'dis ɔhord' : one woman’s experience of confronting and understanding the lived experience of birth.

This paper is a collaborative piece written by a midwifery academic and an artist. It presents and interprets a number of mixed media art works created by Caroline Calonder in response to the traumatic birth of her son, and utilises findings derived from Lesley Kay’s doctoral study about birth and birth stories as a means of contextualising, understanding and interpreting the work (Kay et al 2017). In sharing elements of Caroline’s experience, the psychological harm it caused her, and the means she used (and continues to use) to understand, and come to terms with the experience, the paper highlights some of the distressing and harmful sequelae which can arise when a woman’s disembodied experience of birth is accepted as normal and mainstream. Furthermore, it emphasises the need for health care professionals to actively work towards safeguarding women’s emotional health, and the value of art as a means of confronting and recovering from birth trauma

Conservation of the cope of Bishop Ramon de Bellera

El Museu Episcopal de Vic va confiar al taller de conservació tèxtil de l’Abegg-Stiftung la capa pluvial del bisbe Ramon de Bellera, una peça opus anglicanum del segle xiv feta de vellut vermell. Al segle xvii la capa es va dividir en diverses parts per fer-ne dues dalmàtiques, un drap de faristol i una enquadernació de llibre. El 1899 aquestes peces es van desmuntar i es va tornar a reconstruir la capa. Per tant, el dilema amb què es van trobar els conservadors tèxtils fou si s’havia de mantenir aquesta reconstrucció o no. L’article que publiquem és un resum de l’informe de conservació. The Museu Episcopal de Vic entrusted the Abegg-Stiftung’s textile conservation workshop with the cope of Bishop Ramon de Bellera, a 14th-century «opus anglicanum» vestment made of red velvet. The cope had been cut into pieces in the 17th century and made up into two dalmatics, a lectern cloth and a book binding. These, in turn, were dismantled in 1899 and the cope was subsequently reconstructed. Thus, the main question facing the textile conservators was whether this reconstruction should be preserved or not. The article published here is an abridged version of the conservation report

Scalable Full Flow with Learned Binary Descriptors

We propose a method for large displacement optical flow in which local matching costs are learned by a convolutional neural network (CNN) and a smoothness prior is imposed by a conditional random field (CRF). We tackle the computation- and memory-intensive operations on the 4D cost volume by a min-projection which reduces memory complexity from quadratic to linear and binary descriptors for efficient matching. This enables evaluation of the cost on the fly and allows to perform learning and CRF inference on high resolution images without ever storing the 4D cost volume. To address the problem of learning binary descriptors we propose a new hybrid learning scheme. In contrast to current state of the art approaches for learning binary CNNs we can compute the exact non-zero gradient within our model. We compare several methods for training binary descriptors and show results on public available benchmarks.Comment: GCPR 201

Biologically Inspired Vision for Indoor Robot Navigation

Ultrasonic, infrared, laser and other sensors are being applied in robotics. Although combinations of these have allowed robots to navigate, they are only suited for specific scenarios, depending on their limitations. Recent advances in computer vision are turning cameras into useful low-cost sensors that can operate in most types of environments. Cameras enable robots to detect obstacles, recognize objects, obtain visual odometry, detect and recognize people and gestures, among other possibilities. In this paper we present a completely biologically inspired vision system for robot navigation. It comprises stereo vision for obstacle detection, and object recognition for landmark-based navigation. We employ a novel keypoint descriptor which codes responses of cortical complex cells. We also present a biologically inspired saliency component, based on disparity and colour

The brightness clustering transform and locally contrasting keypoints

In recent years a new wave of feature descriptors has been presented to the computer vision community, ORB, BRISK and FREAK amongst others. These new descriptors allow reduced time and memory consumption on the processing and storage stages of tasks such as image matching or visual odometry, enabling real time applications. The problem is now the lack of fast interest point detectors with good repeatability to use with these new descriptors. We present a new blob- detector which can be implemented in real time and is faster than most of the currently used feature-detectors. The detection is achieved with an innovative non-deterministic low-level operator called the Brightness Clustering Transform (BCT). The BCT can be thought as a coarse-to- fine search through scale spaces for the true derivative of the image; it also mimics trans-saccadic perception of human vision. We call the new algorithm Locally Contrasting Keypoints detector or LOCKY. Showing good repeatability and robustness to image transformations included in the Oxford dataset, LOCKY is amongst the fastest affine-covariant feature detectors

EKF SLAM vs. FastSLAM -- A comparison

The two algorithms are described with a planar robot application in mind. Generalization to any spatial SLAM scenarios is straightforward

Evaluation Method, Dataset Size or Dataset Content: How to Evaluate Algorithms for Image Matching?

Most vision papers have to include some evaluation work in order to demonstrate that the algorithm proposed is an improvement on existing ones. Generally, these evaluation results are presented in tabular or graphical forms. Neither of these is ideal because there is no indication as to whether any performance differences are statistically significant. Moreover, the size and nature of the dataset used for evaluation will obviously have a bearing on the results, and neither of these factors are usually discussed. This paper evaluates the effectiveness of commonly used performance characterization metrics for image feature detection and description for matching problems and explores the use of statistical tests such as McNemar’s test and ANOVA as better alternatives

Early predictors of impaired social functioning in male rhesus macaques (Macaca mulatta)

Autism spectrum disorder (ASD) is characterized by social cognition impairments but its basic disease mechanisms remain poorly understood. Progress has been impeded by the absence of animal models that manifest behavioral phenotypes relevant to ASD. Rhesus monkeys are an ideal model organism to address this barrier to progress. Like humans, rhesus monkeys are highly social, possess complex social cognition abilities, and exhibit pronounced individual differences in social functioning. Moreover, we have previously shown that Low-Social (LS) vs. High-Social (HS) adult male monkeys exhibit lower social motivation and poorer social skills. It is not known, however, when these social deficits first emerge. The goals of this study were to test whether juvenile LS and HS monkeys differed as infants in their ability to process social information, and whether infant social abilities predicted later social classification (i.e., LS vs. HS), in order to facilitate earlier identification of monkeys at risk for poor social outcomes. Social classification was determined for N = 25 LS and N = 25 HS male monkeys that were 1–4 years of age. As part of a colony-wide assessment, these monkeys had previously undergone, as infants, tests of face recognition memory and the ability to respond appropriately to conspecific social signals. Monkeys later identified as LS vs. HS showed impairments in recognizing familiar vs. novel faces and in the species-typical adaptive ability to gaze avert to scenes of conspecific aggression. Additionally, multivariate logistic regression using infant social ability measures perfectly predicted later social classification of all N = 50 monkeys. These findings suggest that an early capacity to process important social information may account for differences in rhesus monkeys’ motivation and competence to establish and maintain social relationships later in life. Further development of this model will facilitate identification of novel biological targets for intervention to improve social outcomes in at-risk young monkeys