不同照明条件下菌菇罐头中发丝的准确识别

为了实现菌菇罐头工业化生产过程中发丝异物的自动识别,该文提出了一种基于发丝中轴特征的图像识别方法。由于菌菇罐头中含水且表面不平整,不同照明条件对发丝识别效果有较大影响。因为普通照明条件下反光与阴影干扰远多于理想照明条件下反光与阴影干扰,所以普通照明条件下菌菇罐头中发丝的识别难度更大。该文用带Gaussian滤波的Hessian矩阵法提取发丝中轴特征,通过取阈值、非局部极大值抑制、8邻域连接得到中轴线,结合平行边缘特征,剔除残留的菌菇阴影中轴线,识别出发丝中轴线。试验结果表明:普通照明条件下,该方法的识别性能远好于平行边缘法、正交旋转滤波器法、四阶优化旋转滤波器法和Gaussian核平移线性组合法;对2种照明条件下采集的常见发丝(含黑色、直径70μm)的原始图片和特种发丝(含黄色、直径50μm)的原始图片,该方法都能够准确识别出发丝;尤其对较难识别的普通照明条件下复杂形状的常见发丝和特种发丝,该方法识别结果的准确率分别是0.98637和0.97007。该方法可用于菌菇罐头生产企业的发丝图像识别。

Accurate recognition of hairs in canned mushroom under different kinds of lighting conditions

Abstract: In order to achieve automatic recognition of hair impurities in the canned mushroom production process, an image recognition method based on the hair centerline feature is proposed in this paper. The proposed approach recognizes the hair centerline based on the Hessian matrices with Gaussian filtering. Under ideal lighting conditions and general lighting conditions, Hessian matrices are obtained after the original image is filtered 6 times using one-dimensional Gaussian derivative filters. The centerline pixels of the hairs and the shadows are obtained by calculating the eigenvalues of every pixel's Hessian matrix. After employing non-maximum suppression, 8-neighbour linkage, and parallel edge analysis, the hairs' centerlines are recognized, and the confusing shadow centerlines are eliminated at the same time. Lighting conditions have a great impact on recognizing hairs in canned mushrooms because (a) the canned mushroom contains water and (b) its surface is not flat. Ideal lighting can eliminate the reflection caused by water and reduce the shadow caused by the shape of the mushroom. Under general lighting conditions, reflection and shadow interference are severe. Therefore it is very difficult to recognize hairs on canned mushroom under general lighting conditions. Unfortunately, general lighting conditions are most widely used in industrial applications. In our experiment, the ideal lighting condition is realized using a HDL-160W-type diffuse light source. The general lighting source consists of two 36W/840 fluorescent strip lamps, which form low-angle, fluorescent strip-light illumination. The hardware of the experimental system mainly consists of a computer, a digital monochrome CCD camera, and a 1394 interface card. The monochrome CCD camera is HD-SV2000FM with a resolution of 2 million pixels (1628 pixel?1236 pixel). The lens is a 12-36 mm, 1:2.8, 2/3' Computar lens. The vertical distance between the lens and the subject is 12 cm and the FOV is 5.50 cm×4.15 cm. Under 2 different lighting conditions, we extract 4 images with representative hair shapes to be the original images in this paper, viz. common hair (black, 70 um in diameter) under ideal lighting conditions, common hair under general lighting conditions, special hair (yellow, 50 um in diameter) under ideal lighting conditions, and special hair under general lighting conditions. There are hairs of simple shape and complex shape in each original image. Simple shapes consist of a straight line and a circular curve. Complex shapes consist of two cross laps and two cross hyperbolic characteristics. Under the ideal lighting condition and the general lighting condition, this paper compares the hair centerline feature extraction effects of 5 methods. The recognition results of the 5 methods are also compared. The 5 methods are parallel edges, steerable quadrature filter pair, optimized 4-order steerable filter, linear combination of shifted Gaussian kernels, and the Hessian matrices with Gaussian filtering proposed in this paper. From the feature extraction effects and the recognition results, it can be concluded that under general lighting conditions, the former 4 methods are infeasible and only the proposed method is feasible. Under general lighting conditions, using different characteristic thresholds, we obtain the receiver operating characteristic (ROC) curves for each of the five methods' hair-recognition results. By comparing the five methods' ROC curves, it can be easily seen that: 1) our method far outperforms the other four methods; 2) using the same method, common hairs are easier to recognize, and special hairs are difficult to recognize. Under general lighting conditions, the accuracy rates of our recognition results for complex-shaped common hairs and complex-shaped special hairs are 0.98673 and 0.97007 respectively. These results show that the proposed method also performs well in recognizing complex special hairs under poor lighting conditions. The proposed approach is able to recognize accurately hairs of various types and various shapes in canned mushrooms in either ideal or general lighting conditions. This suggests that the proposed approach can be applied in automatic impurity image recognition for industrial canned mushroom production.