5,334 research outputs found
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Blockout: Dynamic Model Selection for Hierarchical Deep Networks
Most deep architectures for image classification--even those that are trained
to classify a large number of diverse categories--learn shared image
representations with a single model. Intuitively, however, categories that are
more similar should share more information than those that are very different.
While hierarchical deep networks address this problem by learning separate
features for subsets of related categories, current implementations require
simplified models using fixed architectures specified via heuristic clustering
methods. Instead, we propose Blockout, a method for regularization and model
selection that simultaneously learns both the model architecture and
parameters. A generalization of Dropout, our approach gives a novel
parametrization of hierarchical architectures that allows for structure
learning via back-propagation. To demonstrate its utility, we evaluate Blockout
on the CIFAR and ImageNet datasets, demonstrating improved classification
accuracy, better regularization performance, faster training, and the clear
emergence of hierarchical network structures
Cluster-based feedback control of turbulent post-stall separated flows
We propose a novel model-free self-learning cluster-based control strategy
for general nonlinear feedback flow control technique, benchmarked for
high-fidelity simulations of post-stall separated flows over an airfoil. The
present approach partitions the flow trajectories (force measurements) into
clusters, which correspond to characteristic coarse-grained phases in a
low-dimensional feature space. A feedback control law is then sought for each
cluster state through iterative evaluation and downhill simplex search to
minimize power consumption in flight. Unsupervised clustering of the flow
trajectories for in-situ learning and optimization of coarse-grained control
laws are implemented in an automated manner as key enablers. Re-routing the
flow trajectories, the optimized control laws shift the cluster populations to
the aerodynamically favorable states. Utilizing limited number of sensor
measurements for both clustering and optimization, these feedback laws were
determined in only iterations. The objective of the present work is not
necessarily to suppress flow separation but to minimize the desired cost
function to achieve enhanced aerodynamic performance. The present control
approach is applied to the control of two and three-dimensional separated flows
over a NACA 0012 airfoil with large-eddy simulations at an angle of attack of
, Reynolds number and free-stream Mach number . The optimized control laws effectively minimize the flight power
consumption enabling the flows to reach a low-drag state. The present work aims
to address the challenges associated with adaptive feedback control design for
turbulent separated flows at moderate Reynolds number.Comment: 32 pages, 18 figure
Automatic human behaviour anomaly detection in surveillance video
This thesis work focusses upon developing the capability to automatically evaluate
and detect anomalies in human behaviour from surveillance video. We work with
static monocular cameras in crowded urban surveillance scenarios, particularly air-
ports and commercial shopping areas. Typically a person is 100 to 200 pixels high
in a scene ranging from 10 - 20 meters width and depth, populated by 5 to 40 peo-
ple at any given time. Our procedure evaluates human behaviour unobtrusively to
determine outlying behavioural events,
agging abnormal events to the operator.
In order to achieve automatic human behaviour anomaly detection we address
the challenge of interpreting behaviour within the context of the social and physical
environment. We develop and evaluate a process for measuring social connectivity
between individuals in a scene using motion and visual attention features. To do this
we use mutual information and Euclidean distance to build a social similarity matrix
which encodes the social connection strength between any two individuals. We de-
velop a second contextual basis which acts by segmenting a surveillance environment
into behaviourally homogeneous subregions which represent high tra c slow regions
and queuing areas. We model the heterogeneous scene in homogeneous subgroups
using both contextual elements. We bring the social contextual information, the
scene context, the motion, and visual attention features together to demonstrate
a novel human behaviour anomaly detection process which nds outlier behaviour
from a short sequence of video. The method, Nearest Neighbour Ranked Outlier
Clusters (NN-RCO), is based upon modelling behaviour as a time independent se-
quence of behaviour events, can be trained in advance or set upon a single sequence.
We nd that in a crowded scene the application of Mutual Information-based social
context permits the ability to prevent self-justifying groups and propagate anomalies
in a social network, granting a greater anomaly detection capability. Scene context
uniformly improves the detection of anomalies in all the datasets we test upon.
We additionally demonstrate that our work is applicable to other data domains.
We demonstrate upon the Automatic Identi cation Signal data in the maritime
domain. Our work is capable of identifying abnormal shipping behaviour using joint
motion dependency as analogous for social connectivity, and similarly segmenting
the shipping environment into homogeneous regions
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