to investigate the effect of preprocessing techniques including contrast enhancement and illumination correction on retinal imagequality, a comparative study was carried out. we studied and implemented a few illumination correction and contrast enhancementtechniques on color retinal images to find out the best technique for optimum image enhancement. to compare and choose thebest illumination c...

The influence of illumination effects on the optimality of the dNBR (differenced Normalized Burn Ratio) was evaluated for the case of the 2007 Peloponnese (Greece) wildfires using a pre/post-fire Landsat TM (Thematic Mapper) image couple. Well illuminated pixels (south and south-east facing slopes) exhibited more optimal displacements in the bi-spectral feature space than more shaded pixels (no...

The presence of systematic noise in images in high-throughput microscopy experiments can significantly impact the accuracy of downstream results. Among the most common sources of systematic noise is non-homogeneous illumination across the image field. This often adds an unacceptable level of noise, obscures true quantitative differences and precludes biological experiments that rely on accurate...

In this paper some fundamentals and solutions to accompanying problems in vision system design for mobile robot tracking are presented. The main topics are correction of camera lens distortion and compensation of non-uniform illumination. Both correction methods contribute to vision system performance if implemented in the appropriate manner. Their applicability is demonstrated by applying them...

The recent free availability of Landsat historical data provides new potentials for land-cover change studies. Multi-temporal studies require a previous radiometric and geometric homogenization of input images, to better identify true changes. Topographic normalization is one of the key steps to create consistent and radiometricly stable multi-temporal time series, since terrain shadows change ...

This paper presents a new statistical approach for learning automatic color image correction. The goal is to parameterize color independently of illumination and to correct color for changes of illumination. This is useful in many image processing applications, such as color image segmentation or background subtraction. The motivation for using a learning approach is to deal with changes of lig...

RF-Inhomogeneity Correction (aka bias) artifact is an important research field in Magnetic Resonance Imaging (MRI). Bias corrupts MR images altering their illumination even though they are acquired with the most recent scanners. Homomorphic Unsharp Masking (HUM) is a filtering technique aimed at correcting illumination inhomogeneity, but it produces a halo around the edges as a side effect. In ...

Accurate automatical localization of fiducial points in face images is an important step in registration. Although statistical methods of landmark localization reach high accuracies with 2D face images, their performances rapidly deteriorate under illumination changes. 3D information can assist this process by either removing the illumination effects from the 2D image, or by supplying robust fe...

An important challenge of any skin color based tracking system is to accommodate varying illumination conditions. We present a method for automatic transformation of the color planes to match the skin color model learnt for a fixed illumination. The first couple of initial frames are used to automatically extract the palm region which in turn serves as an observed skin color palette which needs...