automatic color constancy algorithm selection and combination in this work we investigate how illuminant estimation techniques can be improved taking into account intrinsic low level properties of the images we show how these properties can be used to drive given a set of illuminant estimation algorithms the selection of the best algorithm for a given image the algorithm selection is made by a decision forest composed of several trees on the basis of the values of a set of heterogeneous features the features represent the image content in terms of low level visual properties the trees are trained to select the algorithm that minimizes the expected error in illuminant estimation we also designed a combination strategy that estimates the illuminant as a weighted sum of the different algorithms estimations experimental results on the widely used ciurea and funt dataset demonstrate the effectiveness of our approach

output feedback control for attitude tracking in this paper the problem of attitude tracking control for a rigid spacecraft is addressed it is assumed that only attitude measurements are available and thus spacecraft s angular velocity has to be properly estimated two alternative schemes are proposed in which the unit quaternion is adopted to represent the orientation in the first scheme a second order model based observer is adopted to estimate the angular velocity used in the control law in the second scheme an estimate of the angular velocity error is obtained through a lead filter sufficient conditions ensuring local exponential stability of the two controllers are derived via lyapunov analysis

color constancy by category correlation finding color representations that are stable to illuminant changes is still an open problem in computer vision until now most approaches have been based on physical constraints or statistical assumptions derived from the scene whereas very little attention has been paid to the effects that selected illuminants have on the final color image representation the novelty of this paper is to propose perceptual constraints that are computed on the corrected images we define the category hypothesis which weights the set of feasible illuminants according to their ability to map the corrected image onto specific colors here we choose these colors as the universal color categories related to basic linguistic terms which have been psychophysically measured these color categories encode natural color statistics and their relevance across different cultures is indicated by the fact that they have received a common color name from this category hypothesis we propose a fast implementation that allows the sampling of a large set of illuminants experiments prove that our method rivals current state of art performance without the need for training algorithmic parameters additionally the method can be used as a framework to insert top down information from other sources thus opening further research directions in solving for color constancy

contour detection and hierarchical image segmentation this paper investigates two fundamental problems in computer vision contour detection and image segmentation we present state of the art algorithms for both of these tasks our contour detector combines multiple local cues into a globalization framework based on spectral clustering our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into a hierarchical region tree in this manner we reduce the problem of image segmentation to that of contour detection extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly outperform competing algorithms the automatically generated hierarchical segmentations can be interactively refined by user specified annotations computation at multiple image resolutions provides a means of coupling our system to recognition applications

a problem of time in the multiplicative scheme for the n site hopper quantum measure theory qmt is an approach to quantum mechanics based on the path integral in which quantum theory is conceived of as a generalised stochastic process one of the postulates of qmt is that events with zero quantum measure do not occur however this is not sufficient to give a full picture of the quantum world determining the other postulates is a work in progress and this paper investigates a proposal called the multiplicative scheme for qmt in which the physical world corresponds essentially to a set of histories from the path integral this scheme is applied to sorkin s n site hopper a discrete unitary model of a single particle on a ring of n sites motivated by free schrodinger propagation it is shown that the multiplicative scheme s global features lead to the conclusion that no non trivial time finite event can occur

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

evaluating combinational illumination estimation methods on real world images illumination estimation is an important component of color constancy and automatic white balancing a number of methods of combining illumination estimates obtained from multiple subordinate illumination estimation methods now appear in the literature these combinational methods aim to provide better illumination estimates by fusing the information embedded in the subordinate solutions the existing combinational methods are surveyed and analyzed here with the goals of determining 1 the effectiveness of fusing illumination estimates from multiple subordinate methods 2 the best method of combination 3 the underlying factors that affect the performance of a combinational method and 4 the effectiveness of combination for illumination estimation in multiple illuminant scenes the various combinational methods are categorized in terms of whether or not they require supervised training and whether or not they rely on high level scene content cues e g indoor versus outdoor extensive tests and enhanced analyzes using three data sets of real world images are conducted for consistency in testing the images were labeled according to their high level features 3d stages indoor outdoor and this label data is made available on line the tests reveal that the trained combinational methods direct combination by support vector regression in particular clearly outperform both the non combinational methods and those combinational methods based on scene content cues

