Multiple light source detection.

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Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

Multispectral photography for earth resources

A guide for producing accurate multispectral results for earth resource applications is presented along with theoretical and analytical concepts of color and multispectral photography. Topics discussed include: capabilities and limitations of color and color infrared films; image color measurements; methods of relating ground phenomena to film density and color measurement; sensitometry; considerations in the selection of multispectral cameras and components; and mission planning

Analysis of Clipping Effect in Color Images Captured by CCD Cameras

Camera sensors (CCD) have a limited dynamic range that constrains the brightness of the incident light that can be quantified. In other words, if the ray of incident light is too intense, the sensor saturates and the value quantified is inadequately represented. This color clipping effect is a common problem in computer vision and it can become specially difficult when dealing with specular objects against a low-intensity background. In this paper, we present a method for analyzing such clipping effects of CCD cameras appearing in color images. Using an averaging technique to estimate the color of the illuminant, we define two types of axes in the RGB color cube: the Illumination Axis and the Clipping Axis. Our study concludes the followings: 1) the clipped pixels from a dielectric object form one or two lines, depending on the number of color channels on which the clipping effect takes place; and 2) these lines are parallel to the Clipping Axes. These two properties allow for a framework for a color-based segmentation that works even in the presence of saturated (specular) regions in the image. Moreover, the captured images can now be obtained under a wider variation of illumination conditions

Separation and contrast enhancement of overlapping cast shadow components using polarization

Shadow is an inseparable aspect of all natural scenes. When there are multiple light sources or multiple reflections several different shadows may overlap at the same location and create complicated patterns. Shadows are a potentially good source of information about a scene if the shadow regions can be properly identified and segmented. However, shadow region identification and segmentation is a difficult task and improperly identified shadows often interfere with machine vision tasks like object recognition and tracking. We propose here a new shadow separation and contrast enhancement method based on the polarization of light. Polarization information of the scene captured by our polarization-sensitive camera is shown to separate shadows from different light sources effectively. Such shadow separation is almost impossible to realize with conventional, polarization-insensitive imaging

Color image-based shape reconstruction of multi-color objects under general illumination conditions

Humans have the ability to infer the surface reflectance properties and three-dimensional shape of objects from two-dimensional photographs under simple and complex illumination fields. Unfortunately, the reported algorithms in the area of shape reconstruction require a number of simplifying assumptions that result in poor performance in uncontrolled imaging environments. Of all these simplifications, the assumptions of non-constant surface reflectance, globally consistent illumination, and multiple surface views are the most likely to be contradicted in typical environments. In this dissertation, three automatic algorithms for the recovery of surface shape given non-constant reflectance using a single-color image acquired are presented. In addition, a novel method for the identification and removal of shadows from simple scenes is discussed.In existing shape reconstruction algorithms for surfaces of constant reflectance, constraints based on the assumed smoothness of the objects are not explicitly used. Through Explicit incorporation of surface smoothness properties, the algorithms presented in this work are able to overcome the limitations of the previously reported algorithms and accurately estimate shape in the presence of varying reflectance. The three techniques developed for recovering the shape of multi-color surfaces differ in the method through which they exploit the surface smoothness property. They are summarized below:• Surface Recovery using Pre-Segmentation - this algorithm pre-segments the image into distinct color regions and employs smoothness constraints at the color-change boundaries to constrain and recover surface shape. This technique is computationally efficient and works well for images with distinct color regions, but does not perform well in the presence of high-frequency color textures that are difficult to segment.iv• Surface Recovery via Normal Propagation - this approach utilizes local gradient information to propagate a smooth surface solution from points of known orientation. While solution propagation eliminates the need for color-based image segmentation, the quality of the recovered surface can be degraded by high degrees of image noise due to reliance on local information.• Surface Recovery by Global Variational Optimization - this algorithm utilizes a normal gradient smoothness constraint in a non-linear optimization strategy, to iteratively solve for the globally optimal object surface. Because of its global nature, this approach is much less sensitive to noise than the normal propagation is, but requires significantly more computational resources.Results acquired through application of the above algorithms to various synthetic and real image data sets are presented for qualitative evaluation. A quantitative analysis of the algorithms is also discussed for quadratic shapes. The robustness of the three approaches to factors such as segmentation error and random image noise is also explored

Optimum Illuminant Determination Based on Reduced and Optimized Multispectral Spectroscopy to Enhance Vein Detection

Venepuncture as a mode of gaining intravenous access has been a prime practice in surgical procedures and other conventional drug administering into a patient. Biomedical engineering has stressed relatively high scale of importance in the spectroscopic analysis of vein imaging as a sparky approach to promote a non-invasive catheterization. However, medical personnel are challenged by the physiological circumstances of skin tone, presence of scars and irregularity of the epidermal topology, when performing subcutaneous vein localization, which led them to increase number of insertion attempts. Hence, this paper proposes an optimized solution to provide enhanced visual aids for personnel to achieve successful vein catheterization at first attempt