Topic
Distance transform
About: Distance transform is a research topic. Over the lifetime, 2886 publications have been published within this topic receiving 59481 citations.
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01 Jan 2013
TL;DR: A new approach of Watershed Algorithm using Distance Transform is applied to Image Segmentation using Laplacian of Gaussian edge detector and will detect a detailed and an accurate image.
Abstract: A new approach of Watershed Algorithm using Distance Transform is applied to Image Segmentation is discussed in this paper. After applying Watershed Algorithm we get an over-segmented image. The watershed algorithm with Laplacian of Gaussian (LoG) edge detector is used to detect the edges of the image and produce an image which is less over-segmented. The proposed algorithm will detect a detailed and an accurate image.
16 citations
01 Jan 1999
TL;DR: This paper presents a 3D (volume) surface skeletonization algorithm that uses iterative, topology preserving thinning guided by the D26 distance transform, which is the 3D equivalent of t transforms.
Abstract: This paper presents a 3D (volume) surface skeletonization algorithm. Our algorithm uses iterative, topology preserving thinning guided by the D26 distance transform, which is the 3D equivalent of t ...
16 citations
Patent•
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30 Jan 2015
TL;DR: In this paper, a 2D image analyzer consisting of an image scaler, an image generator, and a pattern finder is proposed. But the authors do not specify the specific pattern to be searched.
Abstract: The invention relates to a 2D image analyzer comprising an image scaler, an image generator, and a pattern finder. The image scaler is designed to scale an image according to a scaling factor. The image generator is designed to generate an overview image which has a plurality of copies of the received and scaled image. Each copy is scaled by a different scaling factor. In the process, the respective position can be calculated using an algorithm which takes into consideration a distance between the scaled images in the overview image, a distance from the scaled images to one or more of the boundaries of the overview image, and/or other predefined conditions. The pattern finder is designed to carry out a characteristic transformation and classification of the overview image and to output a position where the searched pattern maximally matches the specified pattern. Optionally, a post-processing device can also be provided for smoothing and correcting the position of local maxima in the classified overview image.
16 citations
TL;DR: The new algorithm achieves the computational complexity of EDT to be linear to the size of an image by using the relative X and Y coordinates computed from the object pixel to the source mapping pixel of its neighbors as well as correction of particular cases.
16 citations
TL;DR: A patchwise scaling method to resize an image to emphasize the important areas and preserve the globally visual effect (smoothness, coherence and integrity) based on optimizing the image distance presented in this paper.
16 citations