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arXiv 2024年

作者： Bogensperger, Lea Narnhofer, Dominik Falk, Alexander Schindler, Konrad Pock, Thomas Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Photogrammetry and Remote Sensing ETH Zurich Zurich Switzerland Institute of Computer Graphics and Vision Graz University of Technology Austria Photogrammetry and Remote Sensing ETH Zurich Switzerland

Medical image segmentation plays an important role in accurately identifying and isolating regions of interest within medical images. Generative approaches are particularly effective in modeling the statistical properties of segmentation masks that are closely related to the respective structures. In this work we introduce FlowSDF, an image-guided conditional flow matching framework, designed to represent the signed distance function (SDF), and, in turn, to represent an implicit distribution of segmentation masks. The advantage of leveraging the SDF is a more natural distortion when compared to that of binary masks. Through the learning of a vector field associated with the probability path of conditional SDF distributions, our framework enables accurate sampling of segmentation masks and the computation of relevant statistical measures. This probabilistic approach also facilitates the generation of uncertainty maps represented by the variance, thereby supporting enhanced robustness in prediction and further analysis. We qualitatively and quantitatively illustrate competitive performance of the proposed method on a public nuclei and gland segmentation data set, highlighting its utility in medical image segmentation applications. © 2024, CC BY.

关键词： Image segmentation

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Learned Discretization Schemes for the Second-Order Total Generalized Variation 9th

Learned Discretization Schemes for the Second-Order Total ...

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9th International Conference on Scale Space and Variational Methods in computer vision, SSVM 2023

作者： Bogensperger, Lea Chambolle, Antonin Effland, Alexander Pock, Thomas Institute of Computer Graphics and Vision Graz University of Technology Graz Austria CEREMADE CNRS & Université Paris-Dauphine PSL Paris France Institute for Applied Mathematics University of Bonn Bonn Germany

ISBN: (纸本)9783031319747

The total generalized variation extends the total variation by incorporating higher-order smoothness. Thus, it can also suffer from similar discretization issues related to isotropy. Inspired by the success of novel discretization schemes of the total variation, there has been recent work to improve the second-order total generalized variation discretization, based on the same design idea. In this work, we propose to extend this to a general discretization scheme based on interpolation filters, for which we prove variational consistency. We then describe how to learn these interpolation filters to optimize the discretization for various imaging applications. We illustrate the performance of the method on a synthetic data set as well as for natural image denoising. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image denoising

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Domain-specific data characteristics: A study on meaning of stylometric sub-concepts and in-class imbalance

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Procedia computer Science 2024年 246卷 4421-4430页

作者： Urszula Stańczyk Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

In the context of data imbalance probably the most investigated problem is imbalance of classes, as learning from the data with this characteristic makes detection of existing patterns for all classes more difficult. However, other problems related to imbalance also exists and the paper addresses such cases where classes are balanced, but there is in-class imbalance. Such imbalance can be caused by uneven representation of sub-concepts. When there is a noticeable difference between the numbers of samples belonging to sub-concepts, this can turn the under-represented sub-concepts into disjuncts. Data irregularities of this type can hinder recognition, therefore actions are typically taken to restore balance. In the investigations described, the issue was studied in the stylometric domain and various classifiers were applied to the data that was balanced, then imbalanced, and finally with restored balance. The experiments show that the specifics of the domain of application can put its own mark on the data which is difficult to overcome by standard processing such as under- or oversampling. Observed dependence on a learner and dataset makes the issue even more complex and layered, and shows the need for deeper studies.

关键词： Attribute domain Data irregularities Sub-concept Disjunct Pattern recognition Stylometry Authorship attribution

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Filtering Decision Rules Driven by Sequential Forward and Backward Selection of Attributes: An Illustrative Example in Stylometric Domain 18

Filtering Decision Rules Driven by Sequential Forward and Ba...

