A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

Vision-based inspection has been applied for quality control and product sorting in manufacturing processes. Blurred or multiple objects are common causes of poor performance in conventional vision-based inspection systems. Detecting hybrid blurred/multiple objects has long been a challenge in manufacturing. For example, single-feature-based algorithms might fail to exactly extract features when concurrently detecting hybrid blurred/multiple objects. Therefore, to resolve this problem, this study proposes a novel vision-based inspection algorithm that entails selecting a dynamic feature-based method on the basis of a multiclassifier of support vector machines (SVMs) for inspecting hybrid blurred/multiple object images. The proposed algorithm dynamically selects suitable inspection schemes for classifying the hybrid images. The inspection schemes include discrete wavelet transform, spherical wavelet transform, moment invariants, and edge-feature-descriptor-based classification methods. The classification methods for single and multiple objects are adaptive region growing- (ARG-) based and local adaptive region growing- (LARG-) based learning approaches, respectively. The experimental results demonstrate that the proposed algorithm can dynamically select suitable inspection schemes by applying a selection algorithm, which uses SVMs for classifying hybrid blurred/multiple object samples. Moreover, the method applies suitable feature-based schemes on the basis of the classification results for employing the ARG/LARG-based method to inspect the hybrid objects. The method improves conventional methods for inspecting hybrid blurred/multiple objects and achieves high recognition rates for that in manufacturing processes.

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Fast and Adaptive Detection of Pulmonary Nodules in Thoracic CT Images Using a Contextual Clustering Based Region Growing

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.24.12010 ◽

2018 ◽

Vol 7 (2.24) ◽

pp. 106

Author(s):

Bhakkiyalakshmi R ◽

Ponnammal P ◽

Srilekha M K ◽

Abhishikt Sai .K

Keyword(s):

Pulmonary Nodules ◽

Region Of Interest ◽

Region Growing ◽

Support Vector ◽

Adaptive Detection ◽

Fuzzy Classifier ◽

Thoracic Ct ◽

Neuro Fuzzy ◽

Feature Based ◽

Better Than

For segmenting the Region of interest and for analyzing each area separately to locate whether pathologies present in it or not, we use segmentation process as the first step to diagnose lung image using ComputerAided Diagnosis. In this paper, ROI is segmented by using supervised Contextual Clustering in addition to the Region growing algorithm. Accurate segmentation of the lungs from the chest volume is obtained from the Contextual clustering which is better than all other thresholding approaches that are simple. Initial Nodule Candidates can be detected and segmented effectively by contextual clustering which is considered to be the most effective approach when compared to the remaining approaches present.We combine rule-based filtering and a feature based support vector machine using which we can reduce the False-positives (FP) ,custom CNN, Alex net, neuro-fuzzy classifier.

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Adapting two-class support vector classification methods to many class problems

Proceedings of the 22nd international conference on Machine learning - ICML '05 ◽

10.1145/1102351.1102391 ◽

2005 ◽

Cited By ~ 4

Author(s):

Simon I. Hill ◽

Arnaud Doucet

Keyword(s):

Support Vector ◽

Classification Methods

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Fugl-Meyer hand motor imagination recognition for brain–computer interfaces using only fNIRS

Complex & Intelligent Systems ◽

10.1007/s40747-020-00266-w ◽

2021 ◽

Author(s):

Chenguang Li ◽

Hongjun Yang ◽

Long Cheng

Keyword(s):

Classification Accuracy ◽

Near Infrared ◽

Support Vector ◽

Classification Methods ◽

Experimental Paradigm ◽

Functional Near Infrared Spectroscopy ◽

Machine Method ◽

Physiological Signal ◽

Computer Interfaces ◽

Mode Decomposition

AbstractAs a relatively new physiological signal of brain, functional near-infrared spectroscopy (fNIRS) is being used more and more in brain–computer interface field, especially in the task of motor imagery. However, the classification accuracy based on this signal is relatively low. To improve the accuracy of classification, this paper proposes a new experimental paradigm and only uses fNIRS signals to complete the classification task of six subjects. Notably, the experiment is carried out in a non-laboratory environment, and movements of motion imagination are properly designed. And when the subjects are imagining the motions, they are also subvocalizing the movements to prevent distraction. Therefore, according to the motor area theory of the cerebral cortex, the positions of the fNIRS probes have been slightly adjusted compared with other methods. Next, the signals are classified by nine classification methods, and the different features and classification methods are compared. The results show that under this new experimental paradigm, the classification accuracy of 89.12% and 88.47% can be achieved using the support vector machine method and the random forest method, respectively, which shows that the paradigm is effective. Finally, by selecting five channels with the largest variance after empirical mode decomposition of the original signal, similar classification results can be achieved.

