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Improving Radial Lens Distortion Correction Using Secondary Learning Task


Sports image analysis is one of the domains that has significantly benefited from advances in computer vision. With the growing adoption of machine learning methods new products and tools have emerged that make sports image analysis both accurate and accessible for professional athletes, amateur and junior athletes, and fans all around the world. Sports images serve as crucial visual records, aiding in post-game or live analysis and decision-making. However, very often they are affected by radial distortion that hinders their accurate interpretation and utilization.

We propose a deep-learning regression-based method to rectify images containing radial lens distortion, that operates on single independent images. Our method hase been trained on the football domain dataset, and has been fine-tuned specifically for images containing small radial distortion, which is more difficult to rectify accurately. Compared to other deep-learning radial distortion correction methods, we introduce the following contributions:

  • Secondary learning task that learns useful distortion features on random combinations of sample pairs, prevents overfitting, and encourages the feature extractor to learn more general distortion features.
  • Penalty term that encourages better accuracy on low-distortion images

The official GitHub repository for our method can be found at : … URL …

The best trained model and training configuration can be downloaded here – improving-radial-best.tar.gz



We have performed a quantitative evaluation of our proposed method on the validation set of the Football360 validation subset, and compared our method with 3 other methods. Our method outperformed all other evaluated methods on all metrics by a significant margin.

Janos and Benesova (2023)0.85223.075 dB0.0172
Li et al. (2019)0.41710.919 dB1.431
Santana-Cendres et al. (2016)0.71421.647 dB0.1132
Our method0.93330.098 dB0.0088

We have performed a qualitative evaluation of our method on the World Cup 2014 dataset. The WorldCup 2014 images have not been used during the training of neither of the evaluated methods.

Input imagesCorrected by our methodJanos and Benesova (2023)Li (2019)Santana-Cedres (2016)




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Football360 – Introducing a New Dataset for Camera Calibration in Sports Domain


The aim of this dataset is to aid the development and evaluation of computer vision algorithms, primarily the radial distortion correction algorithms, in the sports domain.

This dataset contains 268 panorama images, and was created using the PANONO panoramic camera in 3 football arenas in Slovakia. Each arena was covered from numerous locations on all levels of the tribune, and broadcast camera platforms. The images capture regular football game, pitch maintenance, low/challenging lighting conditions, day and night situations.

ArenaNumber of images
Dunajská Streda53

You can download the raw images from the following link: football360-raw.tar.gz (7.7 GB)

There is also an official GitHub repository available at:


Raw Images

Raw images are stored as 16384×8192 JPG, they are the direct result of the PANONO stitching service.


Exported Sets

There are four exported datasets available for direct download and use. Each dataset contains a collection of images (data) distorted with known distortion coefficients (labels).

Set namePurposeImagesPresetDownload Link
ATraining30,000setA.jsonfootball360-setA.h5 (10.5 GB)
BTraining100,000setB.jsonfootball360-setB.h5 (35.2 GB)
CTraining300,000setC.jsonfootball360-setC.h5 (105.5 GB)
VValidation10,000setV.jsonfootball360-setV.h5 (3.5 GB)

You can see an example of how to load and use these files in a linked notebook –

Distorted ImageCorrected Image


Igor Janos

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Explainable 3D convolutional neural network using GMM encoding

Abstract:     The aim of this paper is to propose a novel method to explain, interpret, and support the decision-making process of deep Convolutional Neural Network (CNN). This is achieved by analyzing neuron activations of trained 3D-CNN on selected layers via Gaussian Mixture Model (GMM)   …  more


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Segmentation of anatomical organs in medical data – Master Thesis : Bc. Martin Tamajka

Download: Master Thesis-  Martin Tamajka: Segmentation of anatomical organs in medical data


2016, May
Medical image segmentation is an important part of medical practice. Primarily as far as radiologists are concerned it simplifies their everyday tasks and allows them to use their time more effective, because in most cases radiologists only have a certain amount of time they can spend examining patient’s data. Computer aided diagnosis is also a powerful instrument in elimination of possible human failure.
In this work, we propose a novel approach to human organs segmentation. We primarily concentrate on segmentation of human brain from MR volume. Our method is based on oversegmenting 3D volume to supervoxels using SLIC algorithm. Individual supervoxels are described by features based on intensity distribution of contained voxels and on position within the brain. Supervoxels are classified by neural networks which are trained to classify supervoxels to individual tissues. In order to give our method additional precision, we use information about the shape and inner structure of the organ. In general we propose a 6-step segmentation method based on classification.
We compared our results with those of state-of-the-art methods and we can conclude that the results are clearly comparable.
Apart from the global focus of this thesis, our goal is to apply engineering skills and best practices to implement proposed method and necessary tools in such a way that they can be easily extended and maintained in the future.

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Automatic Music Transcription using WaveNet

Deep generative models such as WaveNet are surprisingly good at various modelling tasks. We exploit the modelling capacity of WaveNet architecture in a setup that is quite different from the original generative case: for feature extraction and pattern recognition in sake of polyphonic music transcription. The model is trained end-to-end to perform the underlying task of multiple fundamental frequency estimation by processing raw waveforms of digital audio signal.   …  more

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Exploring Visual Saliency of Real Objects at Different Depths

Depth cues are important aspects that influence the visual saliency of objects around us. However, the depth aspect and its quantified impact on the visual saliency has not yet been thoroughly examined in real environments. We designed and carried out an experimental study to examine the influence of the depth cues on the visual saliency of the objects at the scene. The experimental study took place with 28 participants under laboratory conditions with the objects in various depth configurations at the real scene. Visual attention data were measured by the wearable eye-tracking glasses. .. more

