Browsing by Author "Mery Quiroz, Domingo Arturo"
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- ItemA fast and self-adaptive on-line learning detection system(2018) Prasad, M.; Zheng, D.-R.; Mery Quiroz, Domingo Arturo; Puthal, D.; Sundaram, S.; Lin, C.-T.
- ItemA Logarithmic X-Ray Imaging Model for Baggage Inspection: Simulation and Object Detection(IEEE, 2017) Mery Quiroz, Domingo Arturo; A. K. KatsaggelosIn the last years, many computer vision algorithms have been developed for X-ray testing tasks. Some of them deal with baggage inspection, in which the aim is to detect automatically target objects. The progress in automated baggage inspection, however, is modest and very limited compared to what is needed because X-ray screening systems are still being manipulated by human inspectors. In this work, we present an X-ray imaging model that can separate foreground from background in baggage screening. The model can be used in two main tasks: i) Simulation of new X-ray images, where simulated images can be used in training programs for human inspectors, or can be used to enhance datasets for computer vision algorithms. ii) Detection of (threat) objects, where new algorithms can be employed to perform automated baggage inspection or to aid an user in the inspection task showing potential threats. In our model, rather than a multiplication of foreground and background, that is typically used in X-ray imaging, we propose the addition of logarithmic images. This allows the use of linear strategies to superimpose images of threat objects onto X-ray images and the use of sparse representations in order to segment target objects. In our experiments, we simulate new X-ray images of handguns, shuriken and razor blades, in which it is impossible to distinguish simulated and real X-ray images. In addition, we show in our experiments the effective detection of shuriken, razor blades and handguns using the proposed algorithm outperforming some alternative state-of- the-art techniques.
- ItemA probabilistic iterative local search algorithm applied to full model selection(2011) Cortazar, E.; Mery Quiroz, Domingo Arturo
- ItemA robust algorithm for nondestructive testing of weld seams(2007) Carrasco, M.A.; Mery Quiroz, Domingo Arturo
- ItemA Robust Face Recognition System for One Sample Problem(2019) Meena, M.S.; Singh, P.; Rana, A.; Mery Quiroz, Domingo Arturo; Prasad, M.
- ItemAccuracy estimation of detection of casting defects in X-ray images using some statistical techniques(2007) Da Silva, R.R.; Mery Quiroz, Domingo Arturo
- ItemAction Recognition in Video Using Sparse Coding and Relative Features(2016) Alfaro, A.; Mery Quiroz, Domingo Arturo; Soto, A.
- ItemAdvances on automated multiple view inspection(2006) Mery Quiroz, Domingo Arturo; Carrasco, M.
- ItemAluminum Casting Inspection using Deep Object Detection Methods and Simulated Ellipsoidal Defects(2021) Mery Quiroz, Domingo Arturo
- ItemAn Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses(2021) Miguel Carrasco; Mery Quiroz, Domingo Arturo; Andrés Concha; Ramiro Velázquez; Roberto De Fazio; Paolo Visconti
- ItemAutomated Design of a Computer Vision System for Visual Food Quality Evaluation(2013) Mery Quiroz, Domingo Arturo; Pedreschi, Franco; Soto, Alvaro
- ItemAutomated detection in complex objects using a tracking algorithm in multiple X-ray views(IEEE, 2011) Mery Quiroz, Domingo ArturoWe propose a new methodology to detect parts of interest inside of complex objects using multiple X-ray views. Our method consists of two steps: `structure estimation', to obtain a geometric model of the multiple views from the object itself, and `parts detection', to detect the object parts of interest. The geometric model is estimated by a bundle adjustment algorithm on stable SIFT keypoints across multiple views that are not necessary sorted. The detection of the object parts of interest is performed by an ad-hoc segmentation algorithm (application dependent) followed by a tracking algorithm based on geometric and appearance constraints. It is not required that the object parts have to be segmented in all views. Additionally, it is allowed to obtain false detections in this step. The tracking is used to eliminate the false detections without discriminating the object parts of interest. In order to illustrate the effectiveness of the proposed method, several applications - like detection of pen tips, razor blades and pins in pencil cases and detection of flaws in aluminum die castings used in the au-tomative industry - are shown yielding a true positive rate of 94.3% and a false positive rate of 5.6% in 18 sequences from 4 to 8 views.
- ItemAutomated 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 MiguelX-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.
- ItemAutomated multiple view inspection based on uncalibrated image sequences(2005) Mery Quiroz, Domingo Arturo; Carrasco, M; Kalviainen, H; Parkkinen, J; Kaarna, A
- ItemAutomated multiple view inspection of metal castings(2007) Mery Quiroz, Domingo Arturo; Carrasco, M.
- ItemAutomated object recognition in baggage screening using multiple X-ray views(2013) Mery Quiroz, Domingo Arturo; Riffo, V.
- ItemAutomated Threat Objects Detection with Synthetic Data for Real-Time X-ray Baggage Inspection(2021) Chaturvedi, Kunal; Braytee, Ali; Vishwakarma, Dinesh Kumar; Saqib, Muhammad; Mery Quiroz, Domingo Arturo; Prasad, Mukesh
- ItemAutomated visual inspection of glass bottles using adapted median filtering(2004) Mery Quiroz, Domingo Arturo; Medina, O
- ItemAutomated x-ray object recognition using an efficient search algorithm in multiple views(2013) Mery Quiroz, Domingo Arturo; Riffo, V.; Zuccar, I.; Pieringer, C.
- ItemAutomatic defect recognition in x-ray testing using computer vision(2017) Mery Quiroz, Domingo Arturo; Arteta, C.