Michal Haindl

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Papers by Michal Haindl

Survival Modeling of Disease Consequences and Post-disease Syndromes

MICAD 2023, 2024

We present a survival model for human maladies, which leave victims with permanent health damages... more We present a survival model for human maladies, which leave victims with permanent health damages requiring lifelong medical observation and treatment. The model allows national health authorities to prepare sufficient medical specialists with adequate capacity in specialized clinics, vaccinations, spas, or rehabilitation facilities. We test the model on Czech Polio (Poliomyelitis Anterior Acuta) victims' data. COVID-19 or Long Covid-19 and the treatment of their wide range of ongoing health problems, where these conditions can last weeks, months, or years, can benefit from Polio and COVID-19 RNA virus similarities.

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Survival Modeling of Disease Consequences and Post-disease Syndromes

Lecture notes in electrical engineering, 2024

Texture Quality Criteria Comparison

Optimal Activation Function for Anisotropic BRDF Modeling

BRDF(双方向反射分布関数)テクセルを用いたBTF(双方向テクスチャ関数)モデリング

Lecture Notes in Computer Science, 2006

Texture Spectral Similarity Criteria Comparison

Computer Science Research Notes, Jul 1, 2023

Criteria capable of texture spectral similarity evaluation are presented and compared. From the f... more Criteria capable of texture spectral similarity evaluation are presented and compared. From the fifteen evaluated criteria, only four criteria guarantee zero or minimal spectral ranking errors. Such criteria can support texture modeling algorithms by comparing the modeled texture with corresponding synthetic simulations. Another possible application is the development of texture retrieval, classification, or texture acquisition system. These criteria thoroughly test monotonicity and mutual correlation on specifically designed extensive monotonously degrading experiments.

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Multiple classifier systems : 7th International Workshop, MCS 2007, Prague, Czech Republic, May 23-25, 2007 : proceedings

Springer eBooks, 2007

... 13 David Windridge, Vadim Mottl, Alexander Tatarchuk, and Andrey Eliseyev Kernel Combination ... more

Texture Segmentation Benchmark

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021

The Prague texture segmentation data-generator and benchmark (mosaic.utia.cas.cz) is a web-based ... more The Prague texture segmentation data-generator and benchmark (mosaic.utia.cas.cz) is a web-based service designed to mutually compare and rank (recently nearly 200) different static and dynamic texture and image segmenters, to find optimal parametrization of a segmenter and support the development of new segmentation and classification methods. The benchmark verifies segmenter performance characteristics on potentially unlimited monospectral, multispectral, satellite, and bidirectional texture function (BTF) data using an extensive set of over forty prevalent criteria. It also enables us to test for noise robustness and scale, rotation, or illumination invariance. It can be used in other applications, such as feature selection, image compression, query by pictorial example, etc.The benchmark's functionalities are demonstrated in evaluating several examples of leading previously published unsupervised and supervised image segmentation algorithms. However, they are used to illustrate the benchmark functionality and not review the recent image segmentation state-of-the-art.

Unsupervised Texture Segmentation Using Multispectral Modelling Approach

A new unsupervised multispectral texture segmentation method with unknown number of classes is pr... more

Bark recognition using novel rotationally invariant multispectral textural features

Pattern Recognition Letters, Jul 1, 2019

Bark recognition using novel rotationally invariant multispectral textural features

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Texture fidelity criterion

Visual texture fidelity evaluation is important but still unsolved problem. Evaluation of how wel... more Visual texture fidelity evaluation is important but still unsolved problem. Evaluation of how well various texture models conform with human visual perception of their original measured pattern is required not only for assessing the visual dissimilarities between a model output and the original measured texture, but also for optimal settings of model parameters, for fair comparison of distinct models, or visual scene understanding. We propose a novel texture fidelity criterion based on the fully multi-spectral generative underlying Markovian texture model, which correlates well with human texture quality ranking verified on the texture fidelity benchmark.

