DCT-based characterization of milk products using diffuse reflectance images

Sharifzadeh, Sara; Skytte, Jacob Lercke; Clemmensen, Line Katrine Harder; Ersbøll, Bjarne Kjær

doi:10.1109/icdsp.2013.6622669

Cited by 6 publications

(4 citation statements)

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“…Fisher’s canonical variables are a widely-used supervised method for dimensionality reduction [ 26 , 27 , 28 ]. The method transforms the vectorized features (2560 in our work) to a maximum of

eigenvectors, where Q refers to the total number of classes.…”

Section: Discussionmentioning

confidence: 99%

Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data

Karayaneva

Sharifzadeh

Jing

et al. 2023

Sensors

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This paper explores the feasibility of using low-resolution infrared (LRIR) image streams for human activity recognition (HAR) with potential application in e-healthcare. Two datasets based on synchronized multichannel LRIR sensors systems are considered for a comprehensive study about optimal data acquisition. A novel noise reduction technique is proposed for alleviating the effects of horizontal and vertical periodic noise in the 2D spatiotemporal activity profiles created by vectorizing and concatenating the LRIR frames. Two main analysis strategies are explored for HAR, including (1) manual feature extraction using texture-based and orthogonal-transformation-based techniques, followed by classification using support vector machine (SVM), random forest (RF), k-nearest neighbor (k-NN), and logistic regression (LR), and (2) deep neural network (DNN) strategy based on a convolutional long short-term memory (LSTM). The proposed periodic noise reduction technique showcases an increase of up to 14.15% using different models. In addition, for the first time, the optimum number of sensors, sensor layout, and distance to subjects are studied, indicating the optimum results based on a single side sensor at a close distance. Reasonable accuracies are achieved in the case of sensor displacement and robustness in detection of multiple subjects. Furthermore, the models show suitability for data collected in different environments.

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eigenvectors, where Q refers to the total number of classes.…”

Section: Discussionmentioning

confidence: 99%

Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data

Karayaneva

Sharifzadeh

Jing

et al. 2023

Sensors

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“…The first strategy is based on the traditional feature extraction and SVM classification techniques, similar example works in different domains are (Jake Bouvrie , Tony Ezzat, 2008;Sharifzadeh, Serrano and Carrabina, 2012;Sharifzadeh et al, 2013). The developed algorithm consists of an unsupervised pixel-based segmentation of vegetation area using NDMI, followed by a two-step supervised step for texture area classification and farm detection; at the first step GLCM and 2-D DCT features are used in an SVM framework for texture classification and in the second step, object-based morphological features were extracted from the textured areas for farm detection.…”

Section: Introductionmentioning

confidence: 99%

Farm Detection based on Deep Convolutional Neural Nets and Semi-supervised Green Texture Detection using VIS-NIR Satellite Image

Sharifzadeh

Tata

Tan

2019

Proceedings of the 8th International Conference on Data Science, Technology and Applications

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Farm detection using low resolution satellite images is an important topic in digital agriculture. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. The current paper addresses the problem of farm detection using low resolution satellite images. In digital agriculture, farm detection has significant role for key applications such as crop yield monitoring. Two main categories of object detection strategies are studied and compared in this paper; First, a two-step semi-supervised methodology is developed using traditional manual feature extraction and modelling techniques; the developed methodology uses the Normalized Difference Moisture Index (NDMI), Grey Level Co-occurrence Matrix (GLCM), 2-D Discrete Cosine Transform (DCT) and morphological features and Support Vector Machine (SVM) for classifier modelling. In the second strategy, high-level features learnt from the massive filter banks of deep Convolutional Neural Networks (CNNs) are utilised. Transfer learning strategies are employed for pretrained Visual Geometry Group Network (VGG-16) networks. Results show the superiority of the high-level features for classification of farm regions.

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“…In our previous contribution, a farm detection strategy was developed at patch level [40]. The analysis include two different strategies; the first one was a semi-supervised strategy based on hand-crafted features combined by classification modeling similar to [40][41][42][43]. The developed algorithm consists of an unsupervised pixel-based segmentation of vegetation area using Normalized Difference Moisture Index (NDMI), followed by a supervised step for texture area classification and farm detection; GLCM and 2-D DCT features are used in an SVM framework for texture classification and in then, object-based morphological features were extracted from the textured areas for farm detection.…”

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confidence: 99%