Distance transform

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

Linear Distance Coding for Image Classification

Abstract: The feature coding-pooling framework is shown to perform well in image classification tasks, because it can generate discriminative and robust image representations. The unavoidable information loss incurred by feature quantization in the coding process and the undesired dependence of pooling on the image spatial layout, however, may severely limit the classification. In this paper, we propose a linear distance coding (LDC) method to capture the discriminative information lost in traditional coding methods while simultaneously alleviating the dependence of pooling on the image spatial layout. The core of the LDC lies in transforming local features of an image into more discriminative distance vectors, where the robust image-to-class distance is employed. These distance vectors are further encoded into sparse codes to capture the salient features of the image. The LDC is theoretically and experimentally shown to be complementary to the traditional coding methods, and thus their combination can achieve higher classification accuracy. We demonstrate the effectiveness of LDC on six data sets, two of each of three types (specific object, scene, and general object), i.e., Flower 102 and PFID 61, Scene 15 and Indoor 67, Caltech 101 and Caltech 256. The results show that our method generally outperforms the traditional coding methods, and achieves or is comparable to the state-of-the-art performance on these data sets.

Robust camera motion estimation using direct edge alignment and sub-gradient method

Abstract: There has been a paradigm shifting trend towards feature-less methods due to their elegant formulation, accuracy and ever increasing computational power. In this work, we present a direct edge alignment approach for 6-DOF tracking. We argue that photo-consistency based methods are plagued by a much smaller convergence basin and are extremely sensitive to noise, changing illumination and fast motion. We propose to use the Distance Transform in the energy formulation which can significantly extend the influence of the edges for tracking. We address the problem of non-differentiability of our cost function and of the previous methods by use of a sub-gradient method. Through extensive experiments we show that the proposed method gives comparable performance to the previous method under nominal conditions and is able to run at 30 Hz in single threaded mode. In addition, under large motion we demonstrate our method outperforms previous methods using the same runtime configuration for our method.

Tree-branch-searching multiresolution approach to skeletonization for virtual endoscopy

Abstract: One of the most important tasks for virtual endoscopy is path planning for viewing the lumen of hollow organs For geometry complex objects, for example the lungs, it remains an unsolved problem While alternative visualization modes have been proposed, for example, cutting and flattening the hollow wall, a skeleton of the lumen is still necessary as a reference for the cutting A general-purpose skeletonization algorithm often generates redundant skeletons because of the local shape variation In this study, a multistage skeletonization method for tree-like volumes, such as airway system, blood vessels, and colon, was presented By appropriately defining the distance between voxels, the distance to the root from each voxel in the volume can be effectively determined with means of region growing techniques The end points of all branches and the shortest path from each end point to the root can be extracted based on this distance map A post-processing algorithm is applied to the shortest paths to remove redundant ones and to centralize the remained ones The skeleton generated is one-voxel wide, along which every branch of the 'tree' can be viewed For effectively processing volume of large size, a modified multiresolution analysis was also developed to scale down the binary segmented volume Tests on airway, vessel, and colon dataset were promising

Analysis of blood and bone marrow smears using digital image processing techniques

Abstract: In the paper, we deal with the analysis of blood and bone marrow smears. The main aim of this long term project is to obtain a relative frequency histogram of the white blood cells of different lineage and maturity. Especially for clinical application, a proper image normalization and segmentation of the color images of blood and bone marrow smears are necessary. For the image normalization, two approaches were adopted: a) active image processing for pre acquisition standardization and b) a histogram based method for post acquisition standardization. Both methods are based on the HSI (Hue Saturation Intensity) Transform. We have developed a robust method for the declustering of the inevitable clusters of white blood cells based on a thresholded distance transform and an extended region growing algorithm that in contrast to active contours does not need any parameterization. For a successful classification, medical morphologic features are translated into feature extraction operators: the mesh structure of the cells' nucleus is analyzed using watershed transform and Gabor features, the shape of cell and nucleus is analyzed using a set of rotational invariant contour based features. The color and granularity of the cytoplasm yield further features for classification. Current work is focused on classification using the presented features.

Char Characterization Using Image Analysis Techniques

Abstract: Three different image analysis techniques for the characterization and analysis of the morphology of chars are presented and discussed in this paper. Each technique attempts to provide objective and repeatable results for the various morphological characteristics of chars produced in a drop-tube furnace or during pulverized fuel combustion. Each technique is described fully, together with the results of several experiments carried out to assess each method for its potential use on a regular basis. The repeatability of the distance transform method is tested and found to be within acceptable limits when 50 images are analyzed. The benefit of automated image-analysis systems is demonstrated by the distance transform method, which produces large quantities of information regarding the whole char sample as opposed to the assessment of single char particles.