13,792 research outputs found
A note on the evaluation of generative models
Probabilistic generative models can be used for compression, denoising,
inpainting, texture synthesis, semi-supervised learning, unsupervised feature
learning, and other tasks. Given this wide range of applications, it is not
surprising that a lot of heterogeneity exists in the way these models are
formulated, trained, and evaluated. As a consequence, direct comparison between
models is often difficult. This article reviews mostly known but often
underappreciated properties relating to the evaluation and interpretation of
generative models with a focus on image models. In particular, we show that
three of the currently most commonly used criteria---average log-likelihood,
Parzen window estimates, and visual fidelity of samples---are largely
independent of each other when the data is high-dimensional. Good performance
with respect to one criterion therefore need not imply good performance with
respect to the other criteria. Our results show that extrapolation from one
criterion to another is not warranted and generative models need to be
evaluated directly with respect to the application(s) they were intended for.
In addition, we provide examples demonstrating that Parzen window estimates
should generally be avoided
High-Precision Localization Using Ground Texture
Location-aware applications play an increasingly critical role in everyday
life. However, satellite-based localization (e.g., GPS) has limited accuracy
and can be unusable in dense urban areas and indoors. We introduce an
image-based global localization system that is accurate to a few millimeters
and performs reliable localization both indoors and outside. The key idea is to
capture and index distinctive local keypoints in ground textures. This is based
on the observation that ground textures including wood, carpet, tile, concrete,
and asphalt may look random and homogeneous, but all contain cracks, scratches,
or unique arrangements of fibers. These imperfections are persistent, and can
serve as local features. Our system incorporates a downward-facing camera to
capture the fine texture of the ground, together with an image processing
pipeline that locates the captured texture patch in a compact database
constructed offline. We demonstrate the capability of our system to robustly,
accurately, and quickly locate test images on various types of outdoor and
indoor ground surfaces
STV-based Video Feature Processing for Action Recognition
In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end
Spatial and Angular Resolution Enhancement of Light Fields Using Convolutional Neural Networks
Light field imaging extends the traditional photography by capturing both
spatial and angular distribution of light, which enables new capabilities,
including post-capture refocusing, post-capture aperture control, and depth
estimation from a single shot. Micro-lens array (MLA) based light field cameras
offer a cost-effective approach to capture light field. A major drawback of MLA
based light field cameras is low spatial resolution, which is due to the fact
that a single image sensor is shared to capture both spatial and angular
information. In this paper, we present a learning based light field enhancement
approach. Both spatial and angular resolution of captured light field is
enhanced using convolutional neural networks. The proposed method is tested
with real light field data captured with a Lytro light field camera, clearly
demonstrating spatial and angular resolution improvement
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