a comparison of computational color constancy algorithms ii experiments with image data for pt i see ibid vol 11 no 9 p 972 84 2002 we test a number of the leading computational color constancy algorithms using a comprehensive set of images these were of 33 different scenes under 11 different sources representative of common illumination conditions the algorithms studied include two gray world methods a version of the retinex method several variants of forsyth s 1990 gamut mapping method cardei et al s 2000 neural net method and finlayson et al s color by correlation method finlayson et al 1997 2001 hubel and finlayson 2000 we discuss a number of issues in applying color constancy ideas to image data and study in depth the effect of different preprocessing strategies we compare the performance of the algorithms on image data with their performance on synthesized data all data used for this study are available online at http www cs sfu ca spl sim color data and implementations for most of the algorithms are also available http www cs sfu ca spl sim color code experiments with synthesized data part one of this paper suggested that the methods which emphasize the use of the input data statistics specifically color by correlation and the neural net algorithm are potentially the most effective at estimating the chromaticity of the scene illuminant unfortunately we were unable to realize comparable performance on real images here exploiting pixel intensity proved to be more beneficial than exploiting the details of image chromaticity statistics and the three dimensional 3 d gamut mapping algorithms gave the best performance

color constancy with spatio spectral statistics we introduce an efficient maximum likelihood approach for one part of the color constancy problem removing from an image the color cast caused by the spectral distribution of the dominating scene illuminant we do this by developing a statistical model for the spatial distribution of colors in white balanced images i e those that have no color cast and then using this model to infer illumination parameters as those being most likely under our model the key observation is that by applying spatial band pass filters to color images one unveils color distributions that are unimodal symmetric and well represented by a simple parametric form once these distributions are fit to training data they enable efficient maximum likelihood estimation of the dominant illuminant in a new image and they can be combined with statistical prior information about the illuminant in a very natural manner experimental evaluation on standard data sets suggests that the approach performs well

a comparison of computational color constancy algorithms i methodology and experiments with synthesized data we introduce a context for testing computational color constancy specify our approach to the implementation of a number of the leading algorithms and report the results of three experiments using synthesized data experiments using synthesized data are important because the ground truth is known possible confounds due to camera characterization and pre processing are absent and various factors affecting color constancy can be efficiently investigated because they can be manipulated individually and precisely the algorithms chosen for close study include two gray world methods a limiting case of a version of the retinex method a number of variants of forsyth s 1990 gamut mapping method cardei et al s 2000 neural net method and finlayson et al s color by correlation method finlayson et al 1997 2001 hubel and finlayson 2000 we investigate the ability of these algorithms to make estimates of three different color constancy quantities the chromaticity of the scene illuminant the overall magnitude of that illuminant and a corrected illumination invariant image we consider algorithm performance as a function of the number of surfaces in scenes generated from reflectance spectra the relative effect on the algorithms of added specularities and the effect of subsequent clipping of the data all data is available on line at http www cs sfu ca spl sim color data and implementations for most of the algorithms are also available http www cs sfu ca spl sim color code

star a structure and texture aware retinex model retinex theory is developed mainly to decompose an image into the illumination and reflectance components by analyzing local image derivatives in this theory larger derivatives are attributed to the changes in reflectance while smaller derivatives are emerged in the smooth illumination in this paper we utilize exponentiated local derivatives with an exponent gamma of an observed image to generate its structure map and texture map the structure map is produced by been amplified with gamma 1 while the texture map is generated by been shrank with gamma to this end we design exponential filters for the local derivatives and present their capability on extracting accurate structure and texture maps influenced by the choices of exponents gamma the extracted structure and texture maps are employed to regularize the illumination and reflectance components in retinex decomposition a novel structure and texture aware retinex star model is further proposed for illumination and reflectance decomposition of a single image we solve the star model by an alternating optimization algorithm each sub problem is transformed into a vectorized least squares regression with closed form solutions comprehensive experiments on commonly tested datasets demonstrate that the proposed star model produce better quantitative and qualitative performance than previous competing methods on illumination and reflectance decomposition low light image enhancement and color correction the code is publicly available at https github com csjunxu star

