There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

https://www.onlinemedicalimages.com/images/medical/publications/8.%20Blood%20vessel%20segmentation%20methodologies.pdf

Blood vessel segmentation methodologies in retinal images - A survey

Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues

Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition

In this paper, a model is presented for the hyperbolic signature of a buried cylindrical target that takes into account the radius of the cylinder, This allows for cylinders of arbitrary radii to be detected and characterized uniquely from a single radargram, resulting in a more accurate estimation of the relative permittivity of the surrounding medium and of the depth, in addition to the radius information. This is achieved by subjecting the radargrams to a series of image processing stages followed by a curve-fitting procedure specifically developed for hyperbolae. The fitting technique is applied on a variety of real hyperbolic signatures that are collected from a controlled test site, The results indicate this technique is fully capable of successfully estimating the depth and radius to within 10%, which validates the method and justify the assumptions used.

Radius estimation for cylindrical objects detected by ground penetrating radar

Glaucoma is a group of eye diseases which can cause vision loss by damaging the optic nerve. Early glaucoma detection is key to preventing vision loss yet there is a lack of noticeable early symptoms. Colour fundus photography allows the optic disc (OD) to be examined to diagnose glaucoma. Typically, this is done by measuring the vertical cup-to-disc ratio (CDR); however, glaucoma is characterised by thinning of the rim asymmetrically in the inferior-superior-temporal-nasal regions in increasing order. Automatic delineation of the OD features has potential to improve glaucoma management by allowing for this asymmetry to be considered in the measurements. Here, we propose a new deep-learning-based method to segment the OD and optic cup (OC). The core of the proposed method is DenseNet with a fully-convolutional network, whose symmetric U-shaped architecture allows pixel-wise classification. The predicted OD and OC boundaries are then used to estimate the CDR on two axes for glaucoma diagnosis. We assess the proposed method’s performance using a large retinal colour fundus dataset, outperforming state-of-the-art segmentation methods. Furthermore, we generalise our method to segment four fundus datasets from different devices without further training, outperforming the state-of-the-art on two and achieving comparable results on the remaining two.

Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis

The retinal fundus photograph is widely used in the diagnosis and treatment of various eye diseases such as diabetic retinopathy and glaucoma. Medical image analysis and processing has great significance in the field of medicine, especially in non-invasive treatment and clinical study. Normally fundus images are manually graded by specially trained clinicians in a time-consuming and resource-intensive process. A computer-aided fundus image analysis could provide an immediate detection and characterisation of retinal features prior to specialist inspection. This paper describes a novel method to automatically localise one such feature: the optic disk. The proposed method consists of two steps: in the first step, a circular region of interest is found by first isolating the brightest area in the image by means of morphological processing, and in the second step, the Hough transform is used to detect the main circular feature (corresponding to the optical disk) within the positive horizontal gradient image within this region of interest. Initial results on a database of fundus images show that the proposed method is effective and favourable in relation to comparable techniques.

Waleed Al-Nuaimy

Papers

Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition

Radius estimation for cylindrical objects detected by ground penetrating radar

Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis

Automated localisation of retinal optic disk using Hough transform

Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc