Reinhold Huber-Mörk

Identification of ancient coins based on fusion of shape and local features

We present a vision-based approach to ancient coins’ identification. The approach is a two-stage procedure. In the first stage an invariant shape description of the coin edge is computed and matching based on shape is performed. The second stage uses preselection by the first stage in order to refine the matching using local descriptors. Results for different descriptors and coin sides are combined using naive Bayesian fusion. Identification rates on a comprehensive data set of 2400 images of ancient coins are on the order of magnitude of 99%.

Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images

Convolutional neural networks (CNNs) achieved impressive recognition rates in image classification tasks recently. In order to exploit those capabilities, we trained CNNs on a database of photometric stereo images of metal surface defects, i.e. rail defects. Those defects are cavities in the rail surface and are indication for further surface degradation right up to rail break. Due to security issues, defects have to be recognized early in order to take countermeasures in time. By means of differently colored light-sources illuminating the rail surfaces from different and constant directions, those cavities are made visible in a photometric dark-field setup. So far, a model-based approach has been used for image classification, which expressed the expected reflection properties of surface defects in contrast to non-defects. In this work, we experimented with classical CNNs trained in pure supervised manner and also explored the impact of regularization methods such as unsupervised layer-wise pre-training and training data-set augmentation. The classical CNN already distinctly outperforms the model-based approach. Moreover, regularization methods yet yield further improvements.

Demosaicing algorithms for area- and line-scan cameras in print inspection

Most color image sensors use color filter arrays (CFA). With this sensor design the captured information at each sensor pixel position is restricted to a specific spectral portion (typically red, green and blue bands). To obtain the missing color responses at each pixel position, so-called CFA demosaicing algorithms are commonly used. We propose two new CFA demosaicing algorithms, which are well suited for industrial print inspection with respect to the requirements in accuracy and speed. As a main contribution, we introduce novel demosaicing algorithms for specific high-speed color digital time delay and integration (DTDI) CFA line-scan cameras. We compare the suggested CFA demosaicing algorithms to state-of-the art algorithms for area and line-scan camera operation modes. We show that the two new algorithms perform superior to conventional algorithms as indicated by reconstruction error.

Image-Based Retrieval and Identification of Ancient Coins

Reliable object identification is an essential task in the process of recognizing and tracing stolen cultural heritage. We investigate the feasibility of using computer-aided identification of ancient coins to search for a given coin on the Internet or in a digital repository. Because a coins shape is a unique feature, we first apply a shape descriptor to capture its characteristics. Then, we use local features to describe the die information. The approach presented here shows promise for reliably identifying objects in the area of cultural heritage.

Statistical Rail Surface Classification Based on 2D and 2 1 / 2 D Image Analysis

We present an approach to high-resolution rail surface analysis combining 2D image texture classification and 21/2D analysis of surface disruptions. Detailed analysis of images of rail surfaces is used to observe the condition of rails and, as a precaution, to avoid rail breaks and further damage. Single rails are observed by a color line scan camera at high resolution of approximately 0.2 millimeters and under special illumination in order to enable 21/2D image analysis. Gabor filter banks are used for 2D texture description and classes are modeled by Gaussian mixtures. A Bayesian classifier, which also incorporates background knowledge, is used to differentiate between surface texture classes. Classes which can be related to surface disruptions are derived from the analysis of the anti-correlation properties between two color channels. Images are illuminated by two light sources mounted at different position and operating at different wavelengths. Results for data gathered in the Vienna metro system are presented.

Quality assurance for document image collections in digital preservation

Maintenance of digital image libraries requires to frequently asses the quality of the images to engage preservation measures if necessary. We present an approach to image based quality assurance for digital image collections based on local descriptor matching. We use spatially distinctive local keypoints of contrast enhanced images and robust symmetric descriptor matching to calculate affine transformations for image registration. Structural similarity of aligned images is used for quality assessment. The results show, that our approach can efficiently asses the quality of digitized documents including images of blank paper.

Analysis of optically variable devices using a photometric light-field approach

Diffractive Optically Variable Image Devices (DOVIDs), sometimes loosely referred to as holograms, are popular security features for protecting banknotes, ID cards, or other security documents. Inspection, authentication, as well as forensic analysis of these security features are still demanding tasks requiring special hardware tools and expert knowledge. Existing equipment for such analyses is based either on a microscopic analysis of the grating structure or a point-wise projection and recording of the diffraction patterns. We investigated approaches for an examination of DOVID security features based on sampling the Bidirectional Reflectance Distribution Function (BRDF) of DOVIDs using photometric stereo- and light-field-based methods. Our approach is demonstrated on the practical task of automated discrimination between genuine and counterfeited DOVIDs on banknotes. For this purpose, we propose a tailored feature descriptor which is robust against several expected sources of inaccuracy but still specific enough for the given task. The suggested approach is analyzed from both theoretical as well as practical viewpoints and w.r.t. analysis based on photometric stereo and light fields. We show that especially the photometric method provides a reliable and robust tool for revealing DOVID behavior and authenticity.

Region based matching for print process identification

For quality inspection of security printing systems it is necessary to measure the displacement between printing processes. We present a new approach for region based matching of color images. Maximally stable extremal regions are extracted from image color channels and are the basis for matching. Binary template matching is performed between pairs of regions taken from the corresponding color channels of different images and a displacement vector is derived for each matching pair of regions. Clustering of measured displacements taken from sequences of sample images allows the estimation of the accuracy of printing processes and the alignment of printing processes. Results of an experimental application to banknote printing process inspection are given.

Invariant characterization of DOVID security features using a photometric descriptor

Diffractive Optically Variable Image Devices (DOVIDs) are popular security features used to protect security documents such as banknotes, ID cards, passports, etc. Checking authenticity of these security features on both user as well as forensic level remains a challenging task, requiring sophisticated hardware tools and expert knowledge. Recently, we proposed a technique exploiting a large-scale photometric behavior of DOVIDs in order to discriminate denominations and detect counterfeits. Here we investigate invariance properties of the proposed method and demonstrate its robustness against various common perturbations, which may have negative impact on the acquisition quality in practice. Presented results show a great potential of this approach primarily for security and forensic purposes, but also for other applications, where automated inspection of DOVIDs is of interest.

Depth and all-in-focus images obtained by multi-line-scan light-field approach

We present a light-field multi-line-scan image acquisition and processing system intended for the 2.5/3-D inspection of fine surface structures, such as small parts, security print, etc. in an industrial environment. The system consists of an area-scan camera, that allows for a small number of sensor lines to be extracted at high frame rates, and a mechanism for transporting the inspected object at a constant speed. During the acquisition, the object is moved orthogonally to the camera’s optical axis as well as the orientation of the sensor lines. In each time step, a predefined subset of lines is read out from the sensor and stored. Afterward, by collecting all corresponding lines acquired over time, a 3-D light field is generated, which consists of multiple views of the object observed from different viewing angles while transported w.r.t. the acquisition device. This structure allows for the construction of so-called epipolar plane images (EPIs) and subsequent EPI-based analysis in order to achieve two main goals: (i) the reliable estimation of a dense depth model and (ii) the construction of an all-in-focus intensity image. Beside specifics of our hardware setup, we also provide a detailed description of algorithmic solutions for the mentioned tasks. Two alternative methods for EPI-based analysis are compared based on artificial and real-world data.