Igor V. Maslov

Using neural network to improve the performance of the hybrid evolutionary algorithm in image registration

The hybrid evolutionary algorithm is used for image registration formulated as an optimization problem of finding a vector of parameters minimizing the difference between images. The reproduction phase of the algorithm is enhanced with a two-level operation of local correction performed on the best genes in the reproduction pool. Random search is performed in the neighborhood of a gene until the time interval reaches a pre-set threshold. If the gene still retains its position in the pool, a refined multi-step search is performed using the Downhill simplex method. In order to improve the computational performance of the local search, local response analysis is used in the following way. All domains of the given reference image are classified according to their local response to a unit variation of the parameter vector. The classification scheme is based on a self-organizing neural network. During the local correction of the reproduction pool, the step size in the Downhill simplex search is modified according to the class of the image domain.

Using image local response for efficient image fusion with the hybrid evolutionary algorithm

Image fusion serves as the basis for automatic target recognition; it maps images of teh same scene received from different sensors into a common reference system. A novel fusion method is described that employs image local response and the hybrid evolutionary algorithm (HEA). Given geometric transformation A(V) under parameter vector V (e.g. affine image transformation) of the images subjected to fusion, image local response is defined as image transform components of the parameter vector V are applied to the image, and the corresponding variations of the least squared differences of the gray levels of the two images (i.e. before and after parameter variation) form the image response matrix. The transform R(V) extracts only the dynamic contents of the image, i.e. the salient features that are most sensitive to geometric transformation A(V). Since R(V) maps the image onto itself, the result of the mapping is largely invariant to the type of the sensor that was used to obtain the image. Once the response matrices are built for all images subjected to fusion, HEA is used to map the images into the common reference system.

Improving local search with neural network in image registration with the hybrid evolutionary algorithm

Image registration is formulated as a nonlinear optimization problem of finding an affine transformation minimizing the difference between images. A particular scheme of the hybrid evolutionary algorithm is used to solve the problem. The reproduction phase of the algorithm is enhanced with a two-phase operation of local search and correction performed on the subset of the best chromosomes in the reproduction pool. In order to reduce the computational cost of the correction, a mechanism of the adaptive control of the local search is designed, based on a micro model of the local image response. The mechanism correlates the step size of the search with the local properties of the gray level surface at different points of an image. To reduce the number of evaluations of the local image response required by the control mechanism, all participating image points are evaluated and classified at once in the preprocessing stage. A self-organizing neural network is employed to classify different points according to their response values, and to build an adaptive, compact response map of the entire image. During the execution of the main algorithm, this map is used as a lookup table, to retrieve the appropriate response values for the points participating in the local search.

Evolutionary approach to human body registration

Human body registration is an important and complex problem that can be found in a variety of real world applications. Registration maps images of a person obtained from different camera views into a common reference system of a scene that contains a human figure. The complexity of the problem stems from the fact that human body can arbitrarily move in the 3-D space while changing its own shape. The registration task is stated as a nonlinear global multimodal optimization problem, i.e., as a search for a proper transformation that provides the best match between the images of the body and the scene. The paper describes an approach to human body registration that utilizes a hybrid evolutionary algorithm and image response analysis. Hybrid evolutionary algorithm provides an efficient procedure of global search in extremely large parameter space. Image response analysis allows to reduce the total amount of information that has to be processed during evaluation of potential solutions. In the process of the evolutionary search, response matrix of each template image is compared against response matrix of the reference image of the scene, in order to find the correct mapping between the images. The efficiency of the proposed approach is demonstrated on a test set of 2-D grayscale images.

Content-based retrieval of distorted 3D images with a hybrid evolutionary algorithm

Content-based image retrieval in many cases involves performing a direct mapping operation between a query image and images stored in a database. Preliminary results are discussed on using image mapping through unsupervised learning, in the form of a Hybrid evolutionary algorithm (HEA), in a search for 3-dimensional objects that can be present in the database images. Content-based retrieval problem is formulated as the optimization problem of finding the proper mapping between the stored and the query images. The paper proposes an extension of the HEA-based method of the 2-dimensional image mapping to the 3-dimensional case. A set of image transformations is sought such that each transformation is applied to a different section of the image subject to mapping. The sought image transformation becomes a piece-wise approximation of the actual 3-D transformation of the object. The 2-D optimization problem of finding a parameter vector minimizing the difference between the images turns into a multi-objective optimization problem of finding a set of feasible parameter vectors that minimize the differences between the sections of the compared images. The search for a proper set of image transformations is conducted in a feature space formed by image local response, as opposed to a pixel-wise comparison of the actual images in the 2-D case. Using image response allows to reduce the computational cost of the search by applying thresholding techniques and a piece-wise approximation of the response matrix. The difference between the images is evaluated in the response space by minimizing the distance between the two-dimensional central moments of the image responses.

