A methodology for feature based 3D face modelling from photographs

In this paper, a new approach to modelling 3D faces based on 2D images is introduced. Here 3D faces are created using two photographs from which we extract facial features based on image manipulation techniques. Through the image manipulation techniques we extract the crucial feature lines of the face in two views. These are then used in modifying a template base mesh which is created in 3D. This base mesh, which has been designed by keeping facial animation in mind, is then subdivided to provide the level of detail required. The methodology, as it stands, is semi-automatic whereby our goal is to automate this process in order to provide an inexpensive and expedient way of producing realistic face models intended for animation purposes. Thus, we show how image manipulation techniques can be used to create binary images which can in turn be used in manipulating a base mesh that can be adapted to a given facial geometry. In order to explain our approach more clearly we discuss a series of examples where we create 3D facial geometry of individuals given the corresponding image data

Detection of Dust Storms Using MODIS Reflective and Emissive Bands

YesDust storms are one of the natural phenomena, which have increased in frequency in recent years in North Africa, Australia and northern China. Satellite remote sensing is the common method for monitoring dust storms but its use for identifying dust storms over sandy ground is still limited as the two share similar characteristics. In this study, an artificial neural network (ANN) is used to detect dust storm using 46 sets of data acquired between 2001 and 2010 over North Africa by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites. The ANN uses image data generated from Brightness Temperature Difference (BTD) between bands 23 and 31 and BTD between bands 31 and 32 with three bands 1, 3, and 4, to classify individual pixels on the basis of their multiple-band values. In comparison with the manually detection of dust storms, the ANN approach gave better result than the Thermal Infrared Integrated Dust Index approach for dust storms detection over the Sahara. The trained ANN using data from the Sahara desert gave an accuracy of 0.88 when tested on data from the Gobi desert and managed to detect 90 out of the 96 dust storm events captured worldwide by Terra and Aqua satellites in 2011 that were classified as dusty images on NASA Earth Observatory.IEEE Geoscience and Remote Sensing Societ

3D modeling of magnetic field lines using SOHO/MDI magnetogram images

YesSolar images, along with other observational data, are very important for solar physicists and space weather researchers aiming to understand the way the Sun works and affects Earth. In this study a 3D modelling technique for visualizing solar magnetic field lines using solar images is presented. Photospheric magnetic field footpoints are detected from magnetogram images and using negative and positive magnetic footpoints, dipole pairs are associated according to their proximity. Then, 3D field line models are built using the calculated dipole coordinates, and mapped to detected pairs after coordinate transformations. Final 3D models are compared to extreme ultraviolet images and existing models and the results of visual comparisons are presented

3D face recognition based on machine learning

3D facial data has a great potential for overcoming the problems of illumination and pose variation in face recognition. In this paper, we present a 3D facial system based on the machine learning. We used landmarks for feature extraction and Cascade Correlation neural network to make the final decision. Experiments are presented using 3D face images from the Face Recognition Grand Challenge database version 2.0. For CCNN using Jack-knife evaluation, an accuracy of 100% has been achieved for 7 faces with different expression, with 100% for both of specificity and sensitivity

Deep learning technology for predicting solar flares from (Geostationary Operational Environmental Satellite) data

YesSolar activity, particularly solar flares can have significant detrimental effects on both space-borne and grounds based systems and industries leading to subsequent impacts on our lives. As a consequence, there is much current interest in creating systems which can make accurate solar flare predictions. This paper aims to develop a novel framework to predict solar flares by making use of the Geostationary Operational Environmental Satellite (GOES) X-ray flux 1-minute time series data. This data is fed to three integrated neural networks to deliver these predictions. The first neural network (NN) is used to convert GOES X-ray flux 1-minute data to Markov Transition Field (MTF) images. The second neural network uses an unsupervised feature learning algorithm to learn the MTF image features. The third neural network uses both the learned features and the MTF images, which are then processed using a Deep Convolutional Neural Network to generate the flares predictions. To the best of our knowledge, this work is the first flare prediction system that is based entirely on the analysis of pre-flare GOES X-ray flux data. The results are evaluated using several performance measurement criteria that are presented in this paper

An efficient system for preprocessing confocal corneal images for subsequent analysis

A confocal microscope provides a sequence of images of the various corneal layers and structures at different depths from which medical clinicians can extract clinical information on the state of health of the patient's cornea. Preprocessing the confocal corneal images to make them suitable for analysis is very challenging due the nature of these images and the amount of the noise present in them. This paper presents an efficient preprocessing approach for confocal corneal images consisting of three main steps including enhancement, binarisation and refinement. Improved visualisation, cell counts and measurements of cell properties have been achieved through this system and an interactive graphical user interface has been developed

