TL;DR: This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction and the resulting methods are accurate, noise resistant and fast.
Abstract: This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction.
Non-linear filtering is used to define which parts of the image are closely related to each individual pixel; each pixel has associated with it a local image region which is of similar brightness to that pixel. The new feature detectors are based on the minimization of this local image region, and the noise reduction method uses this region as the smoothing neighbourhood. The resulting methods are accurate, noise resistant and fast.
Details of the new feature detectors and of the new noise reduction method are described, along with test results.
TL;DR: A fast parallel thinning algorithm that consists of two subiterations: one aimed at deleting the south-east boundary points and the north-west corner points while the other one is aimed at deletion thenorth-west boundarypoints and theSouth-east corner points.
Abstract: A fast parallel thinning algorithm is proposed in this paper It consists of two subiterations: one aimed at deleting the south-east boundary points and the north-west corner points while the other one is aimed at deleting the north-west boundary points and the south-east corner points End points and pixel connectivity are preserved Each pattern is thinned down to a skeleton of unitary thickness Experimental results show that this method is very effective 12 references
TL;DR: In this article, a system for placing a visible watermark on a digital image is disclosed, wherein an image of the watermark is combined with the digital image, and for each pixel whose value is not a specified "transparent" value, the corresponding pixel of the original image is modified by changing its brightness but its chromaticities.
Abstract: A system for placing a visible "watermark" on a digital image is disclosed, wherein an image of the watermark is combined with the digital image. The pixels of the watermark image are examined, and for each pixel whose value is not a specified "transparent" value, the corresponding pixel of the original image is modified by changing its brightness but its chromaticities. This results in a visible mark which allows the contents of the image to be viewed clearly, but which discourages unauthorized use of the image.
TL;DR: This paper proposes a novel extension to MI called regional mutual information (RMI), which efficiently takes neighborhood regions of corresponding pixels into account and demonstrates the usefulness of RMI by applying it to a real-world problem in the medical domain.
Abstract: Mutual information (MI) has emerged in recent years as an effective similarity measure for comparing images. One drawback of MI, however, is that it is calculated on a pixel by pixel basis, meaning that it takes into account only the relationships between corresponding indi- vidual pixels and not those of each pixel's respective neighborhood. As a result, much of the spatial information inherent in images is not utilized. In this paper, we propose a novel extension to MI called regional mutual information (RMI). This extension efficiently takes neighborhood regions of corresponding pixels into account. We demonstrate the usefulness of RMI by applying it to a real-world problem in the medical domain— intensity-based 2D-3D registration of X-ray projection images (2D) to a CT image (3D). Using a gold-standard spine image data set, we show that RMI is a more robust similarity meaure for image registration than MI.
TL;DR: In this paper, a method and system operative to process monochrome image data are disclosed, which can comprise the steps of receiving the image data, segmenting the input pixel values into pixel value ranges, assigning pixel positions in the lowest pixel value range an output pixel value of a first binary value and assigning pixel position in the highest pixel values of a second binary value, wherein the first and second binary values are different.
Abstract: A method and system operative to process monochrome image data are disclosed. In one embodiment, the method can comprise the steps of receiving monochrome image data, segmenting the input pixel values into pixel value ranges, assigning pixel positions in the lowest pixel value range an output pixel value of a first binary value, assigning pixel positions in the highest pixel value range an output pixel value of a second binary value, wherein the first and second binary values are different, and assigning pixel positions in intermediate pixel value ranges output pixel values that correspond to a spatial binary pattern. The resulting binary image data can be written to a file for subsequent storage, transmission, processing, or retrieval and rendering. In further embodiments, a system can be made operative to accomplish the same.