Focus of attention is often attributed to biological vision system where the entire field of view is first monitored and then the attention is focused to the object of interest. We propose using a similar approach for object recognition in a color image sequence. The intention is to locate an object based on a prior motive, concentrate on the detected object so that the imaging device can be guided toward it. We use the abilities of the intelligent image analysis framework developed in our laboratory to generate an algorithm dynamically to detect the particular type of object based on the user's object description.
The proposed method uses color clustering along with segmentation. The segmented image with labeled regions is used to calculate the shape descriptor parameters. These and the color information are matched with the input description. Gaze is then controlled by issuing camera movement commands as appropriate.
We present some preliminary results that demonstrate the success of this approach.
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 develop the expertise of `image processing' tools. We provide two ways to develop this knowledge: 1) by taking feedback from an image processing expert about processing plans 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.
We present a software framework for developing a flexible image analysis system. This framework provides a uniform interface to develop intelligent image analysis tools as well as infrastructure facilities required by these tools for working cooperatively with other tools. This system first automatically generates a processing plan to accomplish a user defined task, and then executes that plan to produce results.
Each processing tool encapsulates an image processing algorithm as well as knowledge about this algorithm. Tools use this knowledge to evaluate their suitability to handle a given task. This approach allows each processing tool the ability of selectively accepting appropriate tasks that belong to its domain.
A processing tool is also able to define subtasks, which must be accomplished to refine input data as required by its underlying image processing algorithm. These subtasks can be broadcast among other tools and enlist appropriate individuals to accomplish task goals.
Our framework is able to accept image analysis tasks defined using abstract conceptual terms used in application domains, and uses production rules to expand detailed fprms of these terms. This facility allows successfully defining an image analysis task without specifying all low level details.
Preliminary results that we obtained from this framework demonstrated the success of our approach.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.