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Immunocytochemical localization of galanin in the rat male and female genital tracts and motor effects in vitro

Galanin, a recently discovered neuropeptide, was studied in the rat male and female reproductive tracts by immunocytochemistry and in vitro pharmacology. Nerve fibers containing galanin immunoreactivity were most abundant in the female paracervical tissue, where they surrounded non-immunoreactive ganglion cells. Galanin nerves were also found in the uterus and Fallopian tubes, as well as in the va

TOA sensor network self-calibration for receiver and transmitter spaces with difference in dimension

We study and solve the previously unstudied problem of finding both transmitter and receiver positions using only time of arrival (TOA) measurements when there is a difference in dimensionality between the affine subspaces spanned by receivers and transmitters. Anchor-free TOA network calibration has uses both in radio, radio strength and sound applications, such as calibrating ad hoc microphone a

Intercepting Dataflow Connections in Diagrams with Inheritance

Control systems are often built using visual dataflow-based languages, and supporting different variants may be challenging. We introduce the concept of connection interception based on inheritance. This mechanism allows a diagram to extend another diagram and intercept connections defined in the supertype, that is, to replace it by two other connections, in order to specialize the behavior. This

Local Refinement for Stereo Regularization

Stereo matching is an inherently difficult problem due to ambiguous and noisy texture. The non-convexity and non- differentiability makes local linear (or quadratic) approximations poor, thereby preventing the use of standard local descent methods. Therefore recent methods are predominantly based on discretization and/or random sampling of some class of approximating surfaces (e.g. planes). While

Generalized Boundaries from Multiple Image Interpretations

Boundary detection is a fundamental computer vision problem that is essential for a variety of tasks, such as contour and region segmentation, symmetry detection and object recognition and categorization. We propose a generalized formulation for boundary detection, with closed-form solution, applicable to the localization of different types of boundaries, such as object edges in natural images and

A Measure of Septum Shape Using Shortest Path Segmentation in Echocardiographic Images of LVAD Patients

Patients waiting for heart transplantation due to a failing heart can get a left ventricular assist device (LVAD) implanted through open chest surgery. The device consists of a pump that pumps blood from the left ventricle into the aorta. To get the correct rotation speed of the pump, the physicians consider a number of measurements as well as a sequence of echocardiographic images. The important

Multitudes of Objects: First Implementation and Case Study for Java

In object-oriented programs, the relationship of an object to many objects is usually implemented using indirection through a collection. This is in contrast to a relationship to one object, which is usually implemented directly. However, using collections for relationships to many objects does not only mean that accessing the related objects always requires accessing the collection first, it also

Partial Symmetry in Polynomial Systems and Its Application in Computer Vision

Algorithms for solving systems of polynomial equations are key components for solving geometry problems in computer vision. Fast and stable polynomial solvers are essential for numerous applications e.g. minimal problems or finding for all stationary points of certain algebraic errors. Recently, full symmetry in the polynomial systems has been utilized to simplify and speed up state-of-the-art pol

Exploratory study of EEG burst characteristics in preterm infants

In this paper, we study machine learning techniques and features of electroencephalography activity bursts for predicting outcome in extremely preterm infants. It was previously shown that the distribution of interburst interval durations predicts clinical outcome, but in previous work the information within the bursts has been neglected. In this paper, we perform exploratory analysis of feature e

Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique.

In this paper the feasibility to extract the proportion of pigs located in different areas of a pig pen by advanced image analysis technique is explored and discussed for possible applications. For example, pigs generally locate themselves in the wet dunging area at high ambient temperatures in order to avoid heat stress, as wetting the body surface is the major path to dissipate the heat by evapo

Probabilistic Joint Image Segmentation and Labeling by Figure-Ground Composition

We propose a layered statistical model for image segmentation and labeling obtained by combining independently extracted, possibly overlapping sets of figure-ground (FG) segmentations. The process of constructing consistent image segmentations, called tilings, is cast as optimization over sets of maximal cliques sampled from a graph connecting all non-overlapping figure-ground segment hypotheses.

Extended structure tensors for multiple directionality estimation

Standard structure tensors provide a robust way of directionality estimation of waves (or edges) but only for the case when they do not intersect. In this work, a structure tensor extension using a one-way wave equation is proposed as a tool for estimating directionality in seismic data and images in the presence of conflicting dips. Detection of two intersecting waves is possible in a two-dimensi

Tractable Algorithms for Robust Model Estimation

What is the computational complexity of geometric model estimation in the presence of noise and outliers? We show that the number of outliers can be minimized in polynomial time with respect to the number of measurements, although exponential in the model dimension. Moreover, for a large class of problems, we prove that the statistically more desirable truncated L2-norm can be optimized with the s

Simultaneous Fusion Moves for 3D-label Stereo

Second derivative regularization methods for dense stereo matching is a topic of intense research. Some of the most successful recent methods employ so called binary fusion moves where the combination of two proposal solutions is computed. In many cases the fusion move can be solved optimally, but the approach is limited to fusing pairs of proposals in each move. For multiple proposals iterative b

A Combinatorial Approach to $L_1$-Matrix Factorization

Recent work on low-rank matrix factorization has focused on the missing data problem and robustness to outliers and therefore the problem has often been studied under the $L_1$-norm. However, due to the non-convexity of the problem, most algorithms are sensitive to initialization and tend to get stuck in a local optimum. In this paper, we present a new theoretical framework aimed at achieving opt

Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments.

We introduce a new dataset, Human3.6M, of 3.6 Million 3D Human poses, acquired by recording the performance of 11 subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models. Besides increasing the size the current state of the art datasets by several orders of magnitude, we aim to complement such datas

A one-dimensional moving-boundary model for tubulin-driven axonal growth.

A one-dimensional continuum-mechanical model of axonal elongation due to assembly of tubulin dimers in the growth cone is presented. The conservation of mass leads to a coupled system of three differential equations. A partial differential equation models the dynamic and the spatial behaviour of the concentration of tubulin that is transported along the axon from the soma to the growth cone. Two o

Structure from Motion Estimation with Positional Cues

We present a system for structure from motion estimation using additional positioning data such as GPS data. The system incorporates the additional data in the outlier detection, the initial estimates and the final bundle adjustment. The initial solution is based on a novel objective function which is solved using convex optimization. This objective function is also used for outlier detection and

Modeling GPP in the Nordic forest landscape with MODIS time series data - comparison with the MODIS GPP product

Satellite sensor-derived data are suitable for regional estimations of several important biophysical variables. Data with a finer spatial resolution should improve regional estimations of GPP (gross primary productivity), since they better capture the variation in a heterogeneous landscape. The main objective of this study was to investigate if MODIS 500 m reflectance data can be used to drive emp

Democratic Tone Mapping Using Optimal K-means Clustering

The field of high dynamic range imaging addresses the problem of capturing and displaying the large range of luminance levels found in the world, using devices with limited dynamic range. In this paper we present a novel tone mapping algorithm that is based on $K$-means clustering. Using dynamic programming we are able to, not only solve the clustering problem efficiently, but also find the global