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A first-person camera, placed at a person's head, captures, which objects are important to the camera wearer. Most prior methods for this task learn to detect such important objects from the manually labeled first-person data in a supervised fashion. However, important objects are strongly related to the camera wearer's internal state such as his intentions and attention, and thus, only the person...
We propose a multigrid extension of convolutional neural networks (CNNs). Rather than manipulating representations living on a single spatial grid, our network layers operate across scale space, on a pyramid of grids. They consume multigrid inputs and produce multigrid outputs, convolutional filters themselves have both within-scale and cross-scale extent. This aspect is distinct from simple multiscale...
Most current semantic segmentation methods rely on fully convolutional networks (FCNs). However, their use of large receptive fields and many pooling layers cause low spatial resolution inside the deep layers. This leads to predictions with poor localization around the boundaries. Prior work has attempted to address this issue by post-processing predictions with CRFs or MRFs. But such models often...
Over the years, Automated Image Analysis Systems (AIAS) have been developed for pavement surface analysis and management. Pavement distress segmentation is a key issue throughout the entire process of analyses. In this paper, an adaptive approach for pavement distress segmentation based on Genetic Algorithms is proposed. After the pavement images are captured, an objective function is defined and...
We present a novel viewpoint which approaches the structural correspondence across an image stack in the 3D space as solving a contour grouping problem. Finding 3D cellular tubes becomes finding closed contours. We derive grouping cues between cells in adjacent slices based on their ability to relate in the 3D space. Those that form a long 3D tube in the space become the most salient contour, while...
Many applications need to segment out all small round regions in an image. This task of finding dots can be viewed as a region segmentation problem where the dots form one region and the areas between dots form the other. We formulate it as a graph cuts problem with two types of grouping cues: short-range attraction based on feature similarity and long-range repulsion based on feature dissimilarity...
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