Ranging the crates of grapes using a robust quality index is a major tool for operators during the Champagne grape harvest. We propose building such an index by processing RGB images of crates of grapes. Each image is segmented into six classes such as healthy grape, crate, diseases (grey rot, powdery mildew, conidia), green elements (stalk, leaf, unripe healthy grape), shadow, dry elements (dry leaf, dry grape, wood) and the index of quality reflects the proportion of healthy part inside the crate. As the main pretreatment, the segmentation must be carefully performed, and a random forest-based solution for each variety of grape is proposed here whose training is done on hand-tagged pixels.
In recent years, driven by new standards and image brand, product quality has become highly critical in the food industry. Particularly concerning the beverage industry, the potential presence of very small foreign objects, especially glass fragments, has to be checked. The visible optical domain offers an advantageous alternative solution to the expensive and intrusive x-ray systems. However, such a solution requires the development of robust processing algorithms satisfying real-time industrial constraints, which is a very challenging task. A detection method based on kurtosis and local information that emphasizes connected components is proposed, as well as two robust criteria that give a greater robustness to the detector. A specific kurtosis is calculated using an inclined frame definition that is based on both time and spatial dimensions. Such kurtosis increases the detection capacity of the moving objects and allows the estimation of their direction without adding more costs. The kurtosis being very sensitive to outliers, noisy objects that are very small are filtered, allowing efficient detection of foreign objects, such as glass fragments, as long as they are bigger than the noisy objects. In case of big foreign objects, the low sensitivity of the kurtosis is compensated for by the large detected surface. The proposed detection method can be directly applied on video sequences acquired by a standard RGB camera in industrial environments. The experimental results show the effectiveness of the method in detecting real random foreign objects, regardless of their size or their transparency, in various semiopaque bottles.
In recent years, driven by new standards and brand image, performant quality control has become highly important in the beverage industry. In the absence of reliable and affordable methods for detecting foreign bodies in semi-opaque bottles, many manufacturers still opt for human visual inspection on the production line. Advanced technological systems such as X-ray based systems started to be used but such technologies are expensive and intrusive. The visible domain offers an alternative and advantageous solution. In this work a recurrent boosting method is proposed and extended to dynamic information to detect small transparent objects. The proposed method works on images video acquired by an RGB camera, it considers an ensemble classification strategies that includes temporal and dynamical features in addition to the classical spatial image features such as texture and color. The experimental results show a better performance with time series and dynamic information, the effectiveness of the method is demonstrated in detection of random foreign objects regardless their size or transparency in different semi-opaque bottles.
In recent years, driven by new standards and brand image, product quality has become highly critical in the beverage industry. Product quality does not concern the beverage in and of itself only, but also the extrinsic aspects, particularly the presence of very small foreign bodies. Without reliable and affordable methods for detecting such foreign bodies in semiopaque bottles, many manufacturers still opt for human visual inspection on the production line. Optical advanced technological systems such as X-ray based systems started to be used but such technologies are expensive and intrusive. Also the visible optical domain offers an alternative and an advantageous solution. Thus a novel detection method based on two robust criteria such as kurtosis and connected components is proposed to give a further validation of the detection output. The method applies on static images acquired by a RGB camera and the experimental results show its effectiveness in detecting random foreign objects regardless their size or their transparency in various semi opaque bottles.
Image registration finds its applications in various fields like environmental monitoring, change detection, automatic inspection, medical imaging and remote sensing. Industrial real time systems need a fast and accurate registration to satisfy real time constraints which are very challenging. In this work a new rigid monomodal registration method is proposed, the method exploits the symmetrical property of objects to speed up registration and to achieve robustness against changing edges and less structural information. Tests on images of a large semi-opaque containers data set show its effectiveness compared to classical methods.
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