370 research outputs found
Color Constancy Convolutional Autoencoder
In this paper, we study the importance of pre-training for the generalization
capability in the color constancy problem. We propose two novel approaches
based on convolutional autoencoders: an unsupervised pre-training algorithm
using a fine-tuned encoder and a semi-supervised pre-training algorithm using a
novel composite-loss function. This enables us to solve the data scarcity
problem and achieve competitive, to the state-of-the-art, results while
requiring much fewer parameters on ColorChecker RECommended dataset. We further
study the over-fitting phenomenon on the recently introduced version of
INTEL-TUT Dataset for Camera Invariant Color Constancy Research, which has both
field and non-field scenes acquired by three different camera models.Comment: 6 pages, 1 figure, 3 table
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
Cavlectometry: Towards Holistic Reconstruction of Large Mirror Objects
We introduce a method based on the deflectometry principle for the
reconstruction of specular objects exhibiting significant size and geometric
complexity. A key feature of our approach is the deployment of an Automatic
Virtual Environment (CAVE) as pattern generator. To unfold the full power of
this extraordinary experimental setup, an optical encoding scheme is developed
which accounts for the distinctive topology of the CAVE. Furthermore, we devise
an algorithm for detecting the object of interest in raw deflectometric images.
The segmented foreground is used for single-view reconstruction, the background
for estimation of the camera pose, necessary for calibrating the sensor system.
Experiments suggest a significant gain of coverage in single measurements
compared to previous methods. To facilitate research on specular surface
reconstruction, we will make our data set publicly available
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
A Neuromorphic Model for Achromatic and Chromatic Surface Representation of Natural Images
This study develops a neuromorphic model of human lightness perception that is inspired by how the mammalian visual system is designed for this function. It is known that biological visual representations can adapt to a billion-fold change in luminance. How such a system determines absolute lightness under varying illumination conditions to generate a consistent interpretation of surface lightness remains an unsolved problem. Such a process, called "anchoring" of lightness, has properties including articulation, insulation, configuration, and area effects. The model quantitatively simulates such psychophysical lightness data, as well as other data such as discounting the illuminant, the double brilliant illusion, and lightness constancy and contrast effects. The model retina embodies gain control at retinal photoreceptors, and spatial contrast adaptation at the negative feedback circuit between mechanisms that model the inner segment of photoreceptors and interacting horizontal cells. The model can thereby adjust its sensitivity to input intensities ranging from dim moonlight to dazzling sunlight. A new anchoring mechanism, called the Blurred-Highest-Luminance-As-White (BHLAW) rule, helps simulate how surface lightness becomes sensitive to the spatial scale of objects in a scene. The model is also able to process natural color images under variable lighting conditions, and is compared with the popular RETINEX model.Air Force Office of Scientific Research (F496201-01-1-0397); Defense Advanced Research Project and the Office of Naval Research (N00014-95-0409, N00014-01-1-0624
The Complementary Brain: From Brain Dynamics To Conscious Experiences
How do our brains so effectively achieve adaptive behavior in a changing world? Evidence is reviewed that brains are organized into parallel processing streams with complementary properties. Hierarchical interactions within each stream and parallel interactions between streams create coherent behavioral representations that overcome the complementary deficiencies of each stream and support unitary conscious experiences. This perspective suggests how brain design reflects the organization of the physical world with which brains interact, and suggests an alternative to the computer metaphor suggesting that brains are organized into independent modules. Examples from perception, learning, cognition, and action are described, and theoretical concepts and mechanisms by which complementarity is accomplished are summarized.Defense Advanced Research Projects and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (ITI-97-20333); Office of Naval Research (N00014-95-1-0657
Probabilistic Color Constancy
In this paper, we propose a novel unsupervised color constancy method, called
Probabilistic Color Constancy (PCC). We define a framework for estimating the
illumination of a scene by weighting the contribution of different image
regions using a graph-based representation of the image. To estimate the weight
of each (super-)pixel, we rely on two assumptions: (Super-)pixels with similar
colors contribute similarly and darker (super-)pixels contribute less. The
resulting system has one global optimum solution. The proposed method achieves
competitive performance, compared to the state-of-the-art, on INTEL-TAU
dataset.Comment: 5 pages, 1 figur
The Complementary Brain: A Unifying View of Brain Specialization and Modularity
Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-I-0409); National Science Foundation (ITI-97-20333); Office of Naval Research (N00014-95-I-0657
- …