53,441 research outputs found
A comparative analysis of neural and statistical classifiers for dimensionality reduction-based face recognition systems.
Human face recognition has received a wide range of attention since 1990s. Recent approaches focus on a combination of dimensionality reduction-based feature extraction algorithms and various types of classifiers. This thesis provides an in depth comparative analysis of neural and statistical classifiers by combining them with existing dimensionality reduction-based algorithms. A set of unified face recognition systems were established for evaluating alternate combinations in terms of recognition performance, processing time, and conditions to achieve certain performance levels. A preprocessing system and four dimensionality reduction-based methods based on Principal Component Analysis (PCA), Two-dimensional PCA, Fisher\u27s Linear Discriminant and Laplacianfaces were utilized and implemented. Classification was achieved by using various types of classifiers including Euclidean Distance, MLP neural network, K-nearest-neighborhood classifier and Fuzzy K-Nearest Neighbor classifier. The statistical model is relatively simple and requires less computation complexity and storage. Experimental results were shown after the algorithms were tested on two databases of known individuals, Yale and AR database. After comparing these algorithms in every aspect, the results of the simulations showed that considering recognition rates, generalization ability, classification performance, the power of noise immunity and processing time, the best results were obtained with the Laplacianfaces, using either Fuzzy K-NN.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .X86. Source: Masters Abstracts International, Volume: 45-01, page: 0428. Thesis (M.A.Sc.)--University of Windsor (Canada), 2006
Matching Image Sets via Adaptive Multi Convex Hull
Traditional nearest points methods use all the samples in an image set to
construct a single convex or affine hull model for classification. However,
strong artificial features and noisy data may be generated from combinations of
training samples when significant intra-class variations and/or noise occur in
the image set. Existing multi-model approaches extract local models by
clustering each image set individually only once, with fixed clusters used for
matching with various image sets. This may not be optimal for discrimination,
as undesirable environmental conditions (eg. illumination and pose variations)
may result in the two closest clusters representing different characteristics
of an object (eg. frontal face being compared to non-frontal face). To address
the above problem, we propose a novel approach to enhance nearest points based
methods by integrating affine/convex hull classification with an adapted
multi-model approach. We first extract multiple local convex hulls from a query
image set via maximum margin clustering to diminish the artificial variations
and constrain the noise in local convex hulls. We then propose adaptive
reference clustering (ARC) to constrain the clustering of each gallery image
set by forcing the clusters to have resemblance to the clusters in the query
image set. By applying ARC, noisy clusters in the query set can be discarded.
Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method
outperforms single model approaches and other recent techniques, such as Sparse
Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant
Analysis.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
201
Scalable Nonlinear Embeddings for Semantic Category-based Image Retrieval
We propose a novel algorithm for the task of supervised discriminative
distance learning by nonlinearly embedding vectors into a low dimensional
Euclidean space. We work in the challenging setting where supervision is with
constraints on similar and dissimilar pairs while training. The proposed method
is derived by an approximate kernelization of a linear Mahalanobis-like
distance metric learning algorithm and can also be seen as a kernel neural
network. The number of model parameters and test time evaluation complexity of
the proposed method are O(dD) where D is the dimensionality of the input
features and d is the dimension of the projection space - this is in contrast
to the usual kernelization methods as, unlike them, the complexity does not
scale linearly with the number of training examples. We propose a stochastic
gradient based learning algorithm which makes the method scalable (w.r.t. the
number of training examples), while being nonlinear. We train the method with
up to half a million training pairs of 4096 dimensional CNN features. We give
empirical comparisons with relevant baselines on seven challenging datasets for
the task of low dimensional semantic category based image retrieval.Comment: ICCV 2015 preprin
KCRC-LCD: Discriminative Kernel Collaborative Representation with Locality Constrained Dictionary for Visual Categorization
We consider the image classification problem via kernel collaborative
representation classification with locality constrained dictionary (KCRC-LCD).
Specifically, we propose a kernel collaborative representation classification
(KCRC) approach in which kernel method is used to improve the discrimination
ability of collaborative representation classification (CRC). We then measure
the similarities between the query and atoms in the global dictionary in order
to construct a locality constrained dictionary (LCD) for KCRC. In addition, we
discuss several similarity measure approaches in LCD and further present a
simple yet effective unified similarity measure whose superiority is validated
in experiments. There are several appealing aspects associated with LCD. First,
LCD can be nicely incorporated under the framework of KCRC. The LCD similarity
measure can be kernelized under KCRC, which theoretically links CRC and LCD
under the kernel method. Second, KCRC-LCD becomes more scalable to both the
training set size and the feature dimension. Example shows that KCRC is able to
perfectly classify data with certain distribution, while conventional CRC fails
completely. Comprehensive experiments on many public datasets also show that
KCRC-LCD is a robust discriminative classifier with both excellent performance
and good scalability, being comparable or outperforming many other
state-of-the-art approaches
Person Recognition in Personal Photo Collections
Recognising persons in everyday photos presents major challenges (occluded
faces, different clothing, locations, etc.) for machine vision. We propose a
convnet based person recognition system on which we provide an in-depth
analysis of informativeness of different body cues, impact of training data,
and the common failure modes of the system. In addition, we discuss the
limitations of existing benchmarks and propose more challenging ones. Our
method is simple and is built on open source and open data, yet it improves the
state of the art results on a large dataset of social media photos (PIPA).Comment: Accepted to ICCV 2015, revise
Analyzing First-Person Stories Based on Socializing, Eating and Sedentary Patterns
First-person stories can be analyzed by means of egocentric pictures acquired
throughout the whole active day with wearable cameras. This manuscript presents
an egocentric dataset with more than 45,000 pictures from four people in
different environments such as working or studying. All the images were
manually labeled to identify three patterns of interest regarding people's
lifestyle: socializing, eating and sedentary. Additionally, two different
approaches are proposed to classify egocentric images into one of the 12 target
categories defined to characterize these three patterns. The approaches are
based on machine learning and deep learning techniques, including traditional
classifiers and state-of-art convolutional neural networks. The experimental
results obtained when applying these methods to the egocentric dataset
demonstrated their adequacy for the problem at hand.Comment: Accepted at First International Workshop on Social Signal Processing
and Beyond, 19th International Conference on Image Analysis and Processing
(ICIAP), September 201
Analyzing First-Person Stories Based on Socializing, Eating and Sedentary Patterns
First-person stories can be analyzed by means of egocentric pictures acquired
throughout the whole active day with wearable cameras. This manuscript presents
an egocentric dataset with more than 45,000 pictures from four people in
different environments such as working or studying. All the images were
manually labeled to identify three patterns of interest regarding people's
lifestyle: socializing, eating and sedentary. Additionally, two different
approaches are proposed to classify egocentric images into one of the 12 target
categories defined to characterize these three patterns. The approaches are
based on machine learning and deep learning techniques, including traditional
classifiers and state-of-art convolutional neural networks. The experimental
results obtained when applying these methods to the egocentric dataset
demonstrated their adequacy for the problem at hand.Comment: Accepted at First International Workshop on Social Signal Processing
and Beyond, 19th International Conference on Image Analysis and Processing
(ICIAP), September 201
Development of position tracking of BLDC motor using adaptive fuzzy logic controller
The brushless DC (BLDC) motor has many advantages including simple to
construct, high torque capability, small inertia, low noise and long life operation.
Unfortunately, it is a non-linear system whose internal parameter values will change
slightly with different input commands and environments. In this proposed
controller, Takagi-Sugeno-Kang method is developed. In this project, a FLC for
position tracking and BLDC motor are modeled and simulated in
MATLAB/SIMULINK. In order to verify the performance of the proposed
controller, various position tracking reference are tested. The simulation results show
that the proposed FLC has better performance compare the conventional PID
controller
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