13 research outputs found
Novel Texture-based Probabilistic Object Recognition and Tracking Techniques for Food Intake Analysis and Traffic Monitoring
More complex image understanding algorithms are increasingly practical in a host of emerging applications. Object tracking has value in surveillance and data farming; and object recognition has applications in surveillance, data management, and industrial automation. In this work we introduce an object recognition application in automated nutritional intake analysis and a tracking application intended for surveillance in low quality videos. Automated food recognition is useful for personal health applications as well as nutritional studies used to improve public health or inform lawmakers. We introduce a complete, end-to-end system for automated food intake measurement. Images taken by a digital camera are analyzed, plates and food are located, food type is determined by neural network, distance and angle of food is determined and 3D volume estimated, the results are cross referenced with a nutritional database, and before and after meal photos are compared to determine nutritional intake. We compare against contemporary systems and provide detailed experimental results of our system\u27s performance. Our tracking systems consider the problem of car and human tracking on potentially very low quality surveillance videos, from fixed camera or high flying \acrfull{uav}. Our agile framework switches among different simple trackers to find the most applicable tracker based on the object and video properties. Our MAPTrack is an evolution of the agile tracker that uses soft switching to optimize between multiple pertinent trackers, and tracks objects based on motion, appearance, and positional data. In both cases we provide comparisons against trackers intended for similar applications i.e., trackers that stress robustness in bad conditions, with competitive results
A Theoretical Analysis of Deep Neural Networks for Texture Classification
We investigate the use of Deep Neural Networks for the classification of
image datasets where texture features are important for generating
class-conditional discriminative representations. To this end, we first derive
the size of the feature space for some standard textural features extracted
from the input dataset and then use the theory of Vapnik-Chervonenkis dimension
to show that hand-crafted feature extraction creates low-dimensional
representations which help in reducing the overall excess error rate. As a
corollary to this analysis, we derive for the first time upper bounds on the VC
dimension of Convolutional Neural Network as well as Dropout and Dropconnect
networks and the relation between excess error rate of Dropout and Dropconnect
networks. The concept of intrinsic dimension is used to validate the intuition
that texture-based datasets are inherently higher dimensional as compared to
handwritten digits or other object recognition datasets and hence more
difficult to be shattered by neural networks. We then derive the mean distance
from the centroid to the nearest and farthest sampling points in an
n-dimensional manifold and show that the Relative Contrast of the sample data
vanishes as dimensionality of the underlying vector space tends to infinity.Comment: Accepted in International Joint Conference on Neural Networks, IJCNN
201
Context-Aware Design of Cyber-Physical Human Systems (CPHS)
Recently, it has been widely accepted by the research community that
interactions between humans and cyber-physical infrastructures have played a
significant role in determining the performance of the latter. The existing
paradigm for designing cyber-physical systems for optimal performance focuses
on developing models based on historical data. The impacts of context factors
driving human system interaction are challenging and are difficult to capture
and replicate in existing design models. As a result, many existing models do
not or only partially address those context factors of a new design owing to
the lack of capabilities to capture the context factors. This limitation in
many existing models often causes performance gaps between predicted and
measured results. We envision a new design environment, a cyber-physical human
system (CPHS) where decision-making processes for physical infrastructures
under design are intelligently connected to distributed resources over
cyberinfrastructure such as experiments on design features and empirical
evidence from operations of existing instances. The framework combines existing
design models with context-aware design-specific data involving
human-infrastructure interactions in new designs, using a machine learning
approach to create augmented design models with improved predictive powers.Comment: Paper was accepted at the 12th International Conference on
Communication Systems and Networks (COMSNETS 2020
A machine learning algorithm to improve building performance modeling during design
Building design involves the optimization of factors affecting building performance such as building functions, comfort, safety, and energy. Building performance models (BPMs) help designers to evaluate and optimize such factors. However, the lack of design capabilities to validly describe human-building interactions for buildings under design may contribute to the development of inaccurate BPMs and the performance discrepancy between predictions and actual buildings. To address this challenge, a computational framework is proposed to increase the estimations performance of BPMs. The framework uses artificial neural networks (ANNs) to combine and describing design-specific human-building interactions captured by using immersive virtual environments (IVEs). The framework produces an augmented BPM that can predict building performance taking human-building interactions specific to a new design into consideration. It incorporates a feature ranking technique allowing designers to assess impacts of contextual factors on human-building interactions. The paper focuses on providing details of theories, experiment and data collection designs, and algorithms behind the framework as a companion paper of [1]. •A framework for combining contextual factors with building performance models to enhance their predictive performance.•Computation for determining impacts of contextual factors on human-building interaction
Deep neural networks for texture classification-A theoretical analysis
We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity