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In this paper, a novel method is proposed for the Traffic Sign Recognition (TSR) using the Principle Component Analysis (PCA) and the Multi-Layer Perceptrons (MLPs) network. In particular, the candidate signs are individually detected from two chroma components in the YCbCr space and then classified into three shape classes: circle, square, and triangle based on computing the rotated version correlations...
The robust dimension reduction for classification of two dimensional data is discussed in this paper. The classification process is done with reference of original data. The classifying of class membership is not easy when more than one variable are loaded with the same information, and they can be written as a near linear combination of other variables. The standard approach to overcome this problem...
In information retrieval, efficient accomplishing the nearest neighbor search on large scale database is a great challenge. Hashing based indexing methods represent each data instance as a binary string to retrieve the approximate nearest neighbors. In this paper, we present a semi-randomized hashing approach to preserve the Euclidean distance by binary codes. Euclidean distance preserving is a classic...
Hand gesture recognition plays a vital role in developing vision-based communication for human-computer interaction. This paper presents a novel static hand gesture recognition method using the two dimensional Zernike moments (2D ZMs) those are considered as effective features when patterns in images possess distortions due to rotation, scaling or viewing angle. The key contribution of this paper...
For the need of actually combining RGB data and depth input in computer vision research, new RGB-D features for object recognition are proposed. We present six kinds of RGB-D kernel matching functions on kernel view. They have the capability of capturing different RGB-depth cues including position, size, shape and distance. Due to the infinite dimensional character in Gaussian space, it is computationally...
In this paper, we propose a novel feature extraction method called double sparse local Fisher discriminant analysis (DSLFDA), which is an extension of the local Fisher discriminant analysis (LFDA) algorithm. The proposed method combines the idea of sparse representation to construct an adaptive graph to describe the structure information of the samples. Meanwhile, to obtain the sparse projection vectors,...
A novel method for liveness detection of dorsal hand vein (DHV) based on AR model is proposed. Firstly, existing real DHV images are used to constitute a projection space based on modified principal component analysis (PCA). Unlike the previous works using the method of PCA, zero eigenvalues with their eigenvectors are used to constitute the projection space in this work. Secondly, test samples, including...
This document proposes a statistical classification model using principal component analysis (PCA) for a data reduction approach combined with self-organizing map (SOM) for a classification purpose, so called, PCA-SOM model compared with SOM model to classify partial discharge pattern (PD) into four categories listed as corona at high voltage side, corona at low voltage side, surface discharge, and...
To improve the class separability and storage efficiency for radar target high resolution range profile (HRRP) recognition task, some linear discriminant analysis (LDA) based feature extraction methods have been successfully applied. However, as an effective feature extraction method, traditional LDA encounters four main drawbacks with respect to Gaussian distribution assumption, small sample size...
Despite the fundamental variability of human appearance, the last several years have seen considerable advances in age estimation from images of faces. Many of these advances have been made possible by artificially removing external sources of variability—they focus on highly constrained images from datasets such as the MORPH face database and FG-NET. We introduce a novel approach to estimating age...
This paper presents a novel locally linear KNN method with an improved marginal Fisher analysis for image classification. First, the discriminating color space (DCS), which is derived by discriminant analysis of the red, green, and blue primary colors, is integrated into the proposed method. Second, an improved marginal Fisher analysis (IMFA) applies an eigenvalue spectrum analysis to improve the...
In the process of multi index analysis and evaluation, the index weight is determined according to each index relative principal component contribution through principal component analysis and principal component extraction. Then, combined evaluation model will be formed through the Grey Relational Analysis, calculation of each index according to grey weighted relative degree. Combined with key performance...
Two methods are presented for the time- or frequency-domain characterization and diagnostics of unintentional radiated emissions from printed circuit boards. Decompositions produced by principal component analysis (PCA) are compared with those obtained using independent component analysis (ICA). While both techniques achieve first- and higher-order orthogonality of their basis functions, the nature...
To improve the robustness against variation in shooting angles, we previously proposed using an asymptotic expansion of the Gabor transform of ear images to compute the Gabor features of other poses and using these estimates in multiple linear discriminant analysis to enhance feature discriminability. Extending this study, the accuracies are compared with other standard methods that can be used to...
This paper reports on a feasibility study of contactless hand biometrics using an RGB-Depth (RGB-D) camera such as the Kinect v2 prototype. The RGB, depth, and near-infrared (near-IR) spectra provide access to information such as palm print, hand shape, finger joint location, and vein patterns. Extraction of the hand is first done using depth data. The frames with the best palm position are selected,...
Due to availability of reliable and low cost devices, range maps (depth maps) are extensively used in many applications. Recent advances in human-computer interaction enabled us to interact with computers in intuitive and friendly way. In this paper, we propose a novel approach for recognizing static hand gestures using depth information captured from Photon Mixing Device (PMD) cameras. We segment...
Since Gaussian Mixture Models (GMM) captures complex densities of the data and has become one of the most significant methods for clustering in unsupervised context; we study and explore the idea of mixture models for image categorization. In this regard, we first segment all image categories in hybrid color space (HCbCr - LUV) to identify the color homogeneity between the neighboring pixels and then...
Biometric template protection is a vital issue in deploying a biometric recognition system. In this paper, a novel face template protection scheme based on Biohashing and permutation approach is proposed. In order to increase the template privacy and security, we propose a new version of Biohashing employing the Chaos map to permute feature vectors before providing secure templates. To evaluate the...
To achieve high performance in object recognition, a high-level feature representation is play an essential role to transform a raw input data (low-level) into a new representation. Unsupervised feature learning is one of the most successful methods that is widely used in machine learning literatures for creating a high-level feature to improve the supervised learning problems. The main concept of...
A large body of work exists concerning uncertainty in ocean current measuring high-frequency radar (HFR) systems. This study investigates the magnitude of uncertainty present in a HFR system in the lower Chesapeake Bay region of Virginia. A method of assessing the fundamental performance of the HFR is comparing the radial velocities measured by two facing HF radars at the centre point of their baseline...
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