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The objective of this research is to investigate the feasibility of using Conway's game of life to solve the problem on printed Lanna character recognition. Pattern recognition can be defined as the objects identification on the basis of information available about it. The problem on character recognition has been discussed to find out the best solution and various recognition methods have been implemented...
Pedestrian detection is of much importance for its practical applications. This paper develops a novel pedestrian detection system which consists of three stages: motion region detection based on background modeling, feature extraction in the guidance of prior information, and map-based classification applying support vector machine (SVM) and Adaboost. First of all, an adaptive Gaussian Mixture Model...
In a pattern recognition sequence consisting of alternating steps of interactive labeling, classifier training, and automated labeling (e.g., CAVIAR systems), the choice of sample size at each step affects the overall amount of human interaction necessary to label all the samples correctly. The appropriate splits depend on the error rate of the classifier as a function of the size of the training...
Most existing methods for action recognition mainly rely on manually engineered features which, despite their good performances, are highly problem dependent. We propose in this paper a fully automated model, which learns to classify human actions without using any prior knowledge. A convolutional sparse autoencoder learns to extract sparse shift-invariant representations of the 2D local patterns...
In cluttered environments the overhead view is often preferred because looking down can afford better visibility and coverage. However detecting people in this or any other extreme view can be challenging as there is a significant variation in a person's appearances depending only on their position in the picture. The Histogram of Oriented Gradient (HOG) algorithm, a standard algorithm for pedestrian...
The entire process of face detection, identification and localization of faces should preferably be almost orientation or rotation invariant. The present paper aims to design one optimal Back Propagation (BP) Network model to perform these face identification tasks. The task is partially independent of orientation or rotation of the faces in the image. Also the identification rate of the faces is...
Local mean classifier can achieve good effect for many real problems and need not explicitly determine the prototypes beforehand. However, it still can not be comparable with human being in classification on the noisy, the sparse, and the high dimensional data. This paper proposes an new approach, called relative local mean classifier(RLMC), to overcome this problem by utilizing the perceptual relativity...
Face detection is a computer technology that determines the locations and sizes of human faces in arbitrary (digital) images. It detects facial features and ignores anything else, such as buildings, trees and other parts of the body. In this paper, we present a face detection system based on the Schneiderman-Kanade method. This system is trained using visual attributes extracted from training samples.
This paper presents preliminary studies on the problem of classification of different kinds of human arm motions based on EMG signals. Methods of change detection, classification, features extraction and selection are considered as an important elements of recognition process. Presented algorithms are part of module to visualise human arm movements. The aim of presented work is to develop system to...
This paper addresses gait recognition, the problem of identifying people by the way of their walk. The proposed system consists of a model-free approach which extracts features directly from the human silhouette. The dynamics of the gait are modeled using Hidden Markov Models. Experiments have been carried out on the CASIA dataset C consisting of 153 people under four walking scenarios: normal walking,...
A Fuzzy Wavelet network (FWN) is proposed to model the characteristics of a speaker in an automatic speaker verification system in this paper. The neural network using wavelet as activation function is wavelet network (Wavenet). Wavenet has the ability to extract the distinguishable and essential features in frequency rich signals. This is required in classification and identification problems such...
Human figure identification is always a challenging move in field of pattern recognition. This paper presents a complete algorithm to find a single object (human body) and identify the object as human being. The algorithm starts the segmentation process with basic frame difference method and use morphological operators, edge detection, feature point generation and finally spline interpolation to find...
In this paper a novel view-invariant movement recognition method is presented. A multi-camera setup is used to capture the movement from different observation angles. Identification of the position of each camera with respect to the subject's body is achieved by a procedure based on morphological operations and the proportions of the human body. Binary body masks from frames of all cameras, consistently...
This paper presents an algorithm based on the ideas of bag of words and sparse representation for action recognition. We assume that all action instances form an action space and all action instances from one action class form a subspace of it. Furthermore, the action space can be represented by an over complete basis and each action instance can be represented by a linear combination of the basis...
This paper describes an approach to structuring behavioral knowledge based on classification of human whole body motions and extraction of the behavioral transitions. The motion patterns are learned by Hidden Markov Models (HMMs), which can be used for classification of the motion patterns. The HMMs are called “motion symbol” since They abstract their corresponding motion patterns. The motion patterns...
The Discriminative Filtering technique performs pattern recognition using a two-dimensional filter. It has a closed-form design, based on the pattern and the statistics of the image set. Here, we investigate the use of Discriminative Filtering for detecting fiducial points in human faces. We show that designing discriminative filters for the principal components increases robustness. The method is...
Recognizing human actions in video sequences is frequently based on analyzing the shape of the human silhouette as the main feature. In this paper we introduce a method for recognizing different actions by comparing signatures of similarities to pre-defined shape prototypes. In training, we build a vocabulary of shape prototypes by clustering a training set of human silhouettes and calculate prototype...
The objective of the current work is to develop an automatic tool to identify microbiological data types using computer vision and pattern recognition. Current systems rely on the subjective reading of profiles by a human expert. This process is time-consuming and prone to errors. Bacteriophage (phage) typing & Fluorescent imaging methods are used to extract representative feature profiles and...
Facial expressions are one important nonverbal communication cue, as they can provide feedback in conversations between people and also in human-robot interaction. This paper presents an evaluation of three standard pattern recognition techniques (active appearance models, gabor energy filters, and raw images) for facial feedback interpretation in terms of valence (success and failure) and compares...
We propose and experimentally evaluate a new method for clustering human behaviors that is suitable for bootstrapping an anomaly detection module for intelligent video surveillance systems. The method uses dynamic time warping, agglomerative hierarchical clustering, and hidden Markov models to provide an initial partitioning of a set of observation sequences then automatically identifies where to...
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