Customized design of hearing aids using statistical shape learning

3D shape modeling is a crucial component of rapid prototyping systems that customize shapes of implants and prosthetic devices to a patient’s anatomy. In this paper, we present a solution to the problem of customized 3D shape modeling using a statistical shape analysis framework. We design a novel method to learn the relationship between two classes of shapes, which are related by certain operations or transformation. The two associated shape classes are represented in a lower dimensional manifold, and the reduced set of parameters obtained in this subspace is utilized in an estimation, which is exemplified by a multivariate regression in this paper.We demonstrate our method with a felicitous application to estimation of customized hearing aid devices

A Variational Framework for the Simultaneous Segmentation and Object Behavior Classification of Image Sequences

In this paper, we advance the state of the art in variational image segmentation through the fusion of bottom-up segmentation and top-down classification of object behavior over an image sequence. Such an approach is beneficial for both tasks and is carried out through a joint optimization, which enables the two tasks to cooperate, such that knowledge relevant to each can aid in the resolution of the other, thereby enhancing the final result. In particular, classification offers dynamic probabilistic priors to guide segmentation, while segmentation supplies its results to classification, ensuring that they are consistent with prior knowledge. The prior models are learned from training data and updated dynamically, based on segmentations of earlier images in the sequence. We demonstrate the potential of our approach in a hand gesture recognition application, where the combined use of segmentation and classification improves robustness in the presence of occlusion and background complexity

A deep level set method for image segmentation

This paper proposes a novel image segmentation approachthat integrates fully convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the integrated method can incorporatesmoothing and prior information to achieve an accurate segmentation.Furthermore, different than using the level set model as a post-processingtool, we integrate it into the training phase to fine-tune the FCN. Thisallows the use of unlabeled data during training in a semi-supervisedsetting. Using two types of medical imaging data (liver CT and left ven-tricle MRI data), we show that the integrated method achieves goodperformance even when little training data is available, outperformingthe FCN or the level set model alone

A theoretical and numerical study of a phase field higher-order active contour model of directed networks.

We address the problem of quasi-automatic extraction of directed networks, which have characteristic geometric features, from images. To include the necessary prior knowledge about these geometric features, we use a phase field higher-order active contour model of directed networks. The model has a large number of unphysical parameters (weights of energy terms), and can favour different geometric structures for different parameter values. To overcome this problem, we perform a stability analysis of a long, straight bar in order to find parameter ranges that favour networks. The resulting constraints necessary to produce stable networks eliminate some parameters, replace others by physical parameters such as network branch width, and place lower and upper bounds on the values of the rest. We validate the theoretical analysis via numerical experiments, and then apply the model to the problem of hydrographic network extraction from multi-spectral VHR satellite images

The application of big data and AI in the upstream supply chain

The use of Big Data has grown in popularity in organisations to exploit the purpose of their primary data to enhance their competitiveness. In conjunction with the increased use of Big Data, there has also been a growth in the use of Artificial Intelligence (AI) to analyse the vast amounts of data generated and provide a mechanism for locating and constructing useable patterns that organisations can incorporate in their supply chain strategy programme. As these organisations embrace the use of technology and embed this in their supply chain strategy, there are questions as to how this may affect their upstream supply chains especially with regards to how SME’s may be able to cope with the potential changes. There exists the opportunity to conduct further research into this area, mainly focusing on three key industry sectors of aerospace, rail and automotive supply chains.N/

Levelset and B-spline deformable model techniques for image segmentation: a pragmatic comparative study

International audienceDeformable contours are now widely used in image segmentation, using different models, criteria and numerical schemes. Some theoretical comparisons between some deformable model methods have already been published. Yet, very few experimental comparative studies on real data have been reported. In this paper,we compare a levelset with a B-spline based deformable model approach in order to understand the mechanisms involved in these widely used methods and to compare both evolution and results on various kinds of image segmentation problems. In general, both methods yield similar results. However, specific differences appear when considering particular problems

Optimization of Divergences Within the Exponential Family for Image Segmentation

International audienceIn this work, we propose novel results for the optimization of divergences within the framework of region-based active contours. We focus on parametric statistical models where the region descriptor is chosen as the probability density function (pdf) of an image feature (e.g. intensity) inside the region and the pdf belongs to the exponential family. The optimization of divergences appears as a flexible tool for segmentation with and without intensity prior. As far as segmentation without reference is concerned, we aim at maximizing the discrepancy between the pdf of the inside region and the pdf of the outside region. Moreover, since the optimization framework is performed within the exponential family, we can cope with difficult segmentation problems including various noise models (Gaussian, Rayleigh, Poisson, Bernoulli ...). We also experimentally show that the maximisation of the KL divergence offers interesting properties compare to some other data terms (e.g. minimization of the anti-log-likelihood). Experimental results on medical images (brain MRI, contrast echocardiography) confirm the applicability of this general setting

PURIFICATION OF URBAN WASTEWATERS BY HELLENIC NATURAL ZEOLITE

Treatment of urban wastewaters (pHinitial 8.2), with 6 g of Hellenic Natural Zeolite (HENAZE) of an grain-size < 1.5 mm, gave overflowed clear water of pH 7.5, free of odors and improved quality parameters by 87% for the suspended particles, 88% for the color, 91% for the P2O5 content, 93% for the chemical oxygen demand (COD) and 97% for the NH4 content. Compared to previous experiment, the decrease of the amount of HENAZE by 1.5 g (20%) resulted to the purification worsening only by 1-2%. The HENAZE comes from Ntrista stream of Petrota village of Trigono Municipality of Evros Prefecture, Northeastern Greece. HENAZE contains 89 wt.% HEU-type zeolite and exhibit an ammonia ion exchange capacity (sorption ability) of 226 meq/100g. The mineralogical composition and the unique physico-chemical properties, make the HENAZE suitable material for numerous environmental, industrial, agricultural and aquacultural applications, such as: Animal nutrition, soil amendment for agriculture, pH soil regulation, greenhouse and flowers substrates, durability and health improvement of lawn, purification of industrial and urban wastewaters, treatment of sewage sludge, odor control, fishery and fish breeding, gas purification and drying systems, oxygen enrichment of aquatic ecosystems, improvement of drinking water quality, constructed wetlands and wastewater treatment units. The treatment gave as precipitate an odorless and cohesive zeo-sewage sludge, suitable for safe deposition and also for the reclamation of agricultural soils. The zeo-sewage sludge, produced either from the urban wastewater treatment or from the mixing of HENAZE and sewage sludge, can be used for the reclamation of agricultural soils. The presence of HENAZE in the agricultural soils, increases the crops yield by 29 57% and improves the quality of agricultural products by 4-46%, reduces the use of fertilizers by 55-100%, reduces the usage of irrigation water by 33-67%, prevents the seepage of dangerous species into the water environment (e.g., NO3 - by 55-57%), protecting thus the quality of surface and underground waters. The usage of HENAZE in vivarium units and in the animal nutrition, increases the production and improves the quality of the relevant products