Browsing by Author "Lillo Valles, Iván Alberto"

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    Automated Detection of Fish Bones in Salmon Fillets Using X-ray Testing
    (IEEE, 2010) Mery Quiroz, Domingo Arturo; Lillo Valles, Iván Alberto; Löbel Díaz, Hans-Albert; Riffo Bouffanais, Vladimir; Soto Arriaza, Álvaro; Cipriano, Aldo; Aguilera Radic, José Miguel
    X-ray testing is playing an increasingly important role in food quality assurance. In the production of fish fillets, however, fish bone detection is performed by human operators using their sense of touch and vision which can lead to misclassification. In countries where fish is often consumed, fish bones are some of the most frequently ingested foreign bodies encountered in foods. Effective detection of fish bones in the quality control process would help avoid this problem. For this reason, we developed an X-ray machine vision approach to automatically detect fish bones in fish fillets. This paper describes our approach and the corresponding validation experiments with salmon fillets. The approach consists of six steps: 1) A digital X-ray image is taken of the fish fillet being tested. 2) The X-ray image is filtered and enhanced to facilitate the detection of fish bones. 3) Potential fish bones in the image are segmented using band pass filtering, thresholding and morphological techniques. 4) Intensity features of the enhanced X-ray image are extracted from small detection windows that are defined in those regions where potential fish bones were segmented. 5) A classifier is used to discriminate between 'bones' and 'no-bones' classes in the detection windows. 6) Finally, fish bones in the X-ray image are isolated using morphological operations applied on the corresponding segments classified as 'bones'. In the experiments we used a high resolution flat panel detector with the capacity to capture up to a 6 million pixel digital X-ray image. In the training phase, we analyzed 20 representative salmon fillets, 7700 detection windows (10×10 pixels) and 279 intensity features. Cross validation yielded a detection performance of 95% using a support vector machine classifier with only 24 selected features. We believe that the proposed approach opens new possibilities in the field of automated visual inspection of salmon and other similar fish.
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    Visual Recognition to Access and Analyze People Density and Flow Patterns in Indoor Environments
    (IEEE, 2015) Ruz Ruz, Cristian Daniel; Pieringer Baeza, Christian Philip; Peralta Marquez, Billy Mark; Lillo Valles, Iván Alberto; Espinace Ronda, Pablo Andrés; Gonzalez, R.; Wendt González, Bruno Nicolás; Mery Quiroz, Domingo Arturo; Soto Arriaza, Álvaro
    This work describes our experience developing a system to access density and flow of people in large indoor spaces using a network of RGB cameras. The proposed system is based on a set of overlapped and calibrated cameras. This facilitates the use of geometric constraints that help to reduce visual ambiguities. These constraints are combined with classifiers based on visual appearance to produce an efficient and robust method to detect and track humans. In this work, we argue that flow and density of people are low level measurements that need to be complemented with suitable analytic tools to bridge semantic gaps and become useful information for a target application. Consequently, we also propose a set of analytic tools that help a human user to effectively take advantage of the measurements provided by the system. Finally, we report results that demonstrate the relevance of the proposed ideas.