The existence of numerous imaging modalities makes it possible to present DIFFERENT data present in DIFFERENT modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the DIFFERENT modalities, are displayed in proper locations. The term image registration is most commonly used to denote the process of alignment of images , that is of transforming them to the common coordinate system. This is done by optimizing a similarity measure between the two images. A widely used measure is Mutual Information (MI). This method requires estimating joint histogram of the two images. Experiments are presented that demonstrate the approach. The technique is intensity-based rather than feature-based. As a comparative assessment the performance based on normalized mutual information and cross correlation as metric have also been presented.
-The existence of numerous imaging modalities makes it possible to present DIFFERENT data present in DIFFERENT modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the DIFFERENT modalities, are displayed in proper locations. Mutual Information is the similarity measure used in this case for optimizing the two images. This method requires estimating joint histogram of the two images. The fusion of images is the process of combining two or more images into a single image retaining important features from each. The Discrete Wavelet Transform (DWT) has become an attractive tool for fusing multimodal images. In this work it has been used to segment the features of the input images to produce a region map. Features of each region are calculated and a region based approach is used to fuse the images in the wavelet domain.
In computer vision, sets of data acquired by sampling the same scene or object at DIFFERENT times, or from DIFFERENT perspectives, will be in DIFFERENT coordinate systems. Image registration is the process of transforming the DIFFERENT sets of data into one coordinate system. Registration is necessary in order to be able to compare or integrate the data obtained from DIFFERENT measurements. Image registration is the process of transforming the DIFFERENT sets of data into one coordinate system. To be precise it involves finding transformations that relate spatial information conveyed in one image to that in another or in physical space. Image registration is performed on a series of at least two images, where one of these images is the reference image to which all the others will be registered. The other images are referred to as target images.
In some graphs, the shortest path is given by optimizing two DIFFERENT metrics: the sum of weights of the edges and the number of edges. For example: if two paths with equal cost exist then, the path with the least number of edges is chosen as the shortest path. Given this metric, you have find out the shortest path between a given pair of vertices in the input graph. The output should be the number of edges on the path, the cost of the shortest path, and the path itself. Input is the adjacency matrix and the two vertices.
