28,067 research outputs found
Template-Cut: A Pattern-Based Segmentation Paradigm
We present a scale-invariant, template-based segmentation paradigm that sets
up a graph and performs a graph cut to separate an object from the background.
Typically graph-based schemes distribute the nodes of the graph uniformly and
equidistantly on the image, and use a regularizer to bias the cut towards a
particular shape. The strategy of uniform and equidistant nodes does not allow
the cut to prefer more complex structures, especially when areas of the object
are indistinguishable from the background. We propose a solution by introducing
the concept of a "template shape" of the target object in which the nodes are
sampled non-uniformly and non-equidistantly on the image. We evaluate it on
2D-images where the object's textures and backgrounds are similar, and large
areas of the object have the same gray level appearance as the background. We
also evaluate it in 3D on 60 brain tumor datasets for neurosurgical planning
purposes.Comment: 8 pages, 6 figures, 3 tables, 6 equations, 51 reference
Advanced content-based semantic scene analysis and information retrieval: the SCHEMA project
The aim of the SCHEMA Network of Excellence is to bring together a critical mass of universities, research centers, industrial partners and end users, in order to design a reference system for content-based semantic scene analysis, interpretation and understanding. Relevant research areas include: content-based multimedia analysis and automatic annotation of semantic multimedia content, combined textual and multimedia information retrieval, semantic -web, MPEG-7 and MPEG-21 standards, user interfaces and human factors. In this paper, recent advances in content-based analysis, indexing and retrieval of digital media within the SCHEMA Network are presented. These advances will be integrated in the SCHEMA module-based, expandable reference system
Ball-Scale Based Hierarchical Multi-Object Recognition in 3D Medical Images
This paper investigates, using prior shape models and the concept of ball
scale (b-scale), ways of automatically recognizing objects in 3D images without
performing elaborate searches or optimization. That is, the goal is to place
the model in a single shot close to the right pose (position, orientation, and
scale) in a given image so that the model boundaries fall in the close vicinity
of object boundaries in the image. This is achieved via the following set of
key ideas: (a) A semi-automatic way of constructing a multi-object shape model
assembly. (b) A novel strategy of encoding, via b-scale, the pose relationship
between objects in the training images and their intensity patterns captured in
b-scale images. (c) A hierarchical mechanism of positioning the model, in a
one-shot way, in a given image from a knowledge of the learnt pose relationship
and the b-scale image of the given image to be segmented. The evaluation
results on a set of 20 routine clinical abdominal female and male CT data sets
indicate the following: (1) Incorporating a large number of objects improves
the recognition accuracy dramatically. (2) The recognition algorithm can be
thought as a hierarchical framework such that quick replacement of the model
assembly is defined as coarse recognition and delineation itself is known as
finest recognition. (3) Scale yields useful information about the relationship
between the model assembly and any given image such that the recognition
results in a placement of the model close to the actual pose without doing any
elaborate searches or optimization. (4) Effective object recognition can make
delineation most accurate.Comment: This paper was published and presented in SPIE Medical Imaging 201
Fuzzy Clustering for Image Segmentation Using Generic Shape Information
The performance of clustering algorithms for image segmentation are highly sensitive to the features used and types of objects in the image, which ultimately limits their generalization capability. This provides strong motivation to investigate integrating shape information into the clustering framework to improve the generality of these algorithms. Existing shape-based clustering techniques mainly focus on circular and elliptical clusters and so are unable to segment arbitrarily-shaped objects. To address this limitation, this paper presents a new shape-based algorithm called fuzzy clustering for image segmentation using generic shape information (FCGS), which exploits the B-spline representation of an object's shape in combination with the Gustafson-Kessel clustering algorithm. Qualitative and quantitative results for FCGS confirm its superior segmentation performance consistently compared to well-established shape-based clustering techniques, for a wide range of test images comprising various regular and arbitrary-shaped objects
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
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