Robustness of texture-based roundwood tracking.

The proof of origin of wood logs is becoming more and more important. In the context of Industry 4.0 and to combat illegal logging, there is an increased interest to track each individual log. There were already previous publications on wood log tracing using image data from logs, but these publications used experimental setups that cannot simulate a practical application where logs are tracked between different stages of the wood processing chain, like e.g. from the forest to the sawmill. In this work, we employ image data from the same 100 logs that were acquired at different stages of the wood processing chain (two datasets at the forest, one at a laboratory and two at the sawmill including one acquired with a CT scanner). Cross-dataset wood tracking experiments are applied using (a) the two forest datasets, (b) one forest and the RGB sawmill dataset and (c) different RGB datasets and the CT sawmill dataset. In our experiments we employ two CNN based method, 2 shape descriptors and two methods from the biometric areas of iris and fingerprint recognition. We will show that wood log tracing between different stages of the wood processing chain is feasible, even if the images at different stages are obtained at different image domains (RGB-CT). But it only works if the log cross sections from different stages of the wood processing chain either offer a good visibility of the annual ring pattern or share the same woodcut pattern.

Iris Image Compression Using Deep Convolutional Neural Networks.

Compression is a way of encoding digital data so that it takes up less storage and requires less network bandwidth to be transmitted, which is currently an imperative need for iris recognition systems due to the large amounts of data involved, while deep neural networks trained as image auto-encoders have recently emerged a promising direction for advancing the state-of-the-art in image compression, yet the generalizability of these schemes to preserve the unique biometric traits has been questioned when utilized in the corresponding recognition systems. For the first time, we thoroughly investigate the compression effectiveness of DSSLIC, a deep-learning-based image compression model specifically well suited for iris data compression, along with an additional deep-learning based lossy image compression technique. In particular, we relate Full-Reference image quality as measured in terms of Multi-scale Structural Similarity Index (MS-SSIM) and Local Feature Based Visual Security (LFBVS), as well as No-Reference images quality as measured in terms of the Blind Reference-less Image Spatial Quality Evaluator (BRISQUE), to the recognition scores as obtained by a set of concrete recognition systems. We further compare the DSSLIC model performance against several state-of-the-art (non-learning-based) lossy image compression techniques including: the ISO standard JPEG2000, JPEG, H.265 derivate BPG, HEVC, VCC, and AV1 to figure out the most suited compression algorithm which can be used for this purpose. The experimental results show superior compression and promising recognition performance of the model over all other techniques on different iris databases.

Secure transport and adaptation of MC-EZBC video utilizing H.264-based transport protocols.

Universal Multimedia Access (UMA) calls for solutions where content is created once and subsequently adapted to given requirements. With regard to UMA and scalability, which is required often due to a wide variety of end clients, the best suited codecs are wavelet based (like the MC-EZBC) due to their inherent high number of scaling options. However, most transport technologies for delivering videos to end clients are targeted toward the H.264/AVC standard or, if scalability is required, the H.264/SVC. In this paper we will introduce a mapping of the MC-EZBC bitstream to existing H.264/SVC based streaming and scaling protocols. This enables the use of highly scalable wavelet based codecs on the one hand and the utilization of already existing network technologies without accruing high implementation costs on the other hand. Furthermore, we will evaluate different scaling options in order to choose the best option for given requirements. Additionally, we will evaluate different encryption options based on transport and bitstream encryption for use cases where digital rights management is required.