A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method

In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCA-KNN) approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform) descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class). The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods

A Novel System for Non-Invasive Method of Animal Tracking and Classification in Designated Area Using Intelligent Camera System

This paper proposed a novel system for non-invasive method of animal tracking and classification in designated area. The system is based on intelligent devices with cameras, which are situated in a designated area and a main computing unit (MCU) acting as a system master. Intelligent devices track animals and then send data to MCU to evaluation. The main purpose of this system is detection and classification of moving animals in a designated area and then creation of migration corridors of wild animals. In the intelligent devices, background subtraction method and CAMShift algorithm are used to detect and track animals in the scene. Then, visual descriptors are used to create representation of unknown objects. In order to achieve the best accuracy in classification, key frame extraction method is used to filtrate an object from detection module. Afterwards, Support Vector Machine is used to classify unknown moving animals

Novel Method for Color Textures Features Extraction Based on GLCM

Texture is one of most popular features for image classification and retrieval. Forasmuch as grayscale textures provide enough information to solve many tasks, the color information was not utilized. But in the recent years, many researchers have begun to take color information into consideration. In the texture analysis field, many algorithms have been enhanced to process color textures and new ones have been researched. In this paper the new method for color GLCM textures and comparing with other good known methods is presented

Classification of animals to determine the migration potential at the construction of new infrastructure

At the planning and construction of new infrastructures, the information about migration potential of animals in a target area is needed. This information will be used to design of migration corridors for wild animals. To determine the migration potential of animals based on distributed video camera system, new methods for object recognition and classification are developed. In general, an object recognition system consists of three steps, namely, the image feature extraction from the training database, training the classifier and evaluation of query image of object/animal. In this paper, an extraction of local key point by SIFT or SURF descriptors, bags of key points method in combination with SVM classifier and two hybrid key points detection methods are proposed in detail

Sinking Skin Flap Syndrome and Paradoxical Herniation Provoked by a Malfunction of External Lumbar Drainage and CSF Leak

The sinking skin flap syndrome represents a less-frequent complication in patients after a decompressive craniectomy. It is defined as a neurological deterioration accompanied by a flat or concave deformity of the craniectomy-related skin flap. The underlying brain parenchyma is distorted correspondingly with its blood flow and metabolism being impaired and cerebrospinal fluid hydrodynamics being disturbed, thus causing cerebral dysfunction and neurological symptomatology. The most important options for reversal of this syndrome include Trendelenburg position, maintaining of the cerebrospinal fluid balance, and cranioplasty as a definite solution. We present a patient who underwent a decompressive craniectomy complicated by a cerebrospinal fluid leak in the operative wound treated by means of an external lumbar drainage. Subsequently he developed the sinking skin flap syndrome and a paradoxical cerebral herniation after the drainage system malfunction with a massive cerebrospinal fluid leak at the site of the lumbar drain insertion parallel to the drain itself. His symptoms were, however, successfully alleviated by a positional change, rehydration, and interruption of the lumbar drainage. This illustrational case suggests that clinicians should be aware that patients after decompressive craniectomy may develop a sinking skin flap syndrome as it may either represent an acute risk of a paradoxical brain herniation or complicate the further postoperative care if developed in a chronic way

GLEBANITE® FOR MODELS AND MOULDS IN SHIPYARDS APPLICATIONS RATHER RESORTING TO MONOMATERIC SOLUTIONS

Despite the recovery of the boating industry today, the crisis in the field sector has brought with it a great amount of boats and related obsolete production equipment (Marsh, 2013). The Glebanite® project aims to create a possibility for all these products considered waste, giving a clear answer to the problem of their disposal, with particular attention to fiber-reinforced composite materials. The project prsented aims to trigger a Circular Economy process for the generation of a new recycled and re-processable materials and for a new economy to support recreational boating. The project has as its starting point the engineering and systemization of the Glebanite material (secondary raw material derived from GRPs waste) for an innovative production strategy of nautical shipbuilding equipment through the use of CNC machine. The project thus straddles the main areas of Eco-Industry, Advanced Manufacturing and Sustainable Mobility

Bluetooth automatic network recognition – the AIR-AWARE approach

Automatic network recognition and classification may prove to be an important concept in the framework of cognitive radio and networks. For practical implementations, these operations must be carried out in a simple way by using simple devices and algorithms that require low computational load. The AIR-AWARE approach proposes to use MAC sub-layer features for technology recognition purposes where a rudimentary device like an energy detector is used for technology-specific feature extraction. The aim of this work is automatic Bluetooth classification. To this purpose, two MAC features reflecting properties, related to the time-varying pattern of MAC packet exchanges, are proposed. Experimental data obtained by using the Universal Software Radio Peripheral as energy detector show that the two proposed features are capable of highlighting MAC sub-layer behavior peculiar to Bluetooth. These features may therefore lead to successful Bluetooth recognition and the results obtained provide support to the validity of the AIR-AWARE approach