13,608 research outputs found
An Algorithm for Learning Shape and Appearance Models without Annotations
This paper presents a framework for automatically learning shape and
appearance models for medical (and certain other) images. It is based on the
idea that having a more accurate shape and appearance model leads to more
accurate image registration, which in turn leads to a more accurate shape and
appearance model. This leads naturally to an iterative scheme, which is based
on a probabilistic generative model that is fit using Gauss-Newton updates
within an EM-like framework. It was developed with the aim of enabling
distributed privacy-preserving analysis of brain image data, such that shared
information (shape and appearance basis functions) may be passed across sites,
whereas latent variables that encode individual images remain secure within
each site. These latent variables are proposed as features for
privacy-preserving data mining applications.
The approach is demonstrated qualitatively on the KDEF dataset of 2D face
images, showing that it can align images that traditionally require shape and
appearance models trained using manually annotated data (manually defined
landmarks etc.). It is applied to MNIST dataset of handwritten digits to show
its potential for machine learning applications, particularly when training
data is limited. The model is able to handle ``missing data'', which allows it
to be cross-validated according to how well it can predict left-out voxels. The
suitability of the derived features for classifying individuals into patient
groups was assessed by applying it to a dataset of over 1,900 segmented
T1-weighted MR images, which included images from the COBRE and ABIDE datasets.Comment: 61 pages, 16 figures (some downsampled by a factor of 4), submitted
to MedI
Pose Induction for Novel Object Categories
We address the task of predicting pose for objects of unannotated object
categories from a small seed set of annotated object classes. We present a
generalized classifier that can reliably induce pose given a single instance of
a novel category. In case of availability of a large collection of novel
instances, our approach then jointly reasons over all instances to improve the
initial estimates. We empirically validate the various components of our
algorithm and quantitatively show that our method produces reliable pose
estimates. We also show qualitative results on a diverse set of classes and
further demonstrate the applicability of our system for learning shape models
of novel object classes
Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion
Facial landmark detection, head pose estimation, and facial deformation
analysis are typical facial behavior analysis tasks in computer vision. The
existing methods usually perform each task independently and sequentially,
ignoring their interactions. To tackle this problem, we propose a unified
framework for simultaneous facial landmark detection, head pose estimation, and
facial deformation analysis, and the proposed model is robust to facial
occlusion. Following a cascade procedure augmented with model-based head pose
estimation, we iteratively update the facial landmark locations, facial
occlusion, head pose and facial de- formation until convergence. The
experimental results on benchmark databases demonstrate the effectiveness of
the proposed method for simultaneous facial landmark detection, head pose and
facial deformation estimation, even if the images are under facial occlusion.Comment: International Conference on Computer Vision and Pattern Recognition,
201
Interspecies Knowledge Transfer for Facial Keypoint Detection
We present a method for localizing facial keypoints on animals by
transferring knowledge gained from human faces. Instead of directly finetuning
a network trained to detect keypoints on human faces to animal faces (which is
sub-optimal since human and animal faces can look quite different), we propose
to first adapt the animal images to the pre-trained human detection network by
correcting for the differences in animal and human face shape. We first find
the nearest human neighbors for each animal image using an unsupervised shape
matching method. We use these matches to train a thin plate spline warping
network to warp each animal face to look more human-like. The warping network
is then jointly finetuned with a pre-trained human facial keypoint detection
network using an animal dataset. We demonstrate state-of-the-art results on
both horse and sheep facial keypoint detection, and significant improvement
over simple finetuning, especially when training data is scarce. Additionally,
we present a new dataset with 3717 images with horse face and facial keypoint
annotations.Comment: CVPR 2017 Camera Read
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