ROBUST EYE FEATURE EXTRACTION FROM FACIAL COLOR IMAGE USING IRIS COLOR COMPENSATION

2012 ◽  
Vol 26 (01) ◽  
pp. 1256004
Author(s):  
VAN HUAN NGUYEN ◽  
HAKIL KIM
Keyword(s):  
Feature Extraction ◽  
Color Image ◽  
Least Mean Square ◽  
Iris Color ◽  
Feature Spaces ◽  
Show Evidence ◽  
Color Compensation ◽  
Novel Method ◽  
The Difference ◽  
Difference Images

This paper presents a novel method of robust eye feature extraction from facial color images by considering the variety of iris colors. Given an eye window containing a single eye, the proposed method assesses the iris color tone based on the difference images between the red and the green channels and the red and the blue channels. A weighted scaling compensation method is then proposed for increasing the separability and homogeneity of the iris region. The extraction of the eye features is performed by an unsupervised K-means clustering on the compensated feature spaces. The eye corners are detected after eyelid fitting using a least mean square cost function. Experiments on a collection of eye images extracted from the FERET face database show evidence of promising performance from color facial images with variation in illumination, pose, eye gazing direction, and race.

Research on the Algorithm of Defect Feature Extraction in Trademark Printing

2015 ◽  
Vol 731 ◽  
pp. 205-209
Author(s):  
Yu Chen ◽  
Bang Gui He ◽  
Wen Juan Gu
Keyword(s):  
Feature Extraction ◽  
High Speed ◽  
Color Image ◽  
Detection System ◽  
Extraction Algorithm ◽  
Time Requirements ◽  
Suitable Structure ◽  
The Difference ◽  
Printing Processes ◽  
Difference Images

A defect feature extraction algorithm which depends on the separation and subtraction of color image and mathematical morphology is proposed in this paper, based on the defects in trademark printing processes, such as smudges, pin holes, misprints, misting etc. The difference images of each monochrome are operated with morphology methods and then the defect information of high precision printing processes in colorful trademark, such as shape, boundary contour, quantity, size and location etc, are extracted and recognized with suitable structure elements. The defect extraction algorithm is programmed and run by MATLAB and the results validated the high speed and precision of the algorithm about extracting some kinds of defect information in color trademark printing processes. The algorithm has met the real-time requirements of defect feature extraction in trademark printing processes. This research laid a theoretical foundation for researching and developing the color trademark quality detection system.

HISTOGRAM OF TRANSITION FOR HUMAN HEAD RECOGNITION

2016 ◽  
Vol 78 (5-9) ◽  
Author(s):  
Panca Mudjirahardjo ◽  
M. Fauzan Edy Purnomo ◽  
Rini Nur Hasanah ◽  
Hadi Suyono
Keyword(s):  
Feature Extraction ◽  
Color Image ◽  
Recognition Rate ◽  
Orientation Angle ◽  
Human Head ◽  
Input Image ◽  
Foreground Extraction ◽  
Multi Scale ◽  
Novel Method ◽  
Main Component

The main component for head recognition is a feature extraction. One of them as our novel method is histogram of transition. This feature is relied on foreground extraction. In this paper we evaluate some pre-processing to get foreground extraction before we calculate the histogram of transition.We evaluate the performance of recognition rate in related with preprocessing of input image, such as color, size and orientation. We evaluate for Red-Green-Blue (RGB) and Hue-saturation-Value (HSV) color image; multi scale of 10×15 pixels, 20×30 pixels and 40×60 pixels; and multi orientation angle of 315o, 330o, 345o, 15o, 30o, and 45o.For comparison, we compare the recognition rate with the existing method of feature extraction, i.e. Histogram of Oriented Gradients (HOG) and Linear Binary Pattern (LBP). The experimental results show Histogram of Transition robust for changing of color, size and orientation angle.

Realization of Enhanced LMS Filter Using Carry Skip Adder And Quantum Dot Cellular Automation In VLSI For Removing Impulse Noises In Medical Image

2019 ◽  
Vol 14 ◽  
Author(s):  
K.R. Shankarkumar ◽  
Gokul Kumar
Keyword(s):  
Adaptive Filtering ◽  
Adaptive Filters ◽  
Pass Band ◽  
Least Mean Square ◽  
Low Pass ◽  
Parallel Prefix ◽  
Loop Feedback ◽  
Band Pass ◽  
The Difference ◽  
Prefix Adder

