Markus Seidl

Mobile phone web browsing: a study on usage and usability of the mobile web

Browsing the Web on mobile phones has finally hit the mass. The visualization of websites on latest mobile phone models comes close to what we are used from desktop computers. Tailoring websites for mobile phones seems to be not mandatory anymore. But still the small display size limits the user experience when browsing the web on these devices. As a result although access to the full web is reasonably well working a tendency to providing additional versions of mobile optimized versions of websites can be observed. This paper presents a multidimensional study where usage scenarios as well as the usability of mobile tailored compared to full websites were investigated. The results show clearly that users prefer and effectively do benefit from mobile optimized versions. However content providers sometimes do not understand the mobile scenarios in which their sites are used and consequently begin optimizing the functionality at the wrong end.

Automated petroglyph image segmentation with interactive classifier fusion

The number of high quality images of rock panels containing petroglyphs grows steadily. Different time-consuming manual methods to determine and document the exact shapes and spatial locations of petroglyphs on a panel have been carried out over decades. The first step for classification and retrieval of petroglyphs is the segmentation of the images. In this paper, we present and evaluate an automated approach to segment petroglyph images.

Detection and Classification of Petroglyphs in Gigapixel Images -- Preliminary Results

With the advances of digital photography, the number of high quality images of rock panels containing petroglyphs grows steadily. Different time-consuming manual methods to determine and document the exact shapes and spatial locations of petroglyphs on a panel have been carried out over decades. We aim at automated methods to a) segment rock images with petroglyphs, b) classify the petroglyphs and c) retrieve similar petroglyphs from different archives. In this short paper, we present an approach for the unsolved problem of rock art image segmentation. A first evaluation demonstrates promising results.

Interactive 3D Segmentation of Rock-Art by Enhanced Depth Maps and Gradient Preserving Regularization

Petroglyphs (rock engravings) have been pecked and engraved by humans into natural rock surfaces thousands of years ago and are among the oldest artifacts that document early human life and culture. Some of these rock engravings have survived until the present and serve today as a unique document of ancient human life. Since petroglyphs are pecked into the surface of natural rocks, they are threatened by environmental factors such as weather and erosion. To document and preserve these valuable artifacts of human history, the 3D digitization of rock surfaces has become a suitable approach due to the development of powerful 3D reconstruction techniques in recent years. The results of 3D reconstruction are huge 3D point clouds which represent the local surface geometry in high resolution. In this article, we present an automatic 3D segmentation approach that is able to extract rock engravings from reconstructed 3D surfaces. To solve this computationally complex problem, we transfer the task of segmentation to the image-space in order to efficiently perform segmentation. Adaptive learning is applied to realize interactive segmentation and a gradient preserving energy minimization assures smooth boundaries for the segmented figures. Our experiments demonstrate the efficiency and the strong segmentation capabilities of the approach. The precise segmentation of petroglyphs from 3D surfaces provides the foundation for compiling large petroglyph databases which can then be indexed and searched automatically.

Efficient image-space extraction and representation of 3D surface topography

Surface topography refers to the geometric micro-structure of a surface and defines its tactile characteristics (typically in the sub-millimeter range). High-resolution 3D scanning techniques developed recently enable the 3D reconstruction of surfaces including their surface topography. In this paper, we present an efficient image-space technique for the extraction of surface topography from high-resolution 3D reconstructions. Additionally, we filter noise and enhance topographic attributes to obtain an improved representation for subsequent topography classification. Comprehensive experiments show that our representation captures topographic attributes well and significantly improves classification performance compared to alternative 2D and 3D representations.

Multi-touch Rocks: Playing with Tangible Virtual Heritage in the Museum -- First User Tests

More than 50.000 petroglyphs are engraved in rock panels on the flanks of the UNESCO world heritage site Val Camonica (Northern Italy). The engravings are not always visible and are often on steep slopes on which it is forbidden to walk for conservation reasons. To overcome these problems, and to be able to transfer the rock art experience to other places, we designed a collaborative computer game for a multi-touch tabletop display. The game contains the image of a full rock panel and several mini games to be played on the panel. This short paper describes the game design as well as the interface and interaction design. We focus on the evaluation of the user interface as an important step in the user centered design approach. Consequently, we perform first user tests on the game in order to evaluate effectivity and efficiency of the user interface. The tests achieve largely good results.

Interactive segmentation of rock-art in high-resolution 3D reconstructions

Petroglyphs (rock engravings) are important artifacts for the documentation and analysis of early human life. Recent improvements in 3D scanning and 3D reconstruction enable the accurate 3D reconstruction of petroglyphs from rock surfaces at sub-millimeter resolution. To enable the indexing, matching, and recognition of petroglyphs in petroglyph databases, the shapes must first be segmented from the reconstructed rock surface. The absence of robust 3D segmentation methods for petroglyphs leaves a gap in the digital processing workflow. In this paper, we present a semi-automatic method for petroglyph segmentation for high-resolution 3D surface reconstructions. A comprehensive evaluation shows that our method is able to robustly segment petroglyphs with high accuracy and that the incorporation of 3D information is crucial to solve the segmentation problem. The presented method represents a major step towards the completion of a full 3D digital processing workflow of petroglyphs.

Topological Descriptors for 3D Surface Analysis

We investigate topological descriptors for 3D surface analysis, i.e. the classification of surfaces according to their geometric fine structure. On a dataset of high-resolution 3D surface reconstructions we compute persistence diagrams for a 2D cubical filtration. In the next step we investigate different topological descriptors and measure their ability to discriminate structurally different 3D surface patches. We evaluate their sensitivity to different parameters and compare the performance of the resulting topological descriptors to alternative non-topological descriptors. We present a comprehensive evaluation that shows that topological descriptors are i robust, ii yield state-of-the-art performance for the task of 3D surface analysis and iii improve classification performance when combined with non-topological descriptors.

A Study on Topological Descriptors for the Analysis of 3D Surface Texture

Abstract Methods from computational topology are becoming more and more popular in computer vision and have shown to improve the state-of-the-art in several tasks. In this paper, we investigate the applicability of topological descriptors in the context of 3D surface analysis for the classification of different surface textures. We present a comprehensive study on topological descriptors, investigate their robustness and expressiveness and compare them with state-of-the-art methods including Convolutional Neural Networks (CNNs). Results show that class-specific information is reflected well in topological descriptors. The investigated descriptors can directly compete with non-topological descriptors and capture complementary information. As a consequence they improve the state-of-the-art when combined with non-topological descriptors.

The 3D-Pitoti Dataset: A Dataset for high-resolution 3D Surface Segmentation

The development of powerful 3D scanning hardware and reconstruction algorithms has strongly promoted the generation of 3D surface reconstructions in different domains. An area of special interest for such 3D reconstructions is the cultural heritage domain, where surface reconstructions are generated to digitally preserve historical artifacts. While reconstruction quality nowadays is sufficient in many cases, the robust analysis (e.g. segmentation, matching, and classification) of reconstructed 3D data is still an open topic. In this paper, we target the automatic segmentation of high-resolution 3D surface reconstructions of petroglyphs. To foster research in this field, we introduce a fully annotated, large-scale 3D surface dataset including high-resolution meshes, depth maps and point clouds as a novel benchmark dataset, which we make publicly available. Additionally, we provide baseline results for a random forest as well as a convolutional neural network based approach. Results show the complementary strengths and weaknesses of both approaches and point out that the provided dataset represents an open challenge for future research.