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| 2014-01-31 05:25:05 -0600 | received badge | ● Student (source) |
| 2013-10-25 13:50:52 -0600 | asked a question | Linear Discriminant Analysis and Fisher Faces "The Principal Component Analysis (PCA), which is the core of the Eigenfaces method, finds a linear combination of features that maximizes the total variance in data. While this is clearly a powerful way to represent data, it doesn’t consider any classes and so a lot of discriminative information may be lost when throwing components away." (Open CV) What is mean by "CLASSES" here???? " Linear Discriminant Analysis maximizes the ratio of between-classes to within-classes scatter, instead of maximizing the overall scatter. The idea is simple: same classes should cluster tightly together, while different classes are as far away as possible from each other in the lower-dimensional representation. in here also what is mean by CLASSES???? Can some one please explain this in image processing view thanx |
| 2013-10-25 09:46:56 -0600 | received badge | ● Editor (source) |
| 2013-10-25 09:43:26 -0600 | asked a question | Match Sketch(Drawing) face photo to digital color photo hey im going to match the sketch face (drawing photo) in to the color photo. so for the research i want to find out what are the challenges that matching sketch drawing in to color faces. for now i have find out that 1. resolution pixel difference 2. texture difference 3. distance difference 4. and color (not much effect) i wanna know that in technical terms what are other challenges and what are available OPEN CV and JAVA CV method and algorithms to overcome that challenges???? please tell me the answers very soon hope some experts will help me thanx |