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Found 7 papers, 1 papers with code
Image-based Deep Learning for the time-dependent prediction of fresh concrete properties
In this paper, a method is presented that makes it possible to predict the properties of fresh concrete during the mixing process based on stereoscopic image sequences of the concretes flow behaviour.
ConsInstancy: Learning Instance Representations for Semi-Supervised Panoptic Segmentation of Concrete Aggregate Particles
In particular, we are able to show that by leveraging completely unlabeled data in our semi-supervised approach the achieved overall accuracy (OA) is increased by up to 5% compared to an entirely supervised training using only labeled data.
Learning to Sieve: Prediction of Grading Curves from Images of Concrete Aggregate
A large component of the building material concrete consists of aggregate with varying particle sizes between 0. 125 and 32 mm.
Semi-Supervised Segmentation of Concrete Aggregate Using Consensus Regularisation and Prior Guidance
To overcome the limitations of standard consistency training, we propose a novel semi-supervised framework for semantic segmentation, introducing additional losses based on prior knowledge.
A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases
In this paper, a hierarchical deep learning framework is proposed to verify the land use information.
CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching
Due to its capability to identify erroneous disparity assignments in dense stereo matching, confidence estimation is beneficial for a wide range of applications, e. g. autonomous driving, which needs a high degree of confidence as mandatory prerequisite.
A two-layer Conditional Random Field for the classification of partially occluded objects
Conditional Random Fields (CRF) are among the most popular techniques for image labelling because of their flexibility in modelling dependencies between the labels and the image features.
