Real-time automated visual inspection system for contaminant removal from wool

Zhang, Liwei; Dehghani, Abbas; Su, Zhenwei; King, Tim; Greenwood, Barry; Levesley, Martin

doi:10.1016/j.rti.2004.09.003

Cited by 19 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typical systems such as [17,18] used the methods of color space transform and three-dimensional techniques, respectively. However, frame rates and resolution in these systems are not high-speed.…”

Section: Conventional Studiesmentioning

confidence: 99%

Real-time scratching behavior quantification system for laboratory mice using high-speed vision

Nie

Ishii

Yamamoto

et al. 2009

J Real-Time Image Proc

View full text Add to dashboard Cite

Scratching is a specific behavior induced by itching; it is also a common symptom of many types of dermatitis. For the itching evaluation in animal models, automatic quantification system is needed for objective and accurate observation. In this study, a dedicated real-time motion analysis system is developed for detecting the scratching behavior of laboratory mice in long-time experiments, which enables automated behavior quantification for the development of new drugs for diseases such as atopic dermatitis. This system can detect laboratory mice scratching in a non-invasive method by introducing a specially designed high-speed vision system that can calculate the frame-to-frame difference at a frame rate of 240 fps. A quantification algorithm is also implemented for distinguishing the scratching behavior from other behaviors. In fact, we evaluate the effectiveness of our system by demonstrating the experimental results of scratching behavior detection during the long-time observation of several ICR mice. The results also show the objectiveness and accuracy.

show abstract

Section: Conventional Studiesmentioning

confidence: 99%

Real-time scratching behavior quantification system for laboratory mice using high-speed vision

Nie

Ishii

Yamamoto

et al. 2009

J Real-Time Image Proc

View full text Add to dashboard Cite

show abstract

“…However, the relatively low speed for ultrasonic transmission has resulted in a slow identification process [4]. With the development of computer technologies, computer vision techniques with the advantages of low costs, fast speed and consistency have been widely used in foreign matter detection [5]- [11]. Existing computer vision techniques depend on the color differences to distinguish foreign matter and cotton.…”

Section: Introductionmentioning

confidence: 99%

Online Sorting of the Film on Cotton Based on Deep Learning and Hyperspectral Imaging

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Mulch film is usually mixed in with cotton during machine-harvesting and processing, which reduces the cotton quality. This paper presents a novel sorting algorithm for the online detection of film on cotton using hyperspectral imaging with a spectral region of 1000-2500 nm. The sorting algorithm consists of a group of stacked autoencoders, two optimization modules and an extreme learning machine (ELM) classifier. The variable-weighted stacked autoencoders (VW-SAE) are constructed to extract the features from hyperspectral images, and an artificial neural network (ANN), which is one optimization module, is applied to optimize the parameters of the VW-SAE. Then, the extracted features are input in the ELM to classify four types of objects: background, film on background, cotton and film on cotton. The ELM is optimized by a new optimizer (grey wolf optimizer), which can adjust the hidden nodes and parameters of the ELM simultaneously. A group of experiments was carried out to evaluate the performance of the proposed sorting algorithm using cotton that was provided by a Xinjiang municipality cotton ginning company. The experimental results show that the VW-SAE can improve the classification accuracies by approximately 15 %. The overall recognition rate of the proposed algorithm is over 95 %, and its recognition time is comparable to some state-of-the-art methods. INDEX TERMS Cotton, sorting system, plastic film, deep learning, hyperspectral imaging, grey wolf optimizer, variable-wise weighted stacked autoencoder.

show abstract

Learning Image-Based Contaminant Detection in Wool Fleece from Noisy Annotations

Patten

Alempijevic

2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Real-time automated visual inspection system for contaminant removal from wool

Cited by 19 publications

References 26 publications

Real-time scratching behavior quantification system for laboratory mice using high-speed vision

Real-time scratching behavior quantification system for laboratory mice using high-speed vision

Online Sorting of the Film on Cotton Based on Deep Learning and Hyperspectral Imaging

Learning Image-Based Contaminant Detection in Wool Fleece from Noisy Annotations

Contact Info

Product

Resources

About