An elementary algorithm for digital arc segmentation

International audienceThis paper concerns the digital circle recognition problem, especially in the form of the circular separation problem. General fundamentals, based on classical tools, as well as algorithmic details are given (the latter by providing pseudo-code for major steps of the algorithm). After recalling the geometrical meaning of the separating circle problem, we present an incremental algorithm to segment a discrete curve into digital arcs

Graph Clustering, Variational Image Segmentation Methods and Hough Transform Scale Detection for Object Measurement in Images

© 2016, Springer Science+Business Media New York. We consider the problem of scale detection in images where a region of interest is present together with a measurement tool (e.g. a ruler). For the segmentation part, we focus on the graph-based method presented in Bertozzi and Flenner (Multiscale Model Simul 10(3):1090–1118, 2012) which reinterprets classical continuous Ginzburg–Landau minimisation models in a totally discrete framework. To overcome the numerical difficulties due to the large size of the images considered, we use matrix completion and splitting techniques. The scale on the measurement tool is detected via a Hough transform-based algorithm. The method is then applied to some measurement tasks arising in real-world applications such as zoology, medicine and archaeology

Reconstructing polygons from moments with connections to array processing

Caption title.Includes bibliographical references (p. 24-26).Supported by the Office of Naval Research. N00014-91-J-1004 Supported by the US Army Research Office. DAAL03-92-G-0115 Supported by the National Science Foundation. MIP-9015281 Supported by the Clement Vaturi Fellowship in Biomedical Imaging Sciences at MIT.Peyman Milanfar ... [et al.]

3D-POLY: A Robot Vision System for Recognizing Objects in Occluded Environments

The two factors that determine the time complexity associated with model-driven interpretation of range maps are: I) the particular strategy used for the generation of object hypotheses; and 2) the manner in which both the model and the sensed data are organized, data organization being a primary determinant of the efficiency of verification of a given hypothesis. In this report, we present 3D-POLY, a working system for recognizing objects in the presence of occlusion and against cluttered backgrounds. The time complexity of this system is only O(n2) for single object recognition, where n is the number of features on the object. The most novel aspect of this system is the manner in which the feature data are organized for the models. We use a data structure called the feature sphere for the purpose. We will present efficient algorithms for assigning a feature to its proper place on a feature sphere, and for extracting the neighbors of a given feature from the feature sphere representation. For hypothesis generation, we use local feature sets, a notion similar to those used before us by Bolles, Shirai and others. The combination of the feature sphere idea for streamlining verification and the local feature sets for hypothesis generation results in a system whose time complexity has a polynomial bound. In addition to recognizing objects in occluded environments, 3D-POLY also possesses model learning capability. Model learning consists of looking at a model object from different views and integrating the resulting information. The 3D-POLY system also contains utilities for range image segmentation and classification of scene surfaces

View generated database

This document represents the final report for the View Generated Database (VGD) project, NAS7-1066. It documents the work done on the project up to the point at which all project work was terminated due to lack of project funds. The VGD was to provide the capability to accurately represent any real-world object or scene as a computer model. Such models include both an accurate spatial/geometric representation of surfaces of the object or scene, as well as any surface detail present on the object. Applications of such models are numerous, including acquisition and maintenance of work models for tele-autonomous systems, generation of accurate 3-D geometric/photometric models for various 3-D vision systems, and graphical models for realistic rendering of 3-D scenes via computer graphics

Fourier Transform to Detect Pine Seedlings in a Digital Image

Each year, u.s. forest nurseries produce approximately 200 million pine seedlings. Forest companies depend on an adequate number of seedlings in order to replant timber land. To monitor the progress of seedlings, nurseries periodically conduct an inventory. The procedure is performed manually and is based on a statistical estimate. The process is slow, tedious, and imprecise. Automating the inventory procedure is subject of this dissertation. A digital image processing technique to visually count pine seedlings is investigated. The technique is based on a proposed imaging system which resides on a platform behind a tractor. As the system passes over the seedling bed, image sensors capture an overhead view of individual seedlings. A computer analyzes the sensor values in order to detect and count individual seedlings. This dissertation is concerned with developing a computer algorithm. Several test images were obtained. Pertinent seedling features in the images are gray level contrast, lines formed by the needles, and circular distribution of the needles. Four different techniques were investigated in an attempt to use these features to detect pine seedlings. These techniques are gray level peaks geometric intersection of needle lines, gray level contour encoding 1 and a technique based on the Fourier transform.Agricultural Engineerin