Zoekresultaten

MultipleXLab: A high-throughput portable live-imaging root phenotyping platform using deep learning and computer vision.

Background: Profiling the plant root architecture is vital for selecting resilient crops that can efficiently take up water and nutrients. The high-performance imaging tools available to study root-growth dynamics with the optimal resolution are costly and stationary. In addition, performing nondestructive high-throughput phenotyping to extract the structural and morphological features of roots remains challenging. Results: We developed the MultipleXLab: a modular, mobile, and cost-effective setup to tackle these limitations. The system can continuously monitor thousands of seeds from germination to root development based on a conventional camera attached to a motorized multiaxis-rotational stage and custom-built 3D-printed plate holder with integrated light-emitting diode lighting. We also developed an image segmentation model based on deep learning that allows the users to analyze the data automatically. We tested the MultipleXLab to monitor seed germination and root growth of Arabidopsis developmental, cell cycle, and auxin transport mutants non-invasively at high-throughput and showed that the system provides robust data and allows precise evaluation of germination index and hourly growth rate between mutants. Conclusion: MultipleXLab provides a flexible and user-friendly root phenotyping platform that is an attractive mobile alternative to high-end imaging platforms and stationary growth chambers. It can be used in numerous applications by plant biologists, the seed industry, crop scientists, and breeding companies.

Machine Vision and Social Media Images: Why Hashtags Matter

Studying images in social media poses specific methodological challenges, which in turn have directed scholarly attention toward the computational interpretation of visual data. When analyzing large numbers of images, both traditional content analysis as well as cultural analytics have proven valuable. However, these techniques do not take into account the contextualization of images within a socio-technical environment. As the meaning of social media images is co-created by online publics, bound through networked practices, these visuals should be analyzed on the level of their networked contextualization. Although machine vision is increasingly adept at recognizing faces and features, its performance in grasping the meaning of social media images remains limited. Combining automated analyses of images with platform data opens up the possibility to study images in the context of their resonance within and across online discursive spaces. This article explores the capacities of hashtags and retweet counts to complement the automated assessment of social media images, doing justice to both the visual elements of an image and the contextual elements encoded through the hashtag practices of networked publics.

The age of total images

In The Age of Total Images, art historian Ana Peraica focuses on the belief that the shape of the planet is two-dimensional which has been reawakened in the late 20th and early 21st centuries, and the ways in which these ‘flat Earth’ conspiracy theories are symptomatic of post-digital image culture. Such theories, proven to be false both in Antiquity and Modernity, but once held to be true in the Medieval Period, have influenced a return to a kind of ‘New Medievalism’.

Critical meme reader

IknowWhatThisIs.

This publication gives an account of the Public Annotation of Cultural Heritage research project (PACE) conducted at the Crossmedialab. The project was carried out between 1 January 2008 and 31 December 2009, and was funded by the Ministry of Education, Culture, and Science. Three members of the Dutch Association of Science Centres (Vereniging Science Centra) actively participated in the execution of the project: the Utrecht University Museum, the National Museum of Natural History (Naturalis), and Museon. In addition, two more knowledge institutes participated: Novay and the Utrecht University of Applied Sciences. BMC Consultancy and Manage¬ment also took part in the project. This broad consortium has enabled us to base the project on both knowledge and experience from a practical and scientific perspective. The purpose of the PACE project was to examine the ways in which social tagging could be deployed as a tool to enrich collections, improve their acces¬sibility and to increase visitor group involvement. The museums’ guiding question for the project was: ‘When is it useful to deploy social tagging as a tool for the benefit of museums and what kind of effect can be expected from such deployment?’ For the Crossmedialab the PACE project presented a unique opportunity to conduct concrete research into the highly interesting phenomenon of social tagging with parties and experts in the field.

Automatic Tagging Suggestion for Database Enrichment

The huge number of images shared on the Web makes effective cataloguing methods for efficient storage and retrieval procedures specifically tailored on the end-user needs a very demanding and crucial issue. In this paper, we investigate the applicability of Automatic Image Annotation (AIA) for image tagging with a focus on the needs of database expansion for a news broadcasting company. First, we determine the feasibility of using AIA in such a context with the aim of minimizing an extensive retraining whenever a new tag needs to be incorporated in the tag set population. Then, an image annotation tool integrating a Convolutional Neural Network model (AlexNet) for feature extraction and a K-Nearest-Neighbours classifier for tag assignment to images is introduced and tested. The obtained performances are very promising addressing the proposed approach as valuable to tackle the problem of image tagging in the framework of a broadcasting company, whilst not yet optimal for integration in the business process.