scholarly journals Inter-Device Periocular Recognition Under Near-Infrared Light

2016 ◽  
Vol 21 (4) ◽  
pp. 33-44
Author(s):  
Michał Włodarczyk ◽  
Paweł Krotewicz ◽  
Damian Kacperski ◽  
Wojciech Sankowski ◽  
Kamil Grabowski
Keyword(s):  
Feature Extraction ◽  
Near Infrared ◽  
Local Binary Patterns ◽  
Extraction Methods ◽  
Infrared Light ◽  
Scale Invariant ◽  
Histograms Of Oriented Gradients ◽  
Decision Module ◽  
The Impact ◽  
Periocular Recognition

Abstract Periocular biometrics is a relatively new field of research, and only several publications on this topic can be found in the literature. It can become a promising feature that can be used independently or as a complement to other biometrics. In this work, the recognition rates of periocular biometrics on a single acquisition device and inter-device database is verified and the impact of different image sources on the performance of recognition algorithms is investigated. For this purpose a NearInfrared Light database was collected. The database contains images taken by two acquisition devices. In order to test the periocular biometric trait, three feature extraction methods are chosen: Histograms of Oriented Gradients, Local Binary Patterns and Scale Invariant Feature Transform. The fusion of these methods is also proposed and it is tested on inter-device database. The feasibility of applying periocular recognition as an individual decision module for a biometric system is assessed. Experimental results yield Equal Error Rate of 17.65 for right eye using inter-device database of 640 gallery periocular images for each eye side taken from 32 different individuals (20 images per individual for each eye side). These results are obtained by the optimal weighted sum fusion of the three feature extraction methods.

scholarly journals CBIR of Brain MR Images Using Histogram of Fuzzy Oriented Gradients and Fuzzy Local Binary Patterns

2017 ◽  
Vol 6 (1) ◽  
pp. 8
Author(s):  
Athira TR ◽  
Abraham Varghese
Keyword(s):  
Feature Extraction ◽  
Local Binary Patterns ◽  
Medical Application ◽  
Extraction Methods ◽  
Mr Images ◽  
Brain Images ◽  
Feature Extraction Method ◽  
Similar Images ◽  
The Impact ◽  
Spatial Support

Retrieval of similar images from large dataset of brain images across patients would help the experts in the decision diagnosis process of diseases. Generally used feature extraction methods are color, texture and shape. In medical images texture and shape features are most efficient. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are good descriptor for brain MR image retrieval. But there are many challenges facing in medical application. An empirical study of the impact of increasing bins number in the HOG descriptor concluded that larger the number is more accurate the descriptor is. In fact this is due to the reduction of orientations range that each bin covers. Despite the efficiency of augmenting the bins number, this technique has limited spatial support as the augmentation of the number of bins used leads to increase the histogram dimension. So here proposed a method called Histogram of Fuzzy Oriented Gradients (HFOG), in which a pixel can belong several bins with different degrees. The Local Binary Patterns feature extraction method is widely used for texture analysis; however, the original LBP is based on hard thresholding the neighborhood of each pixel. Therefore, texture representation with LBP is very sensitive to noise and cannot distinguish between a strong and a weak pattern. In this study, Fuzzy Local Binary Patterns was introduced to improve the original LBP.

scholarly journals Predicting the Particle Size Distributions of Spherical Particle Sets from Synthetic Images: A Comparison of 9 Classic Image Features

2021 ◽  
Author(s):  
javad Manashti ◽  
Francois Duhaime ◽  
Matthew Toews ◽  
Pouyan Pirnia
Keyword(s):  
Particle Size ◽  
Feature Extraction ◽  
Spherical Particle ◽  
Discrete Element ◽  
Local Binary Patterns ◽  
Extraction Methods ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Histograms Of Oriented Gradients ◽  
Synthetic Images

The two objectives of this paper were to demonstrate use the of the discrete element method for generating synthetic images of spherical particle configurations, and to compare the performance of 9 classic feature extraction methods for predicting the particle size distributions (PSD) from these images. The discrete element code YADE was used to generate synthetic images of granular materials to build the dataset. Nine feature extraction methods were compared: Haralick features, Histograms of Oriented Gradients, Entropy, Local Binary Patterns, Local Configuration Pattern, Complete Local Binary Patterns, the Fast Fourier transform, Gabor filters, and Discrete Haar Wavelets. The feature extraction methods were used to generate the inputs of neural networks to predict the PSD. The results show that feature extraction methods can predict the percentage passing with a root-mean-square error (RMSE) on the percentage passing as low as 1.7%. CLBP showed the best result for all particle sizes with a RMSE of 3.8 %. Better RMSE were obtained for the finest sieve (2.1%) compared to coarsest sieve (5.2%).

Robust Facial Expression Recognition with Low-Rank Sparse Error Dictionary Based Probabilistic Collaborative Representation Classification

2017 ◽  
Vol 26 (04) ◽  
pp. 1750017 ◽  
Author(s):  
Zhe Sun ◽  
Zheng-Ping Hu ◽  
Meng Wang ◽  
Fan Bai ◽  
Bo Sun
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Random Noise ◽  
Local Binary Patterns ◽  
Extraction Methods ◽  
Low Rank ◽  
Collaborative Representation ◽  
Expression Recognition ◽  
Maximum Probability

The performance of facial expression recognition (FER) would be degraded due to some factors such as individual differences, Gaussian random noise and so on. Prior feature extraction methods like Local Binary Patterns (LBP) and Gabor filters require explicit expression components, which are always unavailable and difficult to obtain. To make the facial expression recognition (FER) more robust, we propose a novel FER approach based on low-rank sparse error dictionary (LRSE) to remit the side-effect caused by the problems above. Then the query samples can be represented and classified by a probabilistic collaborative representation based classifier (ProCRC), which exploits the maximum likelihood that the query sample belonging to the collaborative subspace of all classes can be better computed. The final classification is performed by seeking which class has the maximum probability. The proposed approach which exploits ProCRC associated with the LRSE features (LRSE ProCRC) for robust FER reaches higher average accuracies on the different databases (i.e., 79.39% on KDEF database, 89.54% on CAS-PEAL database, 84.45% on CK+ database etc.). In addition, our method also leads to state-of-the-art classification results from the aspect of feature extraction methods, training samples, Gaussian noise variances and classification based methods on benchmark databases.

Human–Computer Interactive Gesture Feature Capture and Recognition in Virtual Reality

2020 ◽  
pp. 106480462092413 ◽  
Author(s):  
Fan Zhang
Keyword(s):  
Neural Network ◽  
Virtual Reality ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Extraction Methods ◽  
Scale Invariant ◽  
Feature Vectors ◽  
Accuracy And Precision ◽  
Scale Invariant Feature

With the development of computer technology, the simulation authenticity of virtual reality technology is getting higher and higher, and the accurate recognition of human–computer interaction gestures is also the key technology to enhance the authenticity of virtual reality. This article briefly introduced three different gesture feature extraction methods: scale invariant feature transform, local binary pattern and histogram of oriented gradients (HOG), and back-propagation (BP) neural network for classifying and recognizing different gestures. The gesture feature vectors obtained by three feature extraction methods were used as input data of BP neural network respectively and were simulated in MATLAB software. The results showed that the information of feature gesture diagram extracted by HOG was the closest to the original one; the BP neural network that applied HOG extracted feature vectors converged to stability faster and had the smallest error when it was stable; in the aspect of gesture recognition, the BP neural network that applied HOG extracted feature vector had higher accuracy and precision and lower false alarm rate.

Wavelets-Based Feature Extraction for Texture Classification

2010 ◽  
Vol 97-101 ◽  
pp. 1273-1276 ◽  
Author(s):  
Gang Yu ◽  
Ying Zi Lin ◽  
Sagar Kamarthi
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Energy Content ◽  
Texture Classification ◽  
Wavelet Coefficient ◽  
Discrimination Performance ◽  
Extraction Methods ◽  
Rotation Invariant ◽  
Scale Invariant ◽  
Feature Extraction Method

Texture classification is a necessary task in a wider variety of application areas such as manufacturing, textiles, and medicine. In this paper, we propose a novel wavelet-based feature extraction method for robust, scale invariant and rotation invariant texture classification. The method divides the 2-D wavelet coefficient matrices into 2-D clusters and then computes features from the energies inherent in these clusters. The features that contain the information effective for classifying texture images are computed from the energy content of the clusters, and these feature vectors are input to a neural network for texture classification. The results show that the discrimination performance obtained with the proposed cluster-based feature extraction method is superior to that obtained using conventional feature extraction methods, and robust to the rotation and scale invariant texture classification.

The impact of near-infrared light on dopaminergic cell survival in a transgenic mouse model of parkinsonism

2013 ◽  
Vol 1535 ◽  
pp. 61-70 ◽  
Author(s):  
Sivaraman Purushothuman ◽  
Charith Nandasena ◽  
Daniel M. Johnstone ◽  
Jonathan Stone ◽  
John Mitrofanis
Keyword(s):  
Mouse Model ◽  
Cell Survival ◽  
Transgenic Mouse ◽  
Near Infrared ◽  
Transgenic Mouse Model ◽  
Infrared Light ◽  
Near Infrared Light ◽  
Dopaminergic Cell ◽  
The Impact

Brain tumor classification using modified local binary patterns (LBP) feature extraction methods

2020 ◽  
Vol 139 ◽  
pp. 109696 ◽  
Author(s):  
Kaplan Kaplan ◽  
Yılmaz Kaya ◽  
Melih Kuncan ◽  
H. Metin Ertunç
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Local Binary Patterns ◽  
Extraction Methods ◽  
Tumor Classification

scholarly journals Automatic feature extraction and matching modelling for highly noise near-equatorial satellite images

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Hayder Dibs ◽  
Hashim Ali Hasab ◽  
Hussein Sabah Jaber ◽  
Nadhir Al-Ansari
Keyword(s):  
Feature Extraction ◽  
Satellite Images ◽  
Near Infrared ◽  
Satellite System ◽  
Absolute Difference ◽  
Scale Invariant ◽  
Frame Size ◽  
Automatic Feature Extraction ◽  
Scale Invariant Feature ◽  
Image Band

