Intelligent indexing of crime scene photographs

The Scene of Crime Information System's automatic image-indexing prototype goes beyond extracting keywords and syntactic relations from captions. The semantic information it gathers gives investigators an intuitive, accurate way to search a database of cases for specific photographic evidence. Intelligent, automatic indexing and retrieval of crime scene photographs is one of the main functions of SOCIS, our research prototype developed within the Scene of Crime Information System project. The prototype, now in its final development and evaluation phase, applies advanced natural language processing techniques to text-based image indexing and retrieval to tackle crime investigation needs effectively and efficiently

Automated Intelligent real-time system for aggregate classification

Traditionally, mechanical sieving and manual gauging are used to determine the quality of the aggregates. In order to obtain aggregates with better characteristics, it must pass a series of mechanical, chemical and physical tests which are often performed manually, and are slow, highly subjective and laborious. This research focuses on developing an intelligent real-time classification system called NeuralAgg which consists of 3 major subsystems namely the real-time machine vision, the intelligent classification and the database system. The image capturing system can send high quality images of moving aggregates to the image processing subsystem, and then to the intelligent system for shape classification using artificial neural network. Finally, the classification information is stored in the database system for data archive, which can be used for post analysis purposes. These 3 subsystems are integrated to work in real-time mode which takes an average of 1.23 s for a complete classification process. The system developed in this study has an accuracy of approximately 87% and has the potential to significantly reduce the processing and/or classification time and workload

An intelligent interactive visual database management system for Space Shuttle closeout image management

Status is given of an applications investigation on the potential for using an expert system shell for classification and retrieval of high resolution, digital, color space shuttle closeout photography. This NASA funded activity has focused on the use of integrated information technologies to intelligently classify and retrieve still imagery from a large, electronically stored collection. A space shuttle processing problem is identified, a working prototype system is described, and commercial applications are identified. A conclusion reached is that the developed system has distinct advantages over the present manual system and cost efficiencies will result as the system is implemented. Further, commercial potential exists for this integrated technology

Intelligent Integrated Home Security System Using Raspberry Pi

Security, be it of a small apartment or of a sophisticated, gigantic institute is of arrant concern. In metro cities in India, for a housing complex/small apartment, security personnel are generally employed for the said purpose, who may not be that efficient especially at night. This paper intends to build an “Intelligent Home Security System” based on Digital Image Processing and Speech Processing, using a Raspberry Pi. The system is divided into two sub- systems. 1. Allowing/Disallowing vehicles based on Number Plate Recognition2. Allowing/Disallowing human beings based on Face Detection and  Recognition and Speech RecognitionA database of the residents of the building is prepared. It consists of a pre-recorded security code word and an image of the resident. A separate vehicular database containing the number plates of the cars is also stored in the memory.

Intellectual System Diagnostics Glaucoma

Glaucoma is a chronic eye disease that can lead to permanent vision loss. However, glaucoma is a difficult disease to diagnose because there is no pattern in the distribution of nerve fibers in the ocular fundus. Spectral analysis of the ocular fundus images was performed using the Eidos intelligent system. From the ACRIMA eye image database, 90.7% of healthy eye images were recognized with an average similarity score of 0.588 and 74.42% of glaucoma eye images with an average similarity score of 0.558. The reliability of eye image recognition can be achieved by increasing the number of digitized parameters of eye images obtained, for example, by optical coherence tomography. The research contribution is the digital processing of fundus graphic images by the intelligent system “Eidos”. The scientific contribution lies in the automation of the glaucoma diagnosis process using digitized data. The results of the study can be used at medical faculties of universities to carry out automated diagnostics of glaucoma

Acquisition of Images using Neural Network

The application of computer vision to the image retrieval problem is Content-based image retrieval (CBIR). The interest in digital images is growing day by day. Users in professional fields are make use of the opportunities offered by the ability to access and manipulate remotely-stored images in different ways. The problems in image retrieval are becoming widely accepted, and the finding solution is an active area for research and development. This dissertation work aims at developing a hybrid scheme for intelligent image retrieval system using neural networks. Each image in the database is indexed by a visual feature vector, which is extracted using color moments and discrete cosine transform coefficients. The query is characterized by a set of predefined semantic labels. A novel method of similarity measure using dot product is used for ranking and retrieval for improved performance of the system DOI: 10.17762/ijritcc2321-8169.15050

Design of Participatory Virtual Reality System for visualizing an intelligent adaptive cyberspace

The concept of 'Virtual Intelligence' is proposed as an intelligent adaptive interaction between the simulated 3-D dynamic environment and the 3-D dynamic virtual image of the participant in the cyberspace created by a virtual reality system. A system design for such interaction is realised utilising only a stereoscopic optical head-mounted LCD display with an ultrasonic head tracker, a pair of gesture-controlled fibre optic gloves and, a speech recogni(ion and synthesiser device, which are all connected to a Pentium computer. A 3-D dynamic environment is created by physically-based modelling and rendering in real-time and modification of existing object description files by afractals-based Morph software. It is supported by an extensive library of audio and video functions, and functions characterising the dynamics of various objects. The multimedia database files so created are retrieved or manipulated by intelligent hypermedia navigation and intelligent integration with existing information. Speech commands control the dynamics of the environment and the corresponding multimedia databases. The concept of a virtual camera developed by ZeIter as well as Thalmann and Thalmann, as automated by Noma and Okada, can be applied for dynamically relating the orientation and actions of the virtual image of the participant with respect to the simulated environment. Utilising the fibre optic gloves, gesture-based commands are given by the participant for controlling his 3-D virtual image using a gesture language. Optimal estimation methods and dataflow techniques enable synchronisation between the commands of the participant expressed through the gesture language and his 3-D dynamic virtual image. Utilising a framework, developed earlier by the author, for adaptive computational control of distribute multimedia systems, the data access required for the environment as well as the virtual image of the participant can be endowed with adaptive capability