There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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Remote Sensing And Image Interpretation

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Pixel-level image fusion

The Foundations of Statistics By Prof. Leonard J. Savage. (Wiley Publications in Statistics.) Pp. xv + 294. (New York; John Wiley and Sons, Inc.; London: Chapman and Hall, Ltd., 1954.) 48s. net.

Probability and Statistical Inference

In landscape photography, distant objects often appear blurred with a blue color cast, a degradation caused by atmospheric haze. To enhance image contrast, pleasantness and information content, dehazing can be performed.

/pdf/color-image-dehazing-using-the-near-infrared-4ikrgybgqc.pdf

Color image dehazing using the near-infrared

Current digital camera sensors are inherently sensitive to the near-infrared part of the spectrum To prevent the near-IR contamination of images, an IR blocking filter (hot mirror) is placed in front of the sensor In this work, we start by replacing the camera's hot mirror by a piece of clear glass, thus making the camera sensitive to both visible and near-IR light Using a pair of lens-mounted filters, we explore the differences in operating the camera to take visible and near-IR images of a given scene Our aim is to enhance the visible images using near-IR information To do so, we first discuss the physical causes of differences between visible and near-IR natural images, and remark that these causes are not correlated with a particular colour, but with atmospheric conditions and surface characteristics We then investigate image enhancement by considering the near-IR channel as either colour, luminance, or frequency counterpart to the visible image and conclude that using information from two different colour encodings, depending on the image content, produces vivid, contrasted images that are pleasing to the observers

Colouring the near infrared

Digital camera sensors are inherently sensitive to the near-infrared (NIR) part of the light spectrum. In this paper, we propose a general design for color filter arrays that allow the joint capture of visible/NIR images using a single sensor. We pose the CFA design as a novel spatial domain optimization problem, and provide an efficient iterative procedure that finds (locally) optimal solutions. Numerical experiments confirm the effectiveness of the proposed CFA design, which can simultaneously capture high quality visible and NIR image pairs.

/pdf/designing-color-filter-arrays-for-the-joint-capture-of-5a8mfy675u.pdf

Designing color filter arrays for the joint capture of visible and near-infrared images

We present a method to automatically detect shadows in a fast and accurate manner by taking advantage of the inherent sensitivity of digital camera sensors to the near-infrared (NIR) part of the spectrum. Dark objects, which confound many shadow detection algorithms, often have much higher reflectance in the NIR. We can thus build an accurate shadow candidate map based on image pixels that are dark both in the visible and NIR representations. We further refine the shadow map by incorporating ratios of the visible to the NIR image, based on the observation that commonly encountered light sources have very distinct spectra in the NIR band. The results are validated on a new database, which contains visible/NIR images for a large variety of real-world shadow creating illuminant conditions, as well as manually labeled shadow ground truth. Both quantitative and qualitative evaluations show that our method outperforms current state-of-the-art shadow detection algorithms in terms of accuracy and computational efficiency.

/pdf/automatic-and-accurate-shadow-detection-using-near-infrared-hw9oqs5ihy.pdf

Automatic and Accurate Shadow Detection Using Near-Infrared Information

In this paper we present a surprisingly simple yet powerful method for detecting illumination determining which pixels are lit by different lights in images. Our method is based on the chromagenic camera, which takes two pictures of each scene: one is captured as normal and the other through a coloured filter. Previous research has shown that the relationship between the colours, the RGBs, in the filtered and unfiltered images depends strongly on the colour of the light and this can be used to estimate the colour of the illuminant. While chromagenic illuminant estimation often works well it can and does fail and so is not itself a direct solution to the illuminant detection problem. In this paper we dispense with the goal of illumination estimation and seek only to use the chromagenic effect to find out which parts of a scene are illuminated by the same lights. The simplest implementation of our idea involves a combinatorial search. We precompute a dictionary of possible illuminant relations that might map RGBs to filtered counterparts from which we select a small number m corresponding to the number of distinct lights we think might be present. Each pixel, or region, is assigned the relation from this m-set that best maps filtered to unfiltered RGB. All m-sets are tried in turn and the one that has the minimum prediction error over all is found. At the end of this search process each pixel or region is assigned an integer between 1 and m indicating which of the m lights are thought to have illuminated the region. Our simple search algorithm is possible when m = 2 (and m = 3) and for this case we present experiments that show our method does a remarkable job in detecting illumination in images: if the 2 lights are shadow and non- shadow, we find the shadows almost effortlessly. Compared to ground truth data, our method delivers close to optimal performance.

/pdf/detecting-illumination-in-images-1883l5ynm1.pdf

Clément Fredembach

Papers

Color image dehazing using the near-infrared

Colouring the near infrared

Designing color filter arrays for the joint capture of visible and near-infrared images

Automatic and Accurate Shadow Detection Using Near-Infrared Information

Detecting Illumination in Images