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Peer-reviewed artikel over semantische segmentatie van point clouds.
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THE USE OF MODERN METHODS AND ADVANCED TECHNIQUES FOR A BETTER UNDERSTANDING OF THE FRONTIER DEVELOPMENT
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Publicatie bij de rede, uitgesproken bij de aanvaarding van het ambt als lector Green Biotechnology aan Hogeschool Inholland te Amsterdam op 20 mei2015 door dr. C.M. Kreike
The broad field of environmental ethics, animal welfare, animal liberation, and animal rights literature indicate that all encounters between humans and animals are ethically charged. In this article, I shall examine how environmental ethics or animal welfare/rights/liberation literature translate into public media. The case study will delve into the representation of animals in the Dutch newspapers, using content analysis to provide an empirical basis for monitoring public opinion. Assuming that attitudes to animals are influenced by media coverage, the results of this case study will be brought to bear upon the discussion of the representation of animals beyond a specific national context. LinkedIn: https://www.linkedin.com/in/helenkopnina/
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.
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Studying images on social media introduces several challenges that relate to the size of datasets and the different meaning-making grammars of social visuality; or as aptly pointed out by others in the field, it means ‘studying the qualitative on a quantitative scale’. Although cultural analytics provides an automated process through which patterns can be detected in large numbers of images, this methodology doesn’t account for other modalities of the image than the image itself. However, images circulating social media can (and should) be analyzed on the level of their audience as the latter is co-creating the meaning of images. Bridging the study of platform affordances and affect theory, this paper presents a novel methodology that repurposes Facebook Reactions to infer collective attitudes and performative emotional expressions vis á vis images shared on the large Syrian Revolution Network public page (+2M). We found visual patterns that co-occur with certain collective combinations of buttons, displaying how socio-technical features shape the discursive frameworks of online publics.
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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.
Greenhouses are in need of new monitoring tools, as they size grow bigger and bigger but still using old labour intensive methods ways of caring for the crop. HiPerGreen is set out to create a new tool, which can drive onto the pre-existing heating pipes to provide a birds eye perspective for image analysis purposes. However, clear images are necessary for consistent usable data. This presentation resumes the steps taken during the reporting: the optimisation of a rail based system towards clear images. This is done through analysis of resulting images, understanding vibrations and oscillations, and finally presents results based on prototyping. Moreover, a re-design of the electronics and hardware was also introduce to facilitate prototyping. The results are promising, laying within the requirements.
In November 2019, the High Performance Greenhouse project (HiPerGreen) was nominated for the RAAK Award 2019, as one of the best applied research projects in the Netherlands. This paper discusses the challenges faced, lessons learned and critical factors in making the project into a success.
