Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Simplification and generalization of large scale data for roads : a comparison of two filtering algorithms

This paper reports the results of an in-depth study which investigated two algorithms for line simplification and caricatural generalization (namely, those developed by Douglas and Peucker, and Visvalingam, respectively) in the context of a wider program of research on scale-free mapping. The use of large-scale data for man-designed objects, such as roads, has led to a better understanding of the properties of these algorithms and of their value within the spectrum of scale-free mapping. The Douglas-Peucker algorithm is better at minimal simplification. The large-scale data for roads makes it apparent that Visvalingam's technique is not only capable of removing entire scale-related features, but that it does so in a manner which preserves the shape of retained features. This technique offers some prospects for the construction of scale-free databases since it offers some scope for achieving balanced generalizations of an entire map, consisting of several complex lines. The results also suggest that it may be easier to formulate concepts and strategies for automatic segmentation of in-line features using large-scale road data and Visvalingam's algorithm. In addition, the abstraction of center lines may be facilitated by the inclusion of additional filtering rules with Visvalingam's algorithm

Engineering Innovation (TRIZ based Computer Aided Innovation)

This thesis describes the approach and results of the research to create a TRIZ based computer aided innovation tools (AEGIS and Design for Wow). This research has mainly been based around two tools created under this research: called AEGIS (Accelerated Evolutionary Graphics Interface System), and Design for Wow. Both of these tools are discussed in this thesis in detail, along with the test data, design methodology, test cases, and research. Design for Wow (http://www.designforwow.com) is an attempt to summarize the successful inventions/ designs from all over the world on a web portal which has multiple capabilities. These designs/innovations are then linked to the TRIZ Principles in order to determine whether innovative aspects of these successful innovations are fully covered by the forty TRIZ principles. In Design for Wow, a framework is created which is implemented through a review tool. The Design for Wow website includes this tool which has been used by researcher and the users of the site and reviewers to analyse the uploaded data in terms of strength of TRIZ Principles linked to them. AEGIS (Accelerated Evolutionary Graphics Interface System) is a software tool developed under this research aimed to help the graphic designers to make innovative graphic designs. Again it uses the forty TRIZ Principles as a set of guiding rules in the software. AEGIS creates graphic design prototypes according to the user input and uses TRIZ Principles framework as a guide to generate innovative graphic design samples. The AEGIS tool created is based on TRIZ Principles discussed in Chapter 3 (a subset of them). In AEGIS, the TRIZ Principles are used to create innovative graphic design effects. The literature review on innovative graphic design (in chapter 3) has been analysed for links with TRIZ Principles and then the DNA of AEGIS has been built on the basis of this study. Results from various surveys/ questionnaires indicated were used to collect the innovative graphic design samples and then TRIZ was mapped to it (see section 3.2). The TRIZ effects were mapped to the basic graphic design elements and the anatomy of the graphic design letters was studied to analyse the TRIZ effects in the collected samples. This study was used to build the TRIZ based AEGIS tool. Hence, AEGIS tool applies the innovative effects using TRIZ to basic graphic design elements (as described in section 3.3). the working of AEGIS is designed based on Genetic Algorithms coded specifically to implement TRIZ Principles specialized for Graphic Design, chapter 4 discusses the process followed to apply TRIZ Principles to graphic design and coding them using Genetic Algorithms, hence resulting in AEGIS tool. Similarly, in Design for Wow, the content uploaded has been analysed for its link with TRIZ Principles (see section 3.1 for TRIZ Principles). The tool created in Design for Wow is based on the framework of analysing the TRIZ links in the uploaded content. The ‘Wow’ concept discussed in the section 5.1 and 5.2 is the basis of the concept of Design for Wow website, whereby the users upload the content they classify as ‘Wow’. This content then is further analysed for the ‘Wow factor’ and then mapped to TRIZ Principles as TRIZ tagging methodology is framed (section 5.5). From the results of the research, it appears that the TRIZ Principles are a comprehensive set of innovation basic building blocks. Some surveys suggest that amongst other tools, TRIZ Principles were the first choice and used most .They have thus the potential of being used in other innovation domains, to help in their analysis, understanding and potential development.Great Western Research and Systematic Innovation Ltd U

