An Automatic Grading System for Panels Surfaces Using Artificial Vision

Abstract

This work describes an automatic grading system using artificial vision to improve the quality of wood panels surfaces. The objective is to control stains on the surface. Artificial Vision techniques like Thresholding and Transformed Watershed methods are applied. Defects quantitative measures found on the surface are also presented, in particular quantity, area, intensity and distribution.

Applications of Image Processing for Grading Agriculture products

“…The techniques used in image analysis include image acquisition, image pre-processing and image interpretation, leading to quantification and classification of images and objects of interest within images [5]. The effectiveness of computer vision techniques has been investigated for a large range of agricultural produce like: eggplant grading [6], crack detection in corn shell [7], weed sensing [8], in cotton processing [9], lentils grading [10], cereal grain classification [11], leaf classification [12], fish grading [13], eggshell defect detection [14] and wood panel surface grading [15].…”

Computer vision based date fruit grading system: Design and implementation

“…Therefore, most of the research works are focused on building dedicated systems that can sort a particular fruit or product type. Although, there are efforts to built general fruit sorting and classification systems (Kondo, 2003;Gay and Berruto, 2002) but most of the systems are dedicated systems like the system that can sort tomatoes (Laykin et al, 2002;Polder et al, 2000), apples (Unay andGosselin, 2002, 2005a,b;Mehl et al, 2004;Li and Heinemann, 2007), citrus fruit (Aguilera et al, 2006;Regunathan and Suk Lee, 2005;Calpe et al, 1996), pepper berries (Abdesselam and Abdullah, 2000) and eggplant (Saito et al, 2003). Dedicated quality control vision based systems are also being built for other agricultural products like cereal grain (Choudhary et al, 2008), lentils (Shahin and Symons, 2001), corn products (Gunasekaran et al, 1987), tree leaves (Oskar, 2001), eggshell (Garcia et al, 2000 and fish grading (Hu et al, 1998).…”

“…Dedicated quality control vision based systems are also being built for other agricultural products like cereal grain (Choudhary et al, 2008), lentils (Shahin and Symons, 2001), corn products (Gunasekaran et al, 1987), tree leaves (Oskar, 2001), eggshell (Garcia et al, 2000 and fish grading (Hu et al, 1998). In addition to these applications, the systems are reported for wood processing like panel surface inspection (Aguilera et al, 2006), weed sensing (Polder et al, 2000) and trash measurement (Siddaiah et al, 2002).…”

“…The effectiveness of computer vision techniques has been investigated for a large range of agricultural produce like: eggplant grading (Saito et al, 2003), crack detection in corn shell (Gunasekaran et al, 1987), weed sensing (Steward and Tian, 1998), in cotton processing (Siddaiah et al, 2002), lentils grading (Shahin and Symons, 2001), cereal grain classification (Paliwal et al, 2001), leaf classification (Oskar, 2001), fish grading (Hu et al, 1998), eggshell defect detection (Garcı´a-Alegre et al, 1998) and wood panel surface grading (Aguilera et al, 2006).…”

Surface Roughness Image Analysis using Quasi-Fractal Characteristics and Fuzzy Clustering Methods

“…During experiments however, we observed that this correlation is more complex and depends in a very high degree on the illumination conditions of the probe. Usual image processing phases, of non-object representation images are presented in [14].…”