4,597 research outputs found
A comparative analysis of attribute reduction algorithms applied to wet-blue leather defects classification.
This paper presents an attribute reduction comparative study on four linear discriminant analysis techniques: FisherFace, CLDA, DLDA and YLDA. The attribute reduction has been applied to the problem of leather defect c1assification using four different c1assifiers: C4.5, KNN, Naive Bayes and Support Veetor Machines. Results and analyses on the performance of correct c1assification rates as the number of attributes were reduced are reported.DisponĂvel em: http://www.matmidia.mat.puc-rio.br/sibgrapi2009/media/posters/59602.pdf. Acesso em 13 de novembro de 2009
Deteção automåtica de defeitos em couro
Dissertação de mestrado em Informatics EngineeringEsta dissertação desenvolve-se em torno do problema da deteção de defeitos em couro. A deteção
de defeitos em couro Ă© um problema tradicionalmente resolvido manualmente, usando avaliadores ex perientes na inspeção do couro. No entanto, como esta tarefa Ă© lenta e suscetĂvel ao erro humano, ao
longo dos Ășltimos 20 anos tem-se procurado soluçÔes que automatizem a tarefa. Assim, surgiram vĂĄrias
soluçÔes capazes de resolver o problema eficazmente utilizando técnicas de Machine Learning e Visão
por Computador. No entanto, todas elas requerem um conjunto de dados de grande dimensĂŁo anotado e
balanceado entre as vårias categorias. Assim, esta dissertação pretende automatizar o processo tradicio nal, usando técnicas de Machine Learning, mas sem recorrer a datasets anotados de grandes dimensÔes.
Para tal, são exploradas técnicas de Novelty Detection, as quais permitem resolver a tarefa de inspeção
de defeitos utilizando um conjunto de dados não supervsionado, pequeno e não balanceado. Nesta dis sertação foram analisadas e testadas as seguintes técnicas de novelty detection: MSE Autoencoder, SSIM
Autoencoder, CFLOW, STFPM, Reverse, and DRAEM. Estas técnicas foram treinadas e testadas com dois
conjuntos de dados diferentes: MVTEC e Neadvance. As tĂ©cnicas analisadas detectam e localizam a mai oria dos defeitos das imagens do MVTEC. Contudo, tĂȘm dificuldades em detetar os defeitos das imagens
do dataset da Neadvance. Com base nos resultados obtidos, Ă© proposta a melhor metodologia a usar
para trĂȘs diferentes cenĂĄrios. No caso do poder computacional ser baixo, SSIM Autoencoder deve ser a
técnica usada. No caso onde hå poder computational suficiente e os exemplos a analisar são de uma só
cor, DRAEM deve ser a técnica escolhida. Em qualquer outro caso, o STFPM deve ser a opção escolhida.This dissertation develops around the leather defects detection problem. The leather defects detec tion problem is traditionally manually solved, using experient assorters in the leather inspection. However,
as this task is slow and prone to human error, over the last 20 years the searching for solutions that
automatize this task has continued. In this way, several solutions capable to solve the problem effi ciently emerged using Machine Learning and Computer Vision techniques. Nonetheless, they all require
a high-dimension dataset labeled and balanced between all categories. Thus, this dissertation pretends
to automatize the traditional process, using the Machine Learning techniques without requiring a large
dimensions labelled dataset. To this end, there will be explored Novelty Detection techniques, that in tend to solve the leather inspection task using an unsupervised small and non-balanced dataset. This
dissertation analyzed and tested the following Novelty Detection techniques: MSE Autoencoder, SSIM
Autoencoder, CFLOW, STFPM, Reverse, and DRAEM. These techniques are trained and tested in two
distinct datasets: MVTEC and Neadvance. The analyzed techniques detect and localize most MVTEC
defects. However, they have difficulties in defect detection on Neadvance samples. Based on the ob tained results, it is proposed the best methodology to use for three distinct scenarios. In the case where
the computational power available is low, SSIM Autoencoder should be the technique to use. In the
case where there is enough computational power and the samples to inspect have the same color,
DRAEM should be the chosen technique. In any other case, the STFPM should be the chosen option
Automated Quality Control in Manufacturing Production Lines: A Robust Technique to Perform Product Quality Inspection
Quality control (QC) in manufacturing processes is critical to ensuring consumers receive products with proper functionality and reliability. Faulty products can lead to additional costs for the manufacturer and damage trust in a brand. A growing trend in QC is the use of machine vision (MV) systems because of their noncontact inspection, high repeatability, and efficiency. This thesis presents a robust MV system developed to perform comparative dimensional inspection on diversely shaped samples. Perimeter, area, rectangularity, and circularity are determined in the dimensional inspection algorithm for a base item and test items. A score determined with the four obtained parameter values provides the likeness between the base item and a test item. Additionally, a surface defect inspection is offered capable of identifying scratches, dents, and markings. The dimensional and surface inspections are used in a QC industrial case study. The case study examines the existing QC system for an electric motor manufacturer and proposes the developed QC system to increase product inspection count and efficiency while maintaining accuracy and reliability. Finally, the QC system is integrated in a simulated product inspection line consisting of a robotic arm and conveyor belts. The simulated product inspection line could identify the correct defect in all tested items and demonstrated the systemâs automation capabilities
