GNG based foot reconstruction for custom footwear manufacturing

Custom shoes manufacturing is one of the major challenges facing the footwear industry today. A shoe for everyone: it is a change in the production model in which each individual’s foot is the main focus, replacing traditional size systems based on population means. This paradigm shift represents a major effort for the industry, for which the design and not production becomes the main bottleneck. It is therefore necessary to accelerate the design process by improving the accuracy of current methods. The starting point for making a shoe that fits the client’s foot anatomy is scanning the surface of the foot. Automated foot model reconstruction is accomplished through the use of the self-organising growing neural gas (GNG) network, which is able to topographically map the low dimension of the network to the high dimension of the manifold of the scanner acquisitions without requiring a priori knowledge of the structure of the input space. The GNG obtains a surface representation adapted to the topology of the foot, is accurate, tolerant to noise, and eliminates outliers. It also improves the reconstruction in “dark” areas where the scanner does not obtain information: the heel and toe areas. The method reconstructs the foot surface 4 times more accurately than other well-known methods. The method is generic and easily extensible to other industrial objects that need to be digitized and reconstructed with accuracy and efficiency requirements.This work was partially funded by the Spanish Government DPI2013-40534-R grant, supported with Feder funds, NILS Mobility Project 012-ABEL-CM-2014A, and Fundación Séneca 18946/JLI/13

Understanding Leaves in Natural Images - A Model-Based Approach for Tree Species Identification

International audienceWith the aim of elaborating a mobile application, accessible to anyone and with educational purposes, we present a method for tree species identification that relies on dedicated algorithms and explicit botany-inspired descriptors. Focusing on the analysis of leaves, we developed a working process to help recognize species, starting from a picture of a leaf in a complex natural background. A two-step active contour segmentation algorithm based on a polygonal leaf model processes the image to retrieve the contour of the leaf. Features we use afterwards are high-level geometrical descriptors that make a semantic interpretation possible, and prove to achieve better performance than more generic and statistical shape descriptors alone. We present the results, both in terms of segmentation and classification, considering a database of 50 European broad-leaved tree species, and an implementation of the system is available in the iPhone application Folia

Fast algorithms for wavelet-based analysis of hyperspectral signatures

Hyperspectral sensors promise great improvements in the quality of information gathered for remote sensing applications. However, they also present a huge challenge to data storage and computing systems. Thus there is a great need for reliable compression schemes, as well as analysis tools that can exploit the hyperspectral data in a computationally efficient manner. It has been proposed that wavelet-based methods may be superior to currently used methods for the analysis of hyperspectral signatures. In this thesis, a wavelet-based method, as well as traditional analytical methods, was implemented and applied to hyperspectral images. The computational expense of the various methods are determined analytically and experimentally to show advantages of the wavelet-based methods. Various measures, including cross correlation, signal-to-noise ratios and Euclidean distance, are designed and implemented for comparing the differences that might exist between the outputs of the algorithms

Recognition of Occluded Object Using Wavelets

Ph.DDOCTOR OF PHILOSOPH