Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Despite the existence of various biometric techniques, like fingerprints, iris scan, as well as hand geometry, the most efficient and more widely-used one is face recognition. This is because it is inexpensive, non-intrusive and natural. Therefore, researchers have developed dozens of face recognition techniques over the last few years. These techniques can generally be divided into three categories, based on the face data processing methodology. There are methods that use the entire face as input data for the proposed recognition system, methods that do not consider the whole face, but only some features or areas of the face and methods that use global and local face characteristics simultaneously. In this paper, we present an overview of some well-known methods in each of these categories. First, we expose the benefits of, as well as the challenges to the use of face recognition as a biometric tool. Then, we present a detailed survey of the well-known methods by expressing each method’s principle. After that, a comparison between the three categories of face recognition techniques is provided. Furthermore, the databases used in face recognition are mentioned, and some results of the applications of these methods on face recognition databases are presented. Finally, we highlight some new promising research directions that have recently appeared.

https://www.mdpi.com/2073-431X/5/4/21/pdf

A Survey of 2D Face Recognition Techniques

Learning Continuous Human-Robot Interactions from Human-Human Demonstrations

Face Recognition represents one of the attracting research areas. It has drawn the attention of many researchers due to its varying applications such as database identification, identity authentication, surveillance and security access control human computer interaction.… In this order, different face recognition algorithms are proposed. These methods are based on the face representations. In order to use the most important information in the face representation, we propose to exploit the concept of 2D-DWT for the image compression as a preprocessing for feature extraction. In fact, the DWT is performed at different scales and orientations so it is sensitive to strong lighting conditions and facial details. Based on the DWT advantages, the LL sub-band of the processed image is used as input image for feature extraction process based on ICA, PCA, LDA and SVM algorithms. The importance of the proposed approaches is evaluated by experimental and comparative studies tested on AT&T database.

Robust Face Recognition Approaches Using PCA, ICA, LDA Based on DWT, and SVM Algorithms

The speed of procedures became necessities during recent years; therefore using computers turn out to be the most important factors to increase the speed of implementation especially in security aspect such as recognition of people.There are a lot of waysto recognize the people face recognition is one of them. In this work the details of the face have been taken as blocks and Discrete Cosine Transform (DCT) is used, applied on face image’s blocks. Then without doing inverse DCT Principal Component Analysis (PCA) is applied directly for dimensionality reduction this makes the system very fast. Olivetti Research Laboratory (ORL) database of faces had been used to obtain the results.Each face is considered as a numerical sequence (blocks) that can be easily modelled by HMM. On 400 face images of the (ORL) face database the system has been examined. The experiments showed a recognition rate of 95.211%, using half of the images for training.

Face Recognition System Using PCA and DCT in HMM

Biometric recognition systems have been attracted numerous researchers since they attempt to overcome the problems and factors weakening these systems including problems of obtaining images indeed not appearing the resolution or the object completely. In this work, the object movement reliance was considered to distinguish the human through his/her gait. Some losing features probably weaken the system’s capability in recognizing the people, hence, we propose using all data recorded by the Kinect sensor with no employing the feature extraction methods based on the literature. In these studies, coordinates of 20 points are recorded for each person in various genders and ages, walking with various directions and speeds, creating 8404 constraints. Moreover, pre-processing methods are utilized to measure its influences on the system efficiency through testing on six types of classifiers. Within the proposed approach, a noteworthy recognition rate was obtained reaching 91% without examining the descriptors.

/pdf/human-gait-recognition-using-preprocessing-and-1d1vspyw22.pdf

Human gait recognition using preprocessing and classification techniques

recently It has become essential to search for and retrieve high-resolution and efficient images easily due to swift development of digital images, many present annotation algorithms facing a big challenge which is the variance for represent the image where high level represent image semantic and low level illustrate the features, this issue is known as "semantic gab". This work has been used MPEG-7 standard to extract the features from the images, where the color feature was extracted by using Scalable Color Descriptor (SCD) and Color Layout Descriptor (CLD), whereas the texture feature was extracted by employing Edge Histogram Descriptor (EHD), the CLD produced high dimensionality feature vector therefor it is reduced by Principal Component Analysis (PCA). The features that have extracted by these three descriptors could be passing to the classifiers (Naive Bayes and Decision Tree) for training. Finally, they annotated the query image. In this study TUDarmstadt image bank had been used. The results of tests and comparative performance evaluation indicated better precision and executing time of Naive Bayes classification in comparison with Decision Tree classification.

Automatic image annotation base on Naïve Bayes and Decision Tree classifiers using MPEG-7

Recently, numerous challenging problems have existed for transforming different image types (thermal infrared (TIR), visible spectrum, and near-infrared (NIR)). Other types of cameras may lack the ...

Generating Spectrum Images from Different Types — Visible, Thermal, and Infrared Based on Autoencoder Architecture (GVTI-AE)

Corneal diseases are the most common eye disorders. Deep learning techniques are used to perform automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients.

/pdf/exploiting-the-generative-adversarial-network-approach-to-ec2gaxtg.pdf

Samer Kais Jameel

Papers

Face Recognition System Using PCA and DCT in HMM

Human gait recognition using preprocessing and classification techniques

Automatic image annotation base on Naïve Bayes and Decision Tree classifiers using MPEG-7

Generating Spectrum Images from Different Types — Visible, Thermal, and Infrared Based on Autoencoder Architecture (GVTI-AE)

Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image