A flexible framework for developing a cooperative intelligent image analysis system

Ranaweera, Kamal; Samarabandu, Jagath

doi:10.1117/12.473101

Cited by 3 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…6 This framework is able to use expert image analysis knowledge to generate processing plans to accomplish user defined image analysis task goals. In this paper, we present a generalized approach to incoorporate learning capability into this framework.…”

Section: Discussionmentioning

confidence: 99%

“…This high level knowledge is used by each tool to estimate its suitability to accomplish a given task goal. 7 When a processing tool discoverers its suitability to perform a given image analysis task, the tool cooperatively work with other tools to generate a processing plan to accomplish this task. Such a processing plan consists of one or more processing steps, each representing the contribution of a particular processing tool to achieve the task goal.…”

Section: Processing Toolsmentioning

confidence: 99%

“…6 This framework accepts user defined task descriptions presented using abstract conceptual terms in application domains, and then generates processing plans to accomplish these task goals. Figure 1 illustrates a block digram of this framework.…”

Section: Architecture Of the Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

A knowledge-capturing approach for a cooperative intelligent image analysis system

Ranaweera

Samarabandu

2004

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

We present a generalized approach to dynamically incorporate high level knowledge into a cooperative intelligent image analysis framework. We developed this framework in our laboratory to provide a uniform interface to develop intelligent image analysis tools as well as to provide infrastructure facilities required by these tools in order to work cooperatively for accomplishing complex image analysis task goals. This framework is able to automatically generate processing plans which accomplish user defined image analysis task goals.The approach that we propose in this paper provides a flexible interface to incorporate high level knowledge in image processing tools. We provide two ways to develop this knowledge: 1) by taking a feedback from an image processing expert about a processing plan generated by the system; and 2) by accepting a processing plan which accomplishes a particular task from an expert user, and then extracting the high level knowledge encapsulated in this plan. The generalized nature of our approach allows each individual tool to use machine learning algorithms of its own interest in improving the knowledge-base.Preliminary results that we obtained from this work demonstrates the success of our approach.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Processing Toolsmentioning

confidence: 99%

See 1 more Smart Citation

A knowledge-capturing approach for a cooperative intelligent image analysis system

Ranaweera

Samarabandu

2004

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

show abstract

“…Developing image processing systems is an active area of research 1,2,7,8,9 . Karantzalos and Argialas 10 described a processing scheme with preprocessing, actual and postprocessing operations leading to edge detection from LANDSAT TM, SPOT HRV, IRS 1C satellite images.…”

Section: Introductionmentioning

confidence: 99%

Combining anisotropic diffusion and alternating sequential filtering for satellite image enhancement and smoothing

Καράντζαλος

2004

Image and Signal Processing for Remote Sensing IX

View full text Add to dashboard Cite

Automatic information extraction requires a processing system to encapsulate the content of the image. This is a nontrivial task, because of the complexity of the information stored in images. In this paper satellite image enhancement and smoothing towards automatic feature extraction is accomplished through an effective serial application of anisotropic diffusion processing and alternating sequential filtering. Nonlinear diffusion processes can be found in many recent methods for image processing and computer vision. A robust anisotropic diffusion filtering is used with Tukey's biweight robust error norm for "edge-stopping" function, which preserves sharper boundaries than previous formulations and improves the automatic stopping of the diffusion. A well-known class of morphological filters, alternating sequential filtering is applied afterwards for a more extended enhancement and smoothing. The effective processing scheme is demonstrated with examples; Results appear promising.

show abstract

Unsupervised model based image segmentation using domain knowledge based fuzzy logic and edge enhancement

Nanayakkara

Samarabandu

2003

2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698)

View full text Add to dashboard Cite

In this paper, we present an automatic model based image segmentation system. which combines a multi-resolution Discrete Dynamic Contour (DDC) model refinement procedure and the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining an open DDC model, followed by a contour growing process generates the closed DDC model, which deforms progressively towards higher resolution images. A combination of knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators is used to enhance the edges of interest and to govern the DDC model deformation. With the above process we were able to greatly reduce the sensitivity to the initial model, thus paving the way for automatic segmentation on noisy images. Domain knowledge of a particular class of images is encapsulated within the FIS such that it can he easily changed for different image classes. We applied this algorithm successfully to detect the organ boundary in ultra-sound images of prostates and examples are shown in order to illustrate the advantages of the proposed method.

show abstract

A flexible framework for developing a cooperative intelligent image analysis system

Cited by 3 publications

References 0 publications

A knowledge-capturing approach for a cooperative intelligent image analysis system

A knowledge-capturing approach for a cooperative intelligent image analysis system

Combining anisotropic diffusion and alternating sequential filtering for satellite image enhancement and smoothing

Unsupervised model based image segmentation using domain knowledge based fuzzy logic and edge enhancement

Contact Info

Product

Resources

About