7,492 research outputs found
Automatic landmark annotation and dense correspondence registration for 3D human facial images
Dense surface registration of three-dimensional (3D) human facial images
holds great potential for studies of human trait diversity, disease genetics,
and forensics. Non-rigid registration is particularly useful for establishing
dense anatomical correspondences between faces. Here we describe a novel
non-rigid registration method for fully automatic 3D facial image mapping. This
method comprises two steps: first, seventeen facial landmarks are automatically
annotated, mainly via PCA-based feature recognition following 3D-to-2D data
transformation. Second, an efficient thin-plate spline (TPS) protocol is used
to establish the dense anatomical correspondence between facial images, under
the guidance of the predefined landmarks. We demonstrate that this method is
robust and highly accurate, even for different ethnicities. The average face is
calculated for individuals of Han Chinese and Uyghur origins. While fully
automatic and computationally efficient, this method enables high-throughput
analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl
SuperPoint: Self-Supervised Interest Point Detection and Description
This paper presents a self-supervised framework for training interest point
detectors and descriptors suitable for a large number of multiple-view geometry
problems in computer vision. As opposed to patch-based neural networks, our
fully-convolutional model operates on full-sized images and jointly computes
pixel-level interest point locations and associated descriptors in one forward
pass. We introduce Homographic Adaptation, a multi-scale, multi-homography
approach for boosting interest point detection repeatability and performing
cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on
the MS-COCO generic image dataset using Homographic Adaptation, is able to
repeatedly detect a much richer set of interest points than the initial
pre-adapted deep model and any other traditional corner detector. The final
system gives rise to state-of-the-art homography estimation results on HPatches
when compared to LIFT, SIFT and ORB.Comment: Camera-ready version for CVPR 2018 Deep Learning for Visual SLAM
Workshop (DL4VSLAM2018
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
Recognition of Arabic handwritten words
Recognizing Arabic handwritten words is a difficult problem due to the deformations of different writing styles. Moreover, the cursive nature of the Arabic writing makes correct segmentation of characters an almost impossible task. While there are many sub systems in an Arabic words recognition system, in this work we develop a sub system to recognize Part of Arabic Words (PAW). We try to solve this problem using three different approaches, implicit segmentation and two variants of holistic approach. While Rothacker found similar conclusions while this work is being prepared, we report the difficulty in locating characters in PAW using Scale Invariant Feature Transforms under the first approach. In the second and third approaches, we use holistic approach to recognize PAW using Support Vector Machine (SVM) and Active Shape Models (ASM). While there are few works that use SVM to recognize PAW, they use a small dataset; we use a large dataset and a different set of features. We also explain the errors SVM and ASM make and propose some remedies to these errors as future work
Deep Convolutional Ranking for Multilabel Image Annotation
Multilabel image annotation is one of the most important challenges in
computer vision with many real-world applications. While existing work usually
use conventional visual features for multilabel annotation, features based on
Deep Neural Networks have shown potential to significantly boost performance.
In this work, we propose to leverage the advantage of such features and analyze
key components that lead to better performances. Specifically, we show that a
significant performance gain could be obtained by combining convolutional
architectures with approximate top- ranking objectives, as thye naturally
fit the multilabel tagging problem. Our experiments on the NUS-WIDE dataset
outperforms the conventional visual features by about 10%, obtaining the best
reported performance in the literature
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