: Filtering is an important step in the field of image processing to suppress the required parts or to remove any artifacts present in it. There are different types of filters like low pass, high pass, Band pass, IIR, FIR and adaptive filtering etc.., in these filters adaptive filters is an important filter because it is used to remove the noisy signal and images. Least Mean Square filter is a type of an adaptive filtering which is used to remove the noises present in the medical images. The working of LMS is based on the minimization of the difference between the error images using a closed loop feedback. Therefore presented technique called as Q-CSKA. Here the CSKA performs its operation in stages which is based on the nucleus stage. In the traditional CSKA the nucleus stage is depend on the parallel prefix adder in this work it is replaced by the QCA adder. The QCA adder utilizes the less area compared to PPA and it can be realized in Nanometer range also. For multiplexers, And OR Invert, OR and Invert logic is used to reduce the area and delay. Due to these advantages of the QCA, AOI-OAI logic the proposed method outperformed the LMS implementation in area, power, and accuracy and delay, this based five type image noise of medical pictures related to the best technique is out comes. It helps to medicinal practitioner to resolve the symptoms of patient with ease.

scholarly journals A Multi-Scale Feature Extraction-Based Normalized Attention Neural Network for Image Denoising

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 319
Author(s):  
Yi Wang ◽  
Xiao Song ◽  
Guanghong Gong ◽  
Ni Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Denoising ◽  
Color Image ◽  
Rapid Development ◽  
Similarity Index ◽  
Structural Similarity ◽  
Convolutional Network ◽  
Scale Feature ◽  
Multi Scale

Due to the rapid development of deep learning and artificial intelligence techniques, denoising via neural networks has drawn great attention due to their flexibility and excellent performances. However, for most convolutional network denoising methods, the convolution kernel is only one layer deep, and features of distinct scales are neglected. Moreover, in the convolution operation, all channels are treated equally; the relationships of channels are not considered. In this paper, we propose a multi-scale feature extraction-based normalized attention neural network (MFENANN) for image denoising. In MFENANN, we define a multi-scale feature extraction block to extract and combine features at distinct scales of the noisy image. In addition, we propose a normalized attention network (NAN) to learn the relationships between channels, which smooths the optimization landscape and speeds up the convergence process for training an attention model. Moreover, we introduce the NAN to convolutional network denoising, in which each channel gets gain; channels can play different roles in the subsequent convolution. To testify the effectiveness of the proposed MFENANN, we used both grayscale and color image sets whose noise levels ranged from 0 to 75 to do the experiments. The experimental results show that compared with some state-of-the-art denoising methods, the restored images of MFENANN have larger peak signal-to-noise ratios (PSNR) and structural similarity index measure (SSIM) values and get better overall appearance.

A method of version merging for computer-aided design files based on feature extraction

2010 ◽  
Vol 225 (2) ◽  
pp. 463-471 ◽  
Author(s):  
C Sun ◽  
D Guo ◽  
H Gao ◽  
L Zou ◽  
H Wang
Keyword(s):  
Feature Extraction ◽  
Computer Aided Design ◽  
Application Programming Interface ◽  
Difference Set ◽  
Computer Aided ◽  
Application Programming ◽  
Feature Difference ◽  
The Difference ◽  
Aided Design ◽  
The Given

In order to manage the version files and maintain the latest version of the computer-aided design (CAD) files in asynchronous collaborative systems, one method of version merging for CAD files is proposed to resolve the problem based on feature extraction. First of all, the feature information is extracted based on the feature attribute of CAD files and stored in a XML feature file. Then, analyse the feature file, and the feature difference set is obtained by the given algorithm. Finally, the merging result of the difference set and the master files with application programming interface (API) interface functions is achieved, and then the version merging of CAD files is also realized. The application in Catia validated that the proposed method is feasible and valuable in engineering.

scholarly journals Image Anomaly Detection Using Normal Data Only by Latent Space Resampling

2020 ◽  
Vol 10 (23) ◽  
pp. 8660
Author(s):  
Lu Wang ◽  
Dongkai Zhang ◽  
Jiahao Guo ◽  
Yuexing Han
Keyword(s):  
Anomaly Detection ◽  
Normal Distribution ◽  
Autoregressive Model ◽  
Probability Model ◽  
Latent Space ◽  
Industrial Inspection ◽  
Detection Stage ◽  
Novel Method ◽  
The Difference ◽  
Reconstruction Model

