Graphical image persistence and code generation for object oriented databases

Attached is the detailed description of the design and implementation of graphical image persistence and code generation for object oriented databases. Graphical image persistent is incorporated into a graphics editor called OODINI. OODINI creates and manipulates graphical schemas for object-oriented databases. This graphical image on secondary storage is then translated into an abstract, generic code for dual model databases. This abstract code, DAL can then be converted into different dual model database languages. We provide an example by generating code for the VODAK Data Modeling language. It is also possible to generate a different abstract language code, OODAL from a graphical schema. This language does not have any dual model database architectural dependencies

Car Detecting Method using high Resolution images

A car detection method is implemented using high resolution images, because these images gives high level of object details in the image as compare with satellite images. There are two feature extraction algorithms are used for implementation such as SIFT (Scale Invariant Feature Transform) and HOG (Histogram of Oriented Gradient). SIFT keypoints of objects are first extracted from a set of reference images and stored in a database. HOG descriptors are feature descriptors used in image processing for the purpose of object detection. The HOG technique counts occurrences of gradient orientation in localized portions of an image. The HOG algorithm used for extracting HOG features. These HOG features will be used for classification and object recognition. The classification process is performed using SVM (Support Vector Machine) classifier. The SVM builds a model with a training set that is presented to it and assigns test samples based on the model. Finally get the SIFT results and HOG results, then compare both results to check better accuracy performance. The proposed method detects the number of cars more accuratel

Distributed Object Medical Imaging Model

Abstract- Digital medical informatics and images are commonly used in hospitals today,. Because of the interrelatedness of the radiology department and other departments, especially the intensive care unit and emergency department, the transmission and sharing of medical images has become a critical issue. Our research group has developed a Java-based Distributed Object Medical Imaging Model(DOMIM) to facilitate the rapid development and deployment of medical imaging applications in a distributed environment that can be shared and used by related departments and mobile physiciansDOMIM is a unique suite of multimedia telemedicine applications developed for the use by medical related organizations. The applications support realtime patients’ data, image files, audio and video diagnosis annotation exchanges. The DOMIM enables joint collaboration between radiologists and physicians while they are at distant geographical locations. The DOMIM environment consists of heterogeneous, autonomous, and legacy resources. The Common Object Request Broker Architecture (CORBA), Java Database Connectivity (JDBC), and Java language provide the capability to combine the DOMIM resources into an integrated, interoperable, and scalable system. The underneath technology, including IDL ORB, Event Service, IIOP JDBC/ODBC, legacy system wrapping and Java implementation are explored. This paper explores a distributed collaborative CORBA/JDBC based framework that will enhance medical information management requirements and development. It encompasses a new paradigm for the delivery of health services that requires process reengineering, cultural changes, as well as organizational changes

DOTA: A Large-scale Dataset for Object Detection in Aerial Images

Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of the huge variation in the scale, orientation and shape of the object instances on the earth's surface, but also due to the scarcity of well-annotated datasets of objects in aerial scenes. To advance object detection research in Earth Vision, also known as Earth Observation and Remote Sensing, we introduce a large-scale Dataset for Object deTection in Aerial images (DOTA). To this end, we collect $2806$ aerial images from different sensors and platforms. Each image is of the size about 4000-by-4000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. These DOTA images are then annotated by experts in aerial image interpretation using $15$ common object categories. The fully annotated DOTA images contains $188,282$ instances, each of which is labeled by an arbitrary (8 d.o.f.) quadrilateral To build a baseline for object detection in Earth Vision, we evaluate state-of-the-art object detection algorithms on DOTA. Experiments demonstrate that DOTA well represents real Earth Vision applications and are quite challenging.Comment: Accepted to CVPR 201

Terrestrial applications: An intelligent Earth-sensing information system

For Abstract see A82-2214

Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database

In this paper we present a novel architecture for storing visual data. Effective storing, browsing and searching collections of images is one of the most important challenges of computer science. The design of architecture for storing such data requires a set of tools and frameworks such as SQL database management systems and service-oriented frameworks. The proposed solution is based on a multi-layer architecture, which allows to replace any component without recompilation of other components. The approach contains five components, i.e. Model, Base Engine, Concrete Engine, CBIR service and Presentation. They were based on two well-known design patterns: Dependency Injection and Inverse of Control. For experimental purposes we implemented the SURF local interest point detector as a feature extractor and $K$-means clustering as indexer. The presented architecture is intended for content-based retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan