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I am a student assigned to do a project under sign Language interpretation. I have done all segmentation and morphological operations. Further, its time to classify gestures, and i have gone through different journals. I have little doubt that what features suitably satisfy my classification. I have chosen C# as a programming language and SVM Classifier for classification. Please list me out some possible features. If possible, well document with complete mathematics.

Features I have found: Shape Descriptors like aspect ratio, circularity, spreadness Hu - Variants and moment Features

Hand Segmented ImageFinal Edge Detected Image

I have recently found that re-sizing and normalization of an image is done before feature extraction. And they suggest the algorithm like:

  • Resize into certain resolution say 100*100
  • vertical allignment to first principle component of image.
  • lastly, reconstructing boundary box that fits the image.

I am with doubt that what if the finger alignment. If the training finger is 45 degree aligned with main palm axis and is 10 unit long, what if the testing data appears as it is aligned other than 45 degree and 5 unit long??

And Encountered features: Finger count and principal component analysis(PCA). But what does PCA physically means?

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最后，我选择HU-Moment了手势识别的特征，因为它是平移、旋转和比例不变的，这已经被证明了。对于这一SVM部分，我选择SVM.NET了'LIBSVM'inJAVA和C++asC#作为编程语言的包装器

于 2012-10-09T17:58:40.097 回答
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一类非常强大的特征是傅立叶描述符。这些特征是为曲线提取的。这些特征计算速度很快，并且在比例、平移和方向方面是不变的。

有关如何计算这些的详细信息，请访问：http ://demonstrations.wolfram.com/FourierDescriptors/

几项研究表明傅立叶描述符在形状分析中的有用性。其中一项研究是 H Kauppinen、T Seppanen 的“在 2D 形状分类中对自回归和基于傅立叶的描述符的实验比较”…… - 模式分析和……，1995

于 2012-10-26T21:30:39.403 回答