AbstractFeature extraction plays an important role in pattern recognition because band-to-band registration and geometric correction from different satellite images have linear image distortion. However, new near-equatorial orbital satellite system (NEqO) images is different because they have nonlinear distortion. Conventional techniques cannot overcome this type of distortion and lead to the extraction of false features and incorrect image matching. This research presents a new method by improving the performance of the Scale-Invariant Feature Transformation (SIFT) with a significantly higher rate of true extracted features and their correct matching. The data in this study were obtained from the RazakSAT satellite covering a part of Penang state, Malaysia. The method consists of many stages: image band selection, image band compression, image sharpening, automatic feature extraction, and applying the sum of absolute difference algorithm with an experimental and empirical threshold. We evaluate a refined features scenario by comparing the result of the original extracted SIFT features with corresponding features of the proposed method. The result indicates accurate and precise performance of the proposed method from removing false SIFT extracted features of satellite images and remain only true SIFT extracted features, that leads to reduce the extracted feature from using three frame size: (1) from 2000 to 750, 552 and 92 for the green and red bands image, (2) from 678 extracted control points to be 193, 228 and 73 between the green and blue bands, and (3) from 1995 extracted CPs to be 656, 733, and 556 between the green and near-infrared bands, respectively.

scholarly journals Sunlight Exposure and Phototherapy: Perspectives for Healthy Aging in an Era of COVID-19

2021 ◽  
Vol 18 (20) ◽  
pp. 10950
Author(s):  
Toshiaki Nakano ◽  
Kuei-Chen Chiang ◽  
Chien-Chih Chen ◽  
Po-Jung Chen ◽  
Chia-Yun Lai ◽  
...  
Keyword(s):  
Public Health ◽  
Skin Cancer ◽  
Vitamin D3 ◽  
Healthy Aging ◽  
Near Infrared ◽  
Sunlight Exposure ◽  
Infrared Light ◽  
Clinical Value ◽  
Full Spectrum ◽  
The Impact

Most humans depend on sunlight exposure to satisfy their requirements for vitamin D3. However, the destruction of the ozone layer in the past few decades has increased the risk of skin aging and wrinkling caused by excessive exposure to ultraviolet (UV) radiation, which may also promote the risk of skin cancer development. The promotion of public health recommendations to avoid sunlight exposure would reduce the risk of skin cancer, but it would also enhance the risk of vitamin D3 insufficiency/deficiency, which may cause disease development and progression. In addition, the ongoing global COVID-19 pandemic may further reduce sunlight exposure due to stay-at-home policies, resulting in difficulty in active and healthy aging. In this review article, we performed a literature search in PubMed and provided an overview of basic and clinical data regarding the impact of sunlight exposure and vitamin D3 on public health. We also discuss the potential mechanisms and clinical value of phototherapy with a full-spectrum light (notably blue, red, and near-infrared light) as an alternative to sunlight exposure, which may contribute to combating COVID-19 and promoting active and healthy aging in current aged/superaged societies.

scholarly journals Fundus Image Registration Technique Based on Local Feature of Retinal Vessels

2021 ◽  
Vol 11 (23) ◽  
pp. 11201
Author(s):  
Roziana Ramli ◽  
Khairunnisa Hasikin ◽  
Mohd Yamani Idna Idris ◽  
Noor Khairiah A. Karim ◽  
Ainuddin Wahid Abdul Wahab
Keyword(s):  
Feature Extraction ◽  
Image Registration ◽  
Extraction Methods ◽  
Local Feature ◽  
Retinal Vessels ◽  
Feature Points ◽  
Fundus Image ◽  
Scale Invariant ◽  
Feature Based ◽  
Image Pairs

Feature-based retinal fundus image registration (RIR) technique aligns fundus images according to geometrical transformations estimated between feature point correspondences. To ensure accurate registration, the feature points extracted must be from the retinal vessels and throughout the image. However, noises in the fundus image may resemble retinal vessels in local patches. Therefore, this paper introduces a feature extraction method based on a local feature of retinal vessels (CURVE) that incorporates retinal vessels and noises characteristics to accurately extract feature points on retinal vessels and throughout the fundus image. The CURVE performance is tested on CHASE, DRIVE, HRF and STARE datasets and compared with six feature extraction methods used in the existing feature-based RIR techniques. From the experiment, the feature extraction accuracy of CURVE (86.021%) significantly outperformed the existing feature extraction methods (p ≤ 0.001*). Then, CURVE is paired with a scale-invariant feature transform (SIFT) descriptor to test its registration capability on the fundus image registration (FIRE) dataset. Overall, CURVE-SIFT successfully registered 44.030% of the image pairs while the existing feature-based RIR techniques (GDB-ICP, Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG) only registered less than 27.612% of the image pairs. The one-way ANOVA analysis showed that CURVE-SIFT significantly outperformed GDB-ICP (p = 0.007*), Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG (p ≤ 0.001*).

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