Using Correlation Method For Match Two Digital Images

The correlation method focuses on prediction. It is a statistical measure of two or more variables and the relation between them are assessed. The Correlation coefficient assesses how well you can predict one variable from another. A positive correlation is when the value of one variable increases it is associated with the increase of the other variable. For example height and weight, the more one grows the more he will weigh. A negative correlation is when the value of one variable increases it is associated with the decrease of the other variable. An example is that the

Correlation is usually when two things tend to happen together at the same time and causation is something happens because of something else. I think it is harder to prove causation because

Solomon W. Golomb, G. G. (2005). Signal Design for Good Correlation. New York: Cambridge University Press.

During the identification phase, the user puts the finger on the same sensor, generating a new fingerprint image or template called query print. Minutiae points are carried out from the query print, and the matcher module compares the set of query minutia with the stored minutia templates or image in the enrollment database to find the number of similar minutia points. Because of variations present in finger placement and pressure applied to the sensor, the minutia points take out from the template and query fingerprints must be lined up, or submitted before matching. After line up the fingerprints, the matcher decides the number of pairs of matching minutiae-two minutiae points that have similar location and directions. The system decides the user's identity by comparing the match score to a

A developmental psychologist who used the correlational method found that there was a positive correlation between children's self-esteem and their academic achievement. First, what does a positive correlation mean in this case? Second, how might these results be explained?

Feedback: A strong correlation exists when the plotted ordered pair points are close to the line of best fit. If the points line up exactly on the line of best fit, there is a perfect correlation. The

"Correlation is a measure of association that tests whether a relationship exists between two variables. It indicates both the strength of the association and its direction (direct or inverse). The Pearson product-moment correlation coefficient, written as r, can describe a linear relationship between two variables" Correlation (n.d). As a human service professional and completing research there are advantages and disadvantages to correlational research methods, such as using correlational research it allows us to collect data and determine the strength and direction of what it is we

known components of the image to approximate a real image. Figure 4.5 shows the three

Positive correlation demonstrates the relationship between two variables or events. Therefore, if one variable increases the other variable will increase also. However, a positive correlation can exist if one variable is decreasing and the other variable is decreasing.

Theorist working on a comparison of the biometric template that is created and temporarily stored template with a number of people. And then takes the decision-maker in mind the similarity measures produced by matcher.

Correlational research is the relationship between two variables which lead us to understand how one thing relates to another. As we know, science is constantly creating new studies. Some are true while some are false. To trust on a research claim, other factors needs to be taken into consideration.

A correlation is a positive or negative recognizable relationship between two or more variables that might be associated with each other but may or may not be connected, while a causation is a causal relationship between two or more variables that indicate a certain outcome or event. The difference between correlation and causation is that with causation the event or the change in values of the variables can be predicted while in a correlation it may or may not be. A correlation is much harder to prove since the results may or may not be predictable, visible or certain but the possibility still exists that a specific change in value and/or event

Correlation is much easier to prove than causality or causation. Correlation is looking at two different variables and seeing that there is a relationship, but you do not have to recognize and go into depth to find the real relationship between the two. Causality is more difficult to prove than correlation because it involves more factors, and having to rule out of various explanations

The correlation coefficient allows researchers to determine if there is a possible linear relationship between two variables measured on the same subject (or entity). When these two variables are of a continuous nature (they are measurements such as weight, height, length, etc.) the measure of association most often used is Pearson’s correlation coefficient.

A beneficial feature of correlation analysis is the possibility to foresee the movement in one