Showing papers by "Robert Sablatnig published in 2012"

Writer Retrieval and Writer Identification Using Local Features

Abstract: Writer identification determines the writer of one document among a number of known writers where at least one sample is known. Writer retrieval searches all documents of one particular writer by creating a ranking of the similarity of the handwriting in a dataset. This paper presents a method for writer retrieval and writer identification using local features and therefore the proposed method is not dependent on a binarization step. First the local features of the image are calculated and with the help of a predefined codebook an occurrence histogram can be created. This histogram is compared to determine the identity of the writer or the similarity of other handwritten documents. The proposed method has been evaluated on two datasets, namely the IAM dataset which contains 650 writers and the Trigraph Slant dataset which contains 47 writers. Experiments have shown that it can keep up with previous writer identification approaches. Regarding writer retrieval it outperforms previous methods.

Binarization-Free Text Line Segmentation for Historical Documents Based on Interest Point Clustering

Abstract: Segmenting page images into text lines is a crucial pre-processing step for automated reading of historical documents. Challenging issues in this open research field are given \eg by paper or parchment background noise, ink bleed-through, artifacts due to aging, stains, and touching text lines. In this paper, we present a novel binarization-free line segmentation method that is robust to noise and copes with overlapping and touching text lines. First, interest points representing parts of characters are extracted from gray-scale images. Next, word clusters are identified in high-density regions and touching components such as ascenders and descenders are separated using seam carving. Finally, text lines are generated by concatenating neighboring word clusters, where neighborhood is defined by the prevailing orientation of the words in the document. An experimental evaluation on the Latin manuscript images of the Saint Gall database shows promising results for real-world applications in terms of both accuracy and efficiency.

A performance evaluation of SIFT and SURF for multispectral image matching

Abstract: This paper evaluates the performance of SIFT and SURF for cross band matching of multispectral images. The evaluation is based on matching a reference spectral image with the images acquired at different spectral bands. The reference image possesses scale and (in-plane) rotational differences in addition to spectral variations. Additive white Gaussian noise is also added to compare performance degradation at different noise levels. We use the precision and repeatability criteria for performance evaluation. Experimental results demonstrate that SIFT performs better than SURF in multispectral environment.

Architectural Style Classification of Domes

Abstract: Domes are architectural structural elements characteristic for ecclesiastical and secular monumental buildings, like churches, basilicas, mosques, capitols and city halls In the scope of building facade architectural style classification the current paper addresses the problem of architectural style classification of facade domes Building facade classification by architectural styles is achieved by classification and voting of separate architectural elements, like domes, windows, towers, etc Typical forms of the structural elements bear the signature of each architectural style Our approach classifies domes of three architectural styles - Renaissance, Russian and Islamic We present a three-step approach, which in the first step analyzes the height and width of the dome for the identification of Islamic saucer domes, in the second step detects golden color in YCbCr color space to determine Russian golden onion domes and in the third step performs classification based on dome shapes, using clustering and learning of local features Thus we combine three features - the relation of dome width and height, color and shape, in a single methodology to achieve high classification rate

Multispectral image acquisition of ancient manuscripts

Abstract: This paper presents image acquisition and readability enhancement techniques based on multispectral imaging. In an interdisciplinary manuscript and palimpsest research project an imaging system using a combination of LED illumination and spectral filtering was developed. On basis of the resulting multispectral image information the readability of the texts is enhanced and palimpsest texts are made visible by applying two different methods of Blind Source Separation, namely Principal Component Analysis and Independent Component Analysis.

Skew Estimation of Sparsely Inscribed Document Fragments

Abstract: Document analysis is done to analyze entire forms (e.g. intelligent form analysis, table detection) or to describe the layout/structure of a document for further processing. A pre-processing step of document analysis methods is a skew estimation of scanned or photographed documents. Current skew estimation methods require the existence of large text areas, are dependent on the text type and can be limited on a specific angle range. The proposed method is gradient based in combination with a Focused Nearest Neighbor Clustering of interest points and has no limitations regarding the detectable angle range. The upside/down decision is based on statistical analysis of ascenders and descenders. It can be applied to entire documents as well as to document fragments containing only a few words. Results show that the proposed skew estimation is comparable with state-of-the-art methods and outperforms them on a real dataset consisting of 658 snippets.

Classification of gothic and baroque architectural elements

Abstract: Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectural styles is viewed as a task of classifying separate architectural structural elements. In the scope of building facade architectural style classification the current paper targets the problem of classification of Gothic and Baroque architectural elements called tracery, pediment and balustrade. Since certain gradient directions dominate on the shape of each architectural element, discrimination between dominating gradients means classification of architectural elements and thus architectural styles. We use local features to describe gradient directions. Our approach is based on clustering and learning of local features and yields a high classification rate.

Segmentation of Building Facade Domes

Abstract: Domes are architectural structural elements typical for ecclesiastical and secular grand buildings, like churches, mosques, palaces, capitols and city halls. The current paper targets the problem of segmentation of domes within the framework of architectural style classification of building facades. We perform segmentation of building facade domes by combining bilateral symmetry detection, graph-based segmentation approaches and image analysis and processing technics into a single method. Our algorithm achieves good segmentation results on buildings belonging to variety of architectural styles, such as Renaissanse, Neo-Renaissance, Baroque, Neo-Baroque, Neoclassical and Islamic.

Can modern technologies defeat nazi censorship

Abstract: Censorship of parts of written text was and is a common practice in totalitarian regimes. It is used to destroy information not approved by the political power. Recovering the censored text is of interest for historical studies of the text. This paper raises the question, whether a censored postcard from 1942 can be made legible by applying multispectral imaging in combination with laser cleaning. In the fields of art conservation (e.g. color measurements), investigation (e.g. analysis of underdrawings in paintings), and historical document analysis, multispectral imaging techniques have been applied successfully to give visibility to information hidden to the human eye. The basic principle of laser cleaning is to transfer laser pulse energy to a contamination layer by an absorption process that leads to heating and evaporation of the layer. Partial laser cleaning of postcards is possible; dirt on the surface can be removed and the obscured pictures and writings made visible again. We applied both techniques to the postcard. The text could not be restored since the original ink seems to have suffered severe chemical damage.