Incremental kernel learning algorithms and applications.

Since the Support Vector Machines (SVMs) were introduced in 1995, SVMs have been recognized as essential tools for pattern classification and function approximation. Numerous publications show that SVMs outperform other learning methods in various areas. However, SVMs have a weak performance with large-scale data sets because of high computational complexity. One approach to overcome this limitation is the incremental learning approach where a large-scale data set is divided into several subsets and trained on those subsets updating the core information extracted from the previous subset. This approach also has a drawback that the core information is accumulated during the incremental procedure. When the large-scale data set has a special structure (e.g., in the case of unbalanced data set), the standard SVM might not perform properly. In this study, a novel approach based on the reduced convex hull concept is developed and applied in various applications. In addition, the developed concept is applied to the Support Vector Regression (SVR) to produce better performance. From the performed experiments, the incremental revised SVM significantly reduces the number of support vectors and requires less computing time. In addition the incremental revised SVR produces similar results with the standard SVR by reducing computing time significantly. Furthermore, the filter concept developed in this study may be utilized to reduce the computing time in other learning approach

Revision of basal macropodids from the Riversleigh World Heritage Area with descriptions of new material of Ganguroo bilamina Cooke, 1997 and a new species

The relationship of basal macropodids (Marsupialia: Macropodoidea) from the Oligo-Miocene of Australia have been unclear. Here, we describe a new species from the Bitesantennary Site within the Riversleigh's World Heritage Area (WHA), Ganguroo bites n. sp., new cranial and dental material of G. bilamina, and reassess material previously described as Bulungamaya delicata and 'Nowidgee matrix'. We performed a metric analysis of dental measurements on species of Thylogale which we then used, in combination with morphological features, to determine species boundaries in the fossils. We also performed a phylogenetic analysis to clarify the relationships of basal macropodid species within Macropodoidea. Our results support the distinction of G. bilamina, G. bites and B. delicata, but 'Nowidgee matrix' appears to be a synonym of B. delicata. The results of our phylogenetic analysis are inconclusive, but dental and cranial features suggest a close affinity between G. bilamina and macropodids. Finally, we revise the current understanding of basal macropodid diversity in Oligocene and Miocene sites at Riversleigh WHA

A framework of face recognition with set of testing images

We propose a novel framework to solve the face recognition problem base on set of testing images. Our framework can handle the case that no pose overlap between training set and query set. The main techniques used in this framework are manifold alignment, face normalization and discriminant learning. Experiments on different databases show our system outperforms some state of the art methods

3D Building Synthesis Based on Images and Affine Invariant Salient Features

In this thesis, we introduce a method to synthesize and recognize buildings using a set of at least two 2D images taken from different views. Based on a coarse set of affine invariant salient feature points (corner points) on the images, a 3D high-resolution building model is obtained in accordance with the observed images. Corresponding salient points are found using the ratio of triangle areas formed from a set of four consecutive ordered salient corresponding points that form two triangles. The order is obtained by finding the vertices of the convex hull of the salient points. The salient points are tessellated to form a high-resolution triangular mesh with the appearance of a triangular patch in the image imported onto the personalized 3D model. With multiple images, all coordinates and appearances are reconstructed in accordance with the observed images. The 3D model reconstruction method allows for a 3D classification of a test building to one of many possible buildings stored in the database. The classification is based on a geometric 3D point cloud error. For buildings with very close 3D point cloud errors, a further classification is achieved based on the mean squared error (MSE) on the appearance of corresponding points on the test and base models. Our method can also be used in localization when preloaded location information of each model in the database is stored, hence helping an observer navigate without a GPS system.M.S., Electrical Engineering -- Drexel University, 201

The Taxonomic Status of the Middle Pleistocene Hominins: A 3D Geometric Morphometric Investigation of Variation in the Supraorbital Region

This study investigated the significance of variation in the supraorbital morphology of the Middle Pleistocene hominins (MPH), using 3D geometric morphometric methods and a comparative taxonomic framework. The morphology of the supraorbital and orbital region of fourteen MPH from Africa, Asia, and Europe was recorded using 230 3D landmarks and surface semilandmarks. A comparative sample of 460 primates (Pan, Gorilla, Papio and Macaca) and 237 hominins (Early Pleistocene to Holocene) were included to model supraorbital variation in groups of varying taxonomic classification, ecology, and geographic and temporal range. It was found that the fourteen MPH had relatively low morphological distinctiveness in relation to established species, although they did cluster together in most morphometric analyses. Nevertheless, the variation in the supraorbital and orbital region of the MPH was not larger than could be expected in a single, cross-continental species, and the validity of continental subgroups within the MPH could not be supported based on comparisons to established primate and hominin taxa. Instead, results indicated that some of the MPH may represent transitional specimens or members of other hominin lineages. Sex could not be reliably estimated for the MPH using patterns of sexual dimorphism in other groups of known sex, and while differences in size, allometry, and encephalisation were found to have significant effects on variation in the supraorbital region, geography and chronology did not have significant effects on variation within the MPH. Based on the application of a morphological species concept and comparative primate and hominin data on supraorbital variation, the existence of multiple MPH taxa is indicated by the results found here. It is therefore suggested that the term Homo heidelbergensis (sensu lato) be applied to a restricted, cross-continental group of MPH (Bodo, Kabwe, Saldanha, Petralona, Arago 21, Ceprano, Narmada, and possibly Eliye Springs)

Identifying Data Centers from Satellite Imagery

We develop two different descriptors which can be utilized to describe satellite imagery. The first, the differential-magnitude and radius descriptor, describes a scene by computing the directional gradient of the scene with respect to a vector field whose solutions are circles around a pixel to be described, and then counts pixels in a descriptor matrix according to the magnitude of this gradient and the distance at which this magnitude occurs. The second, the radial Fourier descriptor, extracts from the scene a sequence of annuloid sectors, and uses this to approximate the behavior of the image on a circle around the point to be described. The fast Fourier transform is then used to obtain a description of this function in the frequency domain; the absolute values of these complex-valued frequencies form the descriptor. A set of data to test and perform parameter selection for these procedures using 79 Landsat 8 imagery scenes was constructed. A cellular evolutionary algorithm was then utilized for parameter selection by training and testing support vector machine classifiers using LIBSVM from this dataset utilizing classification accuracy as a fitness function. We then analyze the classification success associated with the two methods equipped with their optimized parameters