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Regularization Of Inverse Problems

Face Recognition is a computer application that is capable of detecting, tracking, identifying or verifying human faces from an image or video captured using a digital camera. Although lot of progress has been made in domain of face detection and recognition for security, identification and attendance purpose, but still there are issues hindering the progress to reach or surpass human level accuracy. These issues are variations in human facial appearance such as; varying lighting condition, noise in face images, scale, pose etc. This research paper presents a new method using Local Binary Pattern (LBP) algorithm combined with advanced image processing techniques such as Contrast Adjustment, Bilateral Filter, Histogram Equalization and Image Blending to address some of the issues hampering face recognition accuracy so as to improve the LBP codes, thus improve the accuracy of the overall face recognition system. Our experiment results show that our method is very accurate, reliable and robust for face recognition system that can be practically implemented in real-life environment as an automatic attendance management system.

An improved face recognition algorithm and its application in attendance management system

Schizophrenia (SZ) is a severe disorder of the human brain which disturbs behavioral characteristics such as interruption in thinking, memory, perception, speech and other living activities. If the patient suffering from SZ is not diagnosed and treated in the early stages, damage to human behavioral abilities in its later stages could become more severe. Therefore, early discovery of SZ may help to cure or limit the effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as SZ due to having high temporal resolution information, and being a noninvasive and inexpensive method. This paper introduces an automatic methodology based on transfer learning with deep convolutional neural networks (CNNs) for the diagnosis of SZ patients from healthy controls. First, EEG signals are converted into images by applying a time–frequency approach called continuous wavelet transform (CWT) method. Then, the images of EEG signals are applied to the four popular pre-trained CNNs: AlexNet, ResNet-18, VGG-19 and Inception-v3. The output of convolutional and pooling layers of these models are used as deep features and are fed into the support vector machine (SVM) classifier. We have tuned the parameters of SVM to classify SZ patients and healthy subjects. The efficiency of the proposed method is evaluated on EEG signals from 14 healthy subjects and 14 SZ patients. The experiments showed that the combination of frontal, central, parietal, and occipital regions applied to the ResNet-18-SVM achieved best results with accuracy, sensitivity and specificity of 98.60% ± 2.29, 99.65% ± 2.35 and 96.92% ± 2.25, respectively. Therefore, the proposed method as a diagnostic tool can help clinicians in detection of the SZ patients for early diagnosis and treatment.

Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals.

Low Resolution Brain Electromagnetic Tomography(LORETA)をもちいた脳機能マッピングの新たな展開

Facial recognition has always gone through a consistent research area due to its non-modelling nature and its diverse applications. As a result, day-to-day activities are increasingly being carried out electronically rather than in pencil and paper. Today, computer vision is a comprehensive field that deals with a high level of programming by feeding the input images/videos to automatically perform tasks such as detection, recognition and classification. Even with deep learning techniques, they are better than the normal human visual system. In this article, we developed a facial recognition system based on the Local Binary Pattern Histogram (LBPH) method to treat the real-time recognition of the human face in the low and high-level images. We aspire to maximize the variation that is relevant to facial expression and open edges so to sort of encode edges in a very cheap way. These highly successful features are called the Local Binary Pattern Histogram (LBPH).

/pdf/lbph-based-enhanced-real-time-face-recognition-4kda8jzusu.pdf

LBPH-based Enhanced Real-Time Face Recognition

Low-resolution medical images can seriously interfere with the medical diagnosis, and poor image quality can lead to loss of detailed information. Therefore, improving the quality of medical images and accelerating the reconstruction is of particular importance for diagnosis. To solve this problem, we propose a wavelet-based mini-grid network medical image super-resolution (WMSR) method, which is similar to the three-layer hidden-layer-based super-resolution convolutional neural network (SRCNN) method. Due to the amplification characteristics of wavelets, a stationary wavelet transform (SWT) is used instead of a discrete wavelet transform (DWT). Also, due to the nature of redundant (scale-by-scale) wavelets, it is possible to retain additional information about the image and restore high-resolution images in detail. For a large amount of training data, wavelet sub-band images, including approximation and frequency subbands are combined into a predefined full-scale factor. The mapping between the wavelet sub-band image and its approximate image is then determined. In order to ensure the reproducibility of the image, a method of adding a sub-pixel layer is proposed to realize the hidden layer, and replacing the small mini-grid-network on the hidden layer is of considerable significance to speed up the image recovery speed. Experimental results on the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) show that the model has better performance.

/pdf/wavelet-based-enhanced-medical-image-super-resolution-1t9lkx5e7d.pdf

Wavelet-Based Enhanced Medical Image Super Resolution

A remote-sensing image enhancement algorithm based on patch-wise dark channel prior and histogram equalisation with colour correction

This paper highlights a novel and simple approach for two-dimensional displacement sensor’s design based on macro-bending loss and optical power coupling effect. Multimode plastic optical fiber offers flexibility in bending which can easily radiate bend loss and easily side coupled with second fiber (receiving fiber RF). In this approach of 2D system, dual-time repeated twisting has been carried out with transmitting fiber (TF). Two RFs were employed in experiment and twisted in such a way that for the measurement of x-axis displacement (First RF is twisted with TF) and for measurement of y-axis displacement (Second RF is twisted with TF. The experimental measurement results of two dimensions sensor have been achieved for forward and backhaul movement with good consistency up to 160 mm and system has linearity from 0 mm to 100 mm having sensitivity of 2.6 nW/mm. The 2D sensor has numerous industrial applications in robotics, precision alignment, bridges and many more. In this context, the aforementioned technique could be considered as more holistic and optimal in comparison to the existing techniques.

Two-dimensional displacement optical fiber sensor based on macro-bending effect

The presence of suspended particles, such as fog, smoke, and dust, in the atmosphere reduces the quality of the captured image. It is essential to overcome these particles, because these particles have a very dire effect on different applications of image processing. We propose a new image dehazing method for remote sensing (RS) applications. Since the hazy RS image is affected by multiple colors and contrast reduction, our goal is to focus on degraded objects, including color correction and color–contrast enhancement. A “Piecewise Linear Transformation (PWLT)” is used to correct the color distortion, and then the color contrast is improved by applying the proposed method. The intensity distribution manipulation is one of the most common methods used to strengthen image contrast in the past. Compared with advanced techniques, the proposed method is easy to implement and suitable for real-time applications. Moreover, it is not necessary to require prior imaging conditions. The experimental results show that, in terms of subjective and visual quality, the color, contrast, naturalness, and high brightness of the object increase in the image to be improved.

Fayaz Ali Dharejo

Papers

LBPH-based Enhanced Real-Time Face Recognition

Wavelet-Based Enhanced Medical Image Super Resolution

A remote-sensing image enhancement algorithm based on patch-wise dark channel prior and histogram equalisation with colour correction

Two-dimensional displacement optical fiber sensor based on macro-bending effect

A Color Enhancement Scene Estimation Approach for Single Image Haze Removal