Quantifying hydraulic roughness coefficients is commonly required in order to calculate flow rate in open channel applications. An assumption typically coupled with the use of Manning’s equation is that a roughness coefficient (n) that is solely dependent upon a boundary roughness characteristic (k) may be applied. Even though Manning reported unique values of n and x’ (the exponent of the hydraulic radius in Manning’s equation) for each of the different boundary roughness materials he tested, he chose x’ = 2/3 as representative, assumed a constant n value, and suggested that it was sufficiently accurate. More recent studies have suggested that in addition to k; Rh, Se, and Fr can influence n. While research points to situations where n may vary, it is always a temptation to simply apply the constant n assumption especially in the case of more complicated channels such as composite channels where different roughness materials line different parts of a channel cross section. This study evaluates the behavior of n as a function of Re, Rh, k, So, and Fr for four different boundary roughness materials ranging from smooth to relatively rough in a rectangular tilting flume. The results indicate that for the relatively rough materials n is best described by its relationship with Rh where it varies over a lower range of Rh but approaches and at a point maintains a constant value as Rh increases. The constant value of n is attributed to both the physically smooth boundary materials and a quasi-smooth flow condition in the rougher boundary materials. The study shows that an x’ = 2/3 (the basis of Manning’s equation) correlated to the assumption of a constant n value only applies to smooth boundary roughness materials and a quasi-smooth flow condition in the rougher boundary materials; otherwise, either n or x’ must vary. These findings are then applied to compare 16 published composite channel relationships. The results identify the importance of applying a varying n where applicable due to the potential for error in assuming and applying a constant n. They also indicate that the more complicated predictive methods do not produce more accurate results than the simpler methods of which the most consistent is the Horton method.

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No / When a planar object is rotated with respect to a directional light source, the reflected luminance changes. If surface lightness is to be a reliable guide to surface identity, observers must compensate for such changes. To the extent they do, observers are said to be lightness constant. We report data from a lightness matching task that assesses lightness constancy with respect to changes in object slant. On each trial, observers viewed an achromatic standard object and indicated the best match from a palette of 36 grayscale samples. The standard object and the palette were visible simultaneously within an experimental chamber. The chamber illumination was provided from above by a theater stage lamp. The standard objects were uniformly-painted flat cards. Different groups of naïve observers made matches under two sets of instructions. In the Neutral Instructions, observers were asked to match the appearance of the standard and palette sample. In the Paint Instructions, observers were asked to choose the palette sample that was painted the same as the standard. Several broad conclusions may be drawn from the results. First, data for most observers were neither luminance matches nor lightness constant matches. Second, there were large and reliable individual differences. To characterize these, a constancy index was obtained for each observer by comparing how well the data were accounted for by both luminance matching and lightness constancy. The index could take on values between 0 (luminance matching) and 1 (lightness constancy). Individual observer indices ranged between 0.17 and 0.63 with mean 0.40 and median 0.40. An auxiliary slant-matching experiment rules out variation in perceived slant as the source of the individual variability. Third, the effect of instructions was small compared to the inter-observer variability. Implications of the data for models of lightness perception are discussed.

No / In the companion study (C. Ripamonti et al., 2004), we present data that measure the effect of surface slant on perceived lightness. Observers are neither perfectly lightness constant nor luminance matchers, and there is considerable individual variation in performance. This work develops a parametric model that accounts for how each observer¿s lightness matches vary as a function of surface slant. The model is derived from consideration of an inverse optics calculation that could achieve constancy. The inverse optics calculation begins with parameters that describe the illumination geometry. If these parameters match those of the physical scene, the calculation achieves constancy. Deviations in the model¿s parameters from those of the scene predict deviations from constancy. We used numerical search to fit the model to each observer¿s data. The model accounts for the diverse range of results seen in the experimental data in a unified manner, and examination of its parameters allows interpretation of the data that goes beyond what is possible with the raw data alone.

No / We present a numerical analysis of rendered pairs of rooms, in which the spectral power distribution of the illuminant in one room matched the surface reflectance function in the other room, and vice versa. We ask whether distinction between the rooms is possible and on what cues this discrimination is based. Using accurately rendered three-dimensional (3D) scenes, we found that room pairs can be distinguished based on indirect illumination, as suggested by A. L. Gilchrist and A. Jacobsen (1984). In a simulated color constancy scenario, we show that indirect illumination plays a pivotal role as areas of indirect illumination undergo a smaller appearance change than areas of direct illumination. Our study confirms that indirect illumination can play a critical role in surface color recovery and shows how computer rendering programs, which model the light¿object interaction according to the laws of physics, are valuable tools that can be used to analyze and explore what image information is available to the visual system from 3D scenes.

