Automatic Analysis of Lens Distortions in Image Registration

Geometric image registration by estimating homographies is an important processing step in a wide variety of computer vision applications. The 2D registration of two images does not require an explicit reconstruction of intrinsic or extrinsic camera parameters. However, correcting images for non-linear lens distortions is highly recommended. Unfortunately, standard calibration techniques are sometimes difficult to apply and reliable estimations of lens distortions can only rarely be obtained. In this paper we present a new technique for automatically detecting and categorising lens distortions in pairs of images by analysing registration results. The approach is based on a new metric for registration quality assessment and facilitates a PCA-based statistical model for classifying distortion effects. In doing so the overall importance for lens calibration and image corrections can be checked, and a measure for the efficiency of accordant correction steps is given

A general approach for discriminative de-novo motif discovery from highthroughput data

De novo motif discovery has been an important challenge of bioinformatics for the past two decades. Since the emergence of high-throughput techniques like ChIP-seq, ChIP-exo and protein-binding microarrays (PBMs), the focus of de novo motif discovery has shifted to runtime and accuracy on large data sets. For this purpose, specialized algorithms have been designed for discovering motifs in ChIP-seq or PBM data. However, none of the existing approaches work perfectly for all three high-throughput techniques. In this article, we propose Dimont, a general approach for fast and accurate de novo motif discovery from high-throughput data. We demonstrate that Dimont yields a higher number of correct motifs from ChIP-seq data than any of the specialized approaches and achieves a higher accuracy for predicting PBM intensities from probe sequence than any of the approaches specifically designed for that purpose. Dimont also reports the expected motifs for several ChIP-exo data sets. Investigating differences between in vitro and in vivo binding, we find that for most transcription factors, the motifs discovered by Dimont are in good accordance between techniques, but we also find notable exceptions. We also observe that modeling intra-motif dependencies may increase accuracy, which indicates that more complex motif models are a worthwhile field of research

Extraction of protein profiles from primary neurons using active contour models and wavelets

AbstractThe function of complex networks in the nervous system relies on the proper formation of neuronal contacts and their remodeling. To decipher the molecular mechanisms underlying these processes, it is essential to establish unbiased automated tools allowing the correlation of neurite morphology and the subcellular distribution of molecules by quantitative means.We developed NeuronAnalyzer2D, a plugin for ImageJ, which allows the extraction of neuronal cell morphologies from two dimensional high resolution images, and in particular their correlation with protein profiles determined by indirect immunostaining of primary neurons. The prominent feature of our approach is the ability to extract subcellular distributions of distinct biomolecules along neurites. To extract the complete areas of neurons, required for this analysis, we employ active contours with a new distance based energy. For locating the structural parts of neurons and various morphological parameters we adopt a wavelet based approach. The presented approach is able to extract distinctive profiles of several proteins and reports detailed morphology measurements on neurites.We compare the detected neurons from NeuronAnalyzer2D with those obtained by NeuriteTracer and Vaa3D-Neuron, two popular tools for automatic neurite tracing. The distinctive profiles extracted for several proteins, for example, of the mRNA binding protein ZBP1, and a comparative evaluation of the neuron segmentation results proves the high quality of the quantitative data and proves its practical utility for biomedical analyses

Thermal Analysis of a Hermetic Reciprocating Compressor Using Numerical Methods

Comprehensive knowledge about the heat transfer mechanisms and the temperature field inside hermetic compressors is very important for the thermal management and thus their performance. A numerical model to predict the temperature field in a hermetic reciprocating compressor for household refrigeration appliances is presented in this work. The model combines a high resolution three-dimensional heat conduction formulation of the compressor’s solid parts, a three-dimensional computational fluid dynamics (CFD) approach for the gas line domain and lumped formulations of the shell gas and the lubrication oil. Heat transfer coefficients are determined by applying CFD to the gas line side and correlations from the literature on the shell gas and oil side, respectively. The valve in the gas line simulation is modelled as a parallel moving flat plate. By means of an iterative loop the temperature field of the solid parts acts as boundary condition for the CFD calculation of the gas line which returns a cycle averaged quantity of heat to the solid parts. Using an iteration method which is based on the temperature deviation between two iteration steps, the total number of iterations and consequently the computational time can be reduced. The loop is continued until a steady-state temperature field is obtained. Calculated temperatures of the solid parts are verified against temperature measurements of a calorimeter test bench. The numerical results show reasonable agreement with the measured data

Experimental Study on the Thermal Behavior of a Domestic Refrigeration Compressor during Transient Operation in a Small Capacity Cooling System

Generally, domestic refrigerators and freezers are running in non-continuous operation mode most of the time, which is a necessity to match cooling capacity to thermal loads. In currently available domestic appliances it can be observed, that this matching is mainly realized in two different ways: On the one hand, a simple on/off control of the hermetic compressor is installed in lower priced appliances with limited energy efficiency for the mass market. On the other hand, modern top efficiency class appliances have a variable frequency controlled compressor installed. Both control strategies have a repetitive and transient change of thermodynamic states of the refrigerant in common. For better understanding of these cyclic patterns in terms of internal temperature distribution, a state of the art domestic refrigeration compressor with a displacement of approximately 6 cubic centimeters is integrated in a commercial freezer. The compressor which has an on/off control is equipped with extensive measurement instrumentation. Several temperature probes are inserted and temperatures on surfaces inside and outside the compressor as well as refrigerant temperatures are logged for both cyclic and steady state behavior. Finally, a comparison between transient experimental data and steady-state data from a standardized calorimeter test bench is done

VOMBAT: prediction of transcription factor binding sites using variable order Bayesian trees

Variable order Markov models and variable order Bayesian trees have been proposed for the recognition of transcription factor binding sites, and it could be demonstrated that they outperform traditional models, such as position weight matrices, Markov models and Bayesian trees. We develop a web server for the recognition of DNA binding sites based on variable order Markov models and variable order Bayesian trees offering the following functionality: (i) given datasets with annotated binding sites and genomic background sequences, variable order Markov models and variable order Bayesian trees can be trained; (ii) given a set of trained models, putative DNA binding sites can be predicted in a given set of genomic sequences and (iii) given a dataset with annotated binding sites and a dataset with genomic background sequences, cross-validation experiments for different model combinations with different parameter settings can be performed. Several of the offered services are computationally demanding, such as genome-wide predictions of DNA binding sites in mammalian genomes or sets of 10(4)-fold cross-validation experiments for different model combinations based on problem-specific data sets. In order to execute these jobs, and in order to serve multiple users at the same time, the web server is attached to a Linux cluster with 150 processors. VOMBAT is available at

Automatic Analysis of Lens Distortions in Image Registration

Geometric image registration by estimating homographies is an important processing step in a wide variety of computer vision applications. The 2D registration of two images does not require an explicit reconstruction of intrinsic or extrinsic camera parameters. However, correcting images for non-linear lens distortions is highly recommended. Unfortunately, standard calibration techniques are sometimes difficult to apply and reliable estimations of lens distortions can only rarely be obtained. In this paper we present a new technique for automatically detecting and categorising lens distortions in pairs of images by analysing registration results. The approach is based on a new metric for registration quality assessment and facilitates a PCA-based statistical model for classifying distortion effects. In doing so the overall importance for lens calibration and image corrections can be checked, and a measure for the efficiency of accordant correction steps is given