Perspectives on management theory’s application in the internet of things research

Information Systems and e-Business Management - The growing use of the internet of things (IoT) has provided businesses with a new opportunity. This study performed a systematic literature review...     

Spatially Varying Mixtures Incorporating Line Processes for Image Segmentation

Spatially varying mixture models are characterized by the dependence of their mixing proportions on location (contextual mixing proportions) and they have been widely used in image segmentation. In this work, Gauss-Markov random field (MRF) priors are employed along with spatially varying mixture models to ensure the preservation of region boundaries in image segmentation. To preserve region boundaries, two distinct models for a line process involved in the MRF prior are proposed. The first model considers edge preservation by imposing a Bernoulli prior on the normally distributed local differences of the contextual mixing proportions. It is a discrete line process model whose parameters are computed by variational inference. The second model imposes Gamma prior on the Student’s-t distributed local differences of the contextual mixing proportions. It is a continuous line process whose parameters are also automatically estimated by the Expectation-Maximization (EM) algorithm. The proposed models are numerically evaluated and two important issues in image segmentation by mixture models are also investigated and discussed: the constraints to be imposed on the contextual mixing proportions to be probability vectors and the MRF optimization strategy in the frameworks of the standard and variational EM algorithm.     

A joint physics-based statistical deformable model for multimodalbrain image analysis

A probabilistic deformable model for the representation of multiple brain structures is described. The statistically learned deformable model represents the relative location of different anatomical surfaces in brain magnetic resonance images (MRIs) and accommodates their significant variability across different individuals. The surfaces of each anatomical structure are parameterized by the amplitudes of the vibration modes of a deformable spherical mesh. For a given MRI in the training set, a vector containing the largest vibration modes describing the different deformable surfaces is created. This random vector is statistically constrained by retaining the most significant variation modes of its Karhunen-Loève expansion on the training population. By these means, the conjunction of surfaces are deformed according to the anatomical variability observed in the training set. Two applications of the joint probabilistic deformable model are presented: isolation of the brain from MRI using the probabilistic constraints embedded in the model and deformable model-based registration of three-dimensional multimodal (magnetic resonance/single photon emission computed tomography) brain images without removing nonbrain structures. The multi-object deformable model may be considered as a first step toward the development of a general purpose probabilistic anatomical atlas of the brain.     

Visual tracking using the Earth Mover's Distance between Gaussian mixtures and Kalman filtering

In this paper, we demonstrate how the differential Earth Mover's Distance (EMD) may be used for visual tracking in synergy with Gaussian mixtures models (GMM). According to our model, motion between adjacent frames results in variations of the mixing proportions of the Gaussian components representing the object to be tracked. These variations are computed in closed form by minimizing the differential EMD between Gaussian mixtures, yielding a very fast algorithm with high accuracy, without recurring to the EM algorithm in each frame. Moreover, we also propose a framework to handle occlusions, where the prediction for the object's location is forwarded to an adaptive Kalman filter whose parameters are estimated on line by the motion model already observed. Experimental results show significant improvement in tracking performance in the presence of occlusion.     

Reconstruction of serially acquired slices using physics-based modeling

This paper presents an accurate, computationally efficient, fast, and fully automated algorithm for the alignment of two-dimensional (2-D) serially acquired sections forming a 3-D volume. The approach relies on the determination of interslice correspondences. The features used for correspondence are extracted by a 2-D physics-based deformable model parameterizing the object shape. Correspondence affinities and global constrains render the method efficient and reliable. The method accounts for one of the major shortcomings of 2-D slices alignment of a 3-D volume, namely variable and nonuniform thickness of the slices. Moreover, no particular alignment direction is privileged, avoiding global offsets, biases, and error propagation. The method was evaluated on real images and the experimental results demonstrated its accuracy, as reconstruction errors were smaller than I degree in rotation and smaller than 1 pixel in translation.     

A class-adaptive spatially variant mixture model for image segmentation.

