A Graph-based Geometric Approach to Contour Extraction from Noisy Binary Images

Abstract

NC simulation emulates the CNC machine tool and the cutter move along its axis. It depicts the material removal to better visualize the machining process. Until now, its main goals are still confined to check an unproved NC program to avoid potential collisions, and to analyze undercuts and overcuts. In this work, by using the simulated output-the in-process model (IPM), and rebuilding a machining feature based model, applications of NC simulation are extended to establish the feedback link from CNC to CAM, which can automatically pass modifications of manufacturing engineers back through the digital chain. In cases when old CAD archives of parts are incompatible with new systems and machines, or are damaged or lost, the original CAD model is neither available nor usable, and the part program is the only data available, rebuilding the feature based model from the NC simulation is necessary. This paper proposes an approach for machining feature recognition from IPM. As per the IPM characteristics, the approach adopts a novel approach of curvature based region segmentation and valuated adjacency graph based feature recognition.

Sampling Shape Contours Using Optimization over a Geometric Graph

“…In our geometric graph, a vertex is indicated by a coordinate and an edge is indicated by a rectangle, which differs from the conventions of graph theory [18]. Representing the contour shape using another geometric graph was demonstrated in [19]. However, the sampling objective in [19] was to extract the closed boundary between an entire object and the background, whereas our objective is to obtain a skeletal configuration of sample points for shape representation.…”

“…Representing the contour shape using another geometric graph was demonstrated in [19]. However, the sampling objective in [19] was to extract the closed boundary between an entire object and the background, whereas our objective is to obtain a skeletal configuration of sample points for shape representation. Using the geometric graph to construct an objective function, we formulate contour sampling as a discrete optimization problem that can be solved using a type of direct search [20].…”

A Survey on The Approaches Used for Detection of Defects on Leather Surfaces Using Image Processing

“…In "A Graph based Geometric Approach to Contour Extraction from Noisy Binary Images" by Amal Dev Parakkat, Philumon Joseph, Jiju Peethambaran, and Ramanathan Muthuganapathy [16], a method to extract the contour of geometric objects in binary digital images using the techniques of computational geometry was presented. An object point set extracted from the image is processed instead of directly dealing with pixels as in traditional contour extraction methods.…”