Diplomatics, the analysis of medieval charters, is a major field of research in which paleography is applied.
Unpaired image-to-image translation of retinal images can efficiently increase the training dataset for deep-learning-based multi-modal retinal registration methods.
Moreover, we developed a system using local font group recognition in order to combine the output of multiple font recognition models, and show that while slower, this approach performs better not only on text lines composed of multiple fonts but on the ones containing a single font only as well.
Our proposed method is able to generate realistic word image samples from different writer styles, by using class index styles and text content prompts without the need of adversarial training, writer recognition, or text recognition.
are present in the data), which exacerbates the difficulty of accurate segmentation.
The Odeuropa Challenge on Olfactory Object Recognition aims to foster the development of object detection in the visual arts and to promote an olfactory perspective on digital heritage.
We investigate the effect of style and category similarity in multiple datasets used for object detection pretraining.
The analysis of digitized historical manuscripts is typically addressed by paleographic experts.
Facial landmark detection plays an important role for the similarity analysis in artworks to compare portraits of the same or similar artists.
For art investigations of paintings, multiple imaging technologies, such as visual light photography, infrared reflectography, ultraviolet fluorescence photography, and x-radiography are often used.
Our method extracts convolutional features from the vessel structure for keypoint detection and description and uses a graph neural network for feature matching.
In this work, we present a novel approach called Image Composition Canvas (ICC++) to compare and retrieve images having similar compositional elements.
One particular machine learning problem in dynamic environments is open world recognition.
We propose the use of fractals as a means of efficient data augmentation.
Ranked #1 on No real Data Binarization on DIBCO and H_DIBCO 2009
With the increasing number of online learning material in the web, search for specific content in lecture videos can be time consuming.
Our method demonstrates the best registration performance on our and a public multi-modal dataset in comparison to competing methods.
Annotating data, especially in the medical domain, requires expert knowledge and a lot of effort.
While these areas can be easily identified from electroluminescense (EL) images, this is much harder for photoluminescence (PL) images.
As of recent generative adversarial networks have allowed for big leaps in the realism of generated images in diverse domains, not the least of which being handwritten text generation.
As a strong baseline, we propose a two-stage DL-based method: first, we create automatically labeled training data by applying a standard sentiment classifier to tweet replies and aggregating its predictions for each original tweet; our rationale is that individual errors made by the classifier are likely to cancel out in the aggregation step.
Ranked #1 on Tweet-Reply Sentiment Analysis on RETWEET
Our verification system is able to identify whether two frontal chest X-ray images are from the same person with an AUC of 0. 9940 and a classification accuracy of 95. 55%.
In this work, we propose to mitigate the class-imbalance between the calving front class and the non-calving front class by reformulating the segmentation problem into a pixel-wise regression task.
Moreover, we propose an improvement to the distance map-based binary cross-entropy (BCE) loss function.
Supervised machine learning requires a large amount of labeled data to achieve proper test results.
An essential climate variable to determine the tidewater glacier status is the location of the calving front position and the separation of seasonal variability from long-term trends.
(2) To improve the already strong results further, we created a small dataset (ClassArch) consisting of ancient Greek vase paintings from the 6-5th century BCE with person and pose annotations.
Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants.
In particular, we investigate the performance of large-scale retrieval of historical document fragments in terms of style and writer identification.
1 code implementation • 30 Sep 2020 • Mathis Hoffmann, Claudia Buerhop-Lutz, Luca Reeb, Tobias Pickel, Thilo Winkler, Bernd Doll, Tobias Würfl, Ian Marius Peters, Christoph Brabec, Andreas Maier, Vincent Christlein
However, knowledge of the power at maximum power point is important as well, since drops in the power of a single module can affect the performance of an entire string.
These compositions are useful in analyzing the interactions in an image to study artists and their artworks.
In classification, notarial instruments are distinguished from other documents, while the notary sign is separated from the certificate in the segmentation task.
The type used to print an early modern book can give scholars valuable information about the time and place of its production as well as its producer.
We show that a re-ranking step based on k-reciprocal nearest neighbor relationships is advantageous for writer identification, even if only a few samples per writer are available.
We present experiments and analysis on three different models and show that the model trained on domain related data gives the best performance for recognizing character.
Then, a method for online handwriting synthesis is used to produce a new realistic-looking text primed with the online input sequence.
To this end, we apply normalized Lp normalization to aggregate the activation maps into single scores for classification.
This competition investigates the performance of large-scale retrieval of historical document images based on writing style.
We compare our method to the state of the art and show that it is superior in presence of perspective distortion while the performance on images, where the module is roughly coplanar to the detector, is similar to the reference method.
Both approaches are trained on 1, 968 cells extracted from high resolution EL intensity images of mono- and polycrystalline PV modules.
1 code implementation • 28 Jun 2018 • Maximilian Seitzer, Guang Yang, Jo Schlemper, Ozan Oktay, Tobias Würfl, Vincent Christlein, Tom Wong, Raad Mohiaddin, David Firmin, Jennifer Keegan, Daniel Rueckert, Andreas Maier
In addition, we introduce a semantic interpretability score, measuring the visibility of the region of interest in both ground truth and reconstructed images, which allows us to objectively quantify the usefulness of the image quality for image post-processing and analysis.
Embedding data into vector spaces is a very popular strategy of pattern recognition methods.
In this work, we propose an image processing pipeline that operates on hyperspectral images to separate such layers.
Our model has high sampling efficiency and is easily applied to any probability distribution, without the need of further analytical or numerical calculations.
In this work, we conducted a survey on different registration algorithms and investigated their suitability for hyperspectral historical image registration applications.
High-resolution imaging has delivered new prospects for detecting the material composition and structure of cultural treasures.
The focus lies on the ICDAR17 competition dataset on historical document writer identification (Historical-WI).