feedback and surround modulated boundary detection edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes the human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations the classical approach assumes that these cells are only responsive to the stimulus present within their receptive fields however recent studies demonstrate that surrounding regions and inter areal feedback connections influence their responses significantly in this work we propose a biologically inspired edge detection model in which orientation selective neurons are represented through the first derivative of a gaussian function resembling double opponent cells in the primary visual cortex v1 in our model we account for four kinds of receptive field surround i e full far iso and orthogonal orientation whose contributions are contrast dependant the output signal from v1 is pooled in its perpendicular direction by larger v2 neurons employing a contrast variant centre surround kernel we further introduce a feedback connection from higher level visual areas to the lower ones the results of our model on three benchmark datasets show a big improvement compared to the current non learning and biologically inspired state of the art algorithms while being competitive to the learning based methods

efficient graph based image segmentation this paper addresses the problem of segmenting an image into regions we define a predicate for measuring the evidence for a boundary between two regions using a graph based representation of the image we then develop an efficient segmentation algorithm based on this predicate and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties we apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph and illustrate the results with both real and synthetic images the algorithm runs in time nearly linear in the number of graph edges and is also fast in practice an important characteristic of the method is its ability to preserve detail in low variability image regions while ignoring detail in high variability regions

color constancy using natural image statistics and scene semantics existing color constancy methods are all based on specific assumptions such as the spatial and spectral characteristics of images as a consequence no algorithm can be considered as universal however with the large variety of available methods the question is how to select the method that performs best for a specific image to achieve selection and combining of color constancy algorithms in this paper natural image statistics are used to identify the most important characteristics of color images then based on these image characteristics the proper color constancy algorithm or best combination of algorithms is selected for a specific image to capture the image characteristics the weibull parameterization e g grain size and contrast is used it is shown that the weibull parameterization is related to the image attributes to which the used color constancy methods are sensitive an mog classifier is used to learn the correlation and weighting between the weibull parameters and the image attributes number of edges amount of texture and snr the output of the classifier is the selection of the best performing color constancy method for a certain image experimental results show a large improvement over state of the art single algorithms on a data set consisting of more than 11 000 images an increase in color constancy performance up to 20 percent median angular error can be obtained compared to the best performing single algorithm further it is shown that for certain scene categories one specific color constancy algorithm can be used instead of the classifier considering several algorithms

global asymptotic stabilization of the attitude and the angular rates of an underactuated non symmetric rigid body the paper deals with the global stabilization of both the attitude and the angular velocities of an underactuated rigid body first a stability theorem is proven for a class of systems subsequently the equations describing the physics of the rigid body are presented showing that the rigid body belongs to the considered class of systems and a sufficient condition for the application of the theorem to the stability of the rigid body equilibrium is pointed out finally some simulation results are reported showing the effectiveness of the proposed methodology

the reproduction angular error for evaluating the performance of illuminant estimation algorithms the angle between the rgbs of the measured illuminant and estimated illuminant colors the recovery angular error has been used to evaluate the performance of the illuminant estimation algorithms however we noticed that this metric is not in line with how the illuminant estimates are used normally the illuminant estimates are divided out from the image to hopefully provide image colors that are not confounded by the color of the light however even though the same reproduction results the same scene might have a large range of recovery errors in this work the scale of the problem with the recovery error is quantified next we propose a new metric for evaluating illuminant estimation algorithms called the reproduction angular error which is defined as the angle between the rgb of a white surface when the actual and estimated illuminations are divided out our new metric ties algorithm performance to how the illuminant estimates are used for a given algorithm adopting the new reproduction angular error leads to different optimal parameters further the ranked list of best to worst algorithms changes when the reproduction angular is used the importance of using an appropriate performance metric is established