A number of studies have been made concerning the constancy of the sociometric structure of a group. This study, though dealing with constancy, is not concerned with the structural constancy of groups, but rather with the constancy of certain types of relationships within the group.

The quality of a digital image pipeline relies greatly on its color reproduction which should at a minimum handle the color constancy, and the final judgment of the excellence of the pipeline is made through subjective observations by humans. This dissertation addresses a few topics surrounding the color processing of digital image pipelines from a practical point of view. Color processing fundamentals will be discussed in the beginning to form a background understanding for the topics that follow.A memory color assisted illuminant estimation algorithm is then introduced after a review of memory colors and some modeling techniques. Spectral sensitivity of the camera is required by many color constancy algorithms but such data is often not readily available. To tackle this problem, an alternative method to the spectral characterization for color constancy parameter calibration is proposed. Hue control in color reproduction can be of great importance especially when memory colors are concerned. A hue constrained matrix optimization algorithm is introduced to address this issue, followed by a psychophysical study to systematically arrive at a recommendation for the optimized preferred color reproduction. At the end, a color constancy algorithm for high dynamic range scenes observing multiple illuminants is proposed. / Graduation date: 2012

Η υπολογιστική εξισορρόπηση χρώματος είναι ένα βασικό προαπαιτούμενο για πολλές εφαρμογές όρασης μηχανών. Στην εργασία αυτή παρουσιάζονται πολλές πρόσφατες εξελίξεις και οι τελευταίες μέθοδοι εξισορρόπησης χρώματος. Προτείνονται επίσης διαφορετικά κριτήρια για την εκτίμηση των προσεγγίσεων αυτών. Επιχειρείται μια ταξινόμηση των μεθόδων αυτών και αυτές χωρίζονται σε τρεις ομάδες: τις στατικές μεθόδους, τις μεθόδους βασισμένες στο χρωματικό εύρος (gamut-based) και τις μεθόδους βασισμένες στην εκπαίδευση (learning-based). Ακόμη, συζητείται η πειραματική διάταξη συμπεριλαμβανομένης μιας επισκόπησης των δημόσια διαθέσιμων συνόλων δεδομένων εικόνας. Τέλος, γίνεται μια αξιολόγηση των μεθόδων που θεωρούνται αιχμής με βάση δύο σύνολα δεδομένων. / Computational color constancy is a fundamental prerequisite for many computer vision applications. This dissertation presents a survey of many recent developments and state-of-the- art methods. Several criteria are proposed that are used to assess the approaches. A taxonomy of existing algorithms is proposed and methods are separated in three groups: static methods, gamut-based methods and learning-based methods. Further, the experimental setup is discussed including an overview of publicly available data sets. Finally, various freely available methods, of which some are considered to be state-of-the-art, are evaluated on two data sets.

This dissertation addresses the problem of inferring scene depth information from a collection of calibrated images taken from different viewpoints via stereo matching. Although it has been heavily investigated for decades, depth from stereo remains a long-standing challenge and popular research topic for several reasons. First of all, in order to be of practical use for many real-time applications such as autonomous driving, accurate depth estimation in real-time is of great importance and one of the core challenges in stereo. Second, for applications such as 3D reconstruction and view synthesis, high-quality depth estimation is crucial to achieve photo realistic results. However, due to the matching ambiguities, accurate dense depth estimates are difficult to achieve. Last but not least, most stereo algorithms rely on identification of corresponding points among images and only work effectively when scenes are Lambertian. For non-Lambertian surfaces, the "brightness constancy" assumption is no longer valid. This dissertation contributes three novel stereo algorithms that are motivated by the specific requirements and limitations imposed by different applications. In addressing high speed depth estimation from images, we present a stereo algorithm that achieves high quality results while maintaining real-time performance. We introduce an adaptive aggregation step in a dynamic-programming framework. Matching costs are aggregated in the vertical direction using a computationally expensive weighting scheme based on color and distance proximity. We utilize the vector processing capability and parallelism in commodity graphics hardware to speed up this process over two orders of magnitude. In addressing high accuracy depth estimation, we present a stereo model that makes use of constraints from points with known depths - the Ground Control Points (GCPs) as referred to in stereo literature. Our formulation explicitly models the influences of GCPs in a Markov Random Field. A novel regularization prior is naturally integrated into a global inference framework in a principled way using the Bayes rule. Our probabilistic framework allows GCPs to be obtained from various modalities and provides a natural way to integrate information from various sensors. In addressing non-Lambertian reflectance, we introduce a new invariant for stereo correspondence which allows completely arbitrary scene reflectance (bidirectional reflectance distribution functions - BRDFs). This invariant can be used to formulate a rank constraint on stereo matching when the scene is observed by several lighting configurations in which only the lighting intensity varies.