Deep Shape Representations for 3D Object Recognition

Deep learning is a rapidly growing discipline that models high-level features in data as multilayered neural networks. The recent trend toward deep neural networks has been driven, in large part, by a combination of affordable computing hardware, open source software, and the availability of pre-trained networks on large-scale datasets. In this thesis, we propose deep learning approaches to 3D shape recognition using a multilevel feature learning paradigm. We start by comprehensively reviewing recent shape descriptors, including hand-crafted descriptors that are mostly developed in the spectral geometry setting and also the ones obtained via learning-based methods. Then, we introduce novel multi-level feature learning approaches using spectral graph wavelets, bag-of-features and deep learning. Low-level features are first extracted from a 3D shape using spectral graph wavelets. Mid-level features are then generated via the bag-of-features model by employing locality-constrained linear coding as a feature coding method, in conjunction with the biharmonic distance and intrinsic spatial pyramid matching in a bid to effectively measure the spatial relationship between each pair of the bag-offeature descriptors. For the task of 3D shape retrieval, high-level shape features are learned via a deep auto-encoder on mid-level features. Then, we compare the deep learned descriptor of a query shape to the descriptors of all shapes in the dataset using a dissimilarity measure for 3D shape retrieval. For the task of 3D shape classification, mid-level features are represented as 2D images in order to be fed into a pre-trained convolutional neural network to learn high-level features from the penultimate fully-connected layer of the network. Finally, a multiclass support vector machine classifier is trained on these deep learned descriptors, and the classification accuracy is subsequently computed. The proposed 3D shape retrieval and classification approaches are evaluated on three standard 3D shape benchmarks through extensive experiments, and the results show compelling superiority of our approaches over state-of-the-art methods

AI and Non AI Assessments for Dementia

Current progress in the artificial intelligence domain has led to the development of various types of AI-powered dementia assessments, which can be employed to identify patients at the early stage of dementia. It can revolutionize the dementia care settings. It is essential that the medical community be aware of various AI assessments and choose them considering their degrees of validity, efficiency, practicality, reliability, and accuracy concerning the early identification of patients with dementia (PwD). On the other hand, AI developers should be informed about various non-AI assessments as well as recently developed AI assessments. Thus, this paper, which can be readable by both clinicians and AI engineers, fills the gap in the literature in explaining the existing solutions for the recognition of dementia to clinicians, as well as the techniques used and the most widespread dementia datasets to AI engineers. It follows a review of papers on AI and non-AI assessments for dementia to provide valuable information about various dementia assessments for both the AI and medical communities. The discussion and conclusion highlight the most prominent research directions and the maturity of existing solutions.Comment: 49 page

Shape adaptive, robust iris feature extraction from noisy iris images

In the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. Whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. To the best of our knowledge, the effect of noise factors on feature extraction has not been considered in the previous works. This paper investigates the effect of shape adaptive wavelet transform and shape adaptive Gabor-wavelet for feature extraction on the iris recognition performance. In addition, an effective noise-removing approach is proposed in this paper. The contribution is to detect eyelashes and reflections by calculating appropriate thresholds by a procedure called statistical decision making. The eyelids are segmented by parabolic Hough transform in normalized iris image to decrease computational burden through omitting rotation term. The iris is localized by an accurate and fast algorithm based on coarse-to-fine strategy. The principle of mask code generation is to assign the noisy bits in an iris code in order to exclude them in matching step is presented in details. An experimental result shows that by using the shape adaptive Gabor-wavelet technique there is an improvement on the accuracy of recognition rate