The Montage Image Mosaic Service: Custom Image Mosaics On-Demand

The Montage software suite has proven extremely useful as a general engine for reprojecting, background matching, and mosaicking astronomical image data from a wide variety of sources. The processing algorithms support all common World Coordinate System (WCS) projections and have been shown to be both astrometrically accurate and flux conserving. The background ‘matching’ algorithm does not remove background flux but rather finds the best compromise background based on all the input and matches the individual images to that. The Infrared Science Archive (IRSA), part of the Infrared Processing and Analysis Center (IPAC) at Caltech, has now wrapped the Montage software as a CGI service and provided a compute and request management infrastructure capable of producing approximately 2 TBytes / day of image mosaic output (e.g. from 2MASS and SDSS data). Besides the basic Montage engine, this service makes use of a 16-node LINUX cluster (dual processor, dual core) and the ROME request management software developed by the National Virtual Observatory (NVO). ROME uses EJB/database technology to manage user requests, queue processing and load balance between users, and managing job monitoring and user notification. The Montage service will be extended to process userdefined data collections, including private data uploads

Benchmarking SciDB Data Import on HPC Systems

SciDB is a scalable, computational database management system that uses an array model for data storage. The array data model of SciDB makes it ideally suited for storing and managing large amounts of imaging data. SciDB is designed to support advanced analytics in database, thus reducing the need for extracting data for analysis. It is designed to be massively parallel and can run on commodity hardware in a high performance computing (HPC) environment. In this paper, we present the performance of SciDB using simulated image data. The Dynamic Distributed Dimensional Data Model (D4M) software is used to implement the benchmark on a cluster running the MIT SuperCloud software stack. A peak performance of 2.2M database inserts per second was achieved on a single node of this system. We also show that SciDB and the D4M toolbox provide more efficient ways to access random sub-volumes of massive datasets compared to the traditional approaches of reading volumetric data from individual files. This work describes the D4M and SciDB tools we developed and presents the initial performance results. This performance was achieved by using parallel inserts, a in-database merging of arrays as well as supercomputing techniques, such as distributed arrays and single-program-multiple-data programming.Comment: 5 pages, 4 figures, IEEE High Performance Extreme Computing (HPEC) 2016, best paper finalis

YMAGE : a resource for real-time sharing of high resolution digital images.

Digital images have primarily been viewed using desktop applications. These types of programs attempt to load all of an image\u27s graphical data into memory in order to display the image in its entirety. The method is effective for images that are relatively small, but for large, high-resolution images, this tends to be slow and resource-exhaustive. Coupled with degraded system performance are the issues of storage and image access. Some newer programs make efforts to mitigate these problems, but few do so satisfactorily. The Ymage System was designed as an alternative to conventional approaches for managing and viewing images. The system provides high accessibility to data while minimizing demands on the machine from which the image is viewed. The design accomplishes these objectives and even the initial, low-performance implementation presented here can compete with existing systems. Further, it appears that the flexibility of the design lends itself to use for non-visual applications such as distributed image processing. It is possible that development of the Ymage System could make a significant impact on the approach to the development of image-handling software in the future

Finding What You Need, and Knowing What You Can Find: Digital Tools for Palaeographers in Musicology and Beyond

This chapter examines three projects that provide musicologists with a range of resources for managing and exploring their materials: DIAMM (Digital Image Archive of Medieval Music), CMME (Computerized Mensural Music Editing) and the software Gamera. Since 1998, DIAMM has been enhancing research of scholars worldwide by providing them with the best possible quality of digital images. In some cases these images are now the only access that scholars are permitted, since the original documents are lost or considered too fragile for further handling. For many sources, however, simply creating a very high-resolution image is not enough: sources are often damaged by age, misuse (usually Medieval ‘vandalism’), or poor conservation. To deal with damaged materials the project has developed methods of digital restoration using mainstream commercial software, which has revealed lost data in a wide variety of sources. The project also uses light sources ranging from ultraviolet to infrared in order to obtain better readings of erasures or material lost by heat or water damage. The ethics of digital restoration are discussed, as well as the concerns of the document holders. CMME and a database of musical sources and editions, provides scholars with a tool for making fluid editions and diplomatic transcriptions: without the need for a single fixed visual form on a printed page, a computerized edition system can utilize one editor’s transcription to create any number of visual forms and variant versions. Gamera, a toolkit for building document image recognition systems created by Ichiro Fujinaga is a broad recognition engine that grew out of music recognition, which can be adapted and developed to perform a number of tasks on both music and non-musical materials. Its application to several projects is discussed