a novel algorithm for color constancy color constancy is the skill by which it is possible to tell the color of an object even under a colored light i interpret the color of an object as its color under a fixed canonical light rather than as a surface reflectance function this leads to an analysis that shows two distinct sets of circumstances under which color constancy is possible in this framework color constancy requires estimating the illuminant under which the image was taken the estimate is then used to choose one of a set of linear maps which is applied to the image to yield a color descriptor at each point this set of maps is computed in advance the illuminant can be estimated using image measurements alone because given a number of weak assumptions detailed in the text the color of the illuminant is constrained by the colors observed in the image this constraint arises from the fact that surfaces can reflect no more light than is cast on them for example if one observes a patch that excites the red receptor strongly the illuminant cannot have been deep blue two algorithms are possible using this constraint corresponding to different assumptions about the world the first algorithm crule will work for any surface reflectance crule corresponds to a form of coefficient rule but obtains the coefficients by using constraints on illuminant color the set of illuminants for which crule will be successful depends strongly on the choice of photoreceptors for narrowband photoreceptors crule will work in an unrestricted world the second algorithm mwext requires that both surface reflectances and illuminants be chosen from finite dimensional spaces but under these restrictive conditions it can recover a large number of parameters in the illuminant and is not an attractive model of human color constancy crule has been tested on real images of mondriaans and works well i show results for crule and for the retinex algorithm of land land 1971 land 1983 land 1985 operating on a number of real images the experimental work shows that for good constancy a color constancy system will need to adjust the gain of the receptors it employs in a fashion analagous to adaptation in humans

color based object recognition abstract the purpose is to arrive at recognition of multicolored objects invariant to a substantial change in viewpoint object geometry and illumination assuming dichromatic reflectance and white illumination it is shown that normalized color rgb saturation s and hue h and the newly proposed color models c 1 c 2 c 3 and l 1 l 2 l 3 are all invariant to a change in viewing direction object geometry and illumination further it is shown that hue h and l1l2l3 are also invariant to highlights finally a change in spectral power distribution of the illumination is considered to propose a new color constant color model m1m2m3 to evaluate the recognition accuracy differentiated for the various color models experiments have been carried out on a database consisting of 500 images taken from 3 d multicolored man made objects the experimental results show that highest object recognition accuracy is achieved by l1l2l3 and hue h followed by c1c2c3 normalized color rgb and m1m2m3 under the constraint of white illumination also it is demonstrated that recognition accuracy degrades substantially for all color features other than m1m2m3 with a change in illumination color the recognition scheme and images are available within the pictoseek and pic2seek systems on line at http www wins uva nl research isis zomax

cumulative attribute space regression for head pose estimation and color constancy abstract two stage cumulative attribute ca regression has been found effective in regression problems of computer vision such as facial age and crowd density estimation the first stage regression maps input features to cumulative attributes that encode correlations between target values the previous works have dealt with single output regression in this work we propose cumulative attribute spaces for 2 and 3 output multivariate regression we show how the original ca space can be generalized to multiple output by the cartesian product cartca however for target spaces with more than two outputs the cartca becomes computationally infeasible and therefore we propose an approximate solution multi view ca mvca where cartca is applied to output pairs we experimentally verify improved performance of the cartca and mvca spaces in 2d and 3d face pose estimation and three output rgb illuminant estimation for color constancy

uniform semiglobal practical asymptotic stability for non autonomous cascaded systems and applications it is due to the modularity they provide that results for cascaded systems have proved their utility in numerous control applications as well as in the development of general control techniques based on adding integrators nevertheless the standing assumptions in most of the present literature on cascaded systems is that when decoupled the subsystems constituting the cascade are uniformly globally asymptotically stable ugas hence existing results fail in the more general case when the subsystems are uniformly semiglobally practically asymptotically stable uspas this situation is often encountered in control practice e g in control of physical systems with external perturbations measurement noise unmodelled dynamics etc after giving a rigorous framework for the analysis of such stability properties this paper generalizes previous results for cascades by establishing that under a uniform boundedness condition on its solutions the cascade of two uspas systems remains uspas an analogous result is derived for uniformly semiglobally asymptotically stable usas systems in cascade furthermore we show the utility of our results in the pid control of mechanical systems affected by unknown non dissipative forces and considering the dynamics of the dc motors

attitude control without angular velocity measurement a passivity approach it is well known that the linear feedback of the quaternion of the attitude error and the angular velocity globally stabilizes the attitude of a rigid body in this note we show that the angular velocity feedback can be replaced by a nonlinear filter of the quaternion thus removing the need for direct angular velocity measurement in contrast to other approaches this design exploits the inherent passivity of the system a model based observer reconstructing the velocity is not needed an application of the proposed scheme is illustrated for the robot control problem simulation results are included to illustrate the theoretical results

