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This paper addresses the background estimation problem for videos captured by moving cameras, referred to as video grounding. It essentially aims at reconstructing a video, as if it would be without foreground objects, e.g. cars or people. What differentiates video grounding from known background estimation methods is that the camera follows unconstrained motion so that background undergoes ongoing...
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...
In this paper we give a convex optimization approach for scene understanding. Since segmentation, object recognition and scene labeling strongly benefit from each other we propose to solve these tasks within a single convex optimization problem. In contrast to previous approaches we do not rely on pre-processing techniques such as object detectors or super pixels. The central idea is to integrate...
We address the problem of joint detection and segmentation of multiple object instances in an image, a key step towards scene understanding. Inspired by data-driven methods, we propose an exemplar-based approach to the task of multi-instance segmentation using a small set of annotated reference images. We design a novel CRF model that jointly models object appearance, shape deformation, and object...
In this paper, we present Infinite Latent Conditional Random Fields (ILCRFs) that model the data through a mixture of CRFs generated from Dirichlet processes. Each CRF represents one possible explanation of the data. In addition to visible nodes and edges that exist in classic CRFs, it generatively models the distribution of different CRF structures over the latent nodes and corresponding edges, imposing...
This paper introduces a novel method for categorical image labeling, where each pixel is uniquely assigned to one of a set of unordered, discrete labels. Starting from provided label-depending pixel likelihoods we (a) exploit a segment hierarchy as spatial support to define powerful priors and (b) introduce an efficient and effective inference method, that can be implemented in a few lines of code...
This paper presents a learning approach for detecting nematocysts in Scanning Electron Microscope (SEM) images. The image dataset was collected and made available to us by biologists for the purposes of morphological studies of corals, jellyfish, and other species in the phylum Cnidaria. Challenges for computer vision presented by this biological domain are rarely seen in general images of natural...
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