Duplicate Image Detection using Machine Learning

In today\u27s digital age, the amount of data being generated and shared on a daily basis is growing at an unprecedented rate. With this growth comes the challenge of managing this vast amount of data effectively. That being said, there are approximately fifteen billion images shared on social media per day. The same image may exist in multiple locations in different formats, sizes, and with slight variations, making it difficult for end-users to filter and detect duplicate images. This duplication can lead to unnecessary storage costs, reduced data quality, and decreased productivity as users waste time searching for the right image.Detecting duplicate images is a crucial task in various fields and there is a growing need to automate this process. The primary objective of this project is to create a system that can identify duplicate images by comparing two images, even if they have slight differences in color, size, or format. To achieve the goal, we developed a system that detects and flags duplicates. The system utilizes various techniques such as visual similarity, image hashing, computer vision and Machine Learning techniques. The system is integrated into a web application that enables users to upload images and detects duplicates. The system also highlights the differences between the images. Overall, the development of a duplicate image detection web application can offer significant benefits to organizations with extensive image collections. By automating the process of identifying duplicate images, it can save time, reduce costs, and enhance the overall data quality.https://ecommons.udayton.edu/stander_posters/4005/thumbnail.jp

An OAI-based Digital Library Framework for Biodiversity Information Systems

Biodiversity information systems (BISs) involve all kinds of heterogeneous data, which include ecological and geographical features. However, available information systems offer very limited support for managing such data in an integrated fashion, and integration is often based on geographic coordinates alone. Furthermore, such systems do not fully support image content management (e.g., photos of landscapes or living organisms), a requirement of many BIS end-users. In order to meet their needs, these users - e.g., biologists, environmental experts - often have to alternate between distinct biodiversity and image information systems to combine information extracted from them. This cumbersome operational procedure is forced on users by lack of interoperability among these systems. This hampers the addition of new data sources, as well as cooperation among scientists. The approach provided in this paper to meet these issues is based on taking advantage of advances in Digital Library (DL) innovations to integrate networked collections of heterogeneous data. It focuses on creating the basis for a biodiversity information system under the digital library perspective, combining new techniques of content-based image retrieval and database query processing mechanisms. This approach solves the problem of system switching, and provides users with a flexible platform from which to tailor a BIS to their needs

performances evaluation of a novel hadoop and spark based system of image retrieval for huge collections

A novel system of image retrieval, based on Hadoop and Spark, is presented. Managing and extracting information from Big Data is a challenging and fundamental task. For these reasons, the system is scalable and it is designed to be able to manage small collections of images as well as huge collections of images. Hadoop and Spark are based on the MapReduce framework, but they have different characteristics. The proposed system is designed to take advantage of these two technologies. The performances of the proposed system are evaluated and analysed in terms of computational cost in order to understand in which context it could be successfully used. The experimental results show that the proposed system is efficient for both small and huge collections

Vendor Held Stock (HVS) Implementation to Improve and Innovate Fuel Management System: Case Study at Prabumulih Field-Pertamina EP

Management of fuel in PERTAMINA EP has not managed properly. The current system cannot guarantee the availability of fuel. Losses during storage and distribution exceed the allowable tolerance limits. Equipment, facilities, and infrastructure not meet existing standards as result in fuel consumption data at each location is not accurate. The above matter can lead to high operating costs and affect the company\u27s image. For improvements things mentioned above, the first step is to conduct observation and data collection. The data was analyzed and identified what the potential problems. Potential problems are discussed for the problem solving expectations. After knowing the expectations of the solution designed a new concept of the fuel management system. One alternative designed concept is managing fuel by using a Vendor Held Stock system. After the comparison between the old fuel management concept with a new one, select a better concept fuel management and more profitable for the company. By applying good fuel management system, the problems faced in the management of fuel can be overcom

Classification of Damaged Road Images Using the Convolutional Neural Network Method

Objective: Automatic identification is carried out with the help of a tool that can take an image of road conditions and automatically distinguish the types of road damage, the location of road damage in the image and calculate the level of road damage according to the type of road damage.Design/method/approach: Identification of damaged roads usually uses manual RCI system which requires high cost. In this study, a comparison framework is proposed to determine the performance of the image pre-processing model on the image classification algorithm.Results: Based on 733 image data classified using the CNN method from 4 models of pre-processing stages, it can be concluded that training from grayscale images produces the best level of accuracy with a training accuracy value of 88% and validation accuracy reaching 99%.Authenticity/state of the art: Testing of 4 pre-processing models against the classification algorithm used as a comparison resulted in the best algorithm/method for managing road images