quantum dynamics without the wavefunction when suitably generalized and interpreted the path integral offers an alternative to the more familiar quantal formalism based on state vectors self adjoint operators and external observers mathematically one generalizes the path integral as propagator to a quantal measure on the space of all conceivable worlds and this generalized measure expresses the dynamics or law of motion of the theory much as wiener measure expresses the dynamics of brownian motion within such histories based schemes new and more realistic possibilities open up for resolving the philosophical problems of the state vector formalism in particular one can dispense with the need for external agents by locating the predictive content of in its sets of measure zero such sets are to be precluded but unrestricted application of this rule engenders contradictions one possible response would remove the contradictions by circumscribing the application of the preclusion concept another response more in the tradition of quantum logic would accommodate the contradictions by dualizing to a space of co events and effectively identifying reality with an element of this dual space

the kochen specker theorem revisited in quantum measure theory the kochen specker theorem is widely interpreted to imply that non contextual hidden variable theories that agree with the predictions of copenhagen quantum mechanics are impossible the import of the theorem for a novel observer independent interpretation of quantum mechanics due to sorkin is investigated

multi cue illumination estimation via a tree structured group joint sparse representation a multi cue illumination estimation method based on tree structured group joint sparse representation is proposed tests show that the proposed method works better than existing methods most of which are based on using only a single cue type for example a binarized color histogram or simple image statistic such as the mean rgb most existing illumination estimation methods make their estimates using only one of three kinds of cues they differ in which cue type they use but the chosen cue is either based on 1 properties of the low level rgb color distribution 2 mid level initial illuminant estimates provided by subordinate methods or 3 high level knowledge of scene content e g indoor versus outdoor scene the proposed multi cue method combines the information provided by cues of all three of these types within the framework of a tree structured group joint sparse representation tgjsr in tgjsr the training data is grouped into a tree of subgroups a test image under an unknown illuminant has its features reconstructed in terms of a joint sparse representation model derived from the grouped training data the test image s illumination is then estimated based on the weights involved in the joint sparse representation model as a general framework the proposed tgjsr framework can also easily be extended to incorporate any new features or cues that might be discovered in the future for illumination estimation

multiple illuminant color estimation via statistical inference on factor graphs this paper presents a method to recover a spatially varying illuminant color estimate from scenes lit by multiple light sources starting with the image formation process we formulate the illuminant recovery problem in a statistically data driven setting to do this we use a factor graph defined across the scale space of the input image in the graph we utilize a set of illuminant prototypes computed using a data driven approach as a result our method delivers a pixelwise illuminant color estimate being devoid of libraries or user input the use of a factor graph also allows for the illuminant estimates to be recovered making use of a maximum a posteriori inference process moreover we compute the probability marginals by performing a delaunay triangulation on our factor graph we illustrate the utility of our method for pixelwise illuminant color recovery on widely available data sets and compare against a number of alternatives we also show sample color correction results on real world images

unit quaternion based output feedback for the attitude tracking problem in this note we propose a quaternion based dynamic output feedback for the attitude tracking problem of a rigid body without velocity measurement our approach consists of introducing an auxiliary dynamical system whose output which is also a unit quaternion is used in the control law together with the unit quaternion representing the attitude tracking error roughly speaking the necessary damping that would have been achieved by the direct use of the angular velocity can be achieved in our approach by the vector part mathtilde q of the error signal between the output of the auxiliary system and the unit quaternion tracking error the resulting velocity free control scheme guarantees almost global asymptotic stability which is as strong as the topology of the motion space can permit in the regulation case our control law is a pure quaternion feedback i e consisting of two terms that are vector parts of unit quaternion and hence the control torques are naturally bounded by the control gains simulation results are provided to show the effectiveness of the proposed control scheme

