Difference image analysis: automatic kernel design using information criteria

V-l Design Criteria for Automatic Information Systems

s (150 words) Titles Only Abstracts Null Summaries Nulls Null Summaries Null Synonym Recognition (Thesaurus) only Word Stem Match Synonym Recognition Statistical Phrase Recognition Simple Word Stems Synonym Recognition Index Term Match Abstracts with Thesarus » "much greater than > "greater than" ~ "about equal to" (based on experiments with 4 collections in 3 topic areas) Overall Evaluation Re...

Automatic Image Annotation Using Auxiliary Text Information

The availability of databases of images labeled with keywords is necessary for developing and evaluating image annotation models. Dataset collection is however a costly and time consuming task. In this paper we exploit the vast resource of images available on the web. We create a database of pictures that are naturally embedded into news articles and propose to use their captions as a proxy for...

Semi-Automatic Image Labelling Using Depth Information

Image labeling tools help to extract objects within images to be used as ground truth for learning and testing in object detection processes. The inputs for such tools are usually RGB images. However with new widely available low-cost sensors like Microsoft Kinect it is possible to use depth images in addition to RGB images. Despite many existing powerful tools for image labeling, there is a ne...

Automatic image enhancement using intrinsic geometrical information

Automatic analysis of the difference image for unsupervised change detection

One of the main problems related to unsupervised change detection methods based on the “difference image” lies in the lack of efficient automatic techniques for discriminating between changed and unchanged pixels in the difference image. Such discrimination is usually performed by using empirical strategies or manual trial-and-error procedures, which affect both the accuracy and the reliability...