Modelling the Environment of a Mobile Robot using Feature Based Principal Component Analysis

2006 ◽  
Author(s):  
Tahir Yaqub ◽  
Jayantha Katupitiya
Keyword(s):  
Principal Component Analysis ◽  
Mobile Robot ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Based

CORRIDOR-SCENE CLASSIFYING METHODS FOR MOBILE ROBOT BASED ON MULTI-SONAR-SENSOR INFORMATION FUSION

2007 ◽  
Vol 04 (01) ◽  
pp. 15-26 ◽  
Author(s):  
XIUQING WANG ◽  
ZENG-GUANG HOU ◽  
LONG CHENG ◽  
MIN TAN ◽  
FEI ZHU
Keyword(s):  
Principal Component Analysis ◽  
Mobile Robot ◽  
Principal Component ◽  
Component Analysis ◽  
Experimental Results ◽  
Scene Analysis ◽  
Kernel Principal Component Analysis ◽  
Kernel Pca ◽  
Sonar Sensor ◽  
Pca Method

The ability of cognition and recognition for complex environment is very important for a real autonomous robot. A new scene analysis method using kernel principal component analysis (kernel-PCA) for mobile robot based on multi-sonar-ranger data fusion is put forward. The principle of classification by principal component analysis (PCA), kernel-PCA, and the BP neural network (NN) approach to extract the eigenvectors which have the largest k eigenvalues are introduced briefly. Next the details of PCA, kernel-PCA and the BP NN method applied in the corridor scene analysis and classification for the mobile robots based on sonar data are discussed and the experimental results of those methods are given. In addition, a corridor-scene-classifier based on BP NN is discussed. The experimental results using PCA, kernel-PCA and the methods based on BP neural networks (NNs) are compared and the robustness of those methods are also analyzed. Such conclusions are drawn: in corridor scene classification, the kernel-PCA method has advantage over the ordinary PCA, and the approaches based on BP NNs can also get satisfactory results. The robustness of kernel-PCA is better than that of the methods based on BP NNs.

scholarly journals The Influence of Each Facial Feature on How We Perceive and Interpret Human Faces

i-Perception ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 204166952096112
Author(s):  
Jose A. Diego-Mas ◽  
Felix Fuentes-Hurtado ◽  
Valery Naranjo ◽  
Mariano Alcañiz
Keyword(s):  
Principal Component Analysis ◽  
Personality Traits ◽  
Principal Component ◽  
Facial Feature ◽  
Component Analysis ◽  
Facial Features ◽  
Direct Link ◽  
Facial Information ◽  
Feature Based ◽  
Human Faces

Facial information is processed by our brain in such a way that we immediately make judgments about, for example, attractiveness or masculinity or interpret personality traits or moods of other people. The appearance of each facial feature has an effect on our perception of facial traits. This research addresses the problem of measuring the size of these effects for five facial features (eyes, eyebrows, nose, mouth, and jaw). Our proposal is a mixed feature-based and image-based approach that allows judgments to be made on complete real faces in the categorization tasks, more than on synthetic, noisy, or partial faces that can influence the assessment. Each facial feature of the faces is automatically classified considering their global appearance using principal component analysis. Using this procedure, we establish a reduced set of relevant specific attributes (each one describing a complete facial feature) to characterize faces. In this way, a more direct link can be established between perceived facial traits and what people intuitively consider an eye, an eyebrow, a nose, a mouth, or a jaw. A set of 92 male faces were classified using this procedure, and the results were related to their scores in 15 perceived facial traits. We show that the relevant features greatly depend on what we are trying to judge. Globally, the eyes have the greatest effect. However, other facial features are more relevant for some judgments like the mouth for happiness and femininity or the nose for dominance.

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