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This paper presents a generic method of calculating accurate horizontal and vertical object distances from digital images taken with any digital camera and lens combination, where the object plane is parallel to the image plane or tilted in the vertical plane. This method was developed for a project investigating the size, density and spatial distribution of shrubs growing as hedges. The geometry of objects projected on to parallel and oblique image planes was used to develop an equation for calculating horizontal and vertical distances from image pixel counts. Images of a grid were taken with a variety of digital cameras at different focal lengths, image-object distances and vertical tilt angles. The equations were also tested by taking a set of photos of shrubs in a hedge field experiment. The results show a very strong correlation between calculated distances and physical measurements across a range of cameras, focal lengths, distances and vertical tilt angles. A small constant error was found in two of the three cameras tested suggesting that effective image size may vary from published sensor dimensions in some cameras. Cameras should be calibrated to check for any constant error before using the equations described in this paper. (Contains 1 figure and 2 tables.)

Digital cameras are now commonplace in many classrooms and in the lives of many children in early childhood centres and primary schools. They are regularly used by adults and teachers for "saving special moments and documenting experiences." The use of previously expensive photographic and recording equipment has often remained in the domain of teachers, who have produced innovative digital products for children to use and for teachers to "facilitate the collection of accurate evidence for assessment purposes." However, more and more teachers of young children in current digitally-equipped classrooms are handing over the cameras to their students, with beneficial results. As well as providing opportunities for learning in art, language, science and physical education, digital cameras are proving to be versatile tools that can be used to facilitate mathematics learning and teaching in K-2 classrooms. In this article, the author provides a few examples where digital cameras can be used as tools in the mathematics classroom.

CD-ROM Interactive Multimedia Training

CD-ROM Interactive Multimedia Training

CD-ROM Interactive Multimedia Training

CD-ROM Interactive Multimedia Training

Here is BVG Train Yourself Using Digital Cameras and Scanners In the Using Digital Cameras course, you will learn about comparing picture quality, where to research, digital cameras, questions to ask about camera purchase, USB connection, flash, LCD playback, printing pictures, picture views and ACDSee, hue and saturation, digital video camera and more In the Using Scanners course, you will learn about setting up and maintenance of scanners, twain software, prescan and scan in pictures, scanner controls, photo resolution, sharpen images, moire patterns, interpolation, fixing red-eye, lowlight photos and more Your instructors Jody Wallace and Leslie Thomas will guide you through using cameras and scanners

Spatial resolution is one of the main characteristics of electronic imaging devices such as the digital still-picture camera. It describes the capability of a device to resolve the spatial details of an image formed by the incoming optical information. The overall resolving capability is of great interest although there are various factors, contributed by camera components and signal processing algorithms, affecting the spatial resolution. The spatial frequency response (SFR), analogous to the MTF of an optical imaging system, is one of the four measurements for analysis of spatial resolution defined in ISO/FDIS 12233, and it provides a complete profile of the spatial response of digital still-picture cameras. In that document, a test chart is employed to estimate the spatial resolving capability. The calculations of SFR were conducted by using the slanted edge method in which a scene with a black-to-white or white-to-black edge tilted at a specified angle is captured. An algorithm is used to find the line spread function as well as the SFR. We will present a modified algorithm in which no prior information of the angle of the tilted black-to-white edge is needed. The tilted angle was estimated by assuming that a region around the center of the transition between black and white regions is linear. At a tilted angle of 8 degree the minimum estimation error is about 3%. The advantages of the modified slanted edge method are high accuracy, flexible use, and low cost.