Vision Science and Technology at NASA: Results of a Workshop

A broad review is given of vision science and technology within NASA. The subject is defined and its applications in both NASA and the nation at large are noted. A survey of current NASA efforts is given, noting strengths and weaknesses of the NASA program

Enabling Seamless Access to Digital Graphical Contents for Visually Impaired Individuals via Semantic-Aware Processing

Vision is one of the main sources through which people obtain information from the world, but unfortunately, visually-impaired people are partially or completely deprived of this type of information. With the help of computer technologies, people with visual impairment can independently access digital textual information by using text-to-speech and text-to-Braille software. However, in general, there still exists a major barrier for people who are blind to access the graphical information independently in real-time without the help of sighted people. In this paper, we propose a novel multi-level and multi-modal approach aiming at addressing this challenging and practical problem, with the key idea being semantic-aware visual-to-tactile conversion through semantic image categorization and segmentation, and semantic-driven image simplification. An end-to-end prototype system was built based on the approach. We present the details of the approach and the system, report sample experimental results with realistic data, and compare our approach with current typical practice

Digital Libraries, Intelligent Data Analytics, and Augmented Description: A Demonstration Project

From July 16-to November 8, 2019, the Aida digital libraries research team at the University of Nebraska-Lincoln collaborated with the Library of Congress on “Digital Libraries, Intelligent Data Analytics, and Augmented Description: A Demonstration Project.“ This demonstration project sought to (1) develop and investigate the viability and feasibility of textual and image-based data analytics approaches to support and facilitate discovery; (2) understand technical tools and requirements for the Library of Congress to improve access and discovery of its digital collections; and (3) enable the Library of Congress to plan for future possibilities. In pursuit of these goals, we focused our work around two areas: extracting and foregrounding visual content from Chronicling America (chroniclingamerica.loc.gov) and applying a series of image processing and machine learning methods to minimally processed manuscript collections featured in By the People (crowd.loc.gov). We undertook a series of explorations and investigated a range of issues and challenges related to machine learning and the Library’s collections. This final report details the explorations, addresses social and technical challenges with regard to the explorations and that are critical context for the development of machine learning in the cultural heritage sector, and makes several recommendations to the Library of Congress as it plans for future possibilities. We propose two top-level recommendations. First, the Library should focus the weight of its machine learning efforts and energies on social and technical infrastructures for the development of machine learning in cultural heritage organizations, research libraries, and digital libraries. Second, we recommend that the Library invest in continued, ongoing, intentional explorations and investigations of particular machine learning applications to its collections. Both of these top-level recommendations map to the three goals of the Library’s 2019 digital strategy. Within each top-level recommendation, we offer three more concrete, short- and medium-term recommendations. They include, under social and technical infrastructures: (1) Develop a statement of values or principles that will guide how the Library of Congress pursues the use, application, and development of machine learning for cultural heritage. (2) Create and scope a machine learning roadmap for the Library that looks both internally to the Library of Congress and its needs and goals and externally to the larger cultural heritage and other research communities. (3) Focus efforts on developing ground truth sets and benchmarking data and making these easily available. Nested under the recommendation to support ongoing explorations and investigations, we recommend that the Library: (4) Join the Library of Congress’s emergent efforts in machine learning with its existing expertise and leadership in crowdsourcing. Combine these areas as “informed crowdsourcing” as appropriate. (5) Sponsor challenges for teams to create additional metadata for digital collections in the Library of Congress. As part of these challenges, require teams to engage across a range of social and technical questions and problem areas. (6) Continue to create and support opportunities for researchers to partner in substantive ways with the Library of Congress on machine learning explorations. Each of these recommendations speak to the investigation and challenge areas identified by Thomas Padilla in Responsible Operations: Data Science, Machine Learning, and AI in Libraries. This demonstration project—via its explorations, discussion, and recommendations—shows the potential of machine learning toward a variety of goals and use cases, and it argues that the technology itself will not be the hardest part of this work. The hardest part will be the myriad challenges to undertaking this work in ways that are socially and culturally responsible, while also upholding responsibility to make the Library of Congress’s materials available in timely and accessible ways. Fortunately, the Library of Congress is in a remarkable position to advance machine learning for cultural heritage organizations, through its size, the diversity of its collections, and its commitment to digital strategy