21,053 research outputs found
Location-based indexing for mobile context-aware access to a digital library
Mobile information systems need to collaborate with each other to provide seamless information access to the user. Information about the user and their context provides the points of contact between the systems. Location is the most basic user context.
TIP is a mobile tourist information system that provides location-based access to documents in the digital library Greenstone. This paper identifies the challenges for providing effcient access to location-based information using the various access modes a tourist requires on their travels. We discuss our extended 2DR-tree approach to meet these challenges
The study of probability model for compound similarity searching
Information Retrieval or IR system main task is to retrieve relevant documents according to the users query. One of IR most popular retrieval model is the Vector Space Model. This model assumes relevance based on similarity, which is defined as the distance between query and document in the concept space. All currently existing chemical compound database systems have adapt the vector space model to calculate the similarity of a database entry to a query compound. However, it assumes that fragments represented by the bits are independent of one another, which is not necessarily true. Hence, the possibility of applying another IR model is explored, which is the Probabilistic Model, for chemical compound searching. This model estimates the probabilities of a chemical structure to have the same bioactivity as a target compound. It is envisioned that by ranking chemical structures in decreasing order of their probability of relevance to the query structure, the effectiveness of a molecular similarity searching system can be increased. Both fragment dependencies and independencies assumption are taken into consideration in achieving improvement towards compound similarity searching system. After conducting a series of simulated similarity searching, it is concluded that PM approaches really did perform better than the existing similarity searching. It gave better result in all evaluation criteria to confirm this statement. In terms of which probability model performs better, the BD model shown improvement over the BIR model
Visual Landmark Recognition from Internet Photo Collections: A Large-Scale Evaluation
The task of a visual landmark recognition system is to identify photographed
buildings or objects in query photos and to provide the user with relevant
information on them. With their increasing coverage of the world's landmark
buildings and objects, Internet photo collections are now being used as a
source for building such systems in a fully automatic fashion. This process
typically consists of three steps: clustering large amounts of images by the
objects they depict; determining object names from user-provided tags; and
building a robust, compact, and efficient recognition index. To this date,
however, there is little empirical information on how well current approaches
for those steps perform in a large-scale open-set mining and recognition task.
Furthermore, there is little empirical information on how recognition
performance varies for different types of landmark objects and where there is
still potential for improvement. With this paper, we intend to fill these gaps.
Using a dataset of 500k images from Paris, we analyze each component of the
landmark recognition pipeline in order to answer the following questions: How
many and what kinds of objects can be discovered automatically? How can we best
use the resulting image clusters to recognize the object in a query? How can
the object be efficiently represented in memory for recognition? How reliably
can semantic information be extracted? And finally: What are the limiting
factors in the resulting pipeline from query to semantics? We evaluate how
different choices of methods and parameters for the individual pipeline steps
affect overall system performance and examine their effects for different query
categories such as buildings, paintings or sculptures
Chemoinformatics Research at the University of Sheffield: A History and Citation Analysis
This paper reviews the work of the Chemoinformatics Research Group in the Department of Information Studies at the University of Sheffield, focusing particularly on the work carried out in the period 1985-2002. Four major research areas are discussed, these involving the development of methods for: substructure searching in databases of three-dimensional structures, including both rigid and flexible molecules; the representation and searching of the Markush structures that occur in chemical patents; similarity searching in databases of both two-dimensional and three-dimensional structures; and compound selection and the design of combinatorial libraries. An analysis of citations to 321 publications from the Group shows that it attracted a total of 3725 residual citations during the period 1980-2002. These citations appeared in 411 different journals, and involved 910 different citing organizations from 54 different countries, thus demonstrating the widespread impact of the Group's work
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
Phase Retrieval with Application to Optical Imaging
This review article provides a contemporary overview of phase retrieval in
optical imaging, linking the relevant optical physics to the information
processing methods and algorithms. Its purpose is to describe the current state
of the art in this area, identify challenges, and suggest vision and areas
where signal processing methods can have a large impact on optical imaging and
on the world of imaging at large, with applications in a variety of fields
ranging from biology and chemistry to physics and engineering
3D differential phase contrast microscopy
We demonstrate 3D phase and absorption recovery from partially coherent intensity images captured with a programmable LED array source. Images are captured through-focus with four different illumination patterns. Using first Born and weak object approximations (WOA), a linear 3D differential phase contrast (DPC) model is derived. The partially coherent transfer functions relate the sample's complex refractive index distribution to intensity measurements at varying defocus. Volumetric reconstruction is achieved by a global FFT-based method, without an intermediate 2D phase retrieval step. Because the illumination is spatially partially coherent, the transverse resolution of the reconstructed field achieves twice the NA of coherent systems and improved axial resolution
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
We present a new deep learning architecture (called Kd-network) that is
designed for 3D model recognition tasks and works with unstructured point
clouds. The new architecture performs multiplicative transformations and share
parameters of these transformations according to the subdivisions of the point
clouds imposed onto them by Kd-trees. Unlike the currently dominant
convolutional architectures that usually require rasterization on uniform
two-dimensional or three-dimensional grids, Kd-networks do not rely on such
grids in any way and therefore avoid poor scaling behaviour. In a series of
experiments with popular shape recognition benchmarks, Kd-networks demonstrate
competitive performance in a number of shape recognition tasks such as shape
classification, shape retrieval and shape part segmentation.Comment: Spotlight at ICCV'1
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