Corner Detection and Classification of Simple Objects in Low-Depth Resolution Range Images

This paper presents a method for classifying pen strokes in an on-line sketching system. The method, based on linear least squares fitting to a conic section equation, proposes using the conic equation’s natural classification property to help classify sketch strokes and identify lines, elliptic arcs, and corners composed of two lines with an optional fillet. The hyperbola form of the conic equation is used for corner detection. The proposed method has proven to be fast, suitable for real-time classification, and capable of tolerating noisy input, including cusps and spikes. The classification is obtained in o(n) time in a single path, where n is the number of sampled points. In addition, an improved adaptive method for clustering disconnected end-points is proposed. The notion of in-context analysis is discussed, and examples from a working implementation are given.

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Recognition of Geometric Shapes in Images

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1036.755 ◽

2014 ◽

Vol 1036 ◽

pp. 755-759 ◽

Cited By ~ 1

Author(s):

Viorel Cohal

Keyword(s):

Corner Detection ◽

Transform Method ◽

Geometric Shapes ◽

Chain Codes ◽

Reference Shape ◽

Harris Corner ◽

Unknown Form ◽

Two Phases ◽

Definition Of

This paper proposes finding optimal ways of recognizing geometric shapes (square, circle, rectangle, etc.) of files in different formats. Shapes recognition is a field of artificial intelligence, which includes all representation and decision techniques to automate the process of identifying similarities between objects or phenomena. An application of shapes recognition requires the definition of descriptors and choosing a distance. An application of shapes recognition is done in two phases: learning and recognition. By learning to calculate the set of descriptors of known (reference). In recognition stage, we calculate the same set of descriptors for unknown form and compared with known shape descriptors. The comparison is made through a distance. Recognition is a decision problem if you say unknown form is the same as the reference shape and if you say unknown form is different form the reference. Classification of forms is done in five steps: Reading and image display, image transformation in binary image and get its negative, Discover crossing thresholds between levels of color, Determine the properties of objects and Classification of objects by shape. Are three methods: Chain codes, the conventional Hough transform, method Harris Corner Detection

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