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Multimodality image fusion
Published in Yi-Hwa Liu, Albert J. Sinusas, Hybrid Imaging in Cardiovascular Medicine, 2017
Marina Piccinelli, James R. Galt, Ernest V. Garcia
From the technical point of view, image registration is implemented by the optimal transformation that best aligns one of the images to the other (Hill et al. 2001; Hutton et al. 2002; Maintz and Viergenev 1998). In this optimization process, three main components have to be defined for a registration algorithm to be correctly designed: a transformation model that defines how the coordinates between the two images are related (e.g., rigid versus deformable transformations); a similarity metric that quantifies the degree of alignment between the images (e.g., mutual information, sum of squared differences (SSD), etc.); and a numerical routine that iteratively moves one of the images, evaluates the agreement between the datasets and stops when a predefined level of matching has been reached. Although a wide variety of different algorithms have been developed and proposed throughout the years, all registration techniques can be broadly grouped into two general approaches: surface-based and volume-based registration (Piccinelli and Garcia 2013). In surface-based techniques, landmark points or geometric features (edges, organ contours) are extracted—an operation commonly called segmentation—from both images and are successively aligned. Voxel-based methods directly use the original image pixel intensities to define a similarity metric and perform the spatial alignment.
Overview of pervasive computing
Published in Sonali Goyal, Neera Batra, N.K. Batra, An Integrated Approach to Home Security and Safety Systems, 2021
Face recognition includes several landmark points that make facial features. User’s face can be captured by a camera for authentication purpose. It is an application for automatically recognizing a user from camera [13]. Its main advantage is that it can be used to recognize a user from a particular distance. This system is secure and easily implemented but variation in pose and lighting affects the image. As there are number of features included with face recognition technique, it can be used in various areas such as banks, criminal’s identification and identification of terrorists.
Distance-Shape-Texture Signature Trio for Facial Expression Recognition
Published in Sourav De, Paramartha Dutta, Computational Intelligence for Human Action Recognition, 2020
Asit Barman, Sankhayan Choudhury, Paramartha Dutta
Active Appearance Model: The accurate face alignment has a vital effect in a face recognition system. Active Appearance Model [29] is a well known method for appropriately locating objects. In the training phase of an active appearance model, huge face images with different shapes are considered in training. Then we mark a set of points to annotate face shape. The face shape is represented with the coordinate landmark points.
Hybrid features and exponential moth-flame optimization based deep belief network for face recognition
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2020
Ch. Hima Bindu, K. Manjunatha Chari
Figure 3 presents the experimental results achieved during the feature extraction process, and the results clearly indicate the AAM features obtained from the image. The AAM represents the active appearance features present in the face image. Figure 3.a presents the original image present in the CVL database, and Figure 3.b presents the AAM features extracted from the original image. The AAM is used to obtain the quality outcome of the difficult images. The frameworks of the AAM enable identity information to be separate from other variations. The AAM approach creates optimal use of the evidence from either a single image or image sequence. This model is created by combining a model of shape variation with a model of the appearance variations in a shape-normalised frame. We need a training set of labelled images, where landmark points are marked on each sample face at key positions to outline the main features.