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This paper addresses the problem of pixel-wise semantic labeling of images. To this end, we use a fully convolutional network (FCN) whose input are raw pixels, and output are pixel labels. Our key novelty is that we regularize a supervised learning of FCN, such that FCN correctly predicts pixel labels and additionally does not violate a given set of spatial object relationships of interest. The frequency...
Biologists collect and analyze phenomic (e.g., anatomical or non-genomic) data to discover relationships among species in the Tree of Life. The domain is seeking to modernize this very time-consuming and largely manual process. We have developed an approach to detect and localize object parts in standardized images of bat skulls. This approach has been further developed for unannotated images by leveraging...
Given a video, we would like to recognize group activities, localize video parts where these activities occur, and detect actors involved in them. This advances prior work that typically focuses only on video classification. We make a number of contributions. First, we specify a new, mid-level, video feature aimed at summarizing local visual cues into bags of the right detections (BORDs). BORDs seek...
Given an arbitrary image, our goal is to segment all distinct texture subimages. This is done by discovering distinct, cohesive groups of spatially repeating patterns, called texels, in the image, where each group defines the corresponding texture. Texels occupy image regions, whose photometric, geometric, structural, and spatial-layout properties are samples from an unknown pdf. If the image contains...
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