Changing images of mother/mothering

This paper examines how representations of the mother and mothering practices have altered between a generation of mothers and daughter. It also discusses the varied configurations of mother/mothering which occur at different times in women's lives, in other racial and ethnic situations, and which have been opened up by medical science through reproductive technology. Taking a broad definition of mothering, the paper points to the hierarchical division that have been created between women who pay, and are being paid for the care and education of their children in their early years. It argues the difficult and complex task for early childhood education and care is to keep pace and grapple with the ever-changing cirsumstances of those who nurture and care for the young.

Untangling teachers’ images of their futures through their responses to the futures narratives of children

A significant amount of research has been undertaken exploring individuals’ images of a future. What often remains uninterrogated are the ways in which others’ respond to these images of a future, as they are articulated through narrative discourses. This paper, then, seeks to address this void through the presentation of research which considers the ways that teachers’ perceptions of "the future" are consolidated and challenged, as they work with children in a classroom setting.

Becoming an artist: life histories and visual images

Chapter summary:
In this chapter, we consider the experiences of an art/research experiment that took place in the context of the annual conference of the British Sociaological Association (BSA), held at the University of East London in April 2007. The essay is in four parts: in the first section, the researcher gives the context of the project that underpinned the BSA event, mapping its theoretical directions and methodological moves. In the second section, the artist tells stories of becoming through words and images. The force of the artist’s narrative challenges and reconfigures discursively constructed boundaries between the researcher and the artist, initiating a dialogic encounter that unfolds in the third section as a visual/textual interface. This encounter revolves around the quest for meaning, which is after all what oral history is about (Portelli, 2011). Our quest for meaning actually inspired us to write about and problematize the BSA event. In this light, the final section looks critically into some of the questions that have arisen, situating them within wider problematics in the field of oral histories and narrative research.

Book summary:
Interviews are becoming an increasingly dominant research method in art, craft, design, fashion and textile history. This groundbreaking text demonstrates how artists, writers and historians deploy interviews as creative practice, as 'history', and as a means to insights into the micro-practices of arts production and identity that contribute to questions of 'voice', authenticity, and authorship. Through a wide range of case studies from international scholars and practitioners across a variety of fields, the volume maps how oral history interviews contribute to a relational practice that is creative, rigorous and ethically grounded.

Sparse representation with multi-manifold analysis for texture classification from few training images

Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades. Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k-Nearest-Neighbour for classification. Given enough training images the state-of-the-art texture classification methods could achieve very high classification accuracies on some benchmark databases. However, when the number of training images is limited, which usually happens in real-world applications because of the high cost of obtaining labelled data, the classification accuracies of those state-of-the-art methods would deteriorate due to the overfitting effect. In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images. By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi-manifold analysis framework for texture classification from few training images. A set of new training samples are generated from each training image by a scale and spatial pyramid, and then the training samples belonging to each class are modelled by a manifold based on sparse representation. We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors. The framework provides a more compact model than the template matching based texture classification methods, and mitigates the overfitting effect. Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images, and significantly outperforms the state-of-the-art texture classification approaches on three benchmark datasets. © 2014 Elsevier B.V. All rights reserved.

LGT/VOT tracking performance evaluation of depth images

This paper presents object tracking in depth, RGB and normal-maps images using LGT tracker. The depth and RGB images are rendered using depth imaging plugins. A series of experiments were held to evaluate the tracker performance in tracking objects in different image sequences. The experiments conducted were from the Visual Object Tracking (VOT) challenge that was arranged in association with ICCV'13 The accuracy was chosen as the evaluation measure, where the the tracker's bounding box was compared against the ground truth bounding box. Results show that tracking object using depth images gives better results and is more accurate than tracking using either the RGB or nomal maps images.

A novel method for detecting double compressed Facebook JPEG images

Images published on online social sites such as Facebook are increasingly prone to be misused for malicious purposes. However, existing image forensic research assumes that the investigator can confiscate every piece of evidence and hence overlooks the fact that the original image is difficult to obtain. Because Facebook applies a Discrete Cosine Transform (DCT)-based compression on uploaded images, we are able to detect the modified images which are re-uploaded to Facebook. Specifically, we propose a novel method to effectively detect the presence of double compression via the spatial domain of the image: We select small image patches from a given image, define a distance metric to measure the differences between compressed images, and propose an algorithm to infer whether the given image is double compressed without referring to the original image. To demonstrate the correctness of our algorithm, we correctly predict the number of compressions being applied to a Facebook image.

Inpainting images with curvilinear structures propagation

Inpainting images with smooth curvilinear structures interrupted is a challenging problem, because the structures are salient features sensitive to the human vision system and they are not easy to be completed in a visually pleasing way, especially when gaps are large. In this paper, we propose an approach to address this problem. A curve with a desired nice shape is first created to smoothly extend the missing structure from the known to unknown regions. As the curve partitions the unknown region into separate areas, textures can be filled independently into each area. We then adopt a patch-based texture inpainting method enhanced by a novel similarity measurement of patches. After that, very abrupt edges caused by different inpainted colours on their two sides need to be smoothed for natural colour transition across the curve. Experimental results demonstrate the effectiveness of the proposed approach. © 2014 Springer-Verlag Berlin Heidelberg.

A Bayesian nonparametric model for hierarchical sequence of images

 Scale features are useful for a great number of applications in computer vision. However, it is difficult to tolerate diversities of features in natural scenes by parametric methods. Empirical studies show that object frequencies and segment sizes follow the power law distributions which are well generated by Pitman-Yor (PY) processes. Based on mid-level segments, we propose a hierarchical sequence of images to obtain scale information stored in a hierarchical structure through the hierarchical Pitman-Yor (HPY) model which is expected to tolerate uncertainty of natural images. We also evaluate our representation by the application of segmentation.

Representing images using curvilinear feature driven subdivision surfaces.

This paper presents a subdivision-based vector graphics for image representation and creation. The graphics representation is a subdivision surface defined by a triangular mesh augmented with color attribute at vertices and feature attribute at edges. Special cubic B-splines are proposed to describe curvilinear features of an image. New subdivision rules are then designed accordingly, which are applied to the mesh and the color attribute to define the spatial distribution and piecewise-smoothly varying colors of the image. A sharpness factor is introduced to control the color transition across the curvilinear edges. In addition, an automatic algorithm is developed to convert a raster image into such a vector graphics representation. The algorithm first detects the curvilinear features of the image, then constructs a triangulation based on the curvilinear edges and feature attributes, and finally iteratively optimizes the vertex color attributes and updates the triangulation. Compared with existing vector-based image representations, the proposed representation and algorithm have the following advantages in addition to the common merits (such as editability and scalability): 1) they allow flexible mesh topology and handle images or objects with complicated boundaries or features effectively; 2) they are able to faithfully reconstruct curvilinear features, especially in modeling subtle shading effects around feature curves; and 3) they offer a simple way for the user to create images in a freehand style. The effectiveness of the proposed method has been demonstrated in experiments.

3D Intervertebral disc localization and segmentation from MR images by data-driven regression and classification

In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks.