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In the late 1960s and early 1970s, disability rights found a place on the U.S. policy agenda. However, it did not do so because social movement groups pressured political elites or because politicians were responding to changes in public preferences. Drawing from recent work in neo-institutionalism and social movements, namely the theory of strategic action fields, I posit that exogenous shocks in the 1960s caused a disability policy monopoly to collapse giving way to a new policy community. Using original longitudinal data on congressional committees, hearings, bills, and laws, as well as data from the Policy Agendas Project, I demonstrate the ways in which entrepreneurs pursued a new policy image of rights within a context of increasing committee involvement, issue complexity, and space on the policy agenda, and the consequences this had on policy.

Faced with increasingly tight budgets many museums have come under pressure to explore new sources of income. The marketing of museum assets seems to offer one possibility especially since there is an apparently insatiable demand for content from the multimedia and ‘edutainment’ industries. Furthermore, providing access to the cultural heritage of Europe has come to be seen as a major aim of the EU and various programmes have been set up to encourage and facilitate this. Apart from the economic potential of digital museum content, the concomitant legal and security issues are investigated.

This study aims to examine why users search for images, how users describe their image needs and what the images are used for by analysing questions obtained from two Chinese social Q&A sites, Zhihu and Baidu Zhidao.

A total of 1,402 image questions were collected from Zhihu and Baidu Zhidao. Both quantitative analysis and qualitative content analysis were performed to identify user image needs and the potential differences on the two social Q&A sites.

Question-asker’s intention varies in different platforms. Zhihu users asked questions mainly aiming at a promotion of subsequent discussion, whereas users of Baidu Zhidao often did so to seek information. Syntactic attributes were not frequently used in both two sites. Zhihu users were more likely to express subjective evaluations on images (concept, emotion, theme and style) in their questions than users of Baidu Zhidao. In contrast, questions from Baidu Zhidao showed a tendency to more frequently include descriptive metadata (rights, format, size, quality and authenticity) and semantic attributes (generic activity, specific people, fashion and text) of the images than questions from Zhihu. Learning was an important use on social Q&A sites, especially on Baidu Zhidao. In addition, the images were primarily used to trigger emotion or served a persuasive purpose in Zhihu.

This study contributes to a better understanding of user image search behaviour, and the findings could be used to develop better image services on social Q&A sites. Meanwhile, the image attributes extracted from the questions are conducive to the improvement of image retrieval systems.

This study explored the features of image needs on social Q&A sites, especially considering image use specified in the question. The difference of image needs between two Chinese social Q&A sites (Zhihu and Baidu Zhidao) was identified.

Searching for appropriate images as part of a work task is a non-trivial problem. Journalists and copywriters need to find images that are not only visually appropriate to accompany the documents they are creating, but are acceptably priced and licensed. The paper aims to discuss these issues.

A work-based study methodology and grounded theory are used to collect qualitative data from a variety of creative professionals including journalists.

The authors report the findings of a study to investigate image search, retrieval and use by creative professionals who routinely use images as part of their work in an online environment. The authors describe the commercial constraints that have an impact on the image users’ behaviour that are not reported in other more academic and lab-based studies of image use (Westman, 2009).

The authors show that the commercial image retrieval systems are based on document retrieval systems, and that this is not the most appropriate approach in the journalism domain.

The authors describe the properties of an “information expedition”; the image seeking behaviour exhibited by journalists in an online environment, and contend that it is significantly different to existing image seeking models which represent other user types.

Multiresolution representations of data are classical tools in image processing applications. The purpose of this paper is to discuss a particular problem, obtaining good reconstructions of noise images.

A nonlinear multiresolution scheme within Harten's framework corresponding to a nonlinear cell‐average technique is used for color image denoising.

This paper finds it is possible, for example, to apply the theoretical framework to case studies in internationally operating companies delivering a mix of goods and services.

The present study provides a starting point for further research in the denoising problems using nonlinear techniques.

Moreover, the proposed framework has proven to be useful in improving the classical linear multiresolution approaches.

Palmprint recognition is a very interesting and promising area of research. Much work has already been done in this area, but much more needs to be done to make the systems more efficient. In this paper, a multimodal biometrics system based on fusion of left and right palmprints of a person is proposed to overcome limitations of unimodal systems.

Features are extracted using some proposed multi-block local descriptors in addition to MBLBP. Fusion of extracted features is done at feature level by a simple concatenation of feature vectors. Then, feature selection is performed on the resulting global feature vector using evolutionary algorithms such as genetic algorithms and backtracking search algorithm for a comparison purpose. The benefits of such step selecting the relevant features are known in the literature, such as increasing the recognition accuracy and reducing the feature set size, which results in runtime saving. In matching step, Chi-square similarity measure is used.

The resulting feature vector length representing a person is compact and the runtime is reduced.

Intensive experiments were done on the publicly available IITD database. Experimental results show a recognition accuracy of 99.17 which prove the effectiveness and robustness of the proposed multimodal biometrics system than other unimodal and multimodal biometrics systems.

To explore the phenomenon of stereoscopic vision and its exploitation in engineering and other professional applications, and in entertainment.

Starts with a review of how stereo vision works, and the techniques used in 3D movies to present the illusion of depth and movement at right angles to the screen. Looks at some engineering products that build on these techniques, and then at the development of 3D television, based on a different image separation method. Finally looks at developments in stereo machine vision.

A variety of techniques exist to present left and right views of a scene to the correct eyes and stimulate 3D perception: for example, light‐filtering, alternate‐frame sequencing and optical separation. Fatigue occurs when there is crosstalk between those images, or when the images are presented at too low a frame rate. Many computer modelling software providers produce programs with 3D‐viewing capability for professional engineers. There are some exciting recent developments, such as add‐on PC stereo systems, and 3D TV.

Makes the general scientist aware of the wide range of professional uses of stereo vision, and of the engineering challenges behind 3D film and television.

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.