18,978 research outputs found
Labelled Dataset of Retinal Images for Glaucoma detection
No full textFundus photography is a viable option for glaucoma population screening. In order to facilitate the development of computer-aided glaucoma detection systems, we publish this annotation dataset that contains manual annotations of glaucoma features for seven public fundus image data sets. All manual annotations are made by a specialised ophthalmologist. For each of the fundus images in the seven fundus datasets, the upper, the bottom, the left and the right boundary coordinates of the optic disc and the cup are stored in a .mat file with the corresponding fundus image name.
The seven public fundus image data sets are:
CHASEDB (https://blogs.kingston.ac.uk/retinal/chasedb1/),
Diaretdb1_v_1_1 (https://www.it.lut.fi/project/imageret/diaretdb1/),
DRINSHTI (http://cvit.iiit.ac.in/projects/mip/drishti-gs/mip-dataset2/Home.php),
DRIONS-DB (http://www.ia.uned.es/~ejcarmona/DRIONS-DB.html),
DRIVE (https://www.isi.uu.nl/Research/Databases/DRIVE/),
HRF (https://www5.cs.fau.de/research/data/fundus-images/), and
Messidor (http://www.adcis.net/en/Download-Third-Party/Messidor.html).
Researchers are encouraged to use this set to train or validate their systems for automatic glaucoma detection. When you use this set, please cite our published paper:
J. Guo, G. Azzopardi, C. Shi, N. M. Jansonius and N. Petkov, "Automatic Determination of Vertical Cup-to-Disc Ratio in Retinal Fundus Images for Glaucoma Screening," in IEEE Access, vol. 7, pp. 8527-8541, 2019, doi: 10.1109/ACCESS.2018.2890544.
</ul
Replication Data for: Injury Prediction In Competitive Runners With Machine Learning
No full textThe data set consists of a detailed training log from a Dutch high-level running team over a period of seven years (2012-2019). We included the middle and long distance runners of the team, that is, those competing on distances between the 800 meters and the marathon. This design decision is motivated by the fact that these groups have strong endurance based components in their training, making their training regimes comparable. The head coach of the team did not change during the years of data collection. The data set contains samples from 74 runners, of whom 27 are women and 47 are men. At the moment of data collection, they had been in the team for an average of 3.7 years. Most athletes competed on a national level, and some also on an international level. The study was conducted according to the requirements of the Declaration of Helsinki, and was approved by the ethics committee of the second author’s institution (research code: PSY-1920-S-0007)
Detection of illicit accounts over the Ethereum blockchain
No full textThe recent technological advent of cryptocurrencies and their respective benefits have been shrouded with a number of illegal activities operating over the network such as money laundering, bribery, phishing, fraud, among others. In this work we focus on the Ethereum network, which has seen over 400 million transactions since its inception. Using 2179 accounts flagged by the Ethereum community for their illegal activity coupled with 2502 normal accounts, we seek to detect illicit accounts based on their transaction history using the XGBoost classifier. Using 10 fold cross-validation, XGBoost achieved an average accuracy of 0.963 ( ± 0.006) with an average AUC of 0.994 ( ± 0.0007). The top three features with the largest impact on the final model output were established to be ‘Time diff between first and last (Mins)’, ‘Total Ether balance’ and ‘Min value received’. Based on the results we conclude that the proposed approach is highly effective in detecting illicit accounts over the Ethereum network. Our contribution is multi-faceted; firstly, we propose an effective method to detect illicit accounts over the Ethereum network; secondly, we provide insights about the most important features; and thirdly, we publish the compiled data set as a benchmark for future related works
FaiResGAN: Fair and robust blind face restoration with biometrics preservation
Full text linkModern computer vision technologies enable systems to detect, recognize, and analyze facial features, but challenges arise when images are noisy, blurred, or low quality. Blind face restoration, which aims to recover high-quality facial images without prior knowledge of degradation, addresses this issue. In this paper, we introduce Fair Restoration GAN (FaiResGAN), a novel Generative Adversarial Network (GAN) designed to balance face restoration with the preservation of soft biometrics (identity, ethnicity, age, and gender). Our model incorporates a pseudo-random batch composition algorithm to promote fairness and mitigate bias, alongside a realistic degradation model simulating corruptions typical in surveillance images. Experimental results show that FaiResGAN outperforms state-of-the-art blind face restoration methods, both quantitatively and qualitatively. A user study involving 40 participants showed that FaiResGAN-restored images were preferred by 70% of users. Additionally, tests on VGGFace2, UTKFace, and FairFace datasets demonstrate FaiResGAN's superior performance in preserving soft biometric attributes and ensuring fair restoration across different genders and ethnicities
fseval - A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms
No full textThe fseval Python package allows benchmarking Feature Selection and Feature Ranking algorithms on a large scale, and facilitates the comparison of multiple algorithms in a systematic way. In particular, fseval enables users to run experiments in parallel and distributed over multiple machines, and export the results to an SQL database. The execution of an experiment can be fully determined by a configuration file, which means the experiment results can be reproduced easily, given only the configuration file. fseval has high test coverage, continuous integration, and rich documentation. The package is open source and can be installed through PyPI
Biometric Recognition of African Clawed Frogs
No full textThe dataset of African Claw Frogs consists of 1,647 images for 160 classes, with an average of 10.3 images per class. The frogs are housed in the University of Groningen, following animal welfare laws and approved by the relevant authorities. The frogs are divided into groups of ten to twenty individuals in aquatic tanks. To capture the images, the frogs are placed individually in transparent containers filled with water on a white surface. The setup takes into account the frogs' pigment changes by housing them in transparent boxes on a yellow surface for approximately 24 hours before photography. The pictures are taken using at least four different smartphones per frog, ensuring consistency in angle and distance from the camera to the container. The dataset includes images taken at different times of day, on different days, and with cameras of different smartphones to enhance the model's robustness. The dataset also provides information on the distribution of samples per frog and the number of photos taken per phone.
This dataset has been used to develop an automatic pattern recognition system that is able to determine the individual frogs based on the analysis of their patterns extracted form the iamges. The developed algorithms are very effective and achieve more than 99% recognition rate
Recognition of Holstein Cattle with Thermal and RGB images
No full textThis data set was collected from the Dairy Campus in Leeuwarden (The Netherlands) with a FLIR E6 thermal camera over a period of 9 days. It consists of 3694 images of 383, with each cow represented with an average of 9 images. Each snapshot created two images; 1) RGB and ii) Temperature. The image filenames are in the format [cow_id-4 digits]_[day no-1 digit]_[counter-1 digit].
The timestamp.xlsx file indicates the day number (day 1 to day 9) of when an image in the data set was collected. This allows to design and run leave-one day-out cross validation, the same as we did in our paper.
Here is the link to the scripts that reproduce the results reported in the paper, and the following is the link to the GitHub repository that contains all the script
Fusion of domain-specific and trainable features for gender recognition from face images
Full text linkThe popularity and the appeal of systems which are able to automatically determine the gender from face images is growing rapidly. Such a great interest arises from the wide variety of applications, especially in the fields of retail and video surveillance. In recent years there have been several attempts to address this challenge, but a definitive solution has not yet been found. In this paper we propose a novel approach that fuses domain-specific and trainable features to recognize the gender from face images. In particular, we use the SURF descriptors extracted from 51 facial landmarks related to eyes, nose and mouth as domain dependent features, and the COSFIRE filters as trainable features. The proposed approach turns out to be very robust with respect to the well known face variations, including different poses, expressions and illumination conditions. It achieves state-of-the-art recognition rates on the GENDER- FERET (94.7%) and on the LFW (99.4%) datasets, which are two of the most popular benchmarks for gender recognition. We further evaluated the method on a new dataset acquired in real scenarios, the UNISA- Public, recently made publicly available. It consists of 206 training (144 male, 62 female) and 200 test (139 male, 61 female) images that are acquired with a real-time indoor camera capturing people in regular walking motion. Such experiment has the aim to assess the capability of the algorithm to deal with face images extracted from videos, which are definitely more challenging than the still images available in the standard datasets. Also for this dataset we achieved a high recognition rate of 91.5%, that confirms the generalization capabilities of the proposed approach. Of the two types of features, the trainable COSFIRE filters are the most effective and, given their trainable character, they can be applied in any visual pattern recognition problem
Holstein Cattle Recognition
No full textThe data set consists of 1237 pairs of thermal and RGB (640 x 320 pixels and 320 x 240 pixels) images with 136 classes (i.e. 136 different cows) with a mean of 9 images per class/cow. Each folder name is the collar id of the cattle and contains its respective thermal and RGB images.
The data set was collected at the Dairy Campus in Leeuwarden, The Netherlands. In order to explore the temperature values of the thermal images, FLIR tools can be used by installing the software (https://www.flir.com/products/flir-tools/).
Due to the large number of files the data set is provided in 5 zip files. The first 4 zip files contain the data of 30 cows each and the 5th zip file contains the data of 16 cows
- …
