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Segmentation of Levator Hiatus Using Multi-Scale Local Region Active contours and Boundary Shape Similarity Constraint
In this paper, a multi-scale framework with local region based active co...
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Adaptive Locally Affine-Invariant Shape Matching
Matching deformable objects using their shapes is an important problem i...
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Contour Detection from Deep Patch-level Boundary Prediction
In this paper, we present a novel approach for contour detection with Co...
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DeepSDF x Sim(3): Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimation
Recent advances in computer graphics and computer vision have allowed fo...
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A Solution for Multi-Alignment by Transformation Synchronisation
The alignment of a set of objects by means of transformations plays an i...
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Contour polygonal approximation using shortest path in networks
Contour polygonal approximation is a simplified representation of a cont...
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Unsupervised Learning by Predicting Noise
Convolutional neural networks provide visual features that perform remar...
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ProAlignNet : Unsupervised Learning for Progressively Aligning Noisy Contours
Contour shape alignment is a fundamental but challenging problem in computer vision, especially when the observations are partial, noisy, and largely misaligned. Recent ConvNet-based architectures that were proposed to align image structures tend to fail with contour representation of shapes, mostly due to the use of proximity-insensitive pixel-wise similarity measures as loss functions in their training processes. This work presents a novel ConvNet, "ProAlignNet" that accounts for large scale misalignments and complex transformations between the contour shapes. It infers the warp parameters in a multi-scale fashion with progressively increasing complex transformations over increasing scales. It learns –without supervision– to align contours, agnostic to noise and missing parts, by training with a novel loss function which is derived an upperbound of a proximity-sensitive and local shape-dependent similarity metric that uses classical Morphological Chamfer Distance Transform. We evaluate the reliability of these proposals on a simulated MNIST noisy contours dataset via some basic sanity check experiments. Next, we demonstrate the effectiveness of the proposed models in two real-world applications of (i) aligning geo-parcel data to aerial image maps and (ii) refining coarsely annotated segmentation labels. In both applications, the proposed models consistently perform superior to state-of-the-art methods.
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