Preprocessing Images And Restore The Contours Of Objects Obtained In The Infrared Range

Воронин, В. В.; Semenishchev, Evgeny A.; Balabaeva, Oxana; Pismenskova, Marina

doi:10.1109/icsp.2018.8652422

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Step i): Get the prediction result of softmax equipped at each individual weak classifier. The result is an array of normalized probability distributions denoted as equation (10) and equation (11).…”

Section: Age Predictionmentioning

confidence: 99%

Ensemble Learning for Facial Age Estimation Within Non-Ideal Facial Imagery

Qian

Huang

et al. 2019

IEEE Access

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Human facial age estimation has been widely used in many computer vision applications, including security surveillance, forensics, biometrics, human-computer interaction (HCI), and so on. We propose a facial age estimation method oriented to non-ideal facial imagery. The method consists of image preprocessing, feature extraction, and age predication. First, we preprocess non-ideal input images in RGB stream, luminance modified (LM) stream, and YIQ stream. Then, we leverage the deep convolutional neural networks (DCNNs) to extract the feature of images preprocessed in each stream. To reduce the training data volume and training complexity, we adopt the transfer learning to build the DCNN structure. With the extracted feature, the weak classifier equipped at every stream is designed to obtain a weak classification prediction of the age range. Moreover, in order to generate estimation, we use the ensemble learning to fuse the three weak classifiers. We design an integrated strategy algorithm based on the combination of voting method and weighted average method. The simulation results show that our proposed algorithm can improve the an exact match (AEM) and an error of one age category (AEO) by 4.75% and 6.75% compared with the best AEM and AEO of the three weak classifiers. Furthermore, in comparison with the unweighted average method, our proposed algorithm can improve the AEM and AEO by 8.68% and 12.79%, respectively. INDEX TERMS Facial age estimation, transfer learning, ensemble learning, deep convolutional neural network, image preprocessing.

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Section: Age Predictionmentioning

confidence: 99%

Ensemble Learning for Facial Age Estimation Within Non-Ideal Facial Imagery

Qian

Huang

et al. 2019

IEEE Access

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“…One method for improving the comprehension of visual image information is to use picture preprocessing, as done by [11], [12]. Image preprocessing is the process of enhancing an image for object extraction by lowering noise, adjusting brightness, and enhancing data quality.…”

Section: Introductionmentioning

confidence: 99%

Forensic Analysis of Faces on Low-Quality Images using Detection and Recognition Methods

Kristanto

Riadi

Prayudi

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Facial recognition is an essential aspect of conducting criminal action investigations. Captured images from the camera or the recording video can reveal the perpetrator's identity if their faces are deliberately or accidentally captured. However, many of these digital imagery results display the results of image quality that is not good when seen by the human eye. Hence, the facial recognition process becomes more complex and takes longer. This research aims to analyze face recognition on a low-quality image with noise, blur and brightness problem to help digital forensic investigator do an investigation in recognizing faces that the human eye can’t do. The Viola-Jones algorithm method has several processes such as the Haar feature, integral image, adaboost, and cascade classifier for detecting a face in an image. Detected face will be passed to the next process for recognition call Fisher’s Linear Discriminant (FLD), Local Binary Pattern’s (LBP) and Principal Component analysis (PCA). The software's facial recognition feature shows one of the images in the database that the program suspects has the same face as the analyzed face image. In conclusion, from the analysis we determined that LBP approach is the best among the other recognition methods for blur and brightness problem, bet found PCA method is the best for recognize face in noise problem. The software's facial recognition feature shows one of the images in the database that the program suspects has the same face as the analyzed face image. The position of the face object in the image, whether or not there is an additional object that was not previously included in the image in the dataset, as well as the brightness level of an image and the color of the face's skin, all affect the accuracy rates.

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“…In addition, infrared images can prevent interference from dust or markers and avoid false alarms during the detection process. Infrared images have been widely used in the field of detection, but due to the imaging principle of infrared images, infrared images usually have problems such as low contrast, high noise, and fuzzy edges, and the images often also have uneven intensity during the acquisition process [2][3][4][5][6]. The inhomogeneity of image intensity can have a great impact on image segmentation, and Kass et al proposed a widely used model for inhomogeneous density images, active contour model (ACM) [7].…”

Section: Introductionmentioning

confidence: 99%

The Segmentation of Wind Turbine Defect Based on UAV Infrared Image

Wang,

Jiang,

Wen

et al. 2023

Proccedings of the 2023 International Conference on Quantitative InfraRed Thermography

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During the operation of wind power systems, problems such as damage may occur, in order to keep the system running normally and safely, regular maintenance of wind turbines is essential. Currently, the main treatment is to detect the blades by carrying an infrared camera on board a UAV. However, infrared images can suffer from significant inhomogeneities due to the inconsistent response of the detector array of the infrared camera, the inconsistent spectral response of the infrared detector to environmental variations and incoming background radiation, and the combined effect of the camera's optical lens. The inhomogeneity of an image can greatly affect the accuracy of image segmentation. To solve this problem, In this paper, we propose a method for rapidly segmenting inhomogeneous images. Specifically, we introduce global and local information of the image into the energy term. We also introduce a detail map as the reference map for the edge stopping function, which improves the robustness of the image to the initial contour and improves the segmentation accuracy of inhomogeneous images while speeding up the segmentation. Correction of bias field is also realized simultaneously. The results demonstrate that our method achieves average accuracy of 96% in the task of performing segmentation of wind turbine defects with the shortest running time. Notably, our segmentation efficiency surpasses that of mainstream level-set techniques.

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Preprocessing Images And Restore The Contours Of Objects Obtained In The Infrared Range

Cited by 4 publications

References 4 publications

Ensemble Learning for Facial Age Estimation Within Non-Ideal Facial Imagery

Ensemble Learning for Facial Age Estimation Within Non-Ideal Facial Imagery

Forensic Analysis of Faces on Low-Quality Images using Detection and Recognition Methods

The Segmentation of Wind Turbine Defect Based on UAV Infrared Image

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