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There is an increasing interest in exploiting multiple images for scene understanding, with great progress in areas such as cosegmentation and video segmentation. Jointly analyzing the images in a large set offers the opportunity to exploit a greater source of information than when considering a single image on its own. However, this also yields challenges since, to effectively exploit all the available...
Semantically describing the contents of images is one of the classical problems of computer vision. With huge numbers of images being made available daily, there is increasing interest in methods for semantic pixel labelling that exploit large image sets. Graph transduction provides a framework for the flexible inclusion of labeled data that can be exploited in the classification of unlabeled samples...
Truncated signed distance function (TSDF) based volumetric surface reconstructions of static environments can be readily acquired using recent RGB-D camera based mapping systems. If objects in the environment move then a previously obtained TSDF reconstruction is no longer current. Handling this problem requires segmenting moving objects from the reconstruction. To this end, we present a novel solution...
In this paper, we tackle the problem of road detection from RGB images. In particular, we follow a data-driven approach to segmenting the road pixels in an image. To this end, we introduce two road detection methods: A top-down approach that builds an image-level road prior based on the traffic pattern observed in an input image, and a bottom-up technique that estimates the probability that an image...
Until recently, inference on fully connected graphs of pixel labels for scene understanding has been computationally expensive, so fast methods have focussed on neighbour connections and unary computation. However, with efficient CRF methods for inference on fully connected graphs, the opportunity exists for exploring other approaches. In this paper, we present a fast approach that calculates unary...
This paper provides a novel and efficient approach to dense stereo matching. The first contribution is, rather than applying disparity distribution inside a segment as hard constraint to directly project the likelihood of corresponding candidates to each pixel individually, our method treat segmentation and corresponding disparity distribution as soft constraint, and further partition each segment...
We propose the use of ground surface segmentation to enhance the perception of obstacles in low to medium resolution prosthetic visual representations. We apply a recently proposed algorithm for segmenting traversable space in stereo disparity data, and show how such a scheme may be utilised to enhance the distinction between the ground surface and obstructions (in particular, small trip hazards)...
This paper presents a novel object-oriented stereo matching on multi-scale superpixels to generate a low-resolution depth map. It overcomes the classic downsampling methods' disadvantages, such as boundary blurring, outlier enlargement and foreground objects merging to background, etc. The approach we exploited is to segment the image in three scales' superpixels from dense to sparse ones according...
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