boundary detection using double opponency and spatial sparseness constraint brightness and color are two basic visual features integrated by the human visual system hvs to gain a better understanding of color natural scenes aiming to combine these two cues to maximize the reliability of boundary detection in natural scenes we propose a new framework based on the color opponent mechanisms of a certain type of color sensitive double opponent do cells in the primary visual cortex v1 of hvs this type of do cells has oriented receptive field with both chromatically and spatially opponent structure the proposed framework is a feedforward hierarchical model which has direct counterpart to the color opponent mechanisms involved in from the retina to v1 in addition we employ the spatial sparseness constraint ssc of neural responses to further suppress the unwanted edges of texture elements experimental results show that the do cells we modeled can flexibly capture both the structured chromatic and achromatic boundaries of salient objects in complex scenes when the cone inputs to do cells are unbalanced meanwhile the ssc operator further improves the performance by suppressing redundant texture edges with competitive contour detection accuracy the proposed model has the additional advantage of quite simple implementation with low computational cost

color constancy using 3d scene geometry derived from a single image the aim of color constancy is to remove the effect of the color of the light source as color constancy is inherently an ill posed problem most of the existing color constancy algorithms are based on specific imaging assumptions e g gray world and white patch assumption in this paper 3d geometry models are used to determine which color constancy method to use for the different geometrical regions depth layer found in images the aim is to classify images into stages rough 3d geometry models according to stage models images are divided into stage regions using hard and soft segmentation after that the best color constancy methods are selected for each geometry depth to this end we propose a method to combine color constancy algorithms by investigating the relation between depth local image statistics and color constancy image statistics are then exploited per depth to select the proper color constancy method our approach opens the possibility to estimate multiple illuminations by distinguishing nearby light source from distant illuminations experiments on state of the art data sets show that the proposed algorithm outperforms state of the art single color constancy algorithms with an improvement of almost 50 of median angular error when using a perfect classifier i e all of the test images are correctly classified into stages the performance of the proposed method achieves an improvement of 52 of the median angular error compared with the best performing single color constancy algorithm

formulas relating kl stability estimates of discrete time and sampled data nonlinear systems abstract we provide an explicit kl stability or input to state stability iss estimate for a sampled data nonlinear system in terms of the kl estimate for the corresponding discrete time system and a k function describing inter sample growth it is quite obvious that a uniform inter sample growth condition plus an iss property for the exact discrete time model of a closed loop system implies uniform iss of the sampled data nonlinear system our results serve to quantify these facts by means of comparison functions our results can be used as an alternative to prove and extend results in 1 or extend some results in 4 to a class of nonlinear systems finally the formulas we establish can be used as a tool for some other problems which we indicate

a computational approach to edge detection this paper describes a computational approach to edge detection the success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points these goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution we define detection and localization criteria for a class of edges and present mathematical forms for these criteria as functionals on the operator impulse response a third criterion is then added to ensure that the detector has only one response to a single edge we use the criteria in numerical optimization to derive detectors for several common image features including step edges on specializing the analysis to step edges we find that there is a natural uncertainty principle between detection and localization performance which are the two main goals with this principle we derive a single operator shape which is optimal at any scale the optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a gaussian smoothed image we extend this simple detector using operators of several widths to cope with different signal to noise ratios in the image we present a general method called feature synthesis for the fine to coarse integration of information from operators at different scales finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge

output feedback control for stabilization on se 3 this paper addresses the problem of stabilizing systems that evolve on se 3 the proposed solution consists of an output feedback controller that guarantees almost global asymptotic stability of the desired equilibrium point in the sense that the equilibrium point is stable and we have convergence for all initial conditions except for those in a nowhere dense set of measure zero the output vector is formed by the position coordinates of a collection of landmarks fixed in the environment the resulting closed loop system exhibits the following properties i the position and orientation subsystems are decoupled ii the position error is globally exponentially stable and iii the orientation error is almost globally exponentially stable results are also provided that allow one to select landmark configurations so as to control how the position and orientation of the rigid body converge to their final equilibrium values

a globally convergent angular velocity observer for rigid body motion the problem of obtaining the angular velocity of a rigid body from orientation and torque measurements only without noisy numerical differentiation is considered a novel angular velocity angular momentum observer for rigid body motion is presented using euler quaternions and a mechanical energy function approach it is shown that the observer estimates converge globally and that the convergence is eventually exponential it is hoped that a mechanical energy function approach to rigid body control can be combined with the observer presented to lead to a globally stable nonlinear observer based rigid body controller in which the observer and controller errors can be separated in much the same way as one can separate controller and observer poles in the output feedback controllers of linear system theory

