Semantik renk değişmezliği

Color constancy aims to perceive the actual color of an object, disregarding the effectof the light source. Recent works showed that utilizing the semantic information inan image enhances the performance of the computational color constancy methods.Considering the recent success of the segmentation methods and the increased numberof labeled images, we propose a color constancy method that combines individualilluminant estimations of detected objects which are computed using the classes of theobjects and their associated colors. Then we introduce a weighting system that valuesthe applicability of the object classes to the color constancy problem. Lastly, weintroduce another metric expressing the detected object and how well it fits the learnedmodel of its class. Finally, we evaluate our proposed method on a popular colorconstancy dataset, confirming that each weight addition enhances the performanceof the global illuminant estimation. Experimental results show promising results,outperforming the conventional methods while competing with the state of the artmethods.--M.S. - Master of Scienc

Evaluation and optimal design of spectral sensitivities for digital color imaging

The quality of an image captured by color imaging system primarily depends on three factors: sensor spectral sensitivity, illumination and scene. While illumination is very important to be known, the sensitivity characteristics is critical to the success of imaging applications, and is necessary to be optimally designed under practical constraints. The ultimate image quality is judged subjectively by human visual system. This dissertation addresses the evaluation and optimal design of spectral sensitivity functions for digital color imaging devices. Color imaging fundamentals and device characterization are discussed in the first place. For the evaluation of spectral sensitivity functions, this dissertation concentrates on the consideration of imaging noise characteristics. Both signal-independent and signal-dependent noises form an imaging noise model and noises will be propagated while signal is processed. A new colorimetric quality metric, unified measure of goodness (UMG), which addresses color accuracy and noise performance simultaneously, is introduced and compared with other available quality metrics. Through comparison, UMG is designated as a primary evaluation metric. On the optimal design of spectral sensitivity functions, three generic approaches, optimization through enumeration evaluation, optimization of parameterized functions, and optimization of additional channel, are analyzed in the case of the filter fabrication process is unknown. Otherwise a hierarchical design approach is introduced, which emphasizes the use of the primary metric but the initial optimization results are refined through the application of multiple secondary metrics. Finally the validity of UMG as a primary metric and the hierarchical approach are experimentally tested and verified

Analysis of image noise in multispectral color acquisition

The design of a system for multispectral image capture will be influenced by the imaging application, such as image archiving, vision research, illuminant modification or improved (trichromatic) color reproduction. A key aspect of the system performance is the effect of noise, or error, when acquiring multiple color image records and processing of the data. This research provides an analysis that allows the prediction of the image-noise characteristics of systems for the capture of multispectral images. The effects of both detector noise and image processing quantization on the color information are considered, as is the correlation between the errors in the component signals. The above multivariate error-propagation analysis is then applied to an actual prototype system. Sources of image noise in both digital camera and image processing are related to colorimetric errors. Recommendations for detector characteristics and image processing for future systems are then discussed

Statistical/Geometric Techniques for Object Representation and Recognition

Object modeling and recognition are key areas of research in computer vision and graphics with wide range of applications. Though research in these areas is not new, traditionally most of it has focused on analyzing problems under controlled environments. The challenges posed by real life applications demand for more general and robust solutions. The wide variety of objects with large intra-class variability makes the task very challenging. The difficulty in modeling and matching objects also vary depending on the input modality. In addition, the easy availability of sensors and storage have resulted in tremendous increase in the amount of data that needs to be processed which requires efficient algorithms suitable for large-size databases. In this dissertation, we address some of the challenges involved in modeling and matching of objects in realistic scenarios. Object matching in images require accounting for large variability in the appearance due to changes in illumination and view point. Any real world object is characterized by its underlying shape and albedo, which unlike the image intensity are insensitive to changes in illumination conditions. We propose a stochastic filtering framework for estimating object albedo from a single intensity image by formulating the albedo estimation as an image estimation problem. We also show how this albedo estimate can be used for illumination insensitive object matching and for more accurate shape recovery from a single image using standard shape from shading formulation. We start with the simpler problem where the pose of the object is known and only the illumination varies. We then extend the proposed approach to handle unknown pose in addition to illumination variations. We also use the estimated albedo maps for another important application, which is recognizing faces across age progression. Many approaches which address the problem of modeling and recognizing objects from images assume that the underlying objects are of diffused texture. But most real world objects exhibit a combination of diffused and specular properties. We propose an approach for separating the diffused and specular reflectance from a given color image so that the algorithms proposed for objects of diffused texture become applicable to a much wider range of real world objects. Representing and matching the 2D and 3D geometry of objects is also an integral part of object matching with applications in gesture recognition, activity classification, trademark and logo recognition, etc. The challenge in matching 2D/3D shapes lies in accounting for the different rigid and non-rigid deformations, large intra-class variability, noise and outliers. In addition, since shapes are usually represented as a collection of landmark points, the shape matching algorithm also has to deal with the challenges of missing or unknown correspondence across these data points. We propose an efficient shape indexing approach where the different feature vectors representing the shape are mapped to a hash table. For a query shape, we show how the similar shapes in the database can be efficiently retrieved without the need for establishing correspondence making the algorithm extremely fast and scalable. We also propose an approach for matching and registration of 3D point cloud data across unknown or missing correspondence using an implicit surface representation. Finally, we discuss possible future directions of this research

