The computer implemented method and system disclosed herein, in general, relates to image processing. More particularly, the computer implemented method and system disclosed herein relates to aligning multiple overlapping images in real time using translation invariant feature matching.
Digital image capture is a well known technique used for capturing images using lower cost optical to electronic conversion devices. Advantages of using a digital image capture device is the ability to capture, store and immediately view an image, and the ability to correct defects in the saved image either in situ or later using a computer system. However, to cover wide space, large field view lenses are used resulting in barrel distortion effects on a resultant image which produces bending of space at the sides of the image. A single image capture device when used to obtain a wider field of view produces a low resolution image not useful for surveillance applications. The immediacy and correction ability of digital imaging has stimulated development of application programs that merge overlapping images together while correcting lens distortion, color and brightness across overlapping images.
Panoramic images have also been created using digital movie techniques, but these images are viewed over time rather than as an instantaneous presentation. Image aligning application programs rely upon the ability to locate the same objects appearing in overlapping images in order to provide alignment targets. Images that do not contain easily locatable objects in the overlapping region are problematic for aligning algorithms. Images captured from various camera positions contain local misalignments because of parallax and photometric inconsistencies due to differences in illumination and camera gain. Lenses with a field view exceeding 150 degrees are used for capturing wide images and produce barrel distortion effects on the image. Capturing a large field of view using a single camera also produces a low resolution image not usable in most applications. For example, surveillance applications require panoramic images to be generated in real time.
Image stabilization is a family of techniques for increasing stability of an image. The conventional image stabilization technique comprises optical image stabilization and electronic image stabilization. The optical image stabilization technique detects a rotational angle of a camera using a motion detection sensor, such as an angular velocity sensor and an acceleration sensor, and displaces a lens in an image pickup optical system and an image pickup device that photo electrically converts a subject image based on the rotational angle. Typically hand shake is sensed using one or more gyroscopic sensors and a floating lens element is moved using electromagnets. Such additional components are not feasible in mobile phone cameras due to size and weight, expense and power consumption. On the other hand, one of the electronic image stabilization techniques includes a method of detecting a motion vector between frame images in a motion picture, changes an output area in each frame image based on the motion vector, and obtains a stabilized motion picture image. However, a typical mobile device camera does not capture the appropriate images per second to reconstruct an image adequately and the image processing required to extrapolate a less blurred image is expensive and can increase power consumption. A need exists for a robust image stabilization technique that is repeatable under various conditions and relatively inexpensive.
Conventional techniques of image aligning process entire images due to lack of information regarding the sequence of the images prior to the processing and use wavelet techniques and other techniques, for example, scale invariant feature transform (SIFT) to solve for inter image translation and rotation, and are computationally expensive and difficult to achieve in real time. Therefore, there is a need for a technology that simplifies the relationship between images without leaving undesirable vestiges in the final image and that would greatly improve the speed and quality of aligned images.
Hence, there is an unmet need for an economic and computationally simple technique for aligning multiple images in real time using translation invariant feature matching.