We were discussing Signature verification methods. Let us review the stages involved with Signature verification. We will also compare online and offline verification techniques afterwards.
The first stage for the image processing is the image acquisition. This is a crucial stage of any recognition system as the quality of image may considerably affect the subsequent stages. Moreover the devices capturing the image may wear over time since this is touch based technique. Therefore, the consistency of image quality over time is also an important factor.
The second stage for the image processing is the pre-processing that removes noise and may even introduce normalization. Some pre-processing steps may also involve resizing, binary color conversion and cleaning, rotation, thinning and cropping. Binary image that highlights only the signature may be achieved by determining the extremes of gray values and finding the mid point between them as the threshold. For example, if mu1 and mu2 are the gray values for both groups of pixels, the threshold may be set as ( mu1 + mu2 ) /2
The third stage of the image processing is the feature extraction. This is a critical stage for the signature verification because the type and quality of feature may make the verification accurate, predictable and consistent. While both online and offline verification techniques may vary in feature extraction, both may also involve common techniques. Feature extraction is generally termed global or local depending on the features extracted.
The last stage of the image processing is the signature verification. This may be the Euclidean distance computed in the feature space. If the distance is less than a threshold, the signature may be considered as verified.
#codingexercise
We were discussing a coding exercise as shown below:
A person wants to buy L items from her favorite store such that a subset of N items must contain D distinct items. the items range from 1 to A in price. Determine the maximum amount of money the person can spend.
Since the price has to be maximized, the algorithm has to be greedy in its strategy to select the next item. when we can no longer purchase the highest priced item because it violates the given restriction, we make the subsequent selection from the next lower priced item. we determine the threshold from the range 1 to n/d. The rest is recursive combination as shown earlier.
Another way to do this would be to enumerate all possible combinations and select only the ones that match the criteria and return the one that has the maximum purchase.
The first stage for the image processing is the image acquisition. This is a crucial stage of any recognition system as the quality of image may considerably affect the subsequent stages. Moreover the devices capturing the image may wear over time since this is touch based technique. Therefore, the consistency of image quality over time is also an important factor.
The second stage for the image processing is the pre-processing that removes noise and may even introduce normalization. Some pre-processing steps may also involve resizing, binary color conversion and cleaning, rotation, thinning and cropping. Binary image that highlights only the signature may be achieved by determining the extremes of gray values and finding the mid point between them as the threshold. For example, if mu1 and mu2 are the gray values for both groups of pixels, the threshold may be set as ( mu1 + mu2 ) /2
The third stage of the image processing is the feature extraction. This is a critical stage for the signature verification because the type and quality of feature may make the verification accurate, predictable and consistent. While both online and offline verification techniques may vary in feature extraction, both may also involve common techniques. Feature extraction is generally termed global or local depending on the features extracted.
The last stage of the image processing is the signature verification. This may be the Euclidean distance computed in the feature space. If the distance is less than a threshold, the signature may be considered as verified.
#codingexercise
We were discussing a coding exercise as shown below:
A person wants to buy L items from her favorite store such that a subset of N items must contain D distinct items. the items range from 1 to A in price. Determine the maximum amount of money the person can spend.
Since the price has to be maximized, the algorithm has to be greedy in its strategy to select the next item. when we can no longer purchase the highest priced item because it violates the given restriction, we make the subsequent selection from the next lower priced item. we determine the threshold from the range 1 to n/d. The rest is recursive combination as shown earlier.
Another way to do this would be to enumerate all possible combinations and select only the ones that match the criteria and return the one that has the maximum purchase.
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