Rapid learning of meaningful image interpretation

Machine Learning for Adaptive Image Interpretation

Automated image interpretation is an important task with numerous applications. Until recently, designing such systems required extensive subject matter and computer vision expertise resulting in poor cross-domain portability and expensive maintenance. Recently, a machine-learned system (ADORE) was successfully applied in an aerial image interpretation domain. Subsequently, it was re-trained fo...

RAPID: RAPid Interpretation of Data

The RAPID tool facilitates the analysis and the visualization of data residing in arbitrary text files. It differs from other analysis applications in the way it selects and operates on data. In RAPID, data elements are directly retrieved from the unstructured text files using pattern-matching. For this purpose, we use the Perl language. This language was especially designed for efficient text ...

Concept maps and meaningful learning in medical education

Perceptually Meaningful Image Editing: Depth

We introduce the concept of perceptually meaningful image editing and present two techniques for manipulating the apparent depth of objects in an image. The user loads an image, selects an object and specifies whether the object should appear closer or further away. The system automatically determines target values for the object and/or background that achieve the desired depth change. These de...

Adaptive Image Interpretation : A Spectrum Of Machine Learning Problems

Automated image interpretation is an important task in numerous applications ranging from security systems to natural resource inventorization based on remote-sensing. Recently, a second generation of adaptive machine-learned image interpretation systems have shown expertlevel performance in several challenging domains. While demonstrating an unprecedented improvement over hand-engineered or fi...