Optimization for automated assembly of puzzles

The puzzle assembly problem has many application areas such as restoration and reconstruction of archeological findings, repairing of broken objects, solving jigsaw type puzzles, molecular docking problem, etc. The puzzle pieces usually include not only geometrical shape information but also visual information such as texture, color, and continuity of lines. This paper presents a new approach to the puzzle assembly problem that is based on using textural features and geometrical constraints. The texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. Feature values are derived from these original and predicted images of pieces. An affinity measure of corresponding pieces is defined and alignment of the puzzle pieces is formulated as an optimization problem where the optimum assembly of the pieces is achieved by maximizing the total affinity measure. An fft based image registration technique is used to speed up the alignment of the pieces. Experimental results are presented on real and artificial data sets

A texture based approach to reconstruction of archaeological finds

Reconstruction of archaeological finds from fragments, is a tedious task requiring many hours of work from the archaeologists and restoration personnel. In this paper we present a framework for the full reconstruction of the original objects using texture and surface design information on the sherd. The texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. The confidence of this process is also defined. Feature values are derived from these original and predicted images of pieces. A combination of the feature and confidence values is used to generate an affinity measure of corresponding pieces. The optimization of total affinity gives the best assembly of the piece. Experimental results are presented on real and artificial data

Computer aided puzzle assembly based on shape and texture information /

Puzzle assembly’s importance lies into application in many areas such as restoration and reconstruction of archeological findings, the repairing of broken objects, solving of the jigsaw type puzzles, molecular docking problem, etc. Puzzle pieces usually include not only geometrical shape information but also visual information of texture, color, continuity of lines, and so on. Moreover, textural information is mainly used to assembly pieces in some cases, such as classic jigsaw puzzles. This research presents a new approach in that pictorial assembly, in contrast to previous curve matching methods, uses texture information as well as geometric shape. The assembly in this study is performed using textural features and geometrical constraints. First, the texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. The feature values are derived by these original and predicted images of pieces. A combination of the feature and confidence values is used to generate an affinity measure of corresponding pieces. Two new algorithms using Fourier based image registration techniques are developed to optimize the affinity. The algorithms for inpainting, affinity and Fourier based assembly are explained with experimental results on real and artificial data. The main contributions of this research are: The development of a performance measure that indicates the level of success of assembly of pieces based on textural features and geometrical shape. Solution of the assembly problem by using of the Fourier based methods

Robustness of Massively Parallel Sequencing Platforms

The improvements in high throughput sequencing technologies (HTS) made clinical sequencing projects such as ClinSeq and Genomics England feasible. Although there are significant improvements in accuracy and reproducibility of HTS based analyses, the usability of these types of data for diagnostic and prognostic applications necessitates a near perfect data generation. To assess the usability of a widely used HTS platform for accurate and reproducible clinical applications in terms of robustness, we generated whole genome shotgun (WGS) sequence data from the genomes of two human individuals in two different genome sequencing centers. After analyzing the data to characterize SNPs and indels using the same tools (BWA, SAMtools, and GATK), we observed significant number of discrepancies in the call sets. As expected, the most of the disagreements between the call sets were found within genomic regions containing common repeats and segmental duplications, albeit only a small fraction of the discordant variants were within the exons and other functionally relevant regions such as promoters. We conclude that although HTS platforms are sufficiently powerful for providing data for first-pass clinical tests, the variant predictions still need to be confirmed using orthogonal methods before using in clinical applications

Formgeschichtliches zu den Seligpreisungen Jesu

Am Anfang der Feldrede stehen bekanntlich drei (Luk. vi. 20 f.), am Anfang der Bergpredigt acht Seligpreisungen oder Heilrufe (Matth. v. 3-10), wobei der später zugewachsene Spruch über die Verfolgten hier nicht berücksichtigt wird. Bei Lukas sind die Heilrufe durch entsprechende Weherufe ergänzt. Beide Versionen zeigen gegenüber den Vorlagen charakteristische Unterschiede auf, die hier zusammengestellt werden solle

Random Markov field of coupled images and a relevant real - time optimization approach

Bu makalede ilintili görüntülerin Markov Rastgele Alan (MRA) modeline dayalı birleşik uygulaması sunulmuş ve bu uygulamanın gerçek zamanlı kullanılabilirliği için bir en-iyileme yaklaşımı önerilmiştir. 'Beamforming' yöntemi ile elde edilen uzaklık ve güvenilirlik bilgisinden faydalanarak piksel-piksel bir biriyle ilintili olan iki ayrı görüntü çıkartılmıştır. Bu model görüntülerin yeniden oluşturulması ve yenilenmesi işlemi için ortaya konmuştur. Modelin hızlı uygulanabilirliği için çözüm süresini kısaltıcı yaklaşımlarda bulunulmuştur. Ayrıca örnek şekillerin yeniden oluşturulmuş uzaklık ve yenilenmiş güvenilirlik görüntülerinden yararlanarak 3 boyutlu gösterimleri de elde edilmiştir.This paper describes a Markov Random Field model for coupled range and confidence signals. Beamforming is a method used to bring a range image from backscattered echos of acoustic signals. Another information is confidence of signal which associated point by point with this range data. In the proposed algorithm, the range and confidence images are modeled as Markov Random Fields whose probability distributions are specified by a single energy function. The optimization of this model gives reconstructed range and restored confidence images and an approach to the optimization is suggested for the real-time implementation of this method

Performance comparison of Next Generation sequencing platforms

Next Generation DNA Sequencing technologies offer ultra high sequencing throughput for very low prices. The increase in throughput and diminished costs open up new research areas. Moreover, number of clinicians utilizing DNA sequencing keeps growing. One of the main concern for researchers and clinicians who are adopting these platforms is their sequencing accuracy. We compared three of the most commonly used Next Generation Sequencing platforms; Ion Torrent from Life Technologies, GS FLX+ from Roche and HiSeq 2000 from Illumina