pd based output feedback attitude control of rigid bodies we address the problem of output feedback attitude control of a rigid body in quaternion coordinate space via a modified pd based tracking controller angular velocity is replaced by a low gain dynamic extension the controller ensures fast convergence to the desired operating point during transient maneuvers while keeping the gains small this contributes to diminishing the sensitivity to measurement noise hence energy consumption may be expected to drop along with a decrease of the residual more precisely we show uniform practical asymptotic stability of the equilibrium point for the closed loop system in the presence of unknown bounded input disturbances simulation results illustrate the performance improvement with respect to pd based output feedback control with static gains

hilbert spaces from path integrals it is shown that a hilbert space can be constructed for a quantum system starting from a framework in which histories are fundamental the decoherence functional provides the inner product on this history hilbert space it is also shown that the history hilbert space is the standard hilbert space in the case of non relativistic quantum mechanics

approaching the computational color constancy as a classification problem through deep learning abstract computational color constancy refers to the problem of computing the illuminant color so that the images of a scene under varying illumination can be normalized to an image under the canonical illumination in this paper we adopt a deep learning framework for the illumination estimation problem the proposed method works under the assumption of uniform illumination over the scene and aims for the accurate illuminant color computation specifically we trained the convolutional neural network to solve the problem by casting the color constancy problem as an illumination classification problem we designed the deep learning architecture so that the output of the network can be directly used for computing the color of the illumination experimental results show that our deep network is able to extract useful features for the illumination estimation and our method outperforms all previous color constancy methods on multiple test datasets

color by correlation a simple unifying framework for color constancy the paper considers the problem of illuminant estimation how given an image of a scene recorded under an unknown light we can recover an estimate of that light obtaining such an estimate is a central part of solving the color constancy problem thus the work presented will have applications in fields such as color based object recognition and digital photography rather than attempting to recover a single estimate of the illuminant we instead set out to recover a measure of the likelihood that each of a set of possible illuminants was the scene illuminant we begin by determining which image colors can occur and how these colors are distributed under each of a set of possible lights we discuss how for a given camera we can obtain this knowledge we then correlate this information with the colors in a particular image to obtain a measure of the likelihood that each of the possible lights was the scene illuminant finally we use this likelihood information to choose a single light as an estimate of the scene illuminant computation is expressed and performed in a generic correlation framework which we develop we propose a new probabilistic instantiation of this correlation framework and show that it delivers very good color constancy on both synthetic and real images we further show that the proposed framework is rich enough to allow many existing algorithms to be expressed within it the gray world and gamut mapping algorithms are presented in this framework and we also explore the relationship of these algorithms to other probabilistic and neural network approaches to color constancy

edge based color constancy color constancy is the ability to measure colors of objects independent of the color of the light source a well known color constancy method is based on the gray world assumption which assumes that the average reflectance of surfaces in the world is achromatic in this paper we propose a new hypothesis for color constancy namely the gray edge hypothesis which assumes that the average edge difference in a scene is achromatic based on this hypothesis we propose an algorithm for color constancy contrary to existing color constancy algorithms which are computed from the zero order structure of images our method is based on the derivative structure of images furthermore we propose a framework which unifies a variety of known gray world max rgb minkowski norm and the newly proposed gray edge and higher order gray edge algorithms the quality of the various instantiations of the framework is tested and compared to the state of the art color constancy methods on two large data sets of images recording objects under a large number of different light sources the experiments show that the proposed color constancy algorithms obtain comparable results as the state of the art color constancy methods with the merit of being computationally more efficient

the attitude control problem a general framework for the analysis of the attitude tracking control problem for a rigid body is presented a large family of globally stable control laws is obtained by using the globally nonsingular unit quaternion representation in a lyapunov function candidate whose form is motivated by the consideration of the total energy of the rigid body the controllers share the common structure of a proportional derivative feedback plus some feedforward which can be zero the model independent case the coriolis torque compensation or an adaptive compensation these controller structures are compared in terms of the requirement on the a priori model information guaranteed transient performance and robustness the global stability of the luh walker paul robot end effector controller is also analyzed in this framework

