Modern search engines receive large numbers of business related, local aware queries. Such queries are best answered using accurate, up-to-date, business listings, that contain representations of business categories. Creating such listings is a challenging task as businesses often change hands or close down. For businesses with street side locations one can leverage the abundance of street level imagery, such as Google Street View, to automate the process. However, while data is abundant, labeled data is not; the limiting factor is creation of large scale labeled training data. In this work, we utilize an ontology of geographical concepts to automatically propagate business category information and create a large, multi label, training dataset for fine grained storefront classification. Our learner, which is based on the GoogLeNet/Inception Deep Convolutional Network architecture and classifies 208 categories, achieves human level accuracy.

Catheters are routinely inserted via vessels to cavities of the heart during fluoroscopic image guided interventions for electrophysiology (EP) procedures such as ablation. During such interventions, the catheter undergoes nonrigid deformation due to physician interaction, patient's breathing, and cardiac motions. EP clinical applications can benefit from fast and accurate automatic catheter tracking in the fluoroscopic images. The typical low quality in fluoroscopic images and the presence of other medical instruments in the scene make the automatic detection and tracking of catheters in clinical environments very challenging. Toward the development of such an application, a robust and efficient method for detecting and tracking the catheter sheath is developed. The proposed approach exploits the clinical setup knowledge to constrain the search space while boosting both tracking speed and accuracy, and is based on a computationally efficient framework to trace the sheath and simultaneously detect one or multiple catheter tips. The algorithm is based on a modification of the fast marching weighted distance computation that efficiently calculates, on the fly, important geodesic properties in relevant regions of the image. This is followed by a cascade classifier for detecting the catheter tips. The proposed technique is validated on 1107 fluoroscopic images acquired on multiple patients across four different clinics, achieving multiple catheter tracking at a rate of 10 images/s with a very low false positive rate of 1.06.

Cardiac ablation involves the risk of serious complications when thermal injury to the esophagus occurs. This paper proposes to reduce the risk of such injuries by a proactive visualization technique, improving physician awareness of the esophagus location in the absence of or in addition to a reactive monitoring device such as a thermal probe. This is achieved by combining a graphical representation of the esophagus with live fluoroscopy. Toward this goal, we present an automated method to reconstruct and visualize a 3-D esophagus model from fluoroscopy image sequences acquired using different C-arm viewing directions. In order to visualize the esophagus under fluoroscopy, it is first biomarked by swallowing a contrast agent such as barium. Images obtained in this procedure are then used to automatically extract the 2-D esophagus silhouette and reconstruct a 3-D surface of the esophagus internal wall. Once the 3-D representation has been computed, it can be visualized using fluoroscopy overlay techniques. Compared to 3-D esophagus imaging using CT or C-arm CT, our proposed fluoroscopy method requires low radiation dose and enables a simpler workflow on geometry-calibrated standard C-arm systems.

Colorization, the task of coloring a grayscale image or video, involves assigning from the single dimension of intensity or luminance a quantity that varies in three dimensions, such as red, green, and blue channels. Mapping between intensity and color is, therefore, not unique, and colorization is ambiguous in nature and requires some amount of human interaction or external information. A computationally simple, yet effective, approach of colorization is presented in this paper. The method is fast and it can be conveniently used "on the fly," permitting the user to interactively get the desired results promptly after providing a reduced set of chrominance scribbles. Based on the concepts of luminance-weighted chrominance blending and fast intrinsic distance computations, high-quality colorization results for still images and video are obtained at a fraction of the complexity and computational cost of previously reported techniques. Possible extensions of the algorithm introduced here included the capability of changing the colors of an existing color image or video, as well as changing the underlying luminance, and many other special effects demonstrated here.