We propose a new approach for image segmentation based on a hierarchical and spatially variant mixture model. According to this model, the pixel labels are random variables and a smoothness prior is imposed on them. The main novelty of this work is a new family of smoothness priors for the label probabilities in spatially variant mixture models. These Gauss-Markov random field-based priors allow all their parameters to be estimated in closed form via the maximum a posteriori (MAP) estimation using the expectation-maximization methodology. Thus, it is possible to introduce priors with multiple parameters that adapt to different aspects of the data. Numerical experiments are presented where the proposed MAP algorithms were tested in various image segmentation scenarios. These experiments demonstrate that the proposed segmentation scheme compares favorably to both standard and previous spatially constrained mixture model-based segmentation.     

Recovery of polarimetric Stokes images by spatial mixture models

A Bayesian approach for joint restoration and segmentation of polarization encoded images is presented with emphasis on both physical admissibility and smoothness of the solution. Two distinct models for the sought polarized radiances are used: (i) the polarized light at each site of the image is described by its Stokes vector, which directly follows a mixture of truncated Gaussians, explicitly assigning zero probability to inadmissible configurations and (ii) polarization at each site is represented by the coherency matrix, which is parameterized by a set of variables assumed to be generated by a spatially varying mixture of Gaussians. Application on real and synthetic images using the proposed methods assesses the pertinence of the approach.     

Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images

In this work, we present an automated method for the detection and boundary determination of cells nuclei in conventional Pap stained cervical smear images. The detection of the candidate nuclei areas is based on a morphological image reconstruction process and the segmentation of the nuclei boundaries is accomplished with the application of the watershed transform in the morphological color gradient image, using the nuclei markers extracted in the detection step. For the elimination of false positive findings, salient features characterizing the shape, the texture and the image intensity are extracted from the candidate nuclei regions and a classification step is performed to determine the true nuclei. We have examined the performance of two unsupervised (K-means, spectral clustering) and a supervised (Support Vector Machines, SVM) classification technique, employing discriminative features which were selected with a feature selection scheme based on the minimal-Redundancy-Maximal-Relevance criterion. The proposed method was evaluated on a data set of 90 Pap smear images containing 10,248 recognized cell nuclei. Comparisons with the segmentation results of a gradient vector flow deformable (GVF) model and a region based active contour model (ACM) are performed, which indicate that the proposed method produces more accurate nuclei boundaries that are closer to the ground truth.     

Computer assisted measurement of cup placement in total hip replacement

The introduction of image guided systems in total hip replacement surgery provides the ability to plan precisely the alignment of the acetabular cup before surgery, and to perform the surgery according to the preoperative plan. Preoperative planners (interactive computer programs for surgical planning) based on three-dimensional medical images allow planning of optimal placement of implant components based on simulated implant performance. Exact measurement of the cup position during surgery allows precise placement of the cup and accurate measurement of the final position of the cup relative to the pelvis. This measurement is used to evaluate the radiographic techniques for postoperative measurement of cup alignment. Malposition of the acetabular component increases the occurrence of impingement, reduces the safe range of motion, and increases the risk of dislocation and wear. Dislocation of the implant after total hip replacement remains a significant clinical problem. Not fully understanding the interaction between pelvic orientation and final acetabular cup alignment may be one of the main contributing factors in the continued significant incidence of dislocations after total hip replacement. In this study an attempt was made to link the preoperative planning, intraoperative placement, and postoperative measurement of cup placement in total hip replacement using computer assisted techniques.     

Mobile application driven consumer engagement

The growing popularity of mobile technologies and applications, lead many companies to develop relations with consumers through mobile applications. Therefore, it is important to understand how to design applications based on consumer preferences. This research seeks to understand which features of mobile applications stimulate consumer engagement and lead to continuous use of mobile applications. This study used an online questionnaire to collect data from 246 respondents. The data was analyzed making use of Structural Equations Modeling (SEM). The results indicate that perception of such features as design solutions and information quality will result in higher engagement leading to continuous usage of mobile applications. Moreover, consumer engagement positively influenced users’ intention to continuous usage of mobile applications. Inconsistent with expectation, consumer interaction and functionality features are not found to be positively related to consumer engagement with mobile applications.