Prices, planners, and producers : an agency problem in Soviet industry, 1928-1950
Soviet planners developed the âunchanged prices of 1926/27â to facilitate the solution of an
agency problem -- the regulation of self-interested producers as they worked to fulfil plans for
heterogeneous products denominated in rubles. The system limited but did not eliminate
producersâ opportunistic behavior, which took the form of inflating the plan prices of new
products. Through the 1930s and 1940s the âunchangedâ prices proved resistant to reform,
and following their abolition in 1950 the system was soon afterwards reinstated with a new
base year. The history of the âunchangedâ prices illustrates the limits of command
On Deep Machine Learning Methods for Anomaly Detection within Computer Vision
This thesis concerns deep learning approaches for anomaly detection in images. Anomaly detection addresses how to find any kind of pattern that differs from the regularities found in normal data and is receiving increasingly more attention in deep learning research. This is due in part to its wide set of potential applications ranging from automated CCTV surveillance to quality control across a range of industries. We introduce three original methods for anomaly detection applicable to two specific deployment scenarios. In the first, we detect anomalous activity in potentially crowded scenes through imagery captured via CCTV or other video recording devices. In the second, we segment defects in textures and demonstrate use cases representative of automated quality inspection on industrial production lines. In the context of detecting anomalous activity in scenes, we take an existing state-of-the-art method and introduce several enhancements including the use of a region proposal network for region extraction and a more information-preserving feature preprocessing strategy. This results in a simpler method that is significantly faster and suitable for real-time application. In addition, the increased efficiency facilitates building higher-dimensional models capable of improved anomaly detection performance, which we demonstrate on the pedestrian-based UCSD Ped2 dataset. In the context of texture defect detection, we introduce a method based on the idea of texture restoration that surpasses all state-of-the-art methods on the texture classes of the challenging MVTecAD dataset. In the same context, we additionally introduce a method that utilises transformer networks for future pixel and feature prediction. This novel method is able to perform competitive anomaly detection on most of the challenging MVTecAD dataset texture classes and illustrates both the promise and limitations of state-of-the-art deep learning transformers for the task of texture anomaly detection
Optimized Fuzzy C-means Clustering Methods for Defect Detection on Leather Surface
In this paper, captured images are segmented for the defective part, that is used for the further process of grading the quality of the products using automated inspection systems employed in industries such as leather, fabrics, textiles, tiles... etc.. These industries are the greatest conventional industries that need automatic detection systems as a basic part in diminishing investigation time and expanding production rate. Initially in this work, the input image is wet blue leather fed into a contrast enhancement process that improves the visibility of the image features. This contrast-enhanced image is employed with segmentation process that utilizes Fuzzy C-means algorithm (FCM) technique. This paper proposes two different optimization techniques, Grey Wolf Optimization (GWO) & Monarch Butterfly Optimization (MBO) for executing centroid optimization in FCM and results are compared with Modified Region Growing with GWO of leather segmentation method. The results exemplify that incorporation of optimization technique with FCM has a quite evident impact on segmentation accuracy of 96.90% over context techniques
Optimized Fuzzy C-means Clustering Methods for Defect Detection on Leather Surface
833-836In this paper, captured images are segmented for the defective part, that is used for the further process of grading the quality of the products using automated inspection systems employed in industries such as leather, fabrics, textiles, tiles... etc.. These industries are the greatest conventional industries that need automatic detection systems as a basic part in diminishing investigation time and expanding production rate. Initially in this work, the input image is wet blue leather fed into a contrast enhancement process that improves the visibility of the image features. This contrast-enhanced image is employed with segmentation process that utilizes Fuzzy C-means algorithm (FCM) technique. This paper proposes two different optimization techniques, Grey Wolf Optimization (GWO) & Monarch Butterfly Optimization (MBO) for executing centroid optimization in FCM and results are compared with Modified Region Growing with GWO of leather segmentation method. The results exemplify that incorporation of optimization technique with FCM has a quite evident impact on segmentation accuracy of 96.90% over context techniques
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