3-D Granulometry Using Image Processing

Labati, Ruggero Donida; Genovese, Angelo; Muñoz, Enrique; Piuri, Vincenzo; Scotti, Fabio

doi:10.1109/tii.2018.2856466

Cited by 10 publications

(4 citation statements)

References 30 publications

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“…Continuously operating light [28,30] or strobe light [23,33] is used; • Sample pellets are illuminated from front [33,34] or from behind (backlight) [26,31,35]; • Pellet monolayer [23,30] or the pellet structure on conveyor or other surfaces is analysed [36][37][38][39][40][41]; and • Three-dimensional images [42,43] or complex data from hybrid (combining several measuring methods) systems are analysed [44].…”

Section: Methodsmentioning

confidence: 99%

Research of the Operator’s Advisory System Based on Fuzzy Logic for Pelletizing Equipment

et al. 2019

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Fertilizer manufacturing in the chemical industry is closely related with agricultural production. More than a half of raw materials for food products are grown by fertilizing plants. The demand of fertilizers has been constantly increasing along the growth of human population. Fertilizer manufacturers face millions of losses each year due to poor quality products. One of the most common reasons is wrong decisions in control of manufacturing processes. Operator’s experience has the highest influence on this. This paper analyzes the pellet measurement data, collected at the fertilizer plant by using indirect measurements. The results of these measurements are used to construct the model of equipment status control, based on the fuzzy logic. The proposed solution allows to respond to changes in production parameters in a 7–10 times faster manner. On average, a manufacturer with production volumes of up to 80 tonnes/hour could have lost about 8400 tonnes/year of high-quality production. The publication seeks symmetry between human and system decision making.

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Section: Methodsmentioning

confidence: 99%

Research of the Operator’s Advisory System Based on Fuzzy Logic for Pelletizing Equipment

et al. 2019

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“…To verify the robustness of the proposed method, two data sets were evaluated. The two large-scale data sets are named RSLI [55] and ALL-IDB [60], respectively. They were captured under various imaging and staining conditions by different medical microscopy institutes.…”

Section: Data Sets and Data Augmentationmentioning

confidence: 99%

White blood cell detection using saliency detection and CenterNet: A two‐stage approach

Zheng

Tang

et al. 2022

Journal of Biophotonics

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White blood cell (WBC) detection plays a vital role in peripheral blood smear analysis. However, cell detection remains a challenging task due to multi‐cell adhesion, different staining and imaging conditions. Owing to the powerful feature extraction capability of deep learning, object detection methods based on convolutional neural networks (CNNs) have been widely applied in medical image analysis. Nevertheless, the CNN training is time‐consuming and inaccuracy, especially for large‐scale blood smear images, where most of the images are background. To address the problem, we propose a two‐stage approach that treats WBC detection as a small salient object detection task. In the first saliency detection stage, we use the Itti's visual attention model to locate the regions of interest (ROIs), based on the proposed adaptive center‐surround difference (ACSD) operator. In the second WBC detection stage, the modified CenterNet model is performed on ROI sub‐images to obtain a more accurate localization and classification result of each WBC. Experimental results showed that our method exceeds the performance of several existing methods on two different data sets, and achieves a state‐of‐the‐art mAP of over 98.8%.

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“…This instrument is able to detect objects within a range of 10 meters and is hard wired to a DC-regulated power supply. Typically, the perfect record for individual faces is conducted from the 3D face images, with a pose variety of ±45°rotation intensive and ±10°rotation in the image plane [44,45]. In this way, a face image can therefore be captured from different surroundings (Figure 4).…”

Section: Face Recognition Setupmentioning

confidence: 99%

Remote Identity Verification Using Gait Analysis and Face Recognition

Zhang

et al. 2020

Wireless Communications and Mobile Computing

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Biometric identification has verified its effectiveness in personal identity verification because of the uniqueness and noninvasion. In this research, we tend to apply the detection of biometric information to a remote sensing system for the purpose of security area monitoring. Our system is established by collecting signals from the coming individuals via the remote measurement in the specific condition where both kinds of data are detected to determine the identity. Specifically, the measuring of gait signals and facial images is integrated to provide a way of improving the detection accuracy and the robustness. In addition, the fuzzy association rule (FAR) is employed for data analysis in line with the outcomes of different methods. As such, the signals are integrated and transmitted for further processing and remote identification. Experiments are conducted to demonstrate the capability of the proposed system. With the training data increases, a high detection accuracy of 95.2% is obtained, which makes it a promising basis for the realization of remote identity verification.

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3-D Granulometry Using Image Processing

Cited by 10 publications

References 30 publications

Research of the Operator’s Advisory System Based on Fuzzy Logic for Pelletizing Equipment

Research of the Operator’s Advisory System Based on Fuzzy Logic for Pelletizing Equipment

White blood cell detection using saliency detection and CenterNet: A two‐stage approach

Remote Identity Verification Using Gait Analysis and Face Recognition

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