The work aims at developing an image analysis procedure able to distinguish high value fillets of Atlantic cod (Gadus morhua) from those of haddock (Melanogrammus aeglefinus). The images of fresh G. morhua (n=90) and M. aeglefinus (n=91) fillets were collected by a flatbed scanner and processed at different levels. Both untreated and edge-based segmented (Canny algorithm) regions of interest were submitted to surface texture evaluation by Grey Level Co-occurrence Matrix analysis. Twelve surface texture variables selected by Principal Component Analysis or by SELECT algorithm were then used to develop Linear Discriminant Analysis models. An average correct classification rate ranging from 86.05 to 92.31% was obtained in prediction, irrespective the use of raw or segmented images. These findings pave the way for a simple machine vision system to be implemented along fish market chain, in order to provide stakeholders with a simple, rapid and cost-effective system useful in fighting commercial frauds.

该工作旨在开发一种图像分析程序，该程序能够区分大西洋鳕（Gadus morhua）和黑线鳕（Melanogrammus aeglefinus）的高价鱼片。新鲜的G. morhua（n = 90）和M. aeglefinus的图像（n = 91）圆角由平板扫描仪收集，并在不同的水平上进行处理。通过灰度共生矩阵分析，将未处理的和基于边缘的分段（Canny算法）感兴趣区域都提交给表面纹理评估。然后使用主成分分析或SELECT算法选择的十二个表面纹理变量来开发线性判别分析模型。不管使用原始图像还是分割图像，预测中的平均正确分类率在86.05％到92.31％之间。这些发现为在鱼类市场链上实施简单的机器视觉系统铺平了道路，从而为利益相关者提供了一种简单，快速且具有成本效益的系统，可用于打击商业欺诈。