Multi-resolution approach to automatic target recognition with a hybrid evolutionary algorithm and response analysis

A multi-resolution approach to automatic target recognition is described that employs a hybrid evolutionary algorithm (HEA) and image transform in a form of image local response. Given images of the targeted area (TA) and the targeted object (TO) located in TA, the proposed method repeatedly applies cross-correlation on different resolution levels (zooming in), in order to find the area TA and the object TO in the large-scale image of the region of interest (ROI). Both images of ROI and TA undergo peculiar transformation called image local response. Given geometric transformation T(V) of the images under specified parameter vector V, image local response is defined as an image transform R(V) that maps an image into itself, with the small perturbation of the parameter vector V. Unit variations of the components of the parameter vector V are applied to the image, and the corresponding variations of the least squared difference of the gray levels of the two images (i.e., before and after the parameter variation) form an image response matrix M(V). Cross-correlation of the response matrices built for ROI and TA outlines a potential range of resolutions of the TA. A hybrid Evolutionary algorithm can be applied then, in order to find the correct parameters V for TA with the reference to ROI.

Image fusion with the hybrid evolutionary algorithm and response analysis

Information fusion is a rapidly developing research area aimed at creating methods and tools capable of augmenting security and defense systems with the state-of-the-art computational power and intelligence. An important part of information fusion, image fusion serves as the basis for a fully automatic object and target recognition. Image fusion maps images of the same scene received from different sensors into a common reference system. Using sensors of different types gives rise to a problem of finding a set of invariant features that help overrun the imagery difference caused by the different types of sensors. The paper describes an image fusion method based on the combination of the hybrid evolutionary algorithm and image local response. The latter is defined as an image transform R(V) that maps an image into itself after a geometric transformation A(V) defined by a parameter vector V is applied to the image. The transform R(V) identifies the dynamic content of the image, i.e. the salient features that are most responsive to the geometric transformation A(V). Moreover, since R(V) maps the image into itself, the result of the mapping is largely invariant to the type of the sensor used to obtain the image. Image fusion is stated as the global optimization problem of finding a proper transformation A(V) that minimizes the difference between the images subject to fusion. Hybrid evolutionary algorithm can be applied to solving the problem. Since the search for the optimal parameter vector V is conducted in the response space rather than in the actual image space, the differences in the sensor types can be significantly alleviated.

Fusion of self-organizing network and response analysis in object recognition with the hybrid evolutionary algorithm

The paper introduces the approach to the 2D automated content-based object recognition utilizing the hybrid evolutionary algorithm (HEA), self-organizing network (SON), and response analysis. When the object is distorted and spatially misregistered, the recognition system has to solve a nonlinear global search problem, i.e. find simultaneously the global positioning of the object and the parameters of its local distortion. The task is accomplished with the HEA using the operators of selection and recombination for the global search, and the accelerated Downhill simplex method for the local search. The algorithm minimizes the fitness formulated as the normalized least squared difference between the images of the scene and the object, and utilizes image local response. The response adequately captures the dynamics of the image transformation, which makes it particularly well suited for the evolutionary search. The response matrix of the object is evaluated and presented to a SON. The weights are computed during the iterative learning process. The resulting adaptive response map serves as the footprint of the object. The evolutionary procedure identifies potential matches for the object based on the response matrix. The local refining procedure uses the response map to accelerate local search in the vicinity of the potential optimal solution.

Automatic image registration and target recognition with multiresolution hybrid evolutionary algorithm

A novel method for multi-resolution automatic target recognition is described that employs the hybrid evolutionary algorithm and image transform in a form of image local response. The recognition task is re-formulated as a nonlinear global optimization problem, i.e. the search for a proper transformation A(V) that provides the best match between the images of the target and the scene. Given the images of the scene and the targeted object located in the scene, the proposed method repeatedly applies response analysis on different resolution levels (zooming in), in order to find the region of interest (ROI) containing the target in the large-scale image of the scene. On every resolution level, the response matrices are computed for the images of ROI and the target. Cross correlation of the response matrices built for ROI and the target outlines the potential locations of the latter. Once the locations are successfully identified, the algorithm zooms in on the found locations. The hybrid evolutionary algorithm is applied to the response matrices MR; it attempts to minimize the least squared difference of the pixel values of the response matrices corresponding to the images, thus searching for the correct parameter vector V for the targeted object with the reference to ROI.

Global optimization methods in content-based image retrieval

Content-based image retrieval involves a search throughout a database of stored images for the best match for the query image. The task is re-formulated as the global optimization problem of finding the correct mapping between the corresponding points of the query image and the database image. For 2-dimensional grayscale images, the quality of the match is evaluated as the difference between the pixel values in the area of the intersection of the two images: the minimum value of the difference indicates a potential match between the images, with the corresponding optimal values of the parameters defining the mapping. The stated problem is a nonlinear, multimodal global optimization problem. In general form, the mapping includes the rigid body transform and the local object deformation. If there is no prior information available about the images, the search space of potential solutions becomes so large that the brute force approach becomes intractable. The classical optimization techniques fail due to the presence of many local minima and the non-convex shape of the nonlinear function defining the difference between the images. The following stochastic optimization techniques are compared in the paper: parallel simulated annealing, multi-start, and hybrid evolutionary algorithm. The methods differ in the degree to which they utilize global and local search, and in the strategy of the global search. The comparison is presented for the grayscale images, with different initial settings.