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How many megapixels can the human eye capture?

first_imgOne of the characteristics in which we look more when buying a camera, is in its resolution. Every year, cameras and even those with mobile phones, have many improvements that allow us to capture the most realistic images. However, none of them are able to recreate the image that we can see. Image: iStock To get a camera to capture the same images we see through our eyes, it would have to be 576 megapixels, but even so, the image we would capture would not reach the sharpness we are able to see.A camera, even if it has a good lens and one of the best sensors, cannot be compared with all the complex features and elements that make up an eye. In addition, there are also different factors that affect our vision and that with a camera would be impossible to recreate.Our vision can capture an image of 576 megapixelsAn example of this is the morphology of our eyes. This morphology allows us to correctly see the things that are in front of us at all times, while the surroundings look more blurred. In addition, there are other factors that decrease the quality of our vision at some times, such as the blind spots of our eyes or the part of our nose that makes us always have things in between.That said, we can confirm that the resolution that our eyes capture varies at any moment and that although our vision can capture an image at 576 megapixels, at other points it can be compared with a resolution of only 7. This fact although it may seem a bit odd It has its logic: everything we observe is transmitted directly to the brain and continuously, so even if we see in some moments we see badly, what we perceive is a processed and correct image of reality.center_img One of the things we look at in a camera is its resolutionThe resolution of a camera cannot be compared with that of the eyeslast_img read more

Lead Scoring Models Assigning Point Values

first_imgIn my last post, I covered how to start building a lead scoring model and at what point in a company’s lifecycle it should be done (once the company has reached the expansion stage, is focused on sales and marketing, and has a high inflow of leads). To quickly review, the first step is to put together a data set of lead attributes (search phrase used, number of pages viewed, country of origin, etc.) along with whether the lead converted to an opportunity or sale. After the data set is assembled, the second step is to analyze each attribute’s (independent variable’s) correlation with conversion (the dependent variable). The idea in this step is to find attributes that are either strong positive or negative predictors of whether a lead will convert. For example, if leads that trialed the product convert at a higher rate than average, this would be a strong positive predictor of conversion. Alternatively, if leads that only viewed one page of the web converted at a much lower rate than average, this would be a good negative predictor of conversion.After each attribute’s correlation with conversion has been analyzed, the third step is to assign point values for each attribute (positive or negative). If you are creating a manual lead scoring model (as opposed to using multiple regression software to get attribute/variable point values), it is important to be consistent. A simple, consistent method of assigning point values is taking the overall conversion rate and subtracting it from the conversion rate of leads with a specific attribute. For example, if the overall lead to opportunity conversion rate is 10%, and the conversion rate for leads that have trialed the product is 25%, add 15 points to every lead that has trialed the product (25 minus 10). If you find that leads that come from Google Adwords campaigns only convert at 3%, deduct 7 points from every Google Adwords lead (3 minus 10).When manually assigning point values, there are some pitfalls that should be avoided – namely assigning point values to attributes that have low sample sizes and assigning point values to attributes that are highly correlated with one another (multicollinearity). I will cover both in more detail next week.AddThis Sharing ButtonsShare to FacebookFacebookShare to TwitterTwitterShare to PrintPrintShare to EmailEmailShare to MoreAddThislast_img read more