Feature-based Image Registration Algorithm Research Articles

Real-Time Print Quality Diagnostics

The traditional diagnostics of print quality requires to print a professionally designed test-page and visually evaluated by an expert, which is very costly and time-consuming. Instead, a system that could automatically diagnose a customer's printer without any human's interference is proposed in this paper. The system relies on scanning user's printed output from user's printer. Print defects such as banding, streaking, etc. will be reflected on its scanned page and can be captured by comparing to its master image. The master image is the digitally generated original from which the page is printed. Once the print quality drops below a specified acceptance criteria level, the system can notify the user of the presence of print quality issues.. The current process has only concentrated on one type of print defect: text fading. The scanned page will initially be aligned with its master image with a feature based image registration algorithm. Text regions of the two pages are then extracted and compared directly.

Feature-based image registration algorithm for image stitching applications on mobile devices

In this paper, we propose a new feature-based image registration algorithm considering the mobile devices with limited computing power. The algorithm is consisted of two steps: first we reject severe outliers in putative correspondences by considering a similarity transform as a motion model between two views. This approach can find most of the inliers very efficiently, but some non-severe outliers still remain due to its coarse approximation. Therefore, in the second step, we apply an optimization method based on the conjugate gradient algorithm to refine a geometric relation between two views and a set of inliers simultaneously. The proposed method is implemented on a mobile device and experimental results have shown that our method is very efficient and yields good registration performance.

Automatic two-stage IR and MMW image registration algorithm for concealed weapons detection

A two-stage registration scheme for the concealed weapons detection (CWD) problem is developed. The goal is to automatically register images taken simultaneously from two different (infrared (IR) and millimetre wave (MMW)) but parallel sensors whose lines of sight (LOS) are close to each other. The purpose of the first stage is to register the images coarsely. A feature-based image registration algorithm based on human body silhouettes is developed at this stage. The pose parameters found at this stage are used as the starting search point for the second stage of the registration algorithm. At the second stage, maximisation of the mutual information measure between IR and MMW images is performed to improve the pose parameters obtained at the first stage. Two-dimensional partial volume interpolation is employed to estimate the joint histogram that is needed to calculate mutual information. The simplex search algorithm is utilised to maximise the MI measure. In both stages, the distortion between the two images is assumed to be a rigid body transformation. Experimental results indicate that the automated two-stage registration algorithm performs fairly well.

A feature-based image registration algorithm using improved chain-code representation combined with invariant moments

A new feature-based approach to automated image-to-image registration is presented. The characteristic of this approach is that it combines an invariant-moment shape descriptor with improved chain-code matching to establish correspondences between the potentially matched regions detected from the two images. It is robust in that it overcomes the difficulties of control-point correspondence by matching the images both in the feature space, using the principle of minimum distance classifier (based on the combined criteria), and sequentially in the image space, using the rule of root mean-square error (RMSE). In image segmentation, the performance of the Laplacian of Gaussian operators is improved by introducing a new algorithm called thin and robust zero crossing. After the detected edge points are refined and sorted, regions are defined. Region correspondences are then performed by an image-matching algorithm developed in this research. The centers of gravity are then extracted from the matched regions and are used as control points. Transformation parameters are estimated based on the final matched control-point pairs. The algorithm proposed is automated, robust, and of significant value in an operational context. Experimental results using multitemporal Landsat TM imagery are presented.