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 combined X-ray/laser 3D imaging technology has been developed for bone fragment and foreign material detection in boneless poultry products. In this paper, various methods of pattern classification including neural network and statistical approaches are applied to the poultry images obtained by the combined imaging system, and the classification performances are compared and analyzed.
In many hyperspectral applications, it is desirable to extract the texture features for pattern classification. Texture refers to replications, symmetry of certain patterns. In a set of hyperspectral images, the differences of image textures often imply changes in the physical and chemical properties on or underneath the surface. In this paper, we utilize Gabor wavelet based texture analysis method for textural pattern extraction, and combined with integrated PCA-FLD method for hyperspectral band selection in the application of classifying chilling damaged cucumbers from normal ones. The classification performances are compared and analyzed.
An important task in hyperspectral data processing is to reduce the redundancy of the spectral and spatial information without losing any valuable details that are needed for the subsequent detection, discrimination and classification processes. Band selection and combination not only serves as the first step of hyperspectral data processing that leads to a significant decrease in computational complexity in the successive procedures, but also a research tool for determining optimal spectra requirements for different online applications. In order to uniquely characterize the materials of interest, band selection criteria for optimal band was defined. An integrated PCA and Fisher linear discriminant (FLD) method has been developed based on the criteria that used for hyperspectral feature band selection and combination. This method has been compared with other feature extraction and selection methods when applied to detect apple defects, and the performance of each method was evaluated and compared based on the detection results.
Foreign materials such as metal slivers and stones in packed food are listed safety hazards, which could lead to severe health problems. In this paper, a real time X-ray imaging inspection method is investigated for foreign material detection in chili packages. A new image segmentation method combining edge detection and region growing was successfully applied to address the challenges due to the uneven thickness of chili package.
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.
This paper describes a novel approach for detection of foreign materials in deboned poultry patties based on real-time imaging technologies. Uneven thickness of poultry patties could lead to a significant classification error in a typical X-ray imaging system, and we addressed this issue successfully by fusing laser range imaging (3D imaging) into the x-ray inspection system. In order for this synergic technology to work effectively for on-line industrial applications, the vision system should be able to identify various physical contaminations automatically and have viable real-time capabilities. To meet these challenges, a rule-based approach was formulated under a unified framework for detection of diversified subjects, and a multithread scheme was developed for real-time image processing. Algorithms of data fusion, feature extraction and pattern classification of this approach are described in this paper. Detection performance and overall throughput of the system are also discussed.