Parameric Decomposition for Evaluating Metamerism

In those industries in which materials are colored to close specifications, a means of evaluating the degree of metamerism of colored objects is of considerable importance. Based on Wyszecki\u27s hypothesis and its application to quantifying metamerism as described by Fairman, parameric decomposition is a technique to adjust one spectrum of a parameric match in order to achieve a perfect (metameric) match under a specific illumination and observer condition. This method can be viewed as batch correction using three colorants where the color-mixing model is linear in reflectance. The research in this thesis presented these methods using the basis functions from the CIE color-matching functions (CMFs) as well as alternative basis functions derived from dimensionality reduction techniques such as principal component analysis (PCA) and independent component analysis (ICA) for a pre-defined DuPont spectral dataset and Munsell dataset. 1,152 parameric pairs surrounding 24 color centers were synthesized using an automotive finish paint system and two-constant Kubelka-Munk turbid-media theory. Each parameric pair was corrected to a metameric pair using these various methods. The corrected spectra were compared with the formulated spectra using Kubelka-Munk theory to evaluate the parameric decomposition accuracy in terms of special and general metameric indices. The results showed that the estimated metameric indices from the CMFs-based process primaries presented relatively poor correlation to those from Kubelka-Munk theory. The process primaries from ICA for the Munsell IV dataset showed almost indentical performance in estimation of metameric indices to the process primaries from the PCA for Munsell dataset as well as those from ICA for the DuPont dataset. These three sets of process primaries showed slightly better performance in estimation of metameric indices than the process primaries from PCA for the DuPont dataset

The Constructive Nature of Color Vision and Its Neural Basis

Our visual world is made up of colored surfaces. The color of a surface is physically determined by its reflectance, i.e., how much energy it reflects as a function of wavelength. Reflected light, however, provides only ambiguous information about the color of a surface as it depends on the spectral properties of both the surface and the illumination. Despite the confounding effects of illumination on the reflected light, the visual system is remarkably good at inferring the reflectance of a surface, enabling observers to perceive surface colors as stable across illumination changes. This capacity of the visual system is called color constancy and it highlights that color vision is a constructive process. The research presented here investigates the neural basis of some of the most relevant aspects of the constructive nature of human color vision using machine learning algorithms and functional neuroimaging. The experiments demonstrate that color-related prior knowledge influences neural signals already in the earliest area of visual processing in the cortex, area V1, whereas in object imagery, perceived color shared neural representations with the color of the imagined objects in human V4. A direct test for illumination-invariant surface color representation showed that neural coding in V1 as well as a region anterior to human V4 was robust against illumination changes. In sum, the present research shows how different aspects of the constructive nature of color vision can be mapped to different regions in the ventral visual pathway

Expanding Dimensionality in Cinema Color: Impacting Observer Metamerism through Multiprimary Display

Television and cinema display are both trending towards greater ranges and saturation of reproduced colors made possible by near-monochromatic RGB illumination technologies. Through current broadcast and digital cinema standards work, system designs employing laser light sources, narrow-band LED, quantum dots and others are being actively endorsed in promotion of Wide Color Gamut (WCG). Despite artistic benefits brought to creative content producers, spectrally selective excitations of naturally different human color response functions exacerbate variability of observer experience. An exaggerated variation in color-sensing is explicitly counter to the exhaustive controls and calibrations employed in modern motion picture pipelines. Further, singular standard observer summaries of human color vision such as found in the CIE’s 1931 and 1964 color matching functions and used extensively in motion picture color management are deficient in recognizing expected human vision variability. Many researchers have confirmed the magnitude of observer metamerism in color matching in both uniform colors and imagery but few have shown explicit color management with an aim of minimized difference in observer perception variability. This research shows that not only can observer metamerism influences be quantitatively predicted and confirmed psychophysically but that intentionally engineered multiprimary displays employing more than three primaries can offer increased color gamut with drastically improved consistency of experience. To this end, a seven-channel prototype display has been constructed based on observer metamerism models and color difference indices derived from the latest color vision demographic research. This display has been further proven in forced-choice paired comparison tests to deliver superior color matching to reference stimuli versus both contemporary standard RGB cinema projection and recently ratified standard laser projection across a large population of color-normal observers