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Reliable object discovery in realistic indoor scenes is a necessity for many computer vision and service robot applications. In these scenes, semantic segmentation methods have made huge advances in recent years. Such methods can provide useful prior information for object discovery by removing false positives and by delineating object boundaries. We propose a novel method that combines bottom-up...
Traffic panels contain rich text and symbolic information for transportation and scene understanding. Fast detection of traffic panels facilitates text information extraction but has been paid little attention by the community. In this paper, we propose a fast and robust approach for rectangular traffic panel detection from traffic scene images. Considering the rectangular shape of traffic panels,...
This paper presents a method for detecting a pedestrian by leveraging multi-spectral image pairs. Our approach is based on the observation that a multi-spectral image, especially far-infrared (FIR) image, enables us to overcome inherent limitations for pedestrian detection under challenging circumstances, such as even dark environments. For that task, multi-spectral color-FIR image pairs are used...
Scene text detection and recognition have become active research topics in computer vision. In this paper, we focus on the detection of text proposal from wild images. Text proposals attempt to generate a relatively small set of bounding box proposals that are most likely to contain text. Different from previous methods that merge similar region based on property of individual region, we assumed that...
We propose a new approach to segmenting a hand accurately from a single depth image. Given a depth image, we extract first a rough hand region of interest (RoI) including a hand and a part of an arm. Then, the RoI is partitioned into triangles by using a constrained Delaunay triangulation (CDT) approach from which hand segmentation proposals are generated. Each segmentation proposal is evaluated by...
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