Hausdorff-Distance Enhanced Matching of Scale Invariant Feature Transform Descriptors in Context of Image Querying

Reliable and effective matching of visual descriptors is a key step for many vision applications, e.g. image retrieval. In this paper, we propose to integrate the Hausdorff distance matching together with our pairing algorithm, in order to obtain a robust while computationally efficient process of matching feature descriptors for image-to-image querying in standards datasets. For this purpose, Scale Invariant Feature Transform (SIFT) descriptors have been matched using our presented algorithm, followed by the computation of our related similarity measure. This approach has shown excellent performance in both retrieval accuracy and speed

DALES: Automated Tool for Detection, Annotation, Labelling and Segmentation of Multiple Objects in Multi-Camera Video Streams

In this paper, we propose a new software tool called DALES to extract semantic information from multi-view videos based on the analysis of their visual content. Our system is fully automatic and is well suited for multi-camera environment. Once the multi-view video sequences are loaded into DALES, our software performs the detection, counting, and segmentation of the visual objects evolving in the provided video streams. Then, these objects of interest are processed in order to be labelled, and the related frames are thus annotated with the corresponding semantic content. Moreover, a textual script is automatically generated with the video annotations. DALES system shows excellent performance in terms of accuracy and computational speed and is robustly designed to ensure view synchronization