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Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones

Scientific Reports ◽

10.1038/s41598-021-92776-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Andrew P. Creagh ◽

Florian Lipsmeier ◽

Michael Lindemann ◽

Maarten De Vos

Keyword(s):

Multiple Sclerosis ◽

Deep Learning ◽

Inertial Sensor ◽

Heterogeneous Data ◽

Fine Tuning ◽

Sensor Data ◽

Support Vector ◽

Deep Convolutional Neural Networks ◽

Healthcare Applications ◽

Feature Based

AbstractThe emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activity recognition (HAR) tasks collected from wearable smartphone sensor data. It was demonstrated that fine-tuning TL DCNN HAR models towards MS disease recognition tasks outperformed previous Support Vector Machine (SVM) feature-based methods, as well as DCNN models trained end-to-end, by upwards of 8–15%. A lack of transparency of “black-box” deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus people with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions.

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New Multi-View Classification Method with Uncertain Data

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3458282 ◽

2021 ◽

Vol 16 (1) ◽

pp. 1-23

Author(s):

Bo Liu ◽

Haowen Zhong ◽

Yanshan Xiao

Keyword(s):

Learning Strategy ◽

State Of The Art ◽

Uncertain Data ◽

Real Life ◽

Support Vector ◽

Classification Methods ◽

Complementary Information ◽

Novel Approach ◽

Svm Model ◽

Iterative Framework

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

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Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽

10.3390/e23080975 ◽

2021 ◽

Vol 23 (8) ◽

pp. 975

Author(s):

Yancai Xiao ◽

Jinyu Xue ◽

Mengdi Li ◽

Wei Yang

Keyword(s):

Machine Learning ◽

Wavelet Transform ◽

Fault Diagnosis ◽

Wind Turbine ◽

Wind Turbines ◽

Gear Tooth ◽

Support Vector ◽

Grey Wolf Optimizer ◽

Empirical Wavelet Transform ◽

Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

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Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search

Energies ◽

10.3390/en9100827 ◽

2016 ◽

Vol 9 (10) ◽

pp. 827 ◽

Cited By ~ 21

Author(s):

Yi Liang ◽

Dongxiao Niu ◽

Minquan Ye ◽

Wei-Chiang Hong

Keyword(s):

Support Vector Machine ◽

Wavelet Transform ◽

Least Squares ◽

Load Forecasting ◽

Cuckoo Search ◽

Support Vector ◽

Short Term ◽

Short Term Load Forecasting

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Study of Segmentation Threshold Based on Wavelet Transform

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3855 ◽

2013 ◽

Vol 756-759 ◽

pp. 3855-3859

Author(s):

Jian Yi Li ◽

Hui Juan Wang

Keyword(s):

Wavelet Transform ◽

Digital Image ◽

Region Growing ◽

Detection Methods ◽

Mathematical Tool ◽

Matlab Simulation ◽

Segmentation Method ◽

Transform Method ◽

Threshold Segmentation ◽

Digital Image Enhancement

Based on the research of the four kinds of algorithms of digital image segmentation, based on edge detection methods, based on region growing method, threshold segmentation method and digital image threshold segmentation method based on wavelet transform, using MATLAB simulation of all digital image enhancement and segmentation process, the obtained results are analyzed, proving the threshold segmentation wavelet transform method has unparalleled advantages in information extraction in medical image. Wavelet transform is a mathematical tool widely used in recent years, compared with the Fu Liye transform, the window of Fu Liye transform, wavelet transform is the local transform of space and frequency, it can be very effective in extracting information from the signal [[1.

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