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Is it possible to run the CascadeClassifer without preprocessing?

Hello OpenCV Community,

is it possible to run the CascadeClassifier without the Preprocessing 8Sliding Window and Image Pyramid). I mean i want to give really small image fragments as input (e.g. 24x24) and the Classifier should just say positive or negative.

If i put the ScaleFactor and minSize of the multidetect metod to "1" and minSize to "24x24" the method crashes.

This is for research and performance test.

Thank you very much, Kind regards, Derrick

Is it possible to run the CascadeClassifer without preprocessing?

Hello OpenCV Community,

is it possible to run the CascadeClassifier without the Preprocessing 8Sliding (Sliding Window and Image Pyramid). I mean i want to give really small image fragments as input (e.g. 24x24) and the Classifier should just say positive or negative.

If i put the ScaleFactor and minSize of the multidetect metod to "1" and minSize to "24x24" the method crashes.

This is for research and performance test.

Thank you very much, Kind regards, Derrick

Is it possible to run the CascadeClassifer without preprocessing?

Hello OpenCV Community,

is it possible to run the CascadeClassifier without the Preprocessing (Sliding Window and Image Pyramid). I mean i want to give really small image fragments as input (e.g. 24x24) and the Classifier should just say positive or negative.

If i put the ScaleFactor and minSize of the multidetect metod method to "1" and minSize to "24x24" the method crashes.

This is for research and performance test.

Thank you very much, Kind regards, Derrick

Is it possible to run the CascadeClassifer without preprocessing?

Hello OpenCV Community,

is it possible to run the CascadeClassifier without the Preprocessing (Sliding Window and Image Pyramid). I mean i want to give really small image fragments as input (e.g. 24x24) and the Classifier should just say positive or negative.

If i put the ScaleFactor and minSize of the multidetect method to "1" and minSize to "24x24" the method crashes.

This is for research and performance test.

Thank you very much, much,

Kind regards, regards,

Derrick