Colour Constancy: Biologically-inspired Contrast Variant Pooling Mechanism

Pooling is a ubiquitous operation in image processing algorithms that allows for higher-level processes to collect relevant low-level features from a region of interest. Currently, max-pooling is one of the most commonly used operators in the computational literature. However, it can lack robustness to outliers due to the fact that it relies merely on the peak of a function. Pooling mechanisms are also present in the primate visual cortex where neurons of higher cortical areas pool signals from lower ones. The receptive fields of these neurons have been shown to vary according to the contrast by aggregating signals over a larger region in the presence of low contrast stimuli. We hypothesise that this contrast-variant-pooling mechanism can address some of the shortcomings of max-pooling. We modelled this contrast variation through a histogram clipping in which the percentage of pooled signal is inversely proportional to the local contrast of an image. We tested our hypothesis by applying it to the phenomenon of colour constancy where a number of popular algorithms utilise a max-pooling step (e.g. White-Patch, Grey-Edge and Double-Opponency). For each of these methods, we investigated the consequences of replacing their original max-pooling by the proposed contrast-variant-pooling. Our experiments on three colour constancy benchmark datasets suggest that previous results can significantly improve by adopting a contrast-variant-pooling mechanism

Simulation and Modeling of Microfluidic Systems

In the present dissertation, fluid flow and heat transfer in microfluidic systems is investigated numerically. Fluid flow in most applications of microfluidic systems is in the slip flow regime, which is characterized by the slip flow and the jump temperature at the wall. Flow in microfluidic devices shows significant slip since the characteristic length is in the order of the mean free path of the fluid or gas molecules. The slip velocity and the jump temperature at the wall is the most important feature in the micro- or nano scale that differs from conventional internal flow. The slip flow and heat transfer in microchannels are simulated. Microfluidic systems are separated into straight and curved microchannels. A good understanding of fluid flow in microfluidic systems can be obtained when the results of straight and curved channels are considered together. Effects of rarefaction on forced convection heat transfer of laminar, steady and incompressible slip flow in straight and curved microchannels with uniform heat flux are investigated. The slip velocity and the jump temperature boundary conditions at the wall are employed. Effects of centrifugal force in the curved microchannels on the hydraulic and thermal behaviors of fluid flow are studied. The Navier-Stokes and energy equations are discretized using the Finite Volume technique. The calculated results show good agreement with previous numerical data and analytical solutions. The calculated results show that the entrance length and the curvature effects can be neglected, when the Reynolds number is less than 100. As a result, microfluidic systems are simulated with considering a very long straight microchannel, which can be modeled as totally fully developed region. The fully developed equations are obtained with considering the Navier-Stokes equations at the fully developed conditions. The analytical solution, which is an eigenvalue problem, is presented. The calculated results for two- and three-dimensional straight microchannels are presented. Flow velocity and temperature fields are calculated with very low computational time. Employing nanofluids is one of the best and practical methods for increasing heat transfer in microchannels. Thermal and hydraulic behaviors of nanofluid flow in microchannels with consideration of the slip velocity and the jump temperature conditions are investigated. Forced convection nanofluid flow in microchannels is simulated to study effects of rarefaction and Al2O3 nanoparticles concentration on the slip flow regimes. The Brownian motions of nanoparticles are considered to determine the thermal conductivity of nanofluid

Data Management in the APPA System

International audienceCombining Grid and P2P technologies can be exploited to provide high-level data sharing in large-scale distributed environments. However, this combination must deal with two hard problems: the scale of the network and the dynamic behavior of the nodes. In this paper, we present our solution in APPA (Atlas Peer-to-Peer Architecture), a data management system with high-level services for building large-scale distributed applications. We focus on data availability and data discovery which are two main requirements for implementing large-scale Grids. We have validated APPA's services through a combination of experimentation over Grid5000, which is a very large Grid experimental platform, and simulation using SimJava. The results show very good performance in terms of communication cost and response time

Color Name Applications in Computer Vision

In Computer Vision, the association of names to colors is one of the fundamental problems in the field of image understanding. There are numerous computational applications (e.g. image retrieval, visual tracking, person identification, human-machine interaction, etc.) that require pixels to be labelled according to the color perceived by the user. This is relatively easy for focal colors under canonical illuminants, where the agreement is high, but becomes increasingly difficult as perceptions move away from these conditions. For these difficult cases, the traditional solution tends to be a collection of "ad-hoc" strategies, however, new approaches that combine knowledge from anthropology, linguistics, visual perception and machine learning have offered promising results. Specifically, deep neural networks appear to possess all the required building blocks to offer a color naming solution "in the wild". This article reviews the current state of knowledge and discusses open challenges with a multidisciplinary (and non-specialized) readership in mind

Efficient Matrix Profile Computation Using Different Distance Functions

Matrix profile has been recently proposed as a promising technique to the problem of all-pairs-similarity search on time series. Efficient algorithms have been proposed for computing it, e.g., STAMP, STOMP and SCRIMP++. All these algorithms use the z-normalized Euclidean distance to measure the distance between subsequences. However, as we observed, for some datasets other Euclidean measurements are more useful for knowledge discovery from time series. In this paper, we propose efficient algorithms for computing matrix profile for a general class of Euclidean distances. We first propose a simple but efficient algorithm called AAMP for computing matrix profile with the "pure" (non-normalized) Euclidean distance. Then, we extend our algorithm for the p-norm distance. We also propose an algorithm, called ACAMP, that uses the same principle as AAMP, but for the case of z-normalized Euclidean distance. We implemented our algorithms, and evaluated their performance through experimentation. The experiments show excellent performance results. For example, they show that AAMP is very efficient for computing matrix profile for non-normalized Euclidean distances. The results also show that the ACAMP algorithm is significantly faster than SCRIMP++ (the state of the art matrix profile algorithm) for the case of z-normalized Euclidean distance

Colour constancy beyond the classical receptive field

The problem of removing illuminant variations to preserve the colours of objects (colour constancy) has already been solved by the human brain using mechanisms that rely largely on centre-surround computations of local contrast. In this paper we adopt some of these biological solutions described by long known physiological findings into a simple, fully automatic, functional model (termed Adaptive Surround Modulation or ASM). In ASM, the size of a visual neuron's receptive field (RF) as well as the relationship with its surround varies according to the local contrast within the stimulus, which in turn determines the nature of the centre-surround normalisation of cortical neurons higher up in the processing chain. We modelled colour constancy by means of two overlapping asymmetric Gaussian kernels whose sizes are adapted based on the contrast of the surround pixels, resembling the change of RF size. We simulated the contrast-dependent surround modulation by weighting the contribution of each Gaussian according to the centre-surround contrast. In the end, we obtained an estimation of the illuminant from the set of the most activated RFs' outputs. Our results on three single-illuminant and one multi-illuminant benchmark datasets show that ASM is highly competitive against the state-of-the-art and it even outperforms learning-based algorithms in one case. Moreover, the robustness of our model is more tangible if we consider that our results were obtained using the same parameters for all datasets, that is, mimicking how the human visual system operates. These results suggest a dynamical adaptation mechanisms contribute to achieving higher accuracy in computational colour constancy