Dilip K. Prasad

Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution

Color constancy is a well-studied topic in color vision. Methods are generally categorized as (1) low-level statistical methods, (2) gamut-based methods, and (3) learning-based methods. In this work, we distinguish methods depending on whether they work directly from color values (i.e., color domain) or from values obtained from the images spatial information (e.g., image gradients/frequencies). We show that spatial information does not provide any additional information that cannot be obtained directly from the color distribution and that the indirect aim of spatial-domain methods is to obtain large color differences for estimating the illumination direction. This finding allows us to develop a simple and efficient illumination estimation method that chooses bright and dark pixels using a projection distance in the color distribution and then applies principal component analysis to estimate the illumination direction. Our method gives state-of-the-art results on existing public color constancy datasets as well as on our newly collected dataset (NUS dataset) containing 1736 images from eight different high-end consumer cameras.

Prediction of porosity and thermal diffusivity in a porous fin using differential evolution algorithm

Abstract In this paper, simultaneous inverse prediction of two parameters such as the porosity and thermal diffusivity of the fluid in a porous fin is done for satisfying a given temperature distribution. Only three temperature measurements are assumed to be available on the surface of the fin and prediction of the parameters is accomplished by using the differential evolution (DE)-based optimization technique. It is shown that the present problem is inherently ill-posed in terms of the retrieval of the value of fluid thermal diffusivity for which many possible solutions exist, which is expected to adapt the fin under different conditions. In the present work, two numerical examples provide engineering insight into the problem of designing porous fins using good thermal conductors like aluminum and copper along with the working of DE. Finally, the efficacy of DE for the present problem is also shown by comparing its performance with few other optimization methods.

Raw-to-Raw: Mapping between Image Sensor Color Responses

Camera images saved in raw format are being adopted in computer vision tasks since raw values represent minimally processed sensor responses. Camera manufacturers, however, have yet to adopt a standard for raw images and current raw-rgb values are device specific due to different sensors spectral sensitivities. This results in significantly different raw images for the same scene captured with different cameras. This paper focuses on estimating a mapping that can convert a raw image of an arbitrary scene and illumination from one cameras raw space to another. To this end, we examine various mapping strategies including linear and non-linear transformations applied both in a global and illumination-specific manner. We show that illumination-specific mappings give the best result, however, at the expense of requiring a large number of transformations. To address this issue, we introduce an illumination-independent mapping approach that uses white-balancing to assist in reducing the number of required transformations. We show that this approach achieves state-of-the-art results on a range of consumer cameras and images of arbitrary scenes and illuminations.

Online tracking of deformable objects under occlusion using dominant points

This paper deals with tracking of deformable objects in the presence of occlusion using dominant point representation of the boundary contour. A novel nonintegral time propagation model for propagating the dominant points is proposed. It uses an initial guess generated from a linear operation and an analytical conjugate gradient approach for online robust learning of the shape deformation and motion model. A scheme is presented to automatically detect and correct the region of large local deformation. In order to deal with occlusion, admissible restrictions on deformation and motion of the object are automatically determined. The proposed method overcomes the need of offline learning and learns the deformation and motion model of the object using very few initial frames of the input video. The performance of the method is demonstrated using varieties of videos of different objects.

Fabrication imperfection analysis and statistics generation using precision and reliability optimization method.

This paper applies a recently proposed dominant point detection method - precision and reliability optimization (PRO) - for representing shapes in the microscopy images of fabricated structures. This method uses both the local and the global nature of fit for dominant point detection. A smaller value of its control parameter better represents the local curvature properties of the shape while a larger value better indicates the global curvature properties. The applicability of this method to a wide range of microscopy images is demonstrated using four microscopy examples of brightness enhancement films, electromagnetic and photonic band gap materials, and aspherical mirror alignments. It is shown that PRO can clearly highlight several image effects and imperfections which may not be easily identifiable by human eye or may be difficult to analyze and assess. Further, for large scale arrays, it can be used to generate useful fabrication accuracy statistics and detect features with low fidelity or more imperfections.

Quick Approximation of Camera's Spectral Response from Casual Lighting

Knowing the RGB spectral sensitivities of a camera is useful for several image processing applications. However, camera manufacturers seldom provide this information. Calibration methods for determining them can be daunting, requiring either sophisticated instruments or carefully controlled lighting. This paper presents a quick and easy method that provides a reasonable approximation of the camera response functions with only a color chart and casual unknown illuminations. Our approach is enabled by careful design of the cost function that imposes several constraints on the problem to make it tractable. In the components of the cost function, the Luther condition provides the global shape prior and the commercial lighting systems with unknown spectra provide narrow spectral windows for local reconstruction. The quality of reconstruction is comparable with other methods that use known illumination.

DEB: Definite Error Bounded Tangent Estimator for Digital Curves

We propose a simple and fast method for tangent estimation of digital curves. This geometric-based method uses a small local region for tangent estimation and has a definite upper bound error for continuous as well as digital conics, i.e., circles, ellipses, parabolas, and hyperbolas. Explicit expressions of the upper bounds for continuous and digitized curves are derived, which can also be applied to nonconic curves. Our approach is benchmarked against 72 contemporary tangent estimation methods and demonstrates good performance for conic, nonconic, and noisy curves. In addition, we demonstrate a good multigrid and isotropic performance and low computational complexity of O(1) and better performance than most methods in terms of maximum and average errors in tangent computation for a large variety of digital curves.

Rise of international schools in India

The number of international schools is rising in India recently. The trends and various reasons for the observed rising trends are presented. Globalisation of the Indian economy, recent economic growth, perceived value of international education, and elitism are identified as a few major factors influencing the rise of international schools in India. The future impact of the rise of international schools on the Indian school education scenario is analysed.

Design of macro-filter-lens with simultaneous chromatic and geometric aberration correction

A macro-filter-lens design that can correct for chromatic and geometric aberrations simultaneously while providing for a long focal length is presented. The filter is easy to fabricate since it involves two spherical surfaces and a planar surface. Chromatic aberration correction is achieved by making all the rays travel the same optical distance inside the filter element (negative meniscus). Geometric aberration is corrected for by the lens element (plano-convex), which makes the output rays parallel to the optic axis. This macro-filter-lens design does not need additional macro lenses and it provides an inexpensive and optically good (aberration compensated) solution for macro imaging of objects not placed close to the camera.

Strategies for Resolving Camera Metamers Using 3+1 Channel

This paper discusses the problem of reducing camera metamerism by using a fourth spectral channel in addition to the typical red, green, and blue channels of the tristimulus sensor used in the commercial consumer cameras. Specifically, we consider three options for this fourth channel. The first option is to use a color channel from another camera to reduce metamerism. The second option is to use a color channel from an image captured by the same camera but with a color filter as the fourth channel. The third option is to design a specific spectral channel to be fabricated with the existing camera sensor. This option uses the metameric black space to design the channel. The commercial cameras original metamerism is typically more than 20%, as observed in a dataset of 335 spectral images captured in 5 different indoor illuminations. Our results show that the third option is the best since it reduces metamerism down to about 5%. Among the first and second options, the first option is more effective and it reduces metamerism down to about 15%. The channel designed using the third option can be used for advanced applications such as distinguishing objects with different spectral reflectances but similar colors.