In automated apple sorting and grading applications, one persistent problem is to identify apple stem-ends and calyxes from defects. To solve this problem, a Three-dimensional (3D) Shape Enhanced Transform (SET) approach is presented. The SET method enhances the apple stem-end/calyx area according to the 3D surface gradient difference between the stem-end/calyx and the apple surface region. In addition, the proposed SET approach does not depend on the location of the stem-end/calyx on the apple surface, making it more suitable for apples orientated randomly. SET is also an automated and robust method, which detects the apple stem-end/calyx without any human intervention, and performs well with noise and even incomplete image data. A total of 232 Golden Delicious apple images were tested, and an overall detection rate of 93.97% was achieved.
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.
Machine vision methods are widely used in apple defect detection and quality grading applications. Currently, 2D near-infrared (NIR) imaging of apples is often used to detect apple defects because the image intensity of defects is different from normal apple parts. However, a drawback of this method is that the apple calyx also exhibits similar image intensity to the apple defects. Since an apple calyx often appears in the NIR image, the false alarm rate is high with the 2D NIR imaging method.
In this paper, a 2D NIR imaging method is extended to a 3D reconstruction so that the apple calyx can be differentiated from apple defects according to their different 3D depth information. The Lambertian model is used to evaluate the reflectance map of the apple surface, and then Pentland's Shape-From-Shading (SFS) method is applied to reconstruct the 3D surface information of the apple based on Fast Fourier Transform (FFT). Pentland's method is directly derived from human perception properties, making it close to the way human eyes recover 3D information from a 2D scene. In addition, the FFT reduces the computation time significantly. The reconstructed 3D apple surface maps are shown in the results, and different depths of apple calyx and defects are obtained correctly.