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I drastically improved my matching algorithm by combining it with a histogram comparison. My final matching probability is calculated like this: probability = (matchingProbability * 2 + histProbability) / 3 Also I found that inverting images can improve template matching further. this.tmplMeanStdDev = getMeanStdDev(tmpl) const meanArr = tmpl.mean() const ...


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$E(u,v)$ is zero for zero shift ($u=v=0$), where the two patches being compared are equal. As you increase the distance, the difference tends to increase. Depending on the local shape of the image, the $E(u,v)$ surface will have a different shape: at a corner or a small point, it will steeply increase from 0 in all directions. Along an edge, it will remain ...


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