This paper presents a study of image simplification techniques as a first stage to define a multiresolution registration framework. We propose here a new approach for image registration based on the partitioning of the source images in binary-space (BSP) and quad-tree structures. These partitioned images have been obtained with a maximum mutual information gain algorithm. Multimodal registratio...

Image registration has great significance in medicine, astrophotography and satellite imaging, so a lot of techniques have been developed within its perspective of one or the other discipline. This paper presents a method for medical image registration of multimodal brain images using Modified Adaptive Polar Transform (MAPT). This algorithm can be used to register images of the same or differen...

Multi-modal image registration is needed to align medical images collected from different protocols or imaging sources, thereby allowing the mapping of complementary information between images. One challenge of multimodal image registration is that typical similarity measures rely on statistical correlations between image intensities to determine anatomical alignment. The use of alternate image...

Multimodal images fusion has the potential to enrich information gathered by multi-sensor plant phenotyping platforms. Fusion of from multiple sources is, however, hampered technical lock image registration. The aim this paper is provide a solution registration and multimodal wheat in field conditions at close range. Eight methods were tested on nadir acquired pair red, green blue (RGB) cameras...

For several years, mutual information (MI) has been the classic multimodal similarity measure. The robustness of MI is closely restricted by the choice of MI window sizes. For unsupervised human monitoring applications, obtaining appropriate MI window sizes for computing MI in videos with multiple people in different sizes and different levels of occlusion is problematic. In this work, we apply...

Automatic registration of multimodality images is an upcoming field of research for medical diagnosis and surgical planning. We propose a new method to extract common features from different modalities based on the extension to 3D of Rothe’s creases. Extracted images are used as input for a multiresolution search algorithm which brings them into agreement.