A PDE Method to Segment Image Linear Objects with Application to Lens Distortion Removal

In this paper, we propose a partial differential equation based method to segment image objects, which have a given parametric shape based on energy functional. The energy functional is composed of a term that detects object boundaries and a term that constrains the contour to find a shape compatible with the parametric shape. While the shape constraints guiding the PDE may be determined from object's shape statistical models, we demonstrate the proposed approach on the extraction of objects with explicit shape parameterization, such as linear image segments. Several experiments are reported on synthetic and real images to evaluate our approach. We also demonstrate the successful application of the proposed method to the problem of removing camera lens distortion, which can be significant in medium to wide-angle lenses

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support

A PDE Method to Segment Image Linear Objects with Application to Lens Distortion Removal

In this paper, we propose a partial differential equation based method to segment image objects, which have a given parametric shape based on energy functional. The energy functional is composed of a term that detects object boundaries and a term that constrains the contour to find a shape compatible with the parametric shape. While the shape constraints guiding the PDE may be determined from object's shape statistical models, we demonstrate the proposed approach on the extraction of objects with explicit shape parameterization, such as linear image segments. Several experiments are reported on synthetic and real images to evaluate our approach. We also demonstrate the successful application of the proposed method to the problem of removing camera lens distortion, which can be significant in medium to wide-angle lenses

Electronic Letters on Computer Vision and Image Analysis 6(2):9-21, 2007 A PDE Method to Segment Image Linear Objects with Application to Lens Distortion Removal

In this paper, we propose a partial differential equation based method to segment image objects, which have a given parametric shape based on energy functional. The energy functional is composed of a term that detects object boundaries and a term that constrains the contour to find a shape compatible with the parametric shape. While the shape constraints guiding the PDE may be determined from object's shape statistical models, we demonstrate the proposed approach on the extraction of objects with explicit shape parameterization, such as linear image segments. Several experiments are reported on synthetic and real images to evaluate our approach. We also demonstrate the successful application of the proposed method to the problem of removing camera lens distortion, which can be significant in medium to wide-angle lenses

Chain based Leader Selection using Neural Network in Wireless Sensor Networks protocols

The selection of a chain leader is an important issue in wireless sensor networks (WSN). In this paper, we introduce a new method to select chain leaders in chain-based routing protocol using Neural Network (NN). Our proposed method can be applied to any chain based routing protocol such as PEGASIS (Power-Efficient Gathering in Sensor Information Systems), CBERP (Cluster Based Energy Efficient Routing Protocol), CCM (Chain-Cluster Based Mixed Routing Protocol), CCBRP (Chain-Chain Based Routing Protocol), etc. To approve our claim that our idea can be applied to any chain-based routing protocol we have applied our method to two of the most known protocols, PEGASIS (original chain-based routing protocol) and CCBRP. It is very well known that energy consumption is a very important issue for all Wireless Sensors Networks (WSNs). Our proposed method is based on the Neural Networks tool to select chain leaders based on the node’s residual energy. The simulation result shows that the use of our proposed method has improved the performance of both PEGASIS and CCBRP in terms of the consumed energy and the network lifetime

Shape‐from‐shading using sensor and physical object characteristics applied to human teeth surface reconstruction

Image formation involves understanding the sensors characteristics and object reflectance. In dentistry, for example an accurate three‐dimensional (3D) representation of the human jaw may be used for diagnostic and treatment purposes. Photogrammetry can offer a flexible, cost‐effective solution in that regard. Nonetheless there are several challenges, such as non‐friendly image acquisition environment inside the human mouth, problems with lighting (specularity effects because of saliva, gum discolourisation, and occlusion because of the tongue in the lower jaw), and errors because of the data acquisition sensors (e.g. camera calibration errors, lens distortion and so on). In this study, the authors focus on the 3D surface reconstruction aspect for human jaw modelling based on physical surface characteristics and sensor properties. Owing to apparent lens distortion imposed by near‐field imaging, the authors propose a new flexible calibration for lens radial distortion based on a single image of a sphere. The authors propose a non‐Lambertian shape‐from‐shading (SFS) algorithm under perspective projection which benefits from camera calibration parameters. Our experiments provide quantitative metric results for the proposed approach. The reflectance of the tooth surface is modelled by the Oren–Nayar reflectance model for rough surfaces whose roughness parameter is physically computed from an optical surface profiler measurements. As compared to state‐of‐the‐art SFS approaches, our approach is able to recover geometric details of tooth occlusal surface. This work is fundamental for establishing an optical‐based approach for reconstructing the human jaw, that is inexpensive and does not use ionising radiation

Development of a Micro-Gripper Using Piezoelectric Bimorphs

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