scholarly journals A Fast Haar Classifier based Gesture Recognition using camShift algorithm and Curve Fitting Method

2012 ◽  
pp. 295-299
Author(s):  
Bhavish Sushiel Agarwal ◽  
Jyoti R Desai ◽  
Snehanshu Saha
Keyword(s):  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Curve Fitting ◽  
Lead Point ◽  
Hand Gestures ◽  
Curve Fit ◽  
Camshift Algorithm ◽  
Fitting Method ◽  
Wide Range ◽  
Curve Fitting Method

The use of hand gestures opens a wide range of application for human computer interaction. The paper makes use of haar classifiers and camShift algorithm to track the movement of hand. Parallelism is introduced at every step by segmenting the data from camshaft into an NxN grid. Every block of the grid now represents a lead point which is calculated from mean of all the points belonging to the particular grid. Now we have only N2 points to recognize the curve that was performed by the user in his action. Finally the fit that was found is compared to pre-defined curve fit data to find out the curve using Mahalanobis equation. Parallelism used in reducing the number of points to be fitted allows the recognition to be faster.

One Parameter Chi-squared Analysis

2019 ◽  
pp. 18-31
Author(s):  
Carey Witkov ◽  
Keith Zengel
Keyword(s):  
Curve Fitting ◽  
Physical Experiments ◽  
Fitting Method ◽  
Wide Range ◽  
Chi Squared ◽  
Measurement Uncertainties ◽  
Distributed Measurement ◽  
Curve Fitting Method ◽  
The One ◽  
Parameter Case

The chi-squared method for parameter estimation and model testing is developed for the one-parameter case of a line with a slope but no intercept. Curve fitting is motivated, and several methods for curve fitting are introduced. The chi-squared method is shown to be the optimal curve fitting method whenever Gaussian distributed measurement uncertainties and a model are present. The central limit theorem, which assures Gaussian distributed measurement uncertainties for a wide range of physical experiments, is introduced. End-of-chapter problems are included (with solutions in an appendix).

Using A Segmented Voltage Sweep Mode and A Gaussian Curve Fitting Method to Improve Heavy Metal Measurement System Performance

2012 ◽  
Vol 19 (2) ◽  
pp. 381-394
Author(s):  
José Pereira ◽  
Octavian Postolache ◽  
Pedro Girão
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Measurement System ◽  
Curve Fitting ◽  
System Performance ◽  
Redox Potentials ◽  
Voltage Sweep ◽  
Gaussian Curve ◽  
Fitting Method ◽  
Curve Fitting Method

Using A Segmented Voltage Sweep Mode and A Gaussian Curve Fitting Method to Improve Heavy Metal Measurement System PerformanceThis paper presents a voltammetric segmented voltage sweep mode that can be used to identify and measure heavy metals' concentrations. The proposed sweep mode covers a set of voltage ranges that are centered around the redox potentials of the metals that are under analysis. The heavy metal measurement system can take advantage of the historical database of measurements to identify the metals with higher concentrations in a given geographical area, and perform a segmented sweep around predefined voltage ranges or, alternatively, the system can perform a fast linear voltage sweep to identify the voltammetric current peaks and then perform a segmented voltage sweep around the set of voltages that are associated with the voltammetric current peaks. The paper also includes the presentation of two auto-calibration modes that can be used to improve system's reliability and proposes the usage of a Gaussian curve fitting of voltammetric data to identify heavy metals and to evaluate their concentrations. Several simulation and experimental results, that validate the theoretical expectations, are also presented in the paper.

An additional branch free algebraic B-spline curve fitting method

2010 ◽  
Vol 26 (6-8) ◽  
pp. 801-811 ◽  
Author(s):  
Mingxiao Hu ◽  
Jieqing Feng ◽  
Jianmin Zheng
Keyword(s):  
Curve Fitting ◽  
B Spline ◽  
Spline Curve ◽  
Fitting Method ◽  
Additional Branch ◽  
Curve Fitting Method

Evaluation of a predictive curve-fitting method for processing data from flow systems

1993 ◽  
Vol 272 (1) ◽  
pp. 125-134 ◽  
Author(s):  
James M. Jordan ◽  
Michael D. Love ◽  
Harry L. Pardue
Keyword(s):  
Curve Fitting ◽  
Processing Data ◽  
Fitting Method ◽  
Flow Systems ◽  
Curve Fitting Method

A Curve Fitting Method for Detecting Valve Stiction in Oscillating Control Loops

2007 ◽  
Vol 46 (13) ◽  
pp. 4549-4560 ◽  
Author(s):  
Q. Peter He ◽  
Jin Wang ◽  
Martin Pottmann ◽  
S. Joe Qin
Keyword(s):  
Curve Fitting ◽  
Control Loops ◽  
Fitting Method ◽  
Valve Stiction ◽  
Curve Fitting Method

Discussion of Calculation of Power Function with Special Reference to Respiratory Metabolism in Fish

1969 ◽  
Vol 26 (10) ◽  
pp. 2643-2650 ◽  
Author(s):  
Norman R. Glass
Keyword(s):  
Body Weight ◽  
Oxygen Consumption ◽  
Power Function ◽  
Body Weight Data ◽  
Fitting Method ◽  
Wide Range ◽  
Curve Fitting Method ◽  
Parameter Values ◽  
Weight Data ◽  
Range Of Values

The rationale for employing a nonlinear iterative least-squares technique for fitting the well-known power function to oxygen consumption–body weight data is set forth. Twenty-six sets of routine or standard metabolism data from six authors were used to demonstrate the relative merits of two methods of calculating parameter values for the power function. The conclusion was reached that if accuracy in predicting oxygen consumption over a wide range of values of body weight is desired, an iterative curve fitting method may be superior to the much used technique of performing a linear regression on logarithmically transformed data.

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