Michal Haindl - Academia.edu (original) (raw)

Papers by Michal Haindl

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.

Lecture notes in electrical engineering, 2024

Lecture Notes in Computer Science, 2006

Computer Science Research Notes, Jul 1, 2023

Springer eBooks, 2007

... 13 David Windridge, Vadim Mottl, Alexander Tatarchuk, and Andrey Eliseyev Kernel Combination ... more ... 13 David Windridge, Vadim Mottl, Alexander Tatarchuk, and Andrey Eliseyev Kernel Combination Versus Classiﬁer Combination ... 62 Sarunas Raudys, Omer Kaan Baykan, Ahmet Babalik, VitalijDenisov, and Antanas Andrius Bielskis Multiple Classiﬁer Methods for Oﬄine ...

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.

A new unsupervised multispectral texture segmentation method with unknown number of classes is pr... more A new unsupervised multispectral texture segmentation method with unknown number of classes is presented. Mul-tispectral texture mosaics are locally represented by four causal multispectral random field models recursively evalu-ated for each pixel. The segmentation algorithm is ...

Pattern Recognition Letters, Jul 1, 2019

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.

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

Pattern Recognition, Sep 1, 2016

Lecture Notes in Computer Science, 2022

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.

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