56,319 research outputs found
Invariant image descriptors and affine morphological scale-space
In this research report we propose a novel approach to build interest points and descriptors which are invariant to a subclass of affine transformations. Scale and rotation invariant interest points are usually obtained via the linear scale-space representation, and it has been shown that affine invariance can be achieved by warping the smoothing kernel to match the local image structure. Our approach is instead based on the so-called Affine Morphological Scale-Space, a non-linear filtering which has been proved to be the natural equivalent of the classic linear scale-space when affine invariance is required. Simple local image descriptors are then derived from the extracted interest points. We demonstrate the proposed approach by robust matching experiments
Affine Subspace Representation for Feature Description
This paper proposes a novel Affine Subspace Representation (ASR) descriptor
to deal with affine distortions induced by viewpoint changes. Unlike the
traditional local descriptors such as SIFT, ASR inherently encodes local
information of multi-view patches, making it robust to affine distortions while
maintaining a high discriminative ability. To this end, PCA is used to
represent affine-warped patches as PCA-patch vectors for its compactness and
efficiency. Then according to the subspace assumption, which implies that the
PCA-patch vectors of various affine-warped patches of the same keypoint can be
represented by a low-dimensional linear subspace, the ASR descriptor is
obtained by using a simple subspace-to-point mapping. Such a linear subspace
representation could accurately capture the underlying information of a
keypoint (local structure) under multiple views without sacrificing its
distinctiveness. To accelerate the computation of ASR descriptor, a fast
approximate algorithm is proposed by moving the most computational part (ie,
warp patch under various affine transformations) to an offline training stage.
Experimental results show that ASR is not only better than the state-of-the-art
descriptors under various image transformations, but also performs well without
a dedicated affine invariant detector when dealing with viewpoint changes.Comment: To Appear in the 2014 European Conference on Computer Visio
DeepMatching: Hierarchical Deformable Dense Matching
We introduce a novel matching algorithm, called DeepMatching, to compute
dense correspondences between images. DeepMatching relies on a hierarchical,
multi-layer, correlational architecture designed for matching images and was
inspired by deep convolutional approaches. The proposed matching algorithm can
handle non-rigid deformations and repetitive textures and efficiently
determines dense correspondences in the presence of significant changes between
images. We evaluate the performance of DeepMatching, in comparison with
state-of-the-art matching algorithms, on the Mikolajczyk (Mikolajczyk et al
2005), the MPI-Sintel (Butler et al 2012) and the Kitti (Geiger et al 2013)
datasets. DeepMatching outperforms the state-of-the-art algorithms and shows
excellent results in particular for repetitive textures.We also propose a
method for estimating optical flow, called DeepFlow, by integrating
DeepMatching in the large displacement optical flow (LDOF) approach of Brox and
Malik (2011). Compared to existing matching algorithms, additional robustness
to large displacements and complex motion is obtained thanks to our matching
approach. DeepFlow obtains competitive performance on public benchmarks for
optical flow estimation
- …