COLOR IMAGE ARRANGEMENT USING ELASTIC TRANSFORM ON PRINCIPAL COMPONENT AXIS
نویسندگان
چکیده
منابع مشابه
Color Image Processing Using Principal Component Analysis
It is known since 1988 that utilizing linear dimension reduction gives appropriate lower dimensional representation of homogenous swatches of color images of the nature. Though, it is common to see serious research projects, even dated 2005, which are based on the fixed color space paradigm. In this thesis, first experimental evaluations are performed to compare the principal component analysis...
متن کاملColor Image Compression Using 2-Dimensional Principal Component Analysis (2DPCA)
Two dimensional principal component analyses (2DPCA) is recently proposed technique for face representation and recognition. The standard PCA works on 1-dimensional vectors which has inherent problem of dealing with high dimensional vector space data such as images, whereas 2DPCA directly works on matrices i.e. in 2DPCA, PCA technique is applied directly on original image without transforming i...
متن کاملColor image classification via quaternion principal component analysis network
The Principal Component Analysis Network (PCANet), which is one of the recently proposed deep learning architectures, achieves the state-of-the-art classification accuracy in various databases. However, the performance of PCANet may be degraded when dealing with color images. In this paper, a Quaternion Principal Component Analysis Network (QPCANet), which is an extension of PCANet, is proposed...
متن کاملQuantum image classification using principal component analysis
We present a novel quantum algorithm for the classification of images. The algorithm is constructed using principal component analysis and von Neuman quantum measurements. In order to apply the algorithm we present a new quantum representation of grayscale images.
متن کاملImage compression using principal component neural networks
Principal component analysis (PCA) is a well-known statistical processing technique that allows to study the correlations among the components of multivariate data and to reduce redundancy by projecting the data over a proper basis. The PCA may be performed both in a batch method and in a recursive fashion; the latter method has been proven to be very effective in presence of high dimension dat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Transactions of Japan Society of Kansei Engineering
سال: 2009
ISSN: 1882-8930,1884-5258
DOI: 10.5057/jjske.8.667