Real-Time Quality Control of Heat Sealed Bottles Using Thermal Images and Artificial Neural Network

Cruz, Samuel; Paulino, António; Duraes, Joao; Mendes, Mateus

doi:10.3390/jimaging7020024

Cited by 12 publications

(10 citation statements)

References 15 publications

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“…A multilayer weighted fuzzy classifier that fuses match scores via selection priority has been developed to fully leverage the potential of two sets of characteristics. Multiple features for biometric recognition systems were proposed by researchers in [18]. Signature feature extraction, which takes in data from twelve various angles, was proposed using Rotated Complex Wavelet Filters (RCWF) and Dual Tree Complex Wavelet transform (DTCWT).…”

Section: Previous Workmentioning

confidence: 99%

Efficient Handwritten Signatures Identification using Machine Learning

Alharbi¹

2023

IJACSA

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Any agreement or contract between two or more parties requires at least one party to employ a signature as evidence of the other parties' identities and as a means of establishing the parties' intent. As a result, more people are curious about Signature Recognition than other biometric methods like fingerprint scanning. Utilizing both Fourier Descriptors and histogram of oriented gradients (HOG) features, this paper presents an efficient algorithms for signature recognition. The use of Local binary patterns (LBP) features in a signature verification technique has been proposed. Using morphological techniques, the signature is encapsulated within a curve that is both symmetrical and a good match. Measured by the frequency with which incorrect patterns are confirmed by a given system, false acceptance rate (FAR) provides an indication of the effectiveness and precision of the proposed system. Using a local dataset of 60 test signature patterns, this investigation found that 10% were incorrectly accepted for FAR of 0.169. Experiments are conducted on signature photos from a local dataset. Verification of signatures has previously made use of KNN classifier. KNN classifier produced higher FARs and recognition accuracies than prior techniques.

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Section: Previous Workmentioning

confidence: 99%

Efficient Handwritten Signatures Identification using Machine Learning

Alharbi¹

2023

IJACSA

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“…In industry, computer vision is used as a tool to support quality control, object counting, identification of worn equipment, and more [59,60]. In routine inspections, real-time defect recognition and feedback to inspectors is made possible by simplified image capture devices [61].…”

Section: Object Recognition and Trackingmentioning

confidence: 99%

Augmented Reality in Maintenance—History and Perspectives

2023

Self Cite

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Augmented Reality (AR) is a technology that allows virtual elements to be superimposed over images of real contexts, whether these are text elements, graphics, or other types of objects. Smart AR glasses are increasingly optimized, and modern ones have features such as Global Positioning System (GPS), a microphone, and gesture recognition, among others. These devices allow users to have their hands free to perform tasks while they receive instructions in real time through the glasses. This allows maintenance professionals to carry out interventions more efficiently and in a shorter time than would be necessary without the support of this technology. In the present work, a timeline of important achievements is established, including important findings in object recognition, real-time operation. and integration of technologies for shop floor use. Perspectives on future research and related recommendations are proposed as well.

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“…When they are combined with the machine learning algorithms for an accurate classification, high‐resolution thermography images can be a promising technique for accurate and inline seal integrity assessment 52 …”

Section: Nondestructive Techniques For Flaw Detection In Heat‐sealed ...mentioning

confidence: 99%

Heat‐sealing integrity assessment through nondestructive evaluation techniques

et al. 2022

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Sealing is a critical step in different packaging industries. In heat sealing as one of the major types of sealing processes, two layers of materials (typically thermoplastics) are joined together through the application of heat and pressure load for a certain sealing time. Due to the capabilities of the heat‐sealing process to seal different kinds and sizes of packages, it has a wide range of applications in many different industries such as the packaging of food and medical products. The quality and integrity of sealings produced by these techniques are crucial for the quality and safety of the contained materials. In this paper, different types and processes of sealing are discussed. Due to the variations in shape, size, and complexity of the sealing region, quality testing and inspection of these products is a challenging task. Nondestructive testing and evaluation (NDTE) techniques can be promising tools to overcome these challenges. Furthermore, a detailed study of various types of potential defects and their generation mechanism in heat sealing is presented. Then, various types of NDTE techniques that can be potentially used for the inspection of heat‐sealing regions and their working principles, as well as their strength and weakness in sealing inspection, are debated. Finally, several cases of sealing inspection and relevant NDTE techniques that are developed as standard procedures are presented. The discussions in this study provide useful information for the selection and implementation of appropriate quality inspection and inline quality monitoring of sealing parts and processes.

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Real-Time Quality Control of Heat Sealed Bottles Using Thermal Images and Artificial Neural Network

Cited by 12 publications

References 15 publications

Efficient Handwritten Signatures Identification using Machine Learning

Efficient Handwritten Signatures Identification using Machine Learning

Augmented Reality in Maintenance—History and Perspectives

Heat‐sealing integrity assessment through nondestructive evaluation techniques

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