Detecting image anomalies automatically in industrial scenarios can improve economic efficiency, but the scarcity of anomalous samples increases the challenge of the task. Recently, autoencoder has been widely used in image anomaly detection without using anomalous images during training. However, it is hard to determine the proper dimensionality of the latent space, and it often leads to unwanted reconstructions of the anomalous parts. To solve this problem, we propose a novel method based on the autoencoder. In this method, the latent space of the autoencoder is estimated using a discrete probability model. With the estimated probability model, the anomalous components in the latent space can be well excluded and undesirable reconstruction of the anomalous parts can be avoided. Specifically, we first adopt VQ-VAE as the reconstruction model to get a discrete latent space of normal samples. Then, PixelSail, a deep autoregressive model, is used to estimate the probability model of the discrete latent space. In the detection stage, the autoregressive model will determine the parts that deviate from the normal distribution in the input latent space. Then, the deviation code will be resampled from the normal distribution and decoded to yield a restored image, which is closest to the anomaly input. The anomaly is then detected by comparing the difference between the restored image and the anomaly image. Our proposed method is evaluated on the high-resolution industrial inspection image datasets MVTec AD which consist of 15 categories. The results show that the AUROC of the model improves by 15% over autoencoder and also yields competitive performance compared with state-of-the-art methods.

scholarly journals Linear traveltime perturbation interpolation: a novel method to compute 3-D traveltimes

2015 ◽  
Vol 203 (1) ◽  
pp. 548-552 ◽  
Author(s):  
Jianzhong Zhang ◽  
Junjie Shi ◽  
Lin-Ping Song ◽  
Hua-wei Zhou
Keyword(s):  
Seismic Waves ◽  
Homogeneous Medium ◽  
Cell Boundary ◽  
Routine Method ◽  
Bilinear Interpolation ◽  
Linear Distribution ◽  
Heterogeneous Model ◽  
Computational Errors ◽  
Novel Method ◽  
The Difference

Abstract The linear traveltime interpolation has been a routine method to compute first arrivals of seismic waves and trace rays in complex media. The method assumes that traveltimes follow a linear distribution on each boundary of cells. The linearity assumption of traveltimes facilitates the numerical implementation but its violation may result in large computational errors. In this paper, we propose a new way to mitigate the potential shortcoming hidden in the linear traveltime interpolation. We use the vertex traveltimes in a calculated cell to introduce an equivalent homogeneous medium that is specific to the cell boundary from a source. Therefore, we can decompose the traveltime at a point on the cell boundary into two parts: (1) a reference traveltime propagating in the equivalent homogeneous medium and (2) a perturbation traveltime that is defined as the difference between the original and reference traveltimes. We now treat that the traveltime perturbation is linear along each boundary of cells instead of the traveltime. With the new assumption, we carry out the bilinear interpolation over traveltime perturbation to complete traveltime computation in a 3-D heterogeneous model. The numerical experiments show that the new method, the linear traveltime perturbation interpolation, is able to achieve much higher accuracy than that based on the linear traveltime interpolation.

scholarly journals Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2265 ◽  
Author(s):  
Qingqing Feng ◽  
Huaping Xu ◽  
Zhefeng Wu ◽  
Wei Liu
Keyword(s):  
Sar Interferometry ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Frequency Detection ◽  
Interferometric Phase ◽  
Deceptive Jamming ◽  
Jamming Detection ◽  
Novel Method ◽  
The Difference ◽  
Cross Track

Deceptive jamming against synthetic aperture radar (SAR) can create false targets or deceptive scenes in the image effectively. Based on the difference in interferometric phase between the target and deceptive jamming signals, a novel method for detecting deceptive jamming using cross-track interferometry is proposed, where the echoes with deceptive jamming are received by two SAR antennas simultaneously and the false targets are identified through SAR interferometry. Since the derived false phase is close to a constant in interferogram, it is extracted through phase filtering and frequency detection. Finally, the false targets in the SAR image are obtained according to the detected false part in the interferogram. The effectiveness of the proposed method is validated by simulation results based on the TanDEM-X system.

scholarly journals A NOVEL METHOD OF CLOUD AND SHADOW EXTRACTION WITH MULTI-TEMPORAL LANDSAT8 OLI IMAGES

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1301-1305
Author(s):  
Y. Ni ◽  
G. He ◽  
W. Jiang
Keyword(s):  
Remote Sensing ◽  
Large Change ◽  
Remote Sensing Image ◽  
Image Process ◽  
Actual Application ◽  
Significant Step ◽  
Multi Temporal ◽  
Ground Object ◽  
Novel Method ◽  
The Difference

Cloud and Shadow removal is a significant step in remote sensing image process. As we all know, the ground object coverage type of the same area of the remote sensing image has little change in the short term. But for cloud and shadow coverage areas, the ground object coverage type has large change. Therefore, according to the difference between the two Landsat / OLI images caused by changes in the cover, this paper presents a method of extracting clouds and shadows based on differences in luminance values. This method selects two thresholds for the difference of brightness values, and extracts the clouds and shadows respectively, and validates them with random point method, which can obtain high precision of extracting cloud and shadow and satisfy the actual application needs.

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