Scene Illumination Research Articles

Large-Scene Sliding Spotlight SAR Using Multiple Channels in Azimuth

In this letter, we suggest using multiple azimuth channels (MACs) for synthetic aperture radar in the sliding spotlight mode. First, we analyze the scene size constraints of conventional sliding spotlight mode, showing that a large scene size requires long scene illumination time. However, while using multiple channels in azimuth, this could be changed. Dividing a long antenna into small antennas, we can increase the azimuth resolution improvement fact, where the range size will not be reduced. Therefore, with the same illumination time, the azimuth imaging width can be enlarged for a given azimuth resolution. However, using the MAC sliding spotlight mode, the nonuniform sampling problem occurs. Unlike the processing in the MAC stripmap mode, in the MAC sliding spotlight mode, the unambigous reconstruction should be performed after the deramping in azimuth. Finally, an example is given to validate the proposed new configuration and the corresponding algorithms.

Laser Photography Device - Spatial Parameters of Imaging

Laser photography device is an imaging device developed in the Institute of Optoelectronics, Military University of Technology and it is an example of modern image acquisition device. The laser photography device allows to de ne a 3D observation scene thanks to short-time scene illumination and image acquisition method. This device works according to time spatial framing method. In the paper, basics of time spatial framing method are explained. Special attention is given to time parameters of device and their in uence to spatial parameters of registered images. In this paper the laser photography device and results of chosen experiments are presented and described. Experimental results presented in the paper show the potential and possibilities of using the laser photography device as a camera for observation and measurement applications.

FSAM: A fast self-adaptive method for correcting non-uniform illumination for 3D reconstruction

Since three-dimensional surface texture can display texture information of an object better than its two-dimensional counterpart and can vary with scene illumination and the view angles, it is widely used in virtual reality, computer games and animation applications. Photometric Stereo, as one of the most effective technologies for capturing three-dimensional surface information, has attracted wide attention both from academic researchers and industrial fields. Uniform illumination is the essential condition for capturing and reconstructing surface heightmaps. In practice, non-uniform illumination leads to distorted surface heightmaps, such as distortion and aberration during the capture and reconstruction processes. This paper proposes and assesses a fast and self-adaptive method based on inverse-square law to correct non-uniform illumination for reconstruction of 3D surface heightmaps, and to eliminate the distortions. In order to objectively assess the performance of the illumination correction algorithm, average absolute gradient (AAG) is proposed to compare the surface heightmaps reconstructed using corrected illumination with those reconstructed without illumination correction. Experimental results show that the method is efficient and can produce convincing results.

Global illumination with radiance regression functions

We present radiance regression functions for fast rendering of global illumination in scenes with dynamic local light sources. A radiance regression function (RRF) represents a non-linear mapping from local and contextual attributes of surface points, such as position, viewing direction, and lighting condition, to their indirect illumination values. The RRF is obtained from precomputed shading samples through regression analysis, which determines a function that best fits the shading data. For a given scene, the shading samples are precomputed by an offline renderer. The key idea behind our approach is to exploit the nonlinear coherence of the indirect illumination data to make the RRF both compact and fast to evaluate. We model the RRF as a multilayer acyclic feed-forward neural network, which provides a close functional approximation of the indirect illumination and can be efficiently evaluated at run time. To effectively model scenes with spatially variant material properties, we utilize an augmented set of attributes as input to the neural network RRF to reduce the amount of inference that the network needs to perform. To handle scenes with greater geometric complexity, we partition the input space of the RRF model and represent the subspaces with separate, smaller RRFs that can be evaluated more rapidly. As a result, the RRF model scales well to increasingly complex scene geometry and material variation. Because of its compactness and ease of evaluation, the RRF model enables real-time rendering with full global illumination effects, including changing caustics and multiple-bounce high-frequency glossy interreflections.

Efficient Estimation of Reflectance Parameters From Imaging Spectroscopy

In this paper, we address the problem of efficiently recovering reflectance parameters from a single multispectral or hyperspectral image. To do so, we propose a shapelet based estimator that employs shapelets to recover the shading in the image. The optimization setting presented is based upon a three-step process. The first of these concerns the recovery of the surface reflectance and the specular coefficients through a constrained optimization approach. Second, we update the illuminant power spectrum using a simple least-squares formulation. Third, the shading is computed directly once the updated illuminant power spectrum is obtained. This yields a computationally efficient method that achieves speed-ups of nearly an order of magnitude over its closest alternative without compromising performance. We provide results on illuminant power spectrum computation, shading recovery, skin recognition and replacement of the scene illuminant, and object reflectance in real-world images.

Detection of traffic signs based on eigen-color model and saliency model in driver assistance systems

Traffic signs are made in many different shapes and colors that normally stand out from their environment for the purpose of enhancing their visibility to drivers. In most previous research, the methods for detecting traffic signs used shape and color information. However, the shape and color information of traffic signs is sensitive to features of the surroundings, such as the viewpoint and changes in the brightness or background, consequently making such information less reliable in terms of accuracy. To solve the problem, this paper presents a method for detecting traffic signs that is invariant under various environmental changes such as those in the appearance and illumination of traffic scenes. This method uses a saliency model to capture those features of traffic signs that are invariant to shading and shadow. Next, a traffic sign color model is used to extract the color features invariant under illumination changes. By using these models, the presented method can identify all possible shapes and colors for similarly representing the traffic signs. Finally, the traffic signs are detected through a so-called object verification process in which each traffic sign can be recognized by finding a region of overlap between two selected candidate regions. Automatic detection of traffic signs by implementing the presented approach has been examined with traffic scenes of some roads. In particular, the detection rate for traffic signs was 92%, while the processing time per frame was 0.27s.

Estimating Photometric Properties from Image Collections

We address the problem of jointly estimating the scene illumination, the radiometric camera calibration and the reflectance properties of an object using a set of images from a community photo collection. The highly ill-posed nature of this problem is circumvented by using appropriate representations of illumination, an empirical model for the nonlinear function that relates image irradiance with intensity values and additional assumptions on the surface reflectance properties. Using a 3D model recovered from an unstructured set of images, we estimate the coefficients that represent the illumination