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This paper presents a novel random forest learning framework to construct a discriminative and informative mid-level feature from low-level features. Since a single low-level feature based representation is not enough to capture the variations of human appearance, multiple low-level features (i.e., optical flow and histogram of gradient 3D features) are fused to further improve recognition performance...
This paper proposes a functional that assigns low ‘energy’ to sets of subsets of the image domain consisting of a number of possibly overlapping near-circular regions of approximately a given radius: a ‘gas of circles’. The model can be used as a prior for object extraction whenever the objects conform to the ‘gas of circles’ geometry, e.g. cells in biological images. Configurations are represented...
Inter patient shape, size and intensity variations of the prostate in transrectal ultrasound (TRUS) images challenge automatic segmentation of the prostate. In this paper we propose a variational model driven by Mumford-Shah (MS) functional for segmenting the prostate. Parametric representation of the implicit curve is derived from principal component analysis (PCA) of the signed distance representation...
Following [Lempitsky and Zisserman, 2010], we seek to count objects by integrating over an object density map that is predicted from an input image. In contrast to that work, we propose to estimate the object density map by averaging over structured, namely patch-wise, predictions. Using an ensemble of randomized regression trees that use dense features as input, we obtain results that are of similar...
Person re-identification is an important problem in visual surveillance where appearance plays a key role. Color is one of the widely used appearance features and utilizing more color spaces doesn't imply benefit of performance enhancement. That's because the poor performance color spaces influence on the high ones. So it is significant to evaluate the performance of different color spaces for person...
In this paper we present an optimization of the Optimum-Path Forest classifier training procedure, which is based on a theoretical relationship between minimum spanning forest and optimum-path forest for a specific path-cost function. Experiments on public datasets have shown that the proposed approach can obtain similar accuracy to the traditional one but with faster data training.
Connected operators are filtering tools that act by merging elementary regions of an image. A popular strategy is based on tree-based image representations: for example, one can compute an attribute on each node of the tree and keep only the nodes for which the attribute is sufficiently strong. This operation can be seen as a thresholding of the tree, seen as a graph whose nodes are weighted by the...
Accurate segmentation provides a useful contour constraint to alleviate drifting during online learning for tracking. Towards this end, we present a closed-loop method for object tracking that links Hough forests and alpha matting via an effective back-projection scheme for patches. A novel hybrid-Hough-forests-based method first estimates object location. Given the object location, the trimap of...
Quantitative analysis of optical coherence tomography volumes is an important tool for both clinicians and researchers. Until now, most work has focused on segmentation of the intraretinal cell layers, but the segmentation of pathological datasets remains challenging. We propose the application of random forest to detect the locations of drusen in the retinal pigment epithelium. This is an important...
This paper proposes a new method for facial motion extraction to represent, learn and recognize observed expressions, from 4D video sequences. The approach called Deformation Vector Field (DVF) is based on Riemannian facial shape analysis and captures densely dynamic information from the entire face. The resulting temporal vector field is used to build the feature vector for expression recognition...
Accurate segmentation of sidewalks from satellite images can be required in various applications, for example giving walking directions to pedestrians and robot navigation. We propose a framework to construct sidewalk and crosswalk maps from satellite images. This is a challenging task, since typically sidewalks in satellite images are highly occluded by trees and their shadows and also there can...
We generalize recursive baseline extraction algorithms for symbol layout analysis in math expressions so that handwritten strokes may be provided as input. Specifically, baseline extraction is used for lexical analysis in a modified LL(1) parser, returning a set of candidate symbols when the leftmost or next symbol along the current baseline (from left-to-right) is requested by the parser. Candidate...
Variations in inter-patient prostate shape, and size and imaging artifacts in magnetic resonance images (MRI) hinders automatic accurate prostate segmentation. In this paper we propose a graph cut based energy minimization of the posterior probabilities obtained in a supervised learning schema for automatic 3D segmentation of the prostate in MRI. A probabilistic classification of the prostate voxels...
Automated segmentation of electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that utilizes a hierarchical structure and boundary classification for 2D neuron segmentation. With a membrane detection probability map, a watershed merge tree is built for the representation of hierarchical region merging from the watershed algorithm. A boundary classifier...
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