature Extraction Algorithm for Natural Images Based on visual Invariance

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TRAITEMENT DU SIGNAL 2019年第3期36卷 209-216页

作者： Zhao, Yanming Hebei Normal Univ Nationalities Dept Math & Comp Sci Chengde 067000 Peoples R China

The invariance of complex visual information in natural images is not considered in existing slow feature (SF) extraction algorithm. To solve the problem, this paper designs an adaptive SF extraction algorithm for natural images based on visual invariance. Firstly, the principal component analysis (PCA) was improved by the topologically independent component analysis (TICA), aiming to adaptively extract the invariance features of complex visual information in natural images. Next, the Markov chain Monte Carlo (MCMC) algorithm and the visual smoothness theory were combined to solve two defects of the conventional slow feature analysis (SFA): the nonlinear expansion algorithm has nothing to do with the visual invariance of the natural images;the sampling algorithm causes the loss of key visual information. Finally, the probability classification was introduced to our algorithm. The experimental results show that our algorithm achieved higher recognition rate and lower computing complexity than conventional algorithms, and exhibited strong robustness and geometric invariance.

关键词： invariant slow feature (SF) visual computing receptive field topology

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Computer Vision and Machine Learning for Viticulture Technology

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IEEE ACCESS 2018年 6卷 67494-67510页

作者： Seng, Kah Phooi Ang, Li-Minn Schmidtke, Leigh M. Rogiers, Suzy Y. Charles Sturt Univ Ringgold Stand Inst Sch Comp & Math Wagga Wagga Campus Wagga Wagga NSW 2650 Australia Ringgold Stand Inst Natl Wine & Grape Ind Ctr Wagga Wagga NSW 2678 Australia NSW Dept Primary Ind Wagga Wagga NSW 2678 Australia

This paper gives two contributions to the state-of-the-art for viticulture technology research. First, we present a comprehensive review of computer vision, image processing, and machine learning techniques in viticulture. We summarize the latest developments in vision systems and techniques with examples from various representative studies, including, harvest yield estimation, vineyard management and monitoring, grape disease detection, quality evaluation, and grape phenology. We focus on how computer vision and machine learning techniques can be integrated into current vineyard management and vinification processes to achieve industry relevant outcomes. The second component of the paper presents the new GrapeCS-ML database which consists of images of grape varieties at different stages of development together with the corresponding ground truth data (e.g., pH and Brix) obtained from chemical analysis. One of the objectives of this database is to motivate computer vision and machine learning researchers to develop practical solutions for deployment in smart vineyards. We illustrate the usefulness of the database for a color-based berry detection application for white and red cultivars and give baseline comparisons using various machine learning approaches and color spaces. This paper concludes by highlighting future challenges that need to be addressed prior to successful implementation of this technology in the viticulture industry.

关键词： Viticulture computer vision machine vision visual computing image processing machine learning

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Targeted style transfer using cycle consistent generative adversarial networks with quantitative analysis of different loss functions

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INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS 2018年第4期22卷 239-247页

作者： Kaur, Mannat Satapathy, Swapnil Soundrapandiyan, Rajkumar Singh, Jivjot Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India Simon Fraser Univ Big Data Burnaby BC Canada

Targeted style transfer is the visual computing and deep learning problem where the input and target image sets are used to train the network by learning the mapping between those for conversion of the input image to the style of the target image. One of the popular methods for this task is Cycle-GANs (Cycle Consistent Generative Adversarial Networks), with Mean Squared Error, Binary Cross Entropy Error, and L1 loss functions. In this paper, our network is trained for image-to-image translation where the style or content of the Target image is changed by the network by modifying loss functions of Cycle GANs. Most accurate translation could be trained to the network through the use of paired images i.e. Supervised Learning where the input image and output images are known and thus, the network learns to minimize the gap between the expected output and observed output. However, this kind of paired data is not readily available and is strenuous to mass produce. Cycle GANs uses unpaired data, and our work is dedicated to finding the best possible loss function combination for making it even more efficient. In Cycle GANs, there is a combination of 2 networks: Discriminators and Generators for each data set, which compete against each other to out-perform the other. Discriminator network uses Classification loss functions for distinguishing the images for the 2 datasets, while the Generator network uses Regression loss functions for determining Cycle loss and Identity loss. These loss functions play a vital role in the style transfer as they determine how much the images have been modified. We have worked on various loss functions like Mean Square Error loss, Binary Cross Entropy Error loss, Hinge loss, Huber loss, Log loss, Square loss and L1 loss for experimentation for the best losses combination to be used. We discuss the strengths and limitations of the loss functions already used and propose different combinations of loss functions for better accuracy. A separate cl

