Automatic Plant Detection Using HOG and LBP Features With SVM

Plants play a vital role in the cycle of nature. Plants are the only organisms which produce food by converting light energy from the sun.  They also help in maintaining oxygen balance on earth by emitting oxygen and taking carbon dioxide. They have plenty of use in medicine and industry. But plant species are vast in number. To identify this large number of existing plant species in the world is a tedious and time-consuming task for a human. Hence, an automatic plant identification tool is very useful even for experienced botanists to identify the vast number of plants. In this paper, we proposed a technique to identify the plant leaf images. For training and testing, we used a publicly available dataset called Flavia leaf dataset. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are used to extract features and multiclass Support Vector Machine (SVM) is applied to classify the leaf images. We observed that the accuracy of HOG+SVM with HOG feature extraction using cells size of 2 x 2, 4 x 4 and 8 x 8 are 77.5%, 81.25% and 85.31 respectively. The accuracy of LBP+ SVM is 40.6% and the combination of HOG and LBP based features with SVM achieved 91.25% accuracy. The experimental results indicate the effectiveness of HOG+LBP with SVM over HOG+SVM and LBP+SVM techniques.

ANISOTROPIC BIANCHI TYPE-I COSMOLOGICAL MODEL FOR VISCOUS FLUID IN A MODIFIED BRANS-DICKE COSMOLOGY

ABSTRACT We present a new Cosmological solution for a Bianchi type-I Cosmological model filled with viscous fluid in a modified Brans-Dicke theory in which the variable cosmological term is an explicit function of a scalar field. The physical and geometrical properties of this model have been discussed. Finally, this model has been transform to the original form (1961) of Bras-Dicke theory

Diagnostic methods to determine microbiology of postpartum endometritis in South Asia: laboratory methods protocol used in the Postpartum Sepsis Study: A prospective cohort study

Background: The South Asian region has the second highest risk of maternal death in the world. To prevent maternal deaths due to sepsis and to decrease the maternal mortality ratio as per the World Health Organization Millenium Development Goals, a better understanding of the etiology of endometritis and related sepsis is required. We describe microbiological laboratory methods used in the maternal Postpartum Sepsis Study, which was conducted in Bangladesh and Pakistan, two populous countries in South Asia.Methods/Design: Postpartum maternal fever in the community was evaluated by a physician and blood and urine were collected for routine analysis and culture. If endometritis was suspected, an endometrial brush sample was collected in the hospital for aerobic and anaerobic culture and molecular detection of bacterial etiologic agents (previously identified and/or plausible).Discussion: The results emanating from this study will provide microbiologic evidence of the etiology and susceptibility pattern of agents recovered from patients with postpartum fever in South Asia, data critical for the development of evidence-based algorithms for management of postpartum fever in the region

Crowd Detection in Still Images Using Combined HOG and SIFT Feature

Person detection and tracking in crowd is a challenging task. We detect the head region and based on this head region we can detect people from crowd. Individual object detection has been improved significantly in recent times but the crowd detection and tracking contains some challenges. Crowd analysis is a highly focused area for law enforcement, urban engineering and traffic management.  There are a lot of incident occurred in crowd area during some fabulous event. In this research low resolution and verities of image orientation is a key factor as well as overlapping person images in crowd misguided the system. An enhanced system of interest point detection based on gradient orientation information as well as improved feature extraction HOG is used for identifying the human head or face from crowd. We have analyzed different types of images in different varieties and found accuracy 88-90%. In a number of applications, such as document analysis and some industrial machine vision tasks, binary images can be used as the input to algorithms that perform useful tasks. These algorithms can handle tasks ranging from very simple counting tasks to much more complex recognition, localization, and inspection tasks. Thus by studying binary image analysis before going on to gray-tone and color images, one can gain insight into the entire image analysis process