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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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TL;DR: An end-to-end deep neural network (DNN) which can simultaneously segment the left atrial (LA) cavity and quantify LA scars and a shape attention (SA) mechanism is embedded into a two-task network to perform the joint LA segmentation and scar quantification.
Abstract: We propose an end-to-end deep neural network (DNN) which can simultaneously segment the left atrial (LA) cavity and quantify LA scars The framework incorporates the continuous spatial information of the target by introducing a spatially encoded (SE) loss based on the distance transform map Compared to conventional binary label based loss, the proposed SE loss can reduce noisy patches in the resulting segmentation, which is commonly seen for deep learning-based methods To fully utilize the inherent spatial relationship between LA and LA scars, we further propose a shape attention (SA) mechanism through an explicit surface projection to build an end-to-end-trainable model Specifically, the SA scheme is embedded into a two-task network to perform the joint LA segmentation and scar quantification Moreover, the proposed method can alleviate the severe class-imbalance problem when detecting small and discrete targets like scars We evaluated the proposed framework on 60 LGE MRI data from the MICCAI2018 LA challenge For LA segmentation, the proposed method reduced the mean Hausdorff distance from 364 mm to 200 mm compared to the 3D basic U-Net using the binary cross-entropy loss For scar quantification, the method was compared with the results or algorithms reported in the literature and demonstrated better performance

14 citations

Proceedings ArticleDOI
27 Aug 2004
TL;DR: This paper presents an approach to handling collision between deformable objects using tetrahedral decomposition using a modified "Closest Point" algorithm derived from Fast Marching using an approximated distance map to compute a penalty based response.
Abstract: This paper presents an approach to handling collision between deformable objects using tetrahedral decomposition. The tetrahedral volumetric model is often used to simulate deformable objects that handle cuts and splits. Interaction between such objects in a complex environment is still an open problem in interactive simulation. This paper is mainly focused on obtaining a fast computation of a reliable penalty response. The method consists in using an approximated distance map to compute a penalty based response. We propose to compute the distances to the boundary using a modified "Closest Point" algorithm derived from Fast Marching. The presented algorithm, inspired by the [FL01], has the advantage of computing rapidly the "Closest Point" in the volumetric tetrahedral mesh without any use of an additional computation grid. From the resulting distance map a response is computed using a new "segment-in-object" response that offers more reliable results than the "point-in-object" generally used in previous works. Using this collision model, simulation at interactive rate can be considered in an environment composed of objects that can be deformed and cut.

14 citations

Proceedings ArticleDOI
23 Jan 2011
TL;DR: A novel algorithm based on the Chamfer Distance to compute the similarity between shapes of word-parts for historical Arabic documents is presented, enabling the system to cluster similar word- parts, even though they are transformed non-linearly due to the nature of handwriting.
Abstract: A large amount of handwritten historical documents are located in libraries around the world. The desire to access, search, and explore these documents paves the way for a new age of knowledge sharing and promotes collaboration and understanding between human societies. Currently, the indexes for these documents are generated manually, which is very tedious and time consuming. Results produced by state of the art techniques, for converting complete images of handwritten documents into textual representations, are not yet sufficient. Therefore, word-spotting methods have been developed to archive and index images of handwritten documents in order to enable efficient searching within documents. In this paper, we present a new matching algorithm to be used in word-spotting tasks for historical Arabic documents. We present a novel algorithm based on the Chamfer Distance to compute the similarity between shapes of word-parts. Matching results are used to cluster images of Arabic word-parts into different classes using the Nearest Neighbor rule. To compute the distance between two word-part images, the algorithm subdivides each image into equal-sized slices (windows). A modified version of the Chamfer Distance, incorporating geometric gradient features and distance transform data, is used as a similarity distance between the different slices. Finally, the Dynamic Time Warping (DTW) algorithm is used to measure the distance between two images of word-parts. By using the DTW we enabled our system to cluster similar word-parts, even though they are transformed non-linearly due to the nature of handwriting. We tested our implementation of the presented methods using various documents in different writing styles, taken from Juma'a Al Majid Center - Dubai, and obtained encouraging results.

14 citations

Proceedings ArticleDOI
07 Sep 1997
TL;DR: A dual representation strategy is proposed here which exploits the strength of both a feature map and a grid map to achieve mapping navigation in an a priori unknown, imperfectly structured indoor environment.
Abstract: This paper presents an environmental acquisition strategy for a mobile robot using an advanced sonar sensor to achieve mapping navigation in an a priori unknown, imperfectly structured indoor environment. Most existing feature based strategies rely on unrealistic assumptions about the environment, while their grid based counterparts hinder localisation which leads to rapid degradation of map quality. A dual representation strategy is proposed here which exploits the strength of both a feature map and a grid map. With the advantage sensor, the environment is scanned and the obtained features are classified into planes, corners, edges and unknowns. The feature map is only updated with the first three types of features. The grid map is updated with all measurement, including the unknowns resulting from complicated objects, to enable obstacle avoidance. On the grid map, distance transform based exploratory path planning is implemented. Adaptation has been made so that an explore-local-first behaviour is exhibited.

14 citations

Patent
Seiya Shimizu1
25 Aug 2010
TL;DR: In this paper, a monocular camera is used to measure the distance from the lens of the camera to one or more arbitrary points on the object, calculates the diffuse reflection coefficient of the object using the measured distance, the luminance on each of the points, the distance to the calibration image, and the measured luminance.
Abstract: An image capturing apparatus previously captures a calibration image that serves as a reference when a distance to an object is calculated, and calculates, when an image of the object is captured using a monocular camera, the distance from the lens of the camera to the object using the calibration image and the distance from the lens of the camera to the calibration image. In this case, the image capturing apparatus measures the distance from the lens of the camera to one or more arbitrary points on the object, calculates the diffuse reflection coefficient of the object using the measured distance, the luminance on each of one or more arbitrary points, the distance to the calibration image, and the luminance, and calculates the distance to the object from the lens of the camera to the object using the calculated diffuse reflection coefficient.

14 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20235
202217
202161
202099
2019112
201881