Book Chapter10.1016/B978-0-444-87137-4.50007-2
Lily: A Software Package for Image Processing
Piet Dewaele,D. van den Oudenhoven,Johan Vandeneede,Rudi Bartels,Patrick Wambacq,André Oosterlinck +5 more
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TL;DR: LILY, a software package for image processing that has been developed with funding from a large number of Belgian industrial companies who are all interested in visual inspection problems, presents itself to the user as a large toolbox from which the appropriate tools must be taken to accomplish a certain task.
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Abstract: In this paper we will present LILY, a software package for image processing. LILY stands for Leuven Image processing LibrarY and has been developed with funding from a large number of Belgian industrial companies who are all interested in visual inspection problems. Therefore, the algorithms in the package are mainly concerned with this application field, although also other algorithms are incorporated. All procedures have been written in Pascal and Fortran on a VAX running VMS, with current work being done to convert the package to the C-language. Along with the programmed algorithms come a large number of support functions and procedures to facilitate the development of image processing programs. The package thus presents itself to the user as a large toolbox from which the appropriate tools must be taken to accomplish a certain task. The available procedures fall in one of the following categories: segmentation, coding, filtering, feature extraction, classification, texture analysis, relaxation, and pyramidal structures. In the first part of the paper, an overview will be given of the algorithms that are present in all these different classes. Syntactical conventions, documentation, maintenance and development tools will be discussed also. The second part of the paper is devoted to some specific problems that have been solved as a testcase for the algorithms. First Laws' procedure for texture segmentation is implemented and applied to the detection of defects in textiles. The procedure involves filtering the image with one or more suitably chosen masks, squaring the obtained values, computing the energy as a texture feature and classifying the resulting values. Each of these steps corresponds more or less to a different module of the package. A second industrial problem of defect inspection in unexposed radiographic film has been approached using three alternative techniques: one dimensional convolution filtering, Fourier domain filtering and polynomial regression.
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Citations
A visual programming interface for an image processing environment
TL;DR: The interface gives a layman the opportunity to develop powerful image processing applications in a two-dimensional, data flow metaphor that provides a better insight into the structure of an application than a traditional interface.
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A software environment for image interpretation
D.C. Koelma
- 01 Jan 1996
TL;DR: The relatable-object concept is used to determine whether the relationship exists but no relation object is instantiated to keep track of computed relations, so it is not really necessary to store such relations.
11
I-see: An AI tool for image understanding
TL;DR: The described environment, called I-see, is implemented on top of the object oriented KEE ∗ system and focuses on the iconical representation and exploration of visual data.
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LUCI, a portable software package for different fields of image processing research
Erwin Bellon,Patrick Wambacq,Frank Schoeters,Stefaan Luckermans,Guy Marchal,Paul Suetens +5 more
- 30 Apr 1992
TL;DR: The concepts of the LUCI image processing package are presented, which has been developed in order to meet requirements that have been formulated as a result of previous experiences in the research group.
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•Journal Article
Some Visual Inspection Problems in the Belgian Industry (Invited).
Patrick Wambacq,Piet Dewaele,Rudi Bartels,Johan Vandeneede,Dirk Van Den Oudenhoven,André Oosterlinck +5 more
TL;DR: A novel approach is introduced, whereby self-adaptive convolution filters are constructed from test samples, whereby the size and the coefficients of the convolution mask are deternuned from the texture that is to be inspected.
References
Textured Image Segmentation
Kenneth I Laws
- 01 Jan 1980
TL;DR: In this article, texture energy is measured by filtering with small masks, typically 5x5, then with a moving-window average of the absolute image values, leading to a simple class of texture energy transforms, which perform better than any of the preceding methods.
•Proceedings Article
Linear feature extraction and description
Ramakant Nevatia,K. Ramesh Babu +1 more
- 20 Aug 1979
TL;DR: A technique of edge detection and linking for linear feature extraction and its applications to detection of roads and runway like structures is described.
738
In search of a general picture processing operator
TL;DR: In this paper, a general, parallel and hierarchical operator for picture processing is defined which at different levels can detect and describe structure as opposed to uniformity within local regions, whatever structure and uniformity may imply at a particular level.
330
Linear feature extraction and description
Ramakant Nevatia,K. Ramesh Babu +1 more
TL;DR: In this paper, a technique of edge detection and line finding for linear feature extraction is described, where edge detection is by convolution with small edge-like masks, and the resulting output is thinned and linked by using edge positions and orientations and approximated by piecewise linear segments.