The classification of walnuts shell and meat has a potential application in industry walnuts processing. A dark-field illumination method is proposed for the inspection of walnuts. Experiments show that the dark-field illuminated images of walnut shell and meat have distinct text patterns due to the differences in the light transmittance property of each. A number of rotation invariant feature analysis methods are used to characterize and discriminate the unique texture patterns. These methods include local binary pattern operator, wavelet analysis, circular Gabor filters, circularly symmetric gray level co-occurrence matrix and the histogram-related features. A recursive feature elimination method (SVM-RFE), is used to remove uncorrelated and redundant features and to train the SVM classifier at the same time. Experiments show that, by using only the top six ranked features, an average classification accuracy of 99.2% can be achieved.
A combined laser 3D and X-ray imaging system is newly developed for food safety inspection. Two kinds of cameras are used in this system. One is CCD camera which is used to provide an accurate thickness profile of the object and the other is X-ray line-scan camera which is to get the high resolution X-ray image. A unique three-step calibration procedure is proposed to calibrate these two kinds of cameras. Firstly, the CCD camera is calibrated to link the CCD pixels to points in 3D world coordinate system. Secondly, the X-ray line-scan camera is calibrated to link points in 3D world coordinate system to the X-ray line sensors. The X-ray fan beam effect is also compensated in this stage. Finally, direct mapping from CCD pixel to X-ray line sensor is realized using the information from the first two calibration steps. Based on the calibration results, look-up tables are also generated to replace the expensive runtime computation with simpler lookup operation. Results show that high accuracy has been achieved with the whole system calibration.
A laser range imaging system based on the triangulation method was designed and implemented for online high-resolution thickness calculation of poultry fillets. A laser pattern was projected onto the surface of the chicken fillet for calculation of the thickness of the meat. Because chicken fillets are relatively loosely-structured material, a laser light easily penetrates the meat, and scattering occurs both at and under the surface. When laser light is scattered under the surface it is reflected back and further blurs the laser line sharpness. To accurately calculate the thickness of the object, the light transportation has to be considered. In the system, the Bidirectional Reflectance Distribution Function (BSSRDF) was used to model the light transportation and the light pattern reflected into the cameras. BSSRDF gives the reflectance of a target as a function of illumination geometry and viewing geometry. Based on this function, an empirical method has been developed and it has been proven that this method can be used to accurately calculate the thickness of the object from a scattered laser profile. The laser range system is designed as a sub-system that complements the X-ray bone inspection system for non-invasive detection of hazardous materials in boneless poultry meat with irregular thickness.
Orthogonal moments have been successfully used in the field of pattern recognition and image analysis. However, due to the complexity in their calculation, the problem of fast computation of orthogonal moments has not till now been well solved. This paper presents two fast and efficient algorithms for the two dimensional (2D) Legendre moment computation. They are based on a block representation of the image and respectively use cumulative and integral methods. Results on 2D binary images show that these algorithms can decrease the computational complexity in a very important way.