Given microscope images, one can observe 2D cross-sections of 3D micro anatomical structures with high spatial resolutions. Each of the 2D microscope images alone is, though, not suitable for studying the 3D anatomical structures and hence many works have been done on a 3D image reconstruction from a given series of microscope images of histological sections obtained from a single target tissue. For the 3D image reconstruction, an image registration technique is necessary because there exists the independent translation, rotation, and non-rigid deformation of the histological sections. In this paper, a landmark-based method of fully non-rigid image registration for the 3D image reconstruction is proposed. The proposed method first detects landmarks corresponded between given images by using a template matching and then non-rigidly deforms the images so that the corresponding landmarks detected in different images are located along a single smooth curve in the reconstructed 3D image. Most of all conventional methods for the reconstruction of 3D microscope image registers two consecutive images at a time and many micro anatomical structures often have unnatural straight shape along the vertical (z) direction in the resultant 3D image because, roughly speaking, the conventional methods registers two given images so that pixels with the same coordinates in the two images have the same pixel value. The proposed method, on the other hand, determine the deformations of all given images by referring to the all images and deforms them simultaneously. In the experiments, a 3D microscope image of the pancreas of a KPC mouse was reconstructed from a series of microscope images of the histological sections.
We propose a visual tracking system that uses RFID-tags to identify
objects. The system firstly identifies an object in front of the
camera, and pulls up data of the object from the database. The data
includes a cad model of the object that is used for estimating 3D
motion relative to the camera and a set of image features that is used
for detecting the object in the initial image. The set of image
features is generated based on the cad model by means of the AdaBoost
algorithm and distinguishes the object in images from the backgrounds
efficiently. Identifying the object, the system processes images using
models that are specialized in the object in front of the camera.