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Color Saliency

Color is a fundamental component of visual attention. Saliency is usually associated with color contrasts. Besides this bottom-up perspective, some recent works indicate that psychological aspects should be considered too. However, relatively little research has been done on the potential impacts of color psychology on attention. To our best knowledge, a publicly available fixation dataset specialized in color features does not exist. We, therefore, conducted a novel eye-tracking experiment with color stimuli. We studied  fixations of 15 participants to find out whether color differences can reliably model color saliency or particular colors are preferably fixated regardless of scene content, i.e. color prior. … more


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Effects of individual’s emotions on saliency and visual search

Patrik Polatsek, Miroslav Laco, Šimon Dekrét, Wanda Benesova, Martina Baránková, Bronislava Strnádelová, Jana Koróniová, Mária Gablíková


While psychological studies have confirmed a connection between emotional stimuli and visual attention, there is a lack of evidence, how much influence individual’s mood has on visual information processing of emotionally neutral stimuli. In contrast to prior studies, we explored if bottom-up low-level saliency could be affected by positive mood. We therefore induced positive or neutral emotions in 10 subjects using autobiographical memories during free-viewing, memorizing the image content and three visual search tasks. We explored differences in human gaze behavior between both emotions and relate their fixations with bottom-up saliency predicted by a traditional computational model. We observed that positive emotions produce a stronger saliency effect only during free exploration of valence-neutral stimuli. However, the opposite effect was observed during task-based analysis. We also found that tasks could be solved less efficiently when experiencing a positive mood and therefore, we suggest that it rather distracts users from a task.

download: saliency-emotions

Please cite this paper if you use the dataset:

Polatsek, P., Laco, M., Dekrét, Š., Benesova, W., Baránková, M., Strnádelová, B., Koróniová, J., & Gablíková, M. (2019)

Effects of individual’s emotions on saliency and visual search

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Computational Models of Shape Saliency

Patrik Polatsek, Marek Jakab, Wanda Benesova, Matej Kužma

Abstract. Computational models predicting stimulus-driven human visual attention usually incorporate simple visual features, such as intensity, color and orientation. However, saliency of shapes and their contour segments influence attention too. Therefore, we built 30 own shape saliency models based on existing shape representation and matching techniques and compared them with 5 existing saliency methods. Since available fixation datasets were usually recorded on natural scenes where various factors of attention are present, we performed a novel eye-tracking experiment that primarily focuses on shape and contour saliency. Fixations from 47 participants who looked at silhouettes of abstract and realworld objects were used to evaluate the accuracy of proposed saliency models and investigate which shape properties are most attentive. The results showed that visual attention integrates local contour saliency, saliency of global shape features and shape dissimilarities. Fixation data also showed that intensity and orientation contrasts play an important role in shape perception. We found that humans tend to fixate first irregular geometrical shapes and objects whose similarity to a circle is different from other objects.

shapeSal dataset contains an extended version of this eye-tracking experiment including images and fixation data (73 participants, 158 scenes).

download: [V2.0; update: 25.3.2019]

Please cite this paper if you use the dataset:

Polatsek, P., Jakab, M., Benesova, W., & Kužma, M. (2019)
Computational Models of Shape Saliency
11th International Conference on Machine Vision (ICMV 2018) (Vol. 11041)
International Society for Optics and Photonics

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Exploring Visual Attention and Saliency Modeling for Task-Based Visual Analysis

Patrik Polatsek, Manuela Waldner, Ivan Viola, Peter Kapec, Wanda Benesova

Abstract. Memory, visual attention, and perception play a critical role in the design of visualizations. The way users observe a visualization is affected by salient stimuli in a scene as well as by domain knowledge, interest, and the task. While recent saliency models manage to predict the users’ visual attention in visualizations during exploratory analysis, there is little evidence of how much influence bottom-up saliency has on task-based visual analysis. Therefore, we performed an eye-tracking study with 47 users to determine the users’ path of attention when solving three low-level analytical tasks using 30 different charts from the MASSVIS database. We also compared our task-based eye-tracking data to the data from the original memorability experiment by Borkin et al.. We found that solving a task leads to more consistent viewing patterns compared to exploratory visual analysis. However, bottom-up saliency of visualization has negligible influence on users’ fixations and task efficiency when performing a low-level analytical task. Also, the efficiency of visual search for an extreme target data point is barely influenced by the target’s bottom-up saliency. Therefore, we conclude that bottom-up saliency models tailored toward information visualization are not suitable for predicting visual attention when performing task-based visual analysis. We discuss potential reasons and suggest extensions to visual attention models to better account for task-based visual analysis.

TASKVIS dataset contains eye-tracking data from this task-based visual analysis experiment.


Please cite this paper if you use the dataset:

Polatsek, P., Waldner, M., Viola, I., Kapec, P., & Benesova, W. (2018)
Exploring Visual Attention and Saliency Modeling for Task-Based Visual Analysis
Computers & Graphics, 72, 26-38

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Automatic brain segmentation method based on supervoxels

Martin Tamajka, Wanda Benesova


In this work, we present a fully automatic brain segmentation method based on supervoxels (ABSOS). We propose novel features used for classification, that are based on distance and angle in different planes between supervoxel and brain center. These novel features are combined with other prominent features. The presented method is based on machine learning and incorporates also a skull stripping (cranium removing) in the preprocessing step. Neural network – multilayer perceptron (MLP) was trained for the classification process. In this paper we also present thorough analysis, which supports choice of rather small supervoxels, preferring homogeneity over compactness, and value of intensity threshold parameter used in preprocessing for skull stripping. In order to decrease computational complexity and increase segmentation performance we incorporate prior knowledge of typical background intensities acquired in analysis of subjects.

Published in:

2016 International Conference on Systems, Signals and Image Processing (IWSSIP)

Date of Conference:

23-25 May 2016