Digital camera

About: Digital camera is a research topic. Over the lifetime, 12169 publications have been published within this topic receiving 137431 citations. The topic is also known as: digicam & digital still camera.

The HydroColor App: Above Water Measurements of Remote Sensing Reflectance and Turbidity Using a Smartphone Camera

Abstract: HydroColor is a mobile application that utilizes a smartphone's camera and auxiliary sensors to measure the remote sensing reflectance of natural water bodies HydroColor uses the smartphone's digital camera as a three-band radiometer Users are directed by the application to collect a series of three images These images are used to calculate the remote sensing reflectance in the red, green, and blue broad wavelength bands As with satellite measurements, the reflectance can be inverted to estimate the concentration of absorbing and scattering substances in the water, which are predominately composed of suspended sediment, chlorophyll, and dissolved organic matter This publication describes the measurement method and investigates the precision of HydroColor's reflectance and turbidity estimates compared to commercial instruments It is shown that HydroColor can measure the remote sensing reflectance to within 26% of a precision radiometer and turbidity within 24% of a portable turbidimeter HydroColor distinguishes itself from other water quality camera methods in that its operation is based on radiometric measurements instead of image color HydroColor is one of the few mobile applications to use a smartphone as a completely objective sensor, as opposed to subjective user observations or color matching using the human eye This makes HydroColor a powerful tool for crowdsourcing of aquatic optical data

Automatic machine vision microscope slide inspection system and method

Abstract: A system for automatically detecting the presence of contaminants in samples. The system includes a microscope, controllable stage positioner for holding slides under the microscope, a computer for controlling the stage positioner and a digital camera to capture images through the microscope. The system scans microscope views of regions of a slide sample and provides the digital images to the computer. Image processing routines stored in the computer analyze the digital images and determine whether these images may contain certain contaminants by comparing the characteristics of the objects in the image with the known characteristics of the contaminants. The system also contemplates a method for automatically determining the presence of contaminants in samples including the steps of providing a microscope slide containing a sample, obtaining a plurality of digital microscope images of the sample, storing the digital images in a computer, automatically comparing characteristics of each digital image to characteristics of known contaminants and storing the results of the comparison.

Efficient and Handy Texture Mapping on 3D Surfaces

Abstract: There has been a rapid technical progress in three-dimensional (3D) computer graphics. But gathering surface and texture data is yet a laborious task. This paper addresses the problem of mapping photographic images on the surface of a 3D object whose geometric data are already known. We propose an efficient and handy method for acquiring textures and mapping them precisely on the surface, employing a digital camera alone. We describe an algorithm for selecting a minimal number of camera positions that can cover the entire surface of a given object and also an algorithm to determine camera’s position and direction for each photograph taken so as to paste it to the corresponding surfaces precisely. We obtained a matching accuracy within a pixel on a surface through three experimental examples, by which the practicability of our method is demonstrated.

Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: Description of image acquisition and processing

Abstract: There are many aspects of crop management that might benefit from aerial observation. Unmanned aerial vehicle (UAV) platforms are evolving rapidly both technically and with regard to regulations. The purpose of this study was to acquire images with conventional RGB cameras using UAVs and process them to obtain geo-referenced ortho-images with the aim of characterizing the main plant growth parameters required in the management of irrigated crops under semi-arid conditions. The paper is in two parts, the first describes the image acquisition and processing procedures, and the second applies the proposed methodology to a case study. In the first part of the paper, the type of UAV utilized is described. It was a vertical take-off and landing quadracopter aircraft with a conventional RGB compact digital camera. Other types of on-board sensors are also described, such as near-infrared sensors and thermal sensors, and the problems of using these types of expensive sensor is discussed. In addition, software developed by the authors for photogrammetry processing, and information extraction from the geomatic products are described and analysed for agronomic applications. This software can also be used in other applications. To obtain agronomic parameters, different strategies were analysed, such as the use of computer vision for canopy cover extraction, as well as the use of vegetation indices derived from the visible spectrum, as a proper solution when very-high resolution imagery is available. The use of high-resolution images obtained with UAVs together with proper treatment might be considered a useful tool for precision in monitoring crop growth and development, advising farmers on water requirements, yield production, weed and insect infestations, among others. More studies, focusing on the calibration and validation of these relationships in other crops are required.

Unsupervised Learning for Color Constancy.

Abstract: Most digital camera pipelines use color constancy methods to reduce the influence of illumination and camera sensor on the colors of scene objects. The highest accuracy of color correction is obtained with learning-based color constancy methods, but they require a significant amount of calibrated training images with known ground- truth illumination. Such calibration is time consuming, preferably done for each sensor individually, and therefore a major bottleneck in acquiring high color constancy accuracy. Statistics-based methods do not require calibrated training images, but they are less accurate. In this paper an unsupervised learning-based method is proposed that learns its parameter values after approximating the unknown ground-truth illumination of the training images, thus avoiding calibration. In terms of accuracy the proposed method outperforms all statistics-based and many state-of-the-art learning-based methods. The results are presented and discussed.