42,410 research outputs found
Efficient Image Segmentation and Segment-Based Analysis in Computer Vision Applications
This dissertation focuses on efficient image segmentation and
segment-based object recognition in computer vision
applications. Special attention is devoted to analyzing shape, of
particular importance for our two applications: plant species
identification from leaf photos, and object classification in remote
sensing images. Additionally, both problems are bound by efficiency,
constraining the choice of applicable methods: leaf recognition
results are to be used within an interactive system, while remote
sensing image analysis must scale well over very large image sets.
Leafsnap was the first mobile app to provide automatic recognition of
tree species, currently counting with over 1.7 million downloads. We
present an overview of the mobile app and corresponding back end
recognition system, as well as a preliminary analysis of
user-submitted data. More than 1.7 million valid leaf photos have been
uploaded by users, 1.3 million of which are GPS-tagged. We then focus
on the problem of segmenting photos of leaves taken against plain
light-colored backgrounds. These types of photos are used in practice
within Leafsnap for tree species recognition. A good segmentation is
essential in order to make use of the distinctive shape of leaves for
recognition. We present a comparative experimental evaluation of
several segmentation methods, including quantitative and qualitative
results. We then introduce a custom-tailored leaf segmentation method
that shows superior performance while maintaining computational
efficiency.
The other contribution of this work is a set of attributes for
analysis of image segments. The set of attributes is designed for use
in knowledge-based systems, so they are selected to be intuitive and
easily describable. The attributes can also be computed efficiently,
to allow applicability across different problems. We experiment with
several descriptive measures from the literature and encounter certain
limitations, leading us to introduce new attribute formulations and
more efficient computational methods. Finally, we experiment with the
attribute set on our two applications: plant species identification
from leaf photos and object recognition in remote sensing images
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation
Joint segmentation and classification of fine-grained actions is important
for applications of human-robot interaction, video surveillance, and human
skill evaluation. However, despite substantial recent progress in large-scale
action classification, the performance of state-of-the-art fine-grained action
recognition approaches remains low. We propose a model for action segmentation
which combines low-level spatiotemporal features with a high-level segmental
classifier. Our spatiotemporal CNN is comprised of a spatial component that
uses convolutional filters to capture information about objects and their
relationships, and a temporal component that uses large 1D convolutional
filters to capture information about how object relationships change across
time. These features are used in tandem with a semi-Markov model that models
transitions from one action to another. We introduce an efficient constrained
segmental inference algorithm for this model that is orders of magnitude faster
than the current approach. We highlight the effectiveness of our Segmental
Spatiotemporal CNN on cooking and surgical action datasets for which we observe
substantially improved performance relative to recent baseline methods.Comment: Updated from the ECCV 2016 version. We fixed an important
mathematical error and made the section on segmental inference cleare
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