关键词： Cycle GANS image-to-image translation deep learning loss functions neural networks visual computing

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New approaches to plastic language: Prolegomena to a computer-aided approach to pictorial semiotics

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SEMIOTICA 2019年第230期 71-95页

作者： Reyes, Everardo Sonesson, Goran Univ Paris 08 Paragraphe Lab 2 Rue Liberte F-93526 St Denis France Semiotics Ctr Language & Literature Box 201 S-22100 Lund Sweden

In this paper we summarize observations bridging the declared aspirations of pictorial semiotics and its real achievements. Pictorial semiotics is here understood as the general study of pictures as signs and it constituted a fundamental step beyond the art historical captivation with individual images. In the first part of our contribution we present a review of the most important methods that have been proposed as an answer to deal with several pictorial problems (multiple instances, segmentation, non-figurative meaning). In the second part, we offer some positive and on-going implementations designed to remedy the shortcomings observed. What is suggested is the proof of concept that human researchers need the assistance of computing methodologies. However, computers can only do their job once an adequate phenomenology of human experience is fed into the process.

关键词： pictorial semiotics plastic language visual computing digital semiotics data exploration

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An improved saliency detection method based on non-uniform quantification and channel-weighted color distance

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第8期76卷 11037-11050页

作者： Han, Aili Han, Feilin Hao, Jing Yuan, Yahui Shandong Univ Dept Comp Sci & Technol Weihai 264209 Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310007 Zhejiang Peoples R China

We propose a non-uniform quantification method for RGB channels based on the visual sensitivities of human eyes to the red, green and blue colors, and give weights to RGB channels which are used to compute the channel-weighted color distances. Furthermore, we design a saliency detection method using the non-uniform quantification method and the channel-weighted color distances measurement. We first quantify the values in RGB channels in different step-lengths and determine the weights w (r) , w (g) and w (b) based on the visual sensitivities of human eyes to the three-primary colors. And then we convert the quantified image into that in Lab color space, which are segmented into some regions, and compute the channel-weighted color distance using the weights w (L) , w (a) and w (b) , which are computed from the weights w (r) , w (g) and w (b) and the transformation from RGB color space to Lab color space. The saliency of each region is computed using the weighted distances and spatial weights. The proposed non-uniform quantification method and the channel-weighted color distance measurement can be used for the applications based on color features in the field of image processing and computer vision. Experimental results show that our methods can improve the efficiency and performance of saliency detection to some extent.

关键词： Saliency detection visual computing Non-uniform quantification Channel weight

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A multi-level slow intelligence system for visualizing personal health care

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JOURNAL OF visual LANGUAGES AND computing 2017年 42卷 135-148页

作者： Chang, Shi-Kuo Chen, JunHui Gao, Wei Lou, ManSi Yin, XiYao Zhang, Qui Zhao, ZiHao Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15238 USA

This paper describes the design of an experimental multi-level slow intelligence system for visualizing personal health care, called the TDR system, consisting of interacting super-components each with different computation cycles specified by an abstract machine model. The TDR system has three major super-components: Tian (Heaven), Di (Earth) and Ren (Human), which are the essential ingredients of a human-centric psycho-physical system following the Chinese philosophy. Each super-component further consists of interacting components supported by an SIS server. This experimental TDR system provides a platform for exploring, visualizing and integrating different applications in personal health care, emergency management and social networking. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Slow intelligence system Distributed sensor networks Component-based software engineering visual computing visualization

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