noise reduction based on partial reference dual tree complex wavelet transform shrinkage this paper presents a novel way to reduce noise introduced or exacerbated by image enhancement methods in particular algorithms based on the random spray sampling technique but not only according to the nature of sprays output images of spray based methods tend to exhibit noise with unknown statistical distribution to avoid inappropriate assumptions on the statistical characteristics of noise a different one is made in fact the non enhanced image is considered to be either free of noise or affected by non perceivable levels of noise taking advantage of the higher sensitivity of the human visual system to changes in brightness the analysis can be limited to the luma channel of both the non enhanced and enhanced image also given the importance of directional content in human vision the analysis is performed through the dual tree complex wavelet transform dtwct unlike the discrete wavelet transform the dtwct allows for distinction of data directionality in the transform space for each level of the transform the standard deviation of the non enhanced image coefficients is computed across the six orientations of the dtwct then it is normalized the result is a map of the directional structures present in the non enhanced image said map is then used to shrink the coefficients of the enhanced image the shrunk coefficients and the coefficients from the non enhanced image are then mixed according to data directionality finally a noise reduced version of the enhanced image is computed via the inverse transforms a thorough numerical analysis of the results has been performed in order to confirm the validity of the proposed approach

improving color constancy by photometric edge weighting edge based color constancy methods make use of image derivatives to estimate the illuminant however different edge types exist in real world images such as material shadow and highlight edges these different edge types may have a distinctive influence on the performance of the illuminant estimation therefore in this paper an extensive analysis is provided of different edge types on the performance of edge based color constancy methods first an edge based taxonomy is presented classifying edge types based on their photometric properties e g material shadow geometry and highlights then a performance evaluation of edge based color constancy is provided using these different edge types from this performance evaluation it is derived that specular and shadow edge types are more valuable than material edges for the estimation of the illuminant to this end the iterative weighted gray edge algorithm is proposed in which these edge types are more emphasized for the estimation of the illuminant images that are recorded under controlled circumstances demonstrate that the proposed iterative weighted gray edge algorithm based on highlights reduces the median angular error with approximately 25 percent in an uncontrolled environment improvements in angular error up to 11 percent are obtained with respect to regular edge based color constancy

color constancy for multiple light sources color constancy algorithms are generally based on the simplifying assumption that the spectral distribution of a light source is uniform across scenes however in reality this assumption is often violated due to the presence of multiple light sources in this paper we will address more realistic scenarios where the uniform light source assumption is too restrictive first a methodology is proposed to extend existing algorithms by applying color constancy locally to image patches rather than globally to the entire image after local patch based illuminant estimation these estimates are combined into more robust estimations and a local correction is applied based on a modified diagonal model quantitative and qualitative experiments on spectral and real images show that the proposed methodology reduces the influence of two light sources simultaneously present in one scene if the chromatic difference between these two illuminants is more than 1 the proposed framework outperforms algorithms based on the uniform light source assumption with error reduction up to approximately 30 otherwise when the chromatic difference is less than 1 and the scene can be considered to contain one approximately uniform light source the performance of the proposed method framework is similar to global color constancy methods

a novel image representation via local frequency analysis for illumination invariant stereo matching in this paper we propose a novel image representation approach to tackle illumination variations in stereo matching problems images are mapped using their fourier transforms which are convolved with a set of monogenic filters frequency analysis is carried out at different scales to account for most image content the phase congruency and the local weighted mean phase angle are then computed over all the scales the original image is transformed into a new representation using these two mappings this representation is invariant to illumination and contrast variations more importantly it is generic and can be used with most sparse as well as dense stereo matching algorithms in addition sequential feature matching or tracking can also benefit from our approach in varying radiometric conditions we demonstrate the improvements introduced with our image mappings on well established data sets in the literature as well as on our own experimental scenarios that include high dynamic range imagery the experiments include both dense and sparse stereo and sequential matching algorithms where the latter is considered in the very challenging visual odometry framework