Using the Forest to See the Trees: Exploiting Context for Visual Object Detection and Localization

Recognizing objects in images is an active area of research in computer vision. In the last two decades, there has been much progress and there are already object recognition systems operating in commercial products. However, most of the algorithms for detecting objects perform an exhaustive search across all locations and scales in the image comparing local image regions with an object model. That approach ignores the semantic structure of scenes and tries to solve the recognition problem by brute force. In the real world, objects tend to covary with other objects, providing a rich collection of contextual associations. These contextual associations can be used to reduce the search space by looking only in places in which the object is expected to be; this also increases performance, by rejecting patterns that look like the target but appear in unlikely places. Most modeling attempts so far have defined the context of an object in terms of other previously recognized objects. The drawback of this approach is that inferring the context becomes as difficult as detecting each object. An alternative view of context relies on using the entire scene information holistically. This approach is algorithmically attractive since it dispenses with the need for a prior step of individual object recognition. In this paper, we use a probabilistic framework for encoding the relationships between context and object properties and we show how an integrated system provides improved performance. We view this as a significant step toward general purpose machine vision systems.United States. National Geospatial-Intelligence Agency (NEGI-1582-04-0004)United States. Army Research Office. Multidisciplinary University Research Initiative (Grant Number N00014-06-1-0734)National Science Foundation (U.S.). (Contract IIS-0413232)National Defense Science and Engineering Graduate Fellowshi

Direct repeat-mediated deletion of a type IV pilin gene results in major virulence attenuation of Francisella tularensis.

Francisella tularensis, the causative agent of tularaemia, is a highly infectious and virulent intracellular pathogen. There are two main human pathogenic subspecies, Francisella tularensis ssp. tularensis (type A), and Francisella tularensis ssp. holarctica (type B). So far, knowledge regarding key virulence determinants is limited but it is clear that intracellular survival and multiplication is one major virulence strategy of Francisella. In addition, genome sequencing has revealed the presence of genes encoding type IV pili (Tfp). One genomic region encoding three proteins with signatures typical for type IV pilins contained two 120 bp direct repeats. Here we establish that repeat-mediated loss of one of the putative pilin genes in a type B strain results in severe virulence attenuation in mice infected by subcutaneous route. Complementation of the mutant by introduction of the pilin gene in cis resulted in complete restoration of virulence. The level of attenuation was similar to that of the live vaccine strain and this strain was also found to lack the pilin gene as result of a similar deletion event mediated by the direct repeats. Presence of the pilin had no major effect on the ability to interact, survive and multiply inside macrophage-like cell lines. Importantly, the pilin-negative strain was impaired in its ability to spread from the initial site of infection to the spleen. Our findings indicate that this putative pilin is critical for Francisella infections that occur via peripheral routes