Representation of solar features in 3D for creating visual solar catalogues

YesIn this study a method for 3D representation of active regions and sunspots that are detected from Solar and Heliospheric Observatory/Michelson Doppler Imager magnetogram and continuum images is provided. This is our first attempt to create a visual solar catalogue. Because of the difficulty of providing a full description of data in text based catalogues, it can be more accurate and effective for scientist to search 3D solar feature models and descriptions at the same time in such a visual solar catalogue. This catalogue would improve interpretation of solar images, since it would allow us to extract data embedded in various solar images and visualize it at the same time. In this work, active regions that are detected from magnetogram images and sunspots that are detected from continuum images are represented in 3D coordinates. Also their properties extracted from text based catalogues are represented at the same time in 3D environment. This is the first step for creating a 3D solar feature catalogue where automatically detected solar features will be presented visually together with their properties

An automatic corneal subbasal nerve registration system using FFT and phase correlation techniques for an accurate DPN diagnosis

yesConfocal microscopy is employed as a fast and non-invasive way to capture a sequence of images from different layers and membranes of the cornea. The captured images are used to extract useful and helpful clinical information for early diagnosis of corneal diseases such as, Diabetic Peripheral Neuropathy (DPN). In this paper, an automatic corneal subbasal nerve registration system is proposed. The main aim of the proposed system is to produce a new informative corneal image that contains structural and functional information. In addition a colour coded corneal image map is produced by overlaying a sequence of Cornea Confocal Microscopy (CCM) images that differ in their displacement, illumination, scaling, and rotation to each other. An automatic image registration method is proposed based on combining the advantages of Fast Fourier Transform (FFT) and phase correlation techniques. The proposed registration algorithm searches for the best common features between a number of sequenced CCM images in the frequency domain to produce the formative image map. In this generated image map, each colour represents the severity level of a specific clinical feature that can be used to give ophthalmologists a clear and precise representation of the extracted clinical features from each nerve in the image map. Moreover, successful implementation of the proposed system and the availability of the required datasets opens the door for other interesting ideas; for instance, it can be used to give ophthalmologists a summarized and objective description about a diabetic patient’s health status using a sequence of CCM images that have been captured from different imaging devices and/or at different time

A Robust Face Recognition System Based on Curvelet and Fractal Dimension Transforms

yesn this paper, a powerful face recognition system for authentication and identification tasks is presented and a new facial feature extraction approach is proposed. A novel feature extraction method based on combining the characteristics of the Curvelet transform and Fractal dimension transform is proposed. The proposed system consists of four stages. Firstly, a simple preprocessing algorithm based on a sigmoid function is applied to standardize the intensity dynamic range in the input image. Secondly, a face detection stage based on the Viola-Jones algorithm is used for detecting the face region in the input image. After that, the feature extraction stage using a combination of the Digital Curvelet via wrapping transform and a Fractal Dimension transform is implemented. Finally, the K-Nearest Neighbor (K-NN) and Correlation Coefficient (CC) Classifiers are used in the recognition task. Lastly, the performance of the proposed approach has been tested by carrying out a number of experiments on three well-known datasets with high diversity in the facial expressions: SDUMLA-HMT, Faces96 and UMIST datasets. All the experiments conducted indicate the robustness and the effectiveness of the proposed approach for both authentication and identification tasks compared to other established approaches

A Fast and Accurate Iris Localization Technique for Healthcare Security System

yesIn the health care systems, a high security level is required to protect extremely sensitive patient records. The goal is to provide a secure access to the right records at the right time with high patient privacy. As the most accurate biometric system, the iris recognition can play a significant role in healthcare applications for accurate patient identification. In this paper, the corner stone towards building a fast and robust iris recognition system for healthcare applications is addressed, which is known as iris localization. Iris localization is an essential step for efficient iris recognition systems. The presence of extraneous features such as eyelashes, eyelids, pupil and reflection spots make the correct iris localization challenging. In this paper, an efficient and automatic method is presented for the inner and outer iris boundary localization. The inner pupil boundary is detected after eliminating specular reflections using a combination of thresholding and morphological operations. Then, the outer iris boundary is detected using the modified Circular Hough transform. An efficient preprocessing procedure is proposed to enhance the iris boundary by applying 2D Gaussian filter and Histogram equalization processes. In addition, the pupil’s parameters (e.g. radius and center coordinates) are employed to reduce the search time of the Hough transform by discarding the unnecessary edge points within the iris region. Finally, a robust and fast eyelids detection algorithm is developed which employs an anisotropic diffusion filter with Radon transform to fit the upper and lower eyelids boundaries. The performance of the proposed method is tested on two databases: CASIA Version 1.0 and SDUMLA-HMT iris database. The Experimental results demonstrate the efficiency of the proposed method. Moreover, a comparative study with other established methods is also carried out