27,675 research outputs found
Composite Feature-Based Face Detection Using Skin Color Modeling and SVM Classification
This report proposes a face detection algorithm based on skin color modeling and support vector machine (SVM) classification. Said classification is based on various face features used to detect specific faces in an input color image. A YCbCr color space is used to filter the skin color pixels from the input color image. Template matching is used on the result with various window sizes of the template created from an ORL face database. The candidates obtained above, are then classified by SVM classifiers using the histogram of oriented gradients, eigen features, edge ratio, and edge statistics features
Image segmentation and pattern classification using support vector machines
Image segmentation and pattern classification have long been important topics in computer science research. Image segmentation is one of the basic and challenging lower-level image processing tasks. Feature extraction, feature reduction, and classifier design based on selected features are the three essential issues for the pattern classification problem.
In this dissertation, an automatic Seeded Region Growing (SRG) algorithm for color image segmentation is developed. In the SRG algorithm, the initial seeds are automatically determined. An adaptive morphological edge-linking algorithm to fill in the gaps between edge segments is designed. Broken edges are extended along their slope directions by using the adaptive dilation operation with suitably sized elliptical structuring elements. The size and orientation of the structuring element are adjusted according to local properties.
For feature reduction, an improved feature reduction method in input and feature spaces using Support Vector Machines (SVMs) is developed. In the input space, a subset of input features is selected by the ranking of their contributions to the decision function. In the feature space, features are ranked according to the weighted support vectors in each dimension.
For object detection, a fast face detection system using SVMs is designed. Twoeye patterns are first detected using a linear SVM, so that most of the background can be eliminated quickly. Two-layer 2nd-degree polynomial SVMs are trained for further face verification. The detection process is implemented directly in feature space, which leads to a faster SVM. By training a two-layer SVM, higher classification rates can be achieved.
For active learning, an improved incremental training algorithm for SVMs is developed. Instead of selecting training samples randomly, the k-mean clustering algorithm is applied to collect the initial set of training samples. In active query, a weight is assigned to each sample according to its distance to the current separating hyperplane and the confidence factor. The confidence factor, calculated from the upper bounds of SVM errors, is used to indicate the degree of closeness of the current separating hyperplane to the optimal solution
Efficient Localization of Discontinuities in Complex Computational Simulations
Surrogate models for computational simulations are input-output
approximations that allow computationally intensive analyses, such as
uncertainty propagation and inference, to be performed efficiently. When a
simulation output does not depend smoothly on its inputs, the error and
convergence rate of many approximation methods deteriorate substantially. This
paper details a method for efficiently localizing discontinuities in the input
parameter domain, so that the model output can be approximated as a piecewise
smooth function. The approach comprises an initialization phase, which uses
polynomial annihilation to assign function values to different regions and thus
seed an automated labeling procedure, followed by a refinement phase that
adaptively updates a kernel support vector machine representation of the
separating surface via active learning. The overall approach avoids structured
grids and exploits any available simplicity in the geometry of the separating
surface, thus reducing the number of model evaluations required to localize the
discontinuity. The method is illustrated on examples of up to eleven
dimensions, including algebraic models and ODE/PDE systems, and demonstrates
improved scaling and efficiency over other discontinuity localization
approaches
Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network
In recent years, various shadow detection methods from a single image have
been proposed and used in vision systems; however, most of them are not
appropriate for the robotic applications due to the expensive time complexity.
This paper introduces a fast shadow detection method using a deep learning
framework, with a time cost that is appropriate for robotic applications. In
our solution, we first obtain a shadow prior map with the help of multi-class
support vector machine using statistical features. Then, we use a semantic-
aware patch-level Convolutional Neural Network that efficiently trains on
shadow examples by combining the original image and the shadow prior map.
Experiments on benchmark datasets demonstrate the proposed method significantly
decreases the time complexity of shadow detection, by one or two orders of
magnitude compared with state-of-the-art methods, without losing accuracy.Comment: 6 pages, 5 figures, Submitted to IROS 201
Automated detection of block falls in the north polar region of Mars
We developed a change detection method for the identification of ice block
falls using NASA's HiRISE images of the north polar scarps on Mars. Our method
is based on a Support Vector Machine (SVM), trained using Histograms of
Oriented Gradients (HOG), and on blob detection. The SVM detects potential new
blocks between a set of images; the blob detection, then, confirms the
identification of a block inside the area indicated by the SVM and derives the
shape of the block. The results from the automatic analysis were compared with
block statistics from visual inspection. We tested our method in 6 areas
consisting of 1000x1000 pixels, where several hundreds of blocks were
identified. The results for the given test areas produced a true positive rate
of ~75% for blocks with sizes larger than 0.7 m (i.e., approx. 3 times the
available ground pixel size) and a false discovery rate of ~8.5%. Using blob
detection we also recover the size of each block within 3 pixels of their
actual size
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