Narrative Development Later in Life: A Novel Perspective

Prevailing paradigms in gerontology tend to eclipse the creative side of aging, implicitly perceiving it in terms of a narrative of decline. Building on insights from the field of narrative gerontology, this paper proposes an explicitlyliterary metaphor for understanding the subjective experience of aging, one in which our lives themselves are conceived in textual terms: As novels we are continually composing––as author, narrator, protagonist, and reader more or less at once. Drawing on literary theorist Mikhail Bahktin, the paper argues the merits of the metaphor of life-as-novel, notes the entailments it carries with it, and enlists it to deepen our understanding of narrative development in later life, with special emphasis on the challenges such development can face. The paper concludes by discussing the implications of a “novel perspective” for the practice of narrative care with older adults and for future research into the poetics of growing old

Narrative Development Later in Life

Prevailing paradigms in gerontology tend to eclipse the creative side of aging, implicitly perceiving it in terms of a narrative of decline. Building on insights from the field of narrative gerontology, this paper proposes an explicitlyliterary metaphor for understanding the subjective experience of aging, one in which our lives themselves are conceived in textual terms: As novels we are continually composing––as author, narrator, protagonist, and reader more or less at once. Drawing on literary theorist Mikhail Bahktin, the paper argues the merits of the metaphor of life-as-novel, notes the entailments it carries with it, and enlists it to deepen our understanding of narrative development in later life, with special emphasis on the challenges such development can face. The paper concludes by discussing the implications of a “novel perspective” for the practice of narrative care with older adults and for future research into the poetics of growing old

Classification of Graphomotor Impressions Using Convolutional Neural Networks: An Application to Automated Neuro-Psychological Screening Tests

Graphomotor impressions are a product of complex cognitive, perceptual and motor skills and are widely used as psychometric tools for the diagnosis of a variety of neuro-psychological disorders. Apparent deformations in these responses are quantified as errors and are used are indicators of various conditions. Contrary to conventional assessment methods where manual analysis of impressions is carried out by trained clinicians, an automated scoring system is marked by several challenges. Prior to analysis, such computerized systems need to extract and recognize individual shapes drawn by subjects on a sheet of paper as an important pre-processing step. The aim of this study is to apply deep learning methods to recognize visual structures of interest produced by subjects. Experiments on figures of Bender Gestalt Test (BGT), a screening test for visuo-spatial and visuo-constructive disorders, produced by 120 subjects, demonstrate that deep feature representation brings significant improvements over classical approaches. The study is intended to be extended to discriminate coherent visual structures between produced figures and expected prototypes

iVision HHID: Handwritten Hyperspectral Images Dataset for Benchmarking Hyperspectral Imaging-based Document Forensic Analysis

We present a dataset of hyperspectral images of handwriting samples collected from 54 individuals. The purpose of the presented dataset is to further explore the use of hyperspectral imaging in document image analysis and to benchmark the performance of forensic analysis methods for hyperspectral document images. Each hyperspectral cube in the dataset has a spatial resolution of 512 × 650 pixels and contains 149 spectral channels in the spectral range of 478∼901nm. All the individuals have different personalities and have their writing patterns. The information of age and gender of each individual is collected. Each subject has written twenty-eight sentences using 12 different varieties of pens from different brands in blue color, each approximately 9 words or 33 characters long, all English alphabets in capital and small cases, digits from 0-9. The previous methods use synthetic mixed samples created by joining different parts of the images from the UWA WIHSI dataset.Each document consists of real mixed samples written with different pens and by different writers with a variety of mixing ratios of inks and writers for forensic analysis.The standard A4 pages, each weighing 70 grams and manufactured by “AA” company, are used for data collection. The handwritten notes written by each subject with different pens are annotated in rectangular boxes. This dataset can be used for several tasks related to hyperspectral document image analysis and document forensic analysis including, handwritten optical character recognition, ink mismatch detection, writer identification at sentence, word, and character-level, handwriting-based gender classification, handwriting-based age prediction, handwritten word segmentation, and word generation. This dataset was designed and collected by the research team at the Artificial intelligence and Computer Vision Lab (iVision), Institute of Space Technology, Pakistan, and the hyperspectral images were acquired through imaging spectroscopy in the visible wavelength range at Wageningen University Research, the Netherland

Using Co-occurrence and Granulometry Features for Content Based Image Retrieval

This communication presents a novel system for Content Based Image Retrieval (CBIR) using Granulometry and Color Co-occurrence Features (CCF). These features are extracted directly from images using visual codebook. Relative distance measures are used to identify the similarity between the stored images and the query image. Results show that proposed method of using Granulometry and CCF is superior to most state of the art CBIR systems. The proposed system is tested on Wang image database that contains 1000 images having different categories. The performance of the system, quantified using the Average Precision Rate (APR), is very encouraging

Optimized Class-Separability in Hyperspectral Images

International audienceImage visualization techniques are mostly based on three bands as RGB color composite channels for human eye to characterize the scene. This, however, is not effective in case of hyper-spectral images (HSI) because they contain dozens of informative spectral bands. To eliminate redundancy of spectral information among these bands, dimensionality reduction (DR) is applied while at the same trying to retain maximum information. In this paper, we propose a new method of information-preserved hyper-spectral satellite image visualization that is based on fusion of unsupervised band selection techniques and color matching function (CMF) stretching. The results show consistent, edge-preserved and pre-attentive feature less images with high class separability. Different visualization techniques are compared to demonstrate the effectiveness of our scheme that can prompt an important advancement in the field

On the improvement of foreground–background model‐based object tracker

In this study, the authors propose two kinds of improvements to a baseline tracker that employs the tracking‐by‐detection framework. First, they explore different feature spaces by employing features commonly used in object detection to improve the performance of detector in feature space. Second, they propose a robust scale estimation algorithm that estimates the size of the object in the current frame. Their experimental results on the challenging online tracking benchmark‐13 dataset show that reduced dimensionality histogram of oriented gradients boosts the performance of the tracker. The proposed scale estimation algorithm provides a significant gain and reduces the failure of the tracker in challenging scenarios. The improved tracker is compared with 13 state‐of‐the‐art trackers. The quantitative and qualitative results show that the performance of the tracker is comparable with the state of the art against initialisation errors, variations in illumination, scale and motion, out‐of‐plane and in‐plane rotations, deformations and low resolution