Unsupervised Texture Segmentation Using Multiple Segmenters Strategy

Springer eBooks, Jun 21, 2007

A novel unsupervised multi-spectral multiple-segmenter texture segmentation method with unknown n... more A novel unsupervised multi-spectral multiple-segmenter texture segmentation method with unknown number of classes is presented. The unsupervised segmenter is based on a combination of several unsupervised segmentation results, each in different resolution, using the sum rule. Multi-spectral texture mosaics are locally represented by four causal multi-spectral random field models recursively evaluated for each pixel. The single-resolution segmentation part of the

A competition in unsupervised color image segmentation

Pattern Recognition, Sep 1, 2016

A competition in unsupervised color image segmentation took place in conjunction with the 22nd In... more A competition in unsupervised color image segmentation took place in conjunction with the 22nd International Conference on Pattern Recognition (ICPR 2014). It aimed to promote evaluation of unsupervised color image segmentation algorithms using publicly available data sets, and to allow for any subsequent methods to be easily evaluated and compared with the results of the contested methods under identical conditions. Our comparison of different methods is based on the standard methodology of performance assessment using an on-line verification server. We present in this paper the evaluation of the top six results submitted to the ICPR 2014 contest in unsupervised color image segmentation and compare them with 11 other state-of-the-art unsupervised image segmenters.

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Bidirectional Texture Function Modeling

Springer eBooks, 2023

A Psychophysical Evaluation of Texture

Delivering digitally a realistic appearance of materials is one of the most difficult tasks of co... more Delivering digitally a realistic appearance of materials is one of the most difficult tasks of computer vision. Accurate representation of surface texture can be obtained by means of view-and illuminationdependent textures. However, this kind of appearance representation produces massive datasets so their compression is inevitable. For optimal visual performance of compression methods, their parameters should be tuned to a specific material. We propose a set of statistical descriptors motivated

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BRDF Anisotropy Criterion

Lecture Notes in Computer Science, 2022

Image Understanding - Introduction to the Special Theme

Ercim News, 2013

BTF Modelling Using BRDF Texels

Springer eBooks, 2006

The highest fidelity representations of realistic real-world materials currently used comprise Bi... more The highest fidelity representations of realistic real-world materials currently used comprise Bidirectional Texture Functions (BTF). The BTF is a six dimensional function depending on view and illumination directions as well as on planar texture coordinates. The huge size of such measurements, typically in the form of thousands of images covering all possible combinations of illumination and viewing angles, has prohibited their practical exploitation and obviously some compression and modelling method of these enormous BTF data spaces is inevitable. The proposed approach combines BTF spatial clustering with cluster index modelling by means of an efficient Markov random field model. This method allows to generate seamless cluster index of arbitrary size to cover large virtual 3D objects surfaces. The method represents original BTF data using a set of local spatially dependent Bidirectional Reflectance Distribution Function (BRDF) values which are combined according to synthesised cluster index and illumination / viewing directions. BTF data compression using this method is about 1 : 100 and their synthesis is very fast.

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Coniferous Trees Needles-Based Taxonomy Classification

2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)

This paper introduces multispectral rotationally in-variant textural features of the Markovian ty... more This paper introduces multispectral rotationally in-variant textural features of the Markovian type applied for the effective coniferous tree needles categorization. Presented texture features are inferred from the descriptive multispectral spiral wide-sense Markov model. Unlike the alternative texture recognition methods based on various gray-scale discriminative textural descriptions, we take advantage of the needles texture representation, which is fully descriptive multispectral and rotationally invariant.The presented method achieves high accuracy for needles recognition. Thus it can be used for reliable coniferous tree taxon classification. Our classifier is tested on the open source needles database Aff, which contains 716 high-resolution images from 11 diverse coniferous tree species.

Melanoma Recognition

Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2022

Early and reliable melanoma detection is one of today's significant challenges for dermatologists... more Early and reliable melanoma detection is one of today's significant challenges for dermatologists to allow successful cancer treatment. This paper introduces multispectral rotationally invariant textural features of the Markovian type applied to effective skin cancerous lesions classification. Presented texture features are inferred from the descriptive multispectral circular wide-sense Markov model. Unlike the alternative texturebased recognition methods, mainly using different discriminative textural descriptions, our textural representation is fully descriptive multispectral and rotationally invariant. The presented method achieves high accuracy for skin lesion categorization. We tested our classifier on the open-source dermoscopic ISIC database, containing 23 901 benign or malignant lesions images, where the classifier outperformed several deep neural network alternatives while using smaller training data.

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