17,601 research outputs found
Matching Disparate Image Pairs Using Shape-Aware ConvNets
An end-to-end trainable ConvNet architecture, that learns to harness the
power of shape representation for matching disparate image pairs, is proposed.
Disparate image pairs are deemed those that exhibit strong affine variations in
scale, viewpoint and projection parameters accompanied by the presence of
partial or complete occlusion of objects and extreme variations in ambient
illumination. Under these challenging conditions, neither local nor global
feature-based image matching methods, when used in isolation, have been
observed to be effective. The proposed correspondence determination scheme for
matching disparate images exploits high-level shape cues that are derived from
low-level local feature descriptors, thus combining the best of both worlds. A
graph-based representation for the disparate image pair is generated by
constructing an affinity matrix that embeds the distances between feature
points in two images, thus modeling the correspondence determination problem as
one of graph matching. The eigenspectrum of the affinity matrix, i.e., the
learned global shape representation, is then used to further regress the
transformation or homography that defines the correspondence between the source
image and target image. The proposed scheme is shown to yield state-of-the-art
results for both, coarse-level shape matching as well as fine point-wise
correspondence determination.Comment: First two authors contributed equally, to Appear in the IEEE Winter
Conference on Applications of Computer Vision (WACV) 201
Shape Matching and Object Recognition
We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of corresponding geometric blur point descriptors as well as the geometric distortion between pairs of corresponding feature points. The algorithm handles outliers, and thus enables matching of exemplars to query images in the presence of occlusion and clutter. Given the correspondences, we estimate an aligning transform, typically a regularized thin plate spline, resulting in a dense correspondence between the two shapes. Object recognition is handled in a nearest neighbor framework where the distance between exemplar and query is the matching cost between corresponding points. We show results on two datasets. One is the Caltech 101 dataset (Li, Fergus and Perona), a challenging dataset with large intraclass variation. Our approach yields a 45 % correct classification rate in addition to localization. We also show results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces
Robust 3D People Tracking and Positioning System in a Semi-Overlapped Multi-Camera Environment
People positioning and tracking in 3D indoor environments are challenging tasks due to background clutter and occlusions. Current works are focused on solving people occlusions in low-cluttered backgrounds, but fail in high-cluttered scenarios, specially when foreground objects occlude people. In this paper, a novel 3D people positioning and tracking system is presented, which shows itself robust to both possible occlusion sources: static scene objects and other people. The system holds on a set of multiple cameras with partially overlapped fields of view. Moving regions are segmented independently in each camera stream by means of a new background modeling strategy based on Gabor filters. People detection is carried out on these segmentations through a template-based correlation strategy. Detected people are tracked independently in each camera view by means of a graph-based matching strategy, which estimates the best correspondences between consecutive people segmentations. Finally, 3D tracking and positioning of people is achieved by geometrical consistency analysis over the tracked 2D candidates, using head position (instead of object centroids) to increase robustness to foreground occlusions
Going Further with Point Pair Features
Point Pair Features is a widely used method to detect 3D objects in point
clouds, however they are prone to fail in presence of sensor noise and
background clutter. We introduce novel sampling and voting schemes that
significantly reduces the influence of clutter and sensor noise. Our
experiments show that with our improvements, PPFs become competitive against
state-of-the-art methods as it outperforms them on several objects from
challenging benchmarks, at a low computational cost.Comment: Corrected post-print of manuscript accepted to the European
Conference on Computer Vision (ECCV) 2016;
https://link.springer.com/chapter/10.1007/978-3-319-46487-9_5
Fully Automatic Expression-Invariant Face Correspondence
We consider the problem of computing accurate point-to-point correspondences
among a set of human face scans with varying expressions. Our fully automatic
approach does not require any manually placed markers on the scan. Instead, the
approach learns the locations of a set of landmarks present in a database and
uses this knowledge to automatically predict the locations of these landmarks
on a newly available scan. The predicted landmarks are then used to compute
point-to-point correspondences between a template model and the newly available
scan. To accurately fit the expression of the template to the expression of the
scan, we use as template a blendshape model. Our algorithm was tested on a
database of human faces of different ethnic groups with strongly varying
expressions. Experimental results show that the obtained point-to-point
correspondence is both highly accurate and consistent for most of the tested 3D
face models
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