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18th Conference on computer Science and Intelligence Systems, FedCSIS 2023

作者： Zielosko, Beata Stanczyk, Urszula Jablonski, Kamil Institute of Computer Science University of Silesia in Katowice Bedzínska 39 Sosnowiec41-200 Poland Department of Graphics Silesian University of Technology Computer Vision and Digital Systems Akademicka 2A Gliwice44-100 Poland

ISBN: (纸本)9788396744784

The paper presents investigations concerning the decision rule filtering process controlled by the estimated relevance of available attributes. In the conducted study, two search directions were used, sequential forward selection and sequential backward elimination. The steps of sequential search were governed by three rankings obtained for variables, all related to characteristics of data and rules that can be induced, as follows, (i) a ranking based on the weighting factor referring to the occurrence of attributes in generated decision reducts, (ii) the OneR ranking exploiting short rule properties, and (iii) the proposed ranking defined through the operation of greedy algorithm for rule induction. The three rankings were confronted and compared from the perspective of their usefulness for the selection of rules performed in the two directions and with two strategies for rule selection. The resulting sets of rules were analysed with respect to the properties of the constituent decision rules and from the point of performance for all constructed rule-based classifiers. Substantial experiments were carried out in the stylometric domain, treating the task of authorship attribution as classification. The results obtained indicate that for all three rankings and search paths it was possible to obtain a noticeable reduction of attributes while at least maintaining the power of inducers, at the same time improving characteristics of rule sets. © 2023 Polish Information Processing Society.

关键词： Information use

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A Cost-Effective Approach to Smooth A* Path Planning for Autonomous Vehicles

arXiv

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arXiv 2024年

作者： Schichler, Lukas Festl, Karin Solmaz, Selim Watzenig, Daniel Virtual Vehicle Research GmbH Graz Austria Institute of Computer Graphics and Vision TU Graz Austria

component Abstract—Path in the planning field of for automation wheeled mobile and _ robots intelligent is a critical transportation systems. Car-like vehicles, which have non-holonomic constraints on their movement capability impose additional requirements on the planned paths. Traditional path planning algorithms, such as A*, are widely used due to their simplicity and effectiveness in finding optimal paths in complex environments. However, these algorithms often do not consider vehicle grid actual dynamics,cells driving. mayresulting Specifically, stillinbepathstoo curvy a that path areor that infeasiblesharp minimizes forora car-likeimpractical the number vehiclefor of that pathtoa curvatureThis navigate not is smoothpaper only *** finds and drivable. a feasible andtheincorporatingneedBy path adaptingfor but a path also the a costplanningA* ensures algorithm function that solutionthat the for considers the smoothness of possible paths, we aim to bridge the gap between grid based path planning and smooth paths that motion are drivable primitives, by car-like pre-computed vehicles. The using proposed a ribbon method based leverages pathplanner motion primitives that produces guide smooth the A* paths algorithm of minimum in finding curvature. paths that The are optimal in terms of both length and curvature. With the proposed modification constraint to on have the a minimum A"algorithm, turning the radius planned much paths larger can than be the grid size. We demonstrate the effectiveness of the proposed In findspatha smooth a two-stage planninga grid-based path planning algorithmwithin path the and approach, inarea thedifferentribbon of first gridunstructuredbased the cells. modified path The planner resultingenvironments. A* algorithm creates paths are smooth with small curvatures independent of the orientation of the grid axes and even in presence of sharp obstacles. Copyright © 2024, The Authors. All rights reserved.

关键词： Cost functions

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Observations of data characteristics and irregularities through domain-oriented transformations of attributes

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Procedia computer Science 2024年 246卷 2694-2703页

作者： Urszula Stańczyk Grzegorz Baron Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The paper presents research dedicated to observations of relations between attribute properties and discretisation. In the investigations described, the gradually increasing sets of features were discretised by selected approaches, and several variants of data were constructed. The continuous, partially discrete, and completely translated datasets were explored by the chosen classifiers and their performance studied in the context of a number of discretised attributes, discretisation procedures, and the way of processing of features and datasets. The stylometric problem of authorship attribution was the machine learning task under study. The experimental results enable to observe closer the specificity of style-markers employed as characteristic features, and indicate conditions for efficient recognition of authorship. They can be extended to other application domains with similar characteristics.

