Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Driver fatigue is one of the major causes of accidents. This has increased the need for driver fatigue detection mechanism in the vehicles to reduce human and vehicle loss during accidents. In the proposed scheme, we capture videos from a camera mounted inside the vehicle. From the captured video, we localize the eyes using Viola-Jones algorithm. Once the eyes have been localized, they are classified as open or closed using three different techniques namely mean intensity, SVM, and SIFT. If eyes are found closed for a considerable amount of time, it indicates fatigue and consequently an alarm is generated to alert the driver. Our experiments show that SIFT outperforms both mean intensity and SVM, achieving an average accuracy of 97.45% on a dataset of five videos, each having a length of two minutes

Ensemble of Local Texture Descriptor for Accurate Breast Cancer Detection from Histopathologic Images

Histopathological analysis is important for detection of the breast cancer (BC). Computer-aided diagnosis and detection systems are developed to assist the radiologist in the diagnosis process and to relieve the patient from unnecessary pain. In this study, a computer-aided diagnosis system for the early detection of benign and malignant breast cancer is proposed. The proposed system consists of feature extraction, feature ensemble, and classification stages. Various preprocessing steps such as grayscale conversion, noise filtering, and image resizing are employed on the input histopathological images. The local texture descriptors namely Local Binary Pattern (LBP), Frequency Decoded LBP (FDLBP), Binary Gabor Pattern (BGP), Local Phase Quantization (LPQ), Binarized Statistical Image Features (BSIF), CENsus TRansform hISTogram (CENTRIST), and Pyramid Histogram of Oriented Gradients (PHOG) are employed for feature extraction from the histopathologic images. The obtained features are then concatenated for the construction of the ensemble of the features. Three classifiers namely Support Vector Machines (SVM), K-nearest neighbor (KNN), and Neural Networks (NN) are used in the detection of the BC and the classification accuracy score is used for performance evaluation. A dataset called BreaKHis is used in the studies. There are 9109 microscopic pictures in BreaKHis, with 2480 benign samples and 5429 malignant samples. During the collection of the data, 82 patients\u27 breast tumor tissues were envisioned using various magnification factors such as 40X, 100X, 200X, and 400X. The accuracy score is used to assess the acquired findings. The results show that the proposed method has the potential to use accurate BC detection

Implementation of Cognitive Mapping Algorithm for Robot Navigation System

Abstract—Mobile robot is a construction of robot that is equipped with wheel to move around its environment in finishing its specific task. Robot will be given some algorithm for certain task. In this project, we are going to build the cognitive mapping algorithm that can be implemented to the mobile robot. This mobile robot will be given some inputs which are horizontal and vertical value and start and goal point and a task which is to move from start point to goal point. Some obstacles are also located in the arena and the mobile robot must be able to avoid any given obstacles to reach the goal point. After doing all implementation, finally, some comparison between robot mapping and robot edge follower will be done to observe the differences between these algorithms in term of time and path needed

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

A textural deep neural network architecture for mechanical failure analysis

Nowadays, many classification problems are approached with deep learning architectures, and the results are outstanding compared to the ones obtained with traditional computer vision approaches. However, when it comes to texture, deep learning analysis has not had the same success as for other tasks. The texture is an inherent characteristic of objects, and it is the main descriptor for many applications in the computer vision field, however due to its stochastic appearance, it is difficult to obtain a mathematical model for it. According to the state of the art, deep learning techniques have some limitations when it comes to learning textural features; and, to classify texture using deep neural networks, it is essential to integrate them with handcrafted features or develop an architecture that resembles these features. By solving this problem, it would be possible to contribute in different applications, such as fractographic analysis. To achieve the best performance in any industry, it is important that the companies have a failure analysis, able to show the flaws’ causes, offer applications and solutions and generate alternatives that allow the customers to obtain more efficient components and productions. The failure of an industrial element has consequences such as significant economic losses, and in some cases, even human losses. With this analysis it is possible to examine the background of the damaged piece in order to find how and why it fails, and to help prevent future failures, in order to implement safer conditions. The visual inspection is the basis for the generation of every fractographic process in failure analysis and it is the main tool for fracture classification. This process is usually done by non-expert personnel on the topic, and normally they do not have the knowledge or experience required for the job, which, without question, increases the possibilities of generating a wrong classification and negatives results in the whole process. This research focuses on the development of a visual computer system that implements a textural deep learning architecture. Several approaches were taken into account, including combining deep learning techniques with traditional handcrafted features, and the development of a new architecture based on the wavelet transform and the multiresolution analysis. The algorithm was test on textural benchmark datasets and on the classification of mechanical fractures with particular texture and marks on surfaces of crystalline materials.Actualmente, diferentes problemas computacionales utilizan arquitecturas de aprendizaje profundo como enfoque principal. Obteniendo resultados sobresalientes comparados con los obtenidos por métodos tradicionales de visión por computador. Sin embargo, cuando se trata de texturas, los análisis de textura no han tenido el mismo éxito que para otras tareas. La textura es una característica inherente de los objetos y es el descriptor principal para diferentes aplicaciones en el campo de la visión por computador. Debido a su apariencia estocástica difícilmente se puede obtener un modelo matemático para describirla. De acuerdo con el estado-del-arte, las técnicas de aprendizaje profundo presentan limitaciones cuando se trata de aprender características de textura. Para clasificarlas, se hace esencial combinarlas con características tradicionales o desarrollar arquitecturas de aprendizaje profundo que reseemblen estas características. Al solucionar este problema es posible contribuir a diferentes aplicaciones como el análisis fractográfico. Para obtener el mejor desempeño en cualquier tipo de industria es importante obtener análisis fractográfico, el cual permite determinar las causas de los diferentes fallos y generar las alternativas para obtener componentes más eficientes. La falla de un elemento mecánico tiene consecuencias importantes tal como pérdidas económicas y en algunos casos incluso pérdidas humanas. Con estos análisis es posible examinar la historia de las piezas dañadas con el fin de entender porqué y cómo se dio el fallo en primer lugar y la forma de prevenirla. De esta forma implementar condiciones más seguras. La inspección visual es la base para la generación de todo proceso fractográfico en el análisis de falla y constituye la herramienta principal para la clasificación de fracturas. El proceso, usualmente, es realizado por personal no-experto en el tema, que normalmente, no cuenta con el conocimiento o experiencia necesarios requeridos para el trabajo, lo que sin duda incrementa las posibilidades de generar una clasificación errónea y, por lo tanto, obtener resultados negativos en todo el proceso. Esta investigación se centra en el desarrollo de un sistema visual de visión por computado que implementa una arquitectura de aprendizaje profundo enfocada en el análisis de textura. Diferentes enfoques fueron tomados en cuenta, incluyendo la combinación de técnicas de aprendizaje profundo con características tradicionales y el desarrollo de una nueva arquitectura basada en la transformada wavelet y el análisis multiresolución. El algorítmo fue probado en bases de datos de referencia en textura y en la clasificación de fracturas mecánicas en materiales cristalinos, las cuales presentan texturas y marcas características dependiendo del tipo de fallo generado sobre la pieza.Fundación CEIBADoctorad