Distance transform

About: Distance transform is a research topic. Over the lifetime, 2886 publications have been published within this topic receiving 59481 citations.

Road Information Extraction from High-Resolution Remote Sensing Images Based on Road Reconstruction

Abstract: Traditional road extraction algorithms, which focus on improving the accuracy of road surfaces, cannot overcome the interference of shelter caused by vegetation, buildings, and shadows. In this paper, we extract the roads via road centerline extraction, road width extraction, broken centerline connection, and road reconstruction. We use a multiscale segmentation algorithm to segment the images, and feature extraction to get the initial road. The fast marching method (FMM) algorithm is employed to obtain the boundary distance field and the source distance field, and the branch backing-tracking method is used to acquire the initial centerline. Road width of each initial centerline is calculated by combining the boundary distance fields, before a tensor field is applied for connecting the broken centerline to gain the final centerline. The final centerline is matched with its road width when the final road is reconstructed. Three experimental results show that the proposed method improves the accuracy of the centerline and solves the problem of broken centerline, and that the method reconstructing the roads is excellent for maintain their integrity.

Adaptive image filtering based on a distance transform

Abstract: A method for reducing visual artifacts in reconstructed images. One embodiment of the method determines edge energy for each pixel in the image and then compares the edge energy for each pixel to a threshold, producing an edge map. A distance transform is then used to produce a filter map and a filter is applied to pixel in the image, such that the filter applied is dependent upon a filter map value for each pixel. An output value for each pixel is then produced.

Shape-Based Trademark Retrieval Using Cosine Distance Method

Abstract: Measuring perceptual similarity and defining an appropriate similarity measure between trademark images remain largely unanswered. Most researchers used the Euclidean distance. This measure considers the difference in magnitude, rather than just the correlation of the features. We propose a new method based on cosine distance and normalized distance measures. The cosine distance metric normalizes all features vector to unit length and makes it invariant against relative in-plane scaling transformation of the image content. The normalized distance combines two distances measures such as cosine distance and Euclidean distance which shows more accuracy than one method alone. The proposed measures take into account the integration of global features (invariant moments and eccentricity) and local features (entropy histogram and distance histogram). It first indexes trademark images database (DB) in order to search for trademarks in narrowed limit and reduce time computation and then calculates similarities for features vector to obtain the total similarity between features vector. We have used retrieval efficiency equation in order to test the accuracy of our method. The obtained results showed that cosine distance and the normalization of cosine and Euclidean distance provide a significant improvement over the Euclidean distance.

Distance Surface for Event-Based Optical Flow

Abstract: We propose DistSurf-OF, a novel optical flow method for neuromorphic cameras. Neuromorphic cameras (or event detection cameras) are an emerging sensor modality that makes use of dynamic vision sensors (DVS) to report asynchronously the log-intensity changes (called “events”) exceeding a predefined threshold at each pixel. In absence of the intensity value at each pixel location, we introduce a notion of “distance surface”—the distance transform computed from the detected events—as a proxy for object texture. The distance surface is then used as an input to the intensity-based optical flow methods to recover the two dimensional pixel motion. Real sensor experiments verify that the proposed DistSurf-OF accurately estimates the angle and speed of each events.