关键词： Attribute domain Discretisation Data representation Data irregularities Pattern recognition Stylometry Authorship attribution

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Single Image LDR to HDR Conversion Using Conditional Diffusion

Single Image LDR to HDR Conversion Using Conditional Diffusi...

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IEEE International Conference on Image Processing

作者： Dwip Dalal Gautam Vashishtha Prajwal Singh Shanmuganathan Raman Computer Vision Imaging and Graphics Lab Indian Institute of Technology Gandhinagar India

Digital imaging aims to replicate realistic scenes, but Low Dynamic Range (LDR) cameras cannot represent the wide dynamic range of real scenes, resulting in under-/overexposed images. This paper presents a deep learning-based approach for recovering intricate details from shadows and highlights while reconstructing High Dynamic Range (HDR) images. We formulate the problem as an image-to-image (I2I) translation task and propose a conditional Denoising Diffusion Probabilistic Model (DDPM) based framework using classifier-free guidance. We incorporate a deep CNN-based autoencoder in our proposed framework to enhance the quality of the latent representation of the input LDR image used for conditioning. Moreover, we introduce a new loss function for LDR-HDR translation tasks, termed Exposure Loss. This loss helps direct gradients in the opposite direction of the saturation, further improving the results’ quality. By conducting comprehensive quantitative and qualitative experiments, we have effectively demonstrated the proficiency of our proposed method. The results indicate that a simple conditional diffusion-based method can replace the complex camera pipeline-based architectures.

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Single Image LDR to HDR Conversion using Conditional Diffusion

arXiv

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arXiv 2023年

作者： Dalal, Dwip Vashishtha, Gautam Singh, Prajwal Raman, Shanmuganathan Computer Vision Imaging and Graphics Lab Indian Institute of Technology Gandhinagar India

关键词： Cameras

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InfoSeg: Unsupervised Semantic Image Segmentation with Mutual Information Maximization 43rd

InfoSeg: Unsupervised Semantic Image Segmentation with Mutua...

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43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021

作者： Harb, Robert Knöbelreiter, Patrick Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

ISBN: (纸本)9783030926588

We propose a novel method for unsupervised semantic image segmentation based on mutual information maximization between local and global high-level image features. The core idea of our work is to leverage recent progress in self-supervised image representation learning. Representation learning methods compute a single high-level feature capturing an entire image. In contrast, we compute multiple high-level features, each capturing image segments of one particular semantic class. To this end, we propose a novel two-step learning procedure comprising a segmentation and a mutual information maximization step. In the first step, we segment images based on local and global features. In the second step, we maximize the mutual information between local features and high-level features of their respective class. For training, we provide solely unlabeled images and start from random network initialization. For quantitative and qualitative evaluation, we use established benchmarks, and COCO-Persons, whereby we introduce the latter in this paper as a challenging novel benchmark. InfoSeg significantly outperforms the current state-of-the-art, e.g., we achieve a relative increase of 26 % in the Pixel Accuracy metric on the COCO-Stuff dataset. © 2021, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Attribute Relevance and Discretisation in Knowledge Discovery: A Study in Stylometric Domain 23rd

Attribute Relevance and Discretisation in Knowledge Discov...

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23rd International Conference on Computational Science, ICCS 2023

作者： Stańczyk, Urszula Zielosko, Beata Baron, Grzegorz Department of Graphics Computer Vision and Digital Systems Silesian University of Technology Akademicka 2A Gliwice44-100 Poland Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 Sosnowiec41-200 Poland

ISBN: (纸本)9783031360206

The paper demonstrates the research methodology focused on observations of relations between attribute relevance, displayed by rankings, and discretisation. Instead of transforming all continuous attributes before data exploration, the variables were gradually processed, and the impact of such a change on the performance of a classifier was studied. Considerable experiments carried out on stylometric data illustrate that selective discretisation could be more advantageous to predictive accuracy than some uniform transformation of all features. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Metadata

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