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.
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.