'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

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

VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change

Visual place recognition (VPR) is the process of recognising a previously visited place using visual information, often under varying appearance conditions and viewpoint changes and with computational constraints. VPR is related to the concepts of localisation, loop closure, image retrieval and is a critical component of many autonomous navigation systems ranging from autonomous vehicles to drones and computer vision systems. While the concept of place recognition has been around for many years, VPR research has grown rapidly as a field over the past decade due to improving camera hardware and its potential for deep learning-based techniques, and has become a widely studied topic in both the computer vision and robotics communities. This growth however has led to fragmentation and a lack of standardisation in the field, especially concerning performance evaluation. Moreover, the notion of viewpoint and illumination invariance of VPR techniques has largely been assessed qualitatively and hence ambiguously in the past. In this paper, we address these gaps through a new comprehensive open-source framework for assessing the performance of VPR techniques, dubbed “VPR-Bench”. VPR-Bench (Open-sourced at: https://github.com/MubarizZaffar/VPR-Bench) introduces two much-needed capabilities for VPR researchers: firstly, it contains a benchmark of 12 fully-integrated datasets and 10 VPR techniques, and secondly, it integrates a comprehensive variation-quantified dataset for quantifying viewpoint and illumination invariance. We apply and analyse popular evaluation metrics for VPR from both the computer vision and robotics communities, and discuss how these different metrics complement and/or replace each other, depending upon the underlying applications and system requirements. Our analysis reveals that no universal SOTA VPR technique exists, since: (a) state-of-the-art (SOTA) performance is achieved by 8 out of the 10 techniques on at least one dataset, (b) SOTA technique in one community does not necessarily yield SOTA performance in the other given the differences in datasets and metrics. Furthermore, we identify key open challenges since: (c) all 10 techniques suffer greatly in perceptually-aliased and less-structured environments, (d) all techniques suffer from viewpoint variance where lateral change has less effect than 3D change, and (e) directional illumination change has more adverse effects on matching confidence than uniform illumination change. We also present detailed meta-analyses regarding the roles of varying ground-truths, platforms, application requirements and technique parameters. Finally, VPR-Bench provides a unified implementation to deploy these VPR techniques, metrics and datasets, and is extensible through templates

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

An investigation of multiple time graphical analysis applied to projection data: Theory and validation

Purpose: The determination of tissue time-activity course and pharmokinetics in PET is normally performed by region-of-interest analysis of reconstructed images. However, in some cases, the same analysis may equally well be performed on the data in projections before reconstruction, avoiding the reconstruction of large time sequence data sets. This is especially important in 3D mode. Method: We present a theory that shows why multiple time/graphical analysis can be applied equally well to image or projection data. The method is validated using FDG uptake data from five healthy normal volunteers, by applying the technique to determine regional cerebral metabolic rate for glucose (rCMRglu) and the partition coefficient-related parameter P using various time ranges for the analysis. Results: The method is shown to be identical to analysis of image data. Variation with time range of the calculated values for regional cerebral glucose metabolism and the partition coefficient of tissue against plasma is shown to be due to the estimation methodology rather than the choice of analysis on projections or on images. Conclusion: The theory presented is shown to be valid for FDG determination of regional cerebral glucose metabolism. The absolute values of the rCMRglu and P are similar to those shown previously

An investigation of multiple time graphical analysis applied to projection data:Theory and validation

Purpose: The determination of tissue time-activity course and pharmokinetics in PET is normally performed by region-of-interest analysis of reconstructed images. However, in some cases, the same analysis may equally well be performed on the data in projections before reconstruction, avoiding the reconstruction of large time sequence data sets. This is especially important in 3D mode. Method: We present a theory that shows why multiple time/graphical analysis can be applied equally well to image or projection data. The method is validated using FDG uptake data from five healthy normal volunteers, by applying the technique to determine regional cerebral metabolic rate for glucose (rCMRglu) and the partition coefficient-related parameter P using various time ranges for the analysis. Results: The method is shown to be identical to analysis of image data. Variation with time range of the calculated values for regional cerebral glucose metabolism and the partition coefficient of tissue against plasma is shown to be due to the estimation methodology rather than the choice of analysis on projections or on images. Conclusion: The theory presented is shown to be valid for FDG determination of regional cerebral glucose metabolism. The absolute values of the rCMRglu and P are similar to those shown previously