A closely integrated digital image capture device for embedded applications

A modular image capture system with close integration to CCD cameras has been developed. The aim is to produce a system capable of integrating CCD sensor, image capture and image processing into a single compact unit. This close integration provides a direct mapping between CCD pixels and digital image pixels. The system has been interfaced to a digital signal processor board for the development and control of image processing tasks. These have included characterization and enhancement of noisy images from an intensified camera and measurement to subpixel resolutions. A highly compact form of the image capture system is in an advanced stage of development. This consists of a single FPGA device and a single VRAM providing a two chip image capturing system capable of being integrated into a CCD camera. A miniature compact PC has been developed using a novel modular interconnection technique, providing a processing unit in a three dimensional format highly suited to integration into a CCD camera unit. Work is under way to interface the compact capture system to the PC using this interconnection technique, combining CCD sensor, image capture and image processing into a single compact unit. ©2005 Copyright SPIE - The International Society for Optical Engineering.

Enticing general engineering undergrads into signal processing with Photo-Editing and image search

The University of Cambridge is unusual in that its Department of Engineering is a single department which covers virtually all branches of engineering under one roof. In their first two years of study, our undergrads study the full breadth of engineering topics and then have to choose a specialization area for the final two years of study. Here we describe part of a course, given towards the end of their second year, which is designed to entice these students to specialize in signal processing and information engineering topics for years 3 and 4. The course is based around a photo editor and an image search application, and it requires no prior knowledge of the z-transform or of 2-dimensional signal processing. It does assume some knowledge of 1-D convolution and basic Fourier methods and some prior exposure to Matlab. The subject of this paper, the photo editor, is written in standard Matlab m-files which are fully visible to the students and help them to see how specific algorithms are implemented in detail. © 2011 IEEE.

Digital watermarking with complex wavelets

In this paper, we propose a watermarking algorithm in the complex wavelet domain. We then model watermarking as a communication process and show that the complex wavelet domain has relatively high capacity and is a potentially good domain for watermarking. Finally, a technique for registering geometrically distorted images, which is based on motion estimation in the wavelet domain, is described. The registration process can assist watermark detection in a watermarked image attacked by Stirmark, for example.

Particle Image Velocimetry analysis in dynamic centrifuge tests

The Particle Image Velocimetry (PIV) technique is an image processing tool to obtain instantaneous velocity measurements during an experiment. The basic principle of PIV analysis is to divide the image into small patches and calculate the locations of the individual patches in consecutive images with the help of cross correlation functions. This paper focuses on the application of the PIV analysis in dynamic centrifuge tests on small scale tunnels in loose, dry sand. Digital images were captured during the application of the earthquake loading on tunnel models using a fast digital camera capable of taking digital images at 1000 frames per second at 1 Megapixel resolution. This paper discusses the effectiveness of the existing methods used to conduct PIV analyses on dynamic centrifuge tests. Results indicate that PIV analysis in dynamic testing requires special measures in order to obtain reasonable deformation data. Nevertheless, it was possible to obtain interesting mechanisms regarding the behaviour of the tunnels from PIV analyses. © 2010 Taylor & Francis Group, London.

Construction site image retrieval based on material cluster recognition

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of image processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based construction site image retrieval method is presented. This method is based on image retrieval techniques, and specifically those related with material and object identification and matches known material samples with material clusters within the image content. The results demonstrate the suitability of this method for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

Identification of Materials from Construction Site Images Using Content Based Image Retrieval Techniques

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. Examples include surface cracks detection, assessment of fire-damaged mortar, fatigue evaluation of asphalt mixes, aggregate shape measurements, velocimentry, vehicles detection, pore size distribution in geotextiles, damage detection and others. This capability is a product of the technological breakthroughs in the area of Image and Video Processing that has allowed for the development of a large number of digital imaging applications in all industries ranging from the well established medical diagnostic tools (magnetic resonance imaging, spectroscopy and nuclear medical imaging) to image searching mechanisms (image matching, content based image retrieval). Content based image retrieval techniques can also assist in the automated recognition of materials in construction site images and thus enable the development of reliable methods for image classification and retrieval. The amount of original imaging information produced yearly in the construction industry during the last decade has experienced a tremendous growth. Digital cameras and image databases are gradually replacing traditional photography while owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks. However, construction companies tend to store images without following any standardized indexing protocols, thus making the manual searching and retrieval a tedious and time-consuming effort. Alternatively, material and object identification techniques can be used for the development of automated, content based, construction site image retrieval methodology. These methods can utilize automatic material or object based indexing to remove the user from the time-consuming and tedious manual classification process. In this paper, a novel material identification methodology is presented. This method utilizes content based image retrieval concepts to match known material samples with material clusters within the image content. The results demonstrate the suitability of this methodology for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.