KEYWORDS: 3D modeling, Principal component analysis, Head, Solid modeling, Matrices, Feature extraction, 3D scanning, Data modeling, Optical spheres, 3D image processing
With the general availability of 3D digitizers and scanners, 3D graphical models have been used widely in a variety of applications. This has led to the development of search engines for 3D models. Especially, 3D head model classification and retrieval have received more and more attention in view of their many potential applications in criminal identifications, computer animation, movie industry and medical industry. This paper addresses the 3D head model classification problem using 2D subspace analysis methods such as 2D principal component analysis (2D PCA[3]) and 2D fisher discriminant analysis (2DLDA[5]). It takes advantage of the fact that the histogram is a 2D image, and we can extract the most useful information from these 2D images to get a good result accordingingly. As a result, there are two main advantages: First, we can perform less calculation to obtain the same rate of classification; second, we can reduce the dimensionality more than PCA to obtain a higher efficiency.
This paper addresses the problem of image content characterization in the compressed domain for the purpose of facilitating similarity matching in a multimedia database. Specifically, given the disparity of the content characterization power of compressed domain approaches and those based on pixel-domain features, with the latter being usually considered as the more superior one, our objective is to transform the selected set of compressed domain feature histograms in such a way that the retrieval result based on these features is compatible with their spatial domain counterparts. Since there are a large number of possible transformations, we adopt a genetic algorithm approach to search for the optimal one, where each of the binary strings in the population represents a candidate transformation. The fitness of each transformation is defined as a function of the discrepancies between the spatial-domain and compressed-domain retrieval results. In this way, the GA mechanism ensures that transformations which best approximate the performance of spatial domain retrieval will survive into the next generation and are allowed through the operations of crossover and mutation to generate variations of themselves to further improve their performances.
Application of computational intelligence techniques--often called soft computing--to the problem of adaptive image processing is the focus of this text. Imaging professionals and others with a background in mathematical science, computer software, and related fields will find this book a readable, useful resource. It also is suitable as a textbook in graduate-level or professional course in image processing.
Copublished with CRC Press.
The determination of the regularization parameter is an important sub-problem in optimizing the performances of image restoration systems. The parameter controls the relative weightings of the data-conformance and model- conformance terms in the restoration cost function. A small parameter value would lead to noisy appearances in the smooth image regions due to over-emphasis of the data term, while a large parameter results in blurring of the textured regions due to dominance of the model term. Based on the principle of adopting small parameter values for the highly textured regions for detail emphasis while using large values for noise suppression in the smooth regions, a spatially adaptive regularization scheme was derived in this paper. An initial segmentation based on the local image activity was performed and a distinct regularization parameter was associated with each segmented component. The regional value was estimated by viewing the parameter as a set of learnable neuronal weights in a model-based neural network. A stochastic gradient descent algorithm based on the regional spatial characteristics and specific functional form of the neuronal weights was derived to optimize the regional parameter values. The efficacy of the algorithm was demonstrated by our observation of the emergence of small parameter values in textured regions and large values in smooth regions.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.