An emotion detection algorithm using frontal facial image is presented in this paper. The algorithm is composed of three main stages: image processing stage and facial feature extraction stage, and emotion detection stage. In image processing stage, the face region and facial component is extracted by using fuzzy color fllter, virtual face model, and histogram analysis method. The features for emotion detection are extracted from facial component in facial feature extraction stage. In emotion detection stage, the fuzzy classifler is adopted to recognize emotion from extracted features. It is shown by experiment results that the proposed algorithm can detect emotion well.

/pdf/emotion-detection-algorithm-using-frontal-face-image-5etp9cri2h.pdf

Emotion Detection Algorithm Using Frontal Face Image

The purpose of this study was to investigate the brain activation patterns in response to thehuman color preference by using a functional magnetic resonance imaging (fMRI). A total of 31 healthyhumans without color blindness were participated in this study. The brain activation was induced by viewingof eight different colors: black, gray, blue, green, earthy yellow, red, yellow and white. The questionnaires forcolor preference showed that green color was mostly favorite to the subjects, whereas the black color wasmostly unfavorite. During the visual stimulation with favorite colors, the brain areas dominantly activatedincluded the pons, supramarginal gyrus, paracentral lobule, midbrain and globus pallidus. During the stimulation with unfavorite colors, on the other hand, the predominantly activated brain areas included the body of the caudate nucleus, parahippocampal gyrus, anterior cingulate gyrus, splenium of the corpus callosum, hippocampus, orbitofrontal gyrus, amygdala, thalamus and angular gyrus. The differential brain activation patterns associated with color preference are assumed to reflect the human emotional response and status being assessed.

https://ijer.ut.ac.ir/article_566_af8ba4d4173fbcf2049b6e612c46783d.pdf

Neural Responses to the Human Color Preference for Assessment of eco-friendliness: A functional Magnetic Resonance Imaging Study

In this paper, we present the complete isotropy analysis of a caster wheeled omnidirectional mobile robot (COMR) with nonredundant/redundant actuation. It is desirable for robust motion control to keep a COMR close to the isotropy but away from the singularity as much as possible. First, with the characteristic length introduced, the kinematic model of a COMR is obtained based on the orthogonal decomposition of the wheel velocities. Second, a general form of the isotropy conditions of a COMR is given in terms of physically meaningful vector quantities which specify the wheel configuration. Third, for all possible nonredundant and redundant actuation sets, the algebraic expressions of the isotropy conditions are derived so as to identify the isotropic configurations of a COMR. Fourth, the number of the isotropic configurations, the isotropic characteristic length, and the optimal initial configuration are discussed.

/pdf/complete-identification-of-isotropic-configurations-of-a-x9f916h6wa.pdf

Complete Identification of Isotropic Configurations of a Caster Wheeled Mobile Robot with Nonredundant/Redundant Actuation

This paper describes the mechanism of a biped walking robot. The biped robot consists of lower-limbs and a pelvis. Each leg has three segments such as a thigh, shank and sole that are connected through rotational joints each other. It has 12 degrees of freedom (DOF) such as two 2 DOF ankles, two 1 DOF knees and two 3 DOF hips. Its height is about 1.17 m, and its total weight is 43 kg. The movable angles of the biped robot are almost the same as a human. The actuator system of each axis consists of a motor, a timing belt, a harmonic drive gear, and two pulleys. This double speed reduction mechanism makes the biped robot have a high reduction ratio and sets the joint axis apart from the motor axis. Using the biped robot, various walking experiments are conducted and the effectiveness of the robot mechanism is confirmed.

Development of a biped walking robot

We present fast stereo matching algorithm using edge projection. Traditional stereo matching algorithm uses 2D template for the search of corresponding point thus it requires huge the computational cost. In this paper, we reduce the 2D search problem into 1D using edge projection along vertical and horizontal direction inside the region of interest. Also, by accumulation of edge projection along vertical and horizontal direction, the edge projection within the region of interest could simply be obtained by just subtracting two values. Experimental results show that matching algorithm using edge projection also gives comparable discriminating power compared to that of using intensity.

/pdf/fast-stereo-matching-algorithm-using-edge-projection-2ewdg7rn6y.pdf

Fast Stereo Matching Algorithm using Edge Projection

Human ability to perceive colors is a very subjective matter. The task of measuring and analyzing appropriate colors from colored images, which matches human sensitivity for perceiving colors has been a challenge to the research community. In this paper we propose a novel approach, which involves the use of fuzzy logic and reasoning to analyze the RGB color intensities extracted from sensory inputs to understand human sensitivity for various colors. Based on this approach, an intelligent system has been built to predict the subject's personality. The results of experiments conducted with this system are discussed in the paper.

Color preference and personality modeling using fuzzy reasoning rule

We present a method to detect on-road vehicle using geometric invariant of feature points on side planes of the vehicle. The vehicles are assumed into a set of planes and the invariant from motion information of features on the plane segments the plane from the theory that a geometric invariant value defined by five points on a plane is preserved under a projective transform. Harris corners as a salient image point are used to give motion information with the normalized correlation centered at these points. We define a probabilistic criterion to test the similarity of invariant values between sequential frames. Experimental results using images of real road scenes are presented.

http://2005.iccas.org/submission/paper/upload/iccas2005_kang_final.pdf

Moving Vehicle Segmentation from Plane Constraint

Among the processes to manufacture parts from footwear materials like upper leathers, one of the most essential processes is the cutting process for optimally arranging parts on raw footwear materials. A new nesting strategy was proposed for the 2-dimensional part layout by using a two-stage approach which can be effectively used for water jet cutting. In the initial layout stage, a SOAL(Self-Organization Assisted Layout) based on the combination of FCM (Fuzzy C-Means) and SOM(Self-Organizing Maps) were adopted. In the layout improvement stage, SA(Simulated Annealing)-based approach was adopted for a finer layout. The approach proposed in this paper saves much CPU time through a two-stage approach scheme, while other annealing-based algorithms so far reported for nesting problems are computationally expensive. The proposed nesting approach uses the stochastic process, and has a much higher possibility to obtain a global solution than the deterministic searching techniques. We developed the automatic 2-dimensional nesting software NST(ver.1.1) for footwear industry by implementing these proposed algorithms. The optimized automatic nesting algorithm of NST has been applied for cutting without the loss of leathers, if possible, after detecting defected areas. NST can also consider various distinctive qualities in not only natural leathers but artificial ones. Lastly, NST can reduce a required time to generating NC-code, cutting time, and waste of raw materials because it automatically performs parts arrangement, cutting paths generation and finally NC-code generation, which require a lot of efforts and time to generate them manually.

An Implementation of the Vectorizing-Based Automatic Nesting Software NST

This paper presents a method for real-time face detection and tracking which combined Adaboost and Camshift algorithm. Adaboost algorithm is a method which selects an important feature called weak classifier among many possible image features by tuning weight of each feature from learning candidates. Even though excellent performance extracting the object, computing time of the algorithm is very high with window size of multi-scale to search image region. So direct application of the method is not easy for real-time tasks such as multi-task OS, robot, and mobile environment. But CAMshift method is an improvement of Mean-shift algorithm for the video streaming environment and track the interesting object at high speed based on hue value of the target region. The detection efficiency of the method is not good for environment of dynamic illumination. We propose a combined method of Adaboost and CAMshift to improve the computing speed with good face detection performance. The method was proved for real image sequences including single and more faces.

AdaBoost-based Real-Time Face Detection & Tracking System

Color Preference and Personality Modeling Using Fuzzy Reasoning Rule

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