Method of extracting raster line in structural optical through colour coding and comparative method

Abstract

The invention relates to a computer technology, and is a method to pick up the raster line of the structure light using color code and comparative method. The processes are: a) projecting color raster of three codes to the scene orderly, and respectively shooting picture of the scene irradiated by the three raster; b) comparing the size of the color heft of every corresponding pels points of the three pictures, pick up and encode each raster bar; c) according to the number of the pels containing in the pels bar, eliminating the noise point; d) according to the adjacent relation of the pels bar, picking up and encoding the raster lines; e) according to the length of the connective raster lines, eliminating the noise point; f) moving paralleled downward the raster bars of the three kinds of raster with equal distance; g) according to the new picture shot extract and encoding the new raster lines; h) comparing the raster lines and eliminating the repeated parts; j) if need to pick up more raster lines, and repeating the processes.

Description

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AI Technical Summary

Benefits of technology

This technology allows an object's colors or patterns on both sides of its line to change slightly when viewed from one side towards another without affecting how it looks like they were before being captured with regular projection equipment such as LCD displays (Liquid Crystal Display). It does this through special techniques that remove false gratings caused during normal viewing conditions but still allow us to capture detailed details about these scenes quickly enough at all times.

Problems solved by technology

Technological Problem: Current methodologies for reconstructing objects by combining multiple layers with varying levels of detail (LOD), such as adding shading effects during photography). However, there exist challenges related to accurate detection and identification of specific features within each layer's boundaries that help improve the quality of these models. Additionally, current techniques involve capturing data through an unnatural environment where noise interference occurs frequently due to factors like uneven illumination caused by external sources or background clutter.

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