5,527 research outputs found
Intelligent computational sketching support for conceptual design
Sketches, with their flexibility and suggestiveness, are in many ways ideal for expressing emerging design concepts. This can be seen from the fact that the process of representing early designs by free-hand drawings was used as far back as in the early 15th century [1]. On the other hand, CAD systems have become widely accepted as an essential design tool in recent years, not least because they provide a base on which design analysis can be carried out. Efficient transfer of sketches into a CAD representation, therefore, is a powerful addition to the designers' armoury.It has been pointed out by many that a pen-on-paper system is the best tool for sketching. One of the crucial requirements of a computer aided sketching system is its ability to recognise and interpret the elements of sketches. 'Sketch recognition', as it has come to be known, has been widely studied by people working in such fields: as artificial intelligence to human-computer interaction and robotic vision. Despite the continuing efforts to solve the problem of appropriate conceptual design modelling, it is difficult to achieve completely accurate recognition of sketches because usually sketches implicate vague information, and the idiosyncratic expression and understanding differ from each designer
Structured Knowledge Representation for Image Retrieval
We propose a structured approach to the problem of retrieval of images by
content and present a description logic that has been devised for the semantic
indexing and retrieval of images containing complex objects. As other
approaches do, we start from low-level features extracted with image analysis
to detect and characterize regions in an image. However, in contrast with
feature-based approaches, we provide a syntax to describe segmented regions as
basic objects and complex objects as compositions of basic ones. Then we
introduce a companion extensional semantics for defining reasoning services,
such as retrieval, classification, and subsumption. These services can be used
for both exact and approximate matching, using similarity measures. Using our
logical approach as a formal specification, we implemented a complete
client-server image retrieval system, which allows a user to pose both queries
by sketch and queries by example. A set of experiments has been carried out on
a testbed of images to assess the retrieval capabilities of the system in
comparison with expert users ranking. Results are presented adopting a
well-established measure of quality borrowed from textual information
retrieval
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Where and Who? Automatic Semantic-Aware Person Composition
Image compositing is a method used to generate realistic yet fake imagery by
inserting contents from one image to another. Previous work in compositing has
focused on improving appearance compatibility of a user selected foreground
segment and a background image (i.e. color and illumination consistency). In
this work, we instead develop a fully automated compositing model that
additionally learns to select and transform compatible foreground segments from
a large collection given only an input image background. To simplify the task,
we restrict our problem by focusing on human instance composition, because
human segments exhibit strong correlations with their background and because of
the availability of large annotated data. We develop a novel branching
Convolutional Neural Network (CNN) that jointly predicts candidate person
locations given a background image. We then use pre-trained deep feature
representations to retrieve person instances from a large segment database.
Experimental results show that our model can generate composite images that
look visually convincing. We also develop a user interface to demonstrate the
potential application of our method.Comment: 10 pages, 9 figure
Matching software-generated sketches to face photographs with a very deep CNN, morphed faces, and transfer learning
Sketches obtained from eyewitness descriptions of criminals have proven to be useful in apprehending criminals, particularly when there is a lack of evidence. Automated methods to identify subjects depicted in sketches have been proposed in the literature, but their performance is still unsatisfactory when using software-generated sketches and when tested using extensive galleries with a large amount of subjects. Despite the success of deep learning in several applications including face recognition, little work has been done in applying it for face photograph-sketch recognition. This is mainly a consequence of the need to ensure robust training of deep networks by using a large number of images, yet limited quantities are publicly available. Moreover, most algorithms have not been designed to operate on software-generated face composite sketches which are used by numerous law enforcement agencies worldwide. This paper aims to tackle these issues with the following contributions: 1) a very deep convolutional neural network is utilised to determine the identity of a subject in a composite sketch by comparing it to face photographs and is trained by applying transfer learning to a state-of-the-art model pretrained for face photograph recognition; 2) a 3-D morphable model is used to synthesise both photographs and sketches to augment the available training data, an approach that is shown to significantly aid performance; and 3) the UoM-SGFS database is extended to contain twice the number of subjects, now having 1200 sketches of 600 subjects. An extensive evaluation of popular and stateof-the-art algorithms is also performed due to the lack of such information in the literature, where it is demonstrated that the proposed approach comprehensively outperforms state-of-the-art methods on all publicly available composite sketch datasets.peer-reviewe
Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation
This paper proposes to generate and to use barcodes to annotate medical
images and/or their regions of interest such as organs, tumors and tissue
types. A multitude of efficient feature-based image retrieval methods already
exist that can assign a query image to a certain image class. Visual
annotations may help to increase the retrieval accuracy if combined with
existing feature-based classification paradigms. Whereas with annotations we
usually mean textual descriptions, in this paper barcode annotations are
proposed. In particular, Radon barcodes (RBC) are introduced. As well, local
binary patterns (LBP) and local Radon binary patterns (LRBP) are implemented as
barcodes. The IRMA x-ray dataset with 12,677 training images and 1,733 test
images is used to verify how barcodes could facilitate image retrieval.Comment: To be published in proceedings of The IEEE International Conference
on Image Processing (ICIP 2015), September 27-30, 2015, Quebec City, Canad
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