Abstract—We propose a novel algorithm, referred to as enhanced bounded correlation (EBC), that significantly reduces the number of computations required to carry out template matching based on normalized cross correlation (NCC) and yields exactly the same result as the full search algorithm. The algorithm relies on the concept of bounding the matching function: finding an efficiently computable upper bound of the NCC rapidly prunes those candidates that cannot provide a better NCC score with respect to the current best match. In this framework, we apply a succession of increasingly tighter upper bounding functions based on Cauchy–Schwarz inequality. Moreover, by including an online parameter prediction step into EBC, we obtain a parameter free algorithm that, in most cases, affords computational advantages very similar to those attainable by optimal offline parameter tuning. Experimental results show that the proposed algorithm can significantly accelerate a full-search equivalent template matching process and outperforms state-of-the-art methods. Index Terms—Bounded correlation, Cauchy–Schwarz inequality, fast Fourier transform (FFT), normalized cross correlation

Medical image registration is a difficult problem. Not only a registration algorithm needs to capture both large and small scale image deformations, it also has to deal with global and local image intensity variations. In this paper we describe a new multiresolution elastic image registration method that challenges these difficulties in image registration. To capture large and small scale image deformations, we use both global and local affine transformation algorithms. To address global and local image intensity variations, we apply an image intensity standardization algorithm to correct image intensity variations. This transforms image intensities into a standard intensity scale, which allows highly accurate registration of medical images. 1

The task of enhancing the perception of a scene by combining information captured by different sensors is usually known as image fusion. The pyramid decomposition and the Dual-Tree Wavelet Transform have been thoroughly applied in image fusion as analysis and synthesis tools. Using a number of pixel-based and region-based fusion rules, one can combine the important features of the input images in the transform domain to compose an enhanced image. In this paper, the authors test the efficiency of a transform constructed using Independent Component Analysis (ICA) and Topographic Independent Component Analysis bases in image fusion. The bases are obtained by offline training with images of similar context to the observed scene. The images are fused in the transform domain using novel pixelbased or region-based rules. The proposed schemes feature improved performance compared to traditional wavelet approaches with slightly increased computational complexity. Key words: image fusion, image segmentation, Independent Component Analysis, topographic ICA.

This paper presents a method that reduces the computational cost of template matching based on the Zero-mean Normalized Cross-Correlation (ZNCC) without compromising the accuracy of the results. A very effective condition is determined at a small and �xed cost that allow to rapidly detect a large number of mismatching candidates with no need to compute the ZNCC score. Then, thanks to the use of an additional set of conditions, the computation of the whole ZNCC function is typically required only for a very small number of candidates. Experimental results demonstrate the effectiveness of our approach. Index Terms — Template matching, ZNCC, cross correlation, fast, exhaustive

Image Registration (IR) [9, 58], is a fundamental task in computer vision used to finding either a spatial transformation (e.g, rotation, translation,

Change detection is one of the most important issues in computer vision applications. This paper presents a study of an automatic change detection method for multiple images of the same scene acquired by a mobile-camera from different positions with no illumination changes. The proposed method used for this study consists of three steps: (1) automatic image registration, (2) temporal differencing and (3) unimportant changes removal. The results in this paper show that the presented method succesfully detect new objects in the scene from the multiple images. 1

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This paper proposes a novel method for fast pattern matching based on dissimilarity functions derived from the Lp norm, such as the Sum of Squared Differences (SSD) and the Sum of Absolute Differences (SAD). The proposed method is full-search equivalent, i.e. it yields the same results as the Full Search (FS) algorithm. In order to pursue computational savings the method deploys a succession of increasingly tighter lower bounds of the adopted L p norm-based dissimilarity function. Such bounding functions allow for establishing a hierarchy of pruning conditions aimed at skipping rapidly those candidates that cannot satisfy the matching criterion. The paper includes an experimental comparison between the proposed method and other full-search equivalent approaches known in literature, which proves the remarkable computational efficiency of our proposal. I.

journal homepage: www.elsevier.com/locate/patrec

Image registration is concerned with the establishment of correspondence between the images of the same scene. It is a challenge problem especially when multispectral/multisensor images are registered. In general, such images have different gray level characteristics and imaging model, and simple registration techniques based on area methods cannot be applied directly. A registration method between synthesize aperture radar (SAR) image and Optical image is presented in this paper. There are two steps to realize the registration process. Firstly, edge detector and some morphologic methods, such as dilate and thin, are used for the contour extraction in both images. Affine transform model is used to correct the global rigid deformation (rotation, translation, scale) between two images. Then, we will estimate the residual deformation between the two images with similarity measure based on possibilities. The local deformation will be reduced in this step. The proposed registration algorithm has been applied in the experimental area with airborne SAR image and Optical image. The experimental results demonstrate that the algorithm can solve some problems to some extent between the multisensor registrations and can receive high accuracy. 1.