624 research outputs found
Shape Representations Using Nested Descriptors
The problem of shape representation is a core problem in computer vision. It can be argued that shape representation is the most central representational problem for computer vision, since unlike texture or color, shape alone can be used for perceptual tasks such as image matching, object detection and object categorization.
This dissertation introduces a new shape representation called the nested descriptor. A nested descriptor represents shape both globally and locally by pooling salient scaled and oriented complex gradients in a large nested support set. We show that this nesting property introduces a nested correlation structure that enables a new local distance function called the nesting distance, which provides a provably robust similarity function for image matching. Furthermore, the nesting property suggests an elegant flower like normalization strategy called a log-spiral difference. We show that this normalization enables a compact binary representation and is equivalent to a form a bottom up saliency. This suggests that the nested descriptor representational power is due to representing salient edges, which makes a fundamental connection between the saliency and local feature descriptor literature. In this dissertation, we introduce three examples of shape representation using nested descriptors: nested shape descriptors for imagery, nested motion descriptors for video and nested pooling for activities. We show evaluation results for these representations that demonstrate state-of-the-art performance for image matching, wide baseline stereo and activity recognition tasks
Template Adaptation for Face Verification and Identification
Face recognition performance evaluation has traditionally focused on
one-to-one verification, popularized by the Labeled Faces in the Wild dataset
for imagery and the YouTubeFaces dataset for videos. In contrast, the newly
released IJB-A face recognition dataset unifies evaluation of one-to-many face
identification with one-to-one face verification over templates, or sets of
imagery and videos for a subject. In this paper, we study the problem of
template adaptation, a form of transfer learning to the set of media in a
template. Extensive performance evaluations on IJB-A show a surprising result,
that perhaps the simplest method of template adaptation, combining deep
convolutional network features with template specific linear SVMs, outperforms
the state-of-the-art by a wide margin. We study the effects of template size,
negative set construction and classifier fusion on performance, then compare
template adaptation to convolutional networks with metric learning, 2D and 3D
alignment. Our unexpected conclusion is that these other methods, when combined
with template adaptation, all achieve nearly the same top performance on IJB-A
for template-based face verification and identification
Inducing Predictive Uncertainty Estimation for Face Recognition
Knowing when an output can be trusted is critical for reliably using face
recognition systems. While there has been enormous effort in recent research on
improving face verification performance, understanding when a model's
predictions should or should not be trusted has received far less attention.
Our goal is to assign a confidence score for a face image that reflects its
quality in terms of recognizable information. To this end, we propose a method
for generating image quality training data automatically from 'mated-pairs' of
face images, and use the generated data to train a lightweight Predictive
Confidence Network, termed as PCNet, for estimating the confidence score of a
face image. We systematically evaluate the usefulness of PCNet with its error
versus reject performance, and demonstrate that it can be universally paired
with and improve the robustness of any verification model. We describe three
use cases on the public IJB-C face verification benchmark: (i) to improve 1:1
image-based verification error rates by rejecting low-quality face images; (ii)
to improve quality score based fusion performance on the 1:1 set-based
verification benchmark; and (iii) its use as a quality measure for selecting
high quality (unblurred, good lighting, more frontal) faces from a collection,
e.g. for automatic enrolment or display.Comment: To Appear at the British Machine Vision Conference (BMVC), 202
Fine-grained Activities of People Worldwide
Every day, humans perform many closely related activities that involve subtle
discriminative motions, such as putting on a shirt vs. putting on a jacket, or
shaking hands vs. giving a high five. Activity recognition by ethical visual AI
could provide insights into our patterns of daily life, however existing
activity recognition datasets do not capture the massive diversity of these
human activities around the world. To address this limitation, we introduce
Collector, a free mobile app to record video while simultaneously annotating
objects and activities of consented subjects. This new data collection platform
was used to curate the Consented Activities of People (CAP) dataset, the first
large-scale, fine-grained activity dataset of people worldwide. The CAP dataset
contains 1.45M video clips of 512 fine grained activity labels of daily life,
collected by 780 subjects in 33 countries. We provide activity classification
and activity detection benchmarks for this dataset, and analyze baseline
results to gain insight into how people around with world perform common
activities. The dataset, benchmarks, evaluation tools, public leaderboards and
mobile apps are available for use at visym.github.io/cap
Mathematical analysis of a model for the growth of the bovine corpus luteum
The corpus luteum (CL) is an ovarian tissue that grows in the wound space created by follicular rupture. It produces the progesterone needed in the uterus to maintain pregnancy. Rapid growth of the CL and progesterone transport to the uterus require angiogenesis, the creation of new blood vessels from pre-existing ones, a process which is regulated by proteins that include fibroblast growth factor 2 (FGF2).\ud
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In this paper we develop a system of time-dependent ordinary differential equations to model CL growth. The dependent variables represent FGF2, endothelial cells (ECs), luteal cells, and stromal cells (like pericytes), by assuming that the CL volume is a continuum of the three cell types. We assume that if the CL volume exceeds that of the ovulated follicle, then growth is inhibited. This threshold volume partitions the system dynamics into two regimes, so that the model may be classified as a Filippov (piecewise smooth) system.\ud
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We show that normal CL growth requires an appropriate balance between the growth rates of luteal and stromal cells. We investigate how angiogenesis influences CL growth by considering how the system dynamics depend on the dimensionless EC proliferation rate, p5. We find that weak (low p5) or strong (high p5) angiogenesis leads to ‘pathological’ CL growth, since the loss of CL constituents compromises progesterone production or delivery. However, for intermediate values of p5, normal CL growth is predicted. The implications of these results for cow fertility are also discussed. For example, inadequate angiogenesis has been linked to infertility in dairy cows
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