Active Appearance Model: Unlocking the Power of Active Appearance Models in Computer Vision

By Fouad Sabry

What is Active Appearance Model

An active appearance model (AAM) is a computer vision algorithm for matching a statistical model of object shape and appearance to a new image. They are built during a training phase. A set of images, together with coordinates of landmarks that appear in all of the images, is provided to the training supervisor.

How you will benefit

(I) Insights, and validations about the following topics:

Chapter 1: Active appearance model

Chapter 2: Image registration

Chapter 3: Active shape model

Chapter 4: Facial motion capture

Chapter 5: Structure from motion

Chapter 6: Surrogate model

Chapter 7: Mean shift

Chapter 8: Point distribution model

Chapter 9: Articulated body pose estimation

Chapter 10: Bag-of-words model in computer vision

(II) Answering the public top questions about active appearance model.

(III) Real world examples for the usage of active appearance model in many fields.

Who this book is for

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Active Appearance Model.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: May 10, 2024

Book preview

Chapter 1: Active appearance model

An AAM is a computer vision method that compares a fresh image to a statistical model of an object's shape and appearance. They grow during a period of practice. The training supervisor is given a collection of photos together with the GPS coordinates of landmarks shown in each of the images.

At the 1998 Third International Conference on Face and Gesture Recognition, Edwards, Cootes, and Taylor presented the approach for the first time in the context of face analysis. This method is commonly used for facial recognition and tracking as well as in the interpretation of medical images.

The algorithm is optimized based on the dissimilarity between the present appearance estimation and the desired image. It quickly adapts to new photos by using least squares approaches for matching.

It has connections to the model of dynamic shapes (ASM). One drawback of ASM is that it does not make use of all the available information, including the texture across the target object, relying instead just on shape restrictions (along with some information about the picture structure around the landmarks). An AAM can be used to model this.

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Chapter 2: Image registration

To register an image is to convert it from many coordinate systems into a single one. Multiple images, data from various sensors, times, depths, and perspectives are all possible forms of data. military applications of autonomous target recognition and data compilation and analysis from satellites. Data from these many metrics cannot be compared or integrated without first registering for an account.

Algorithms for image registration, also known as picture alignment, fall into two broad categories: intensity-based and feature-based.

It is also possible to categorize image registration algorithms based on the transformation models they employ to establish a correspondence between the spaces of the target image and the reference image. Linear transformations (including rotation, scaling, translation, and other affine transforms) are the first overarching class of transformation models, followed by physical continuum models (viscous fluids), and finally, massive deformation models (diffeomorphisms).

Parametrization is a frequent way to express transformations; the number of parameters is often set by the model. One such parameter is a translation vector, which may be used to define the translation of a whole image. Parametric models are the ones that have parameters. In contrast, non-parametric models do not adhere to any parameterization, allowing for the random displacement of individual image elements.

Several software packages include support for both warp-field estimation and application. It's included in SPM and AIR.

On the other hand, homeomorphisms and diffeomorphisms, which preserve structure by transporting smooth submanifolds, are the foundation of several cutting-edge approaches to spatial normalization. Since diffeomorphisms are not additive even though they constitute a group, but rather a group under the law of function composition, flows are used to construct diffeomorphisms in the cutting-edge science of computational anatomy. As a result, huge topology-preserving deformations can be generated by flows that generalize the ideas of additive groups, offering 1-1 and onto transformations. As the primary computational tool for establishing connections between coordinate systems that correspond to the geodesic flows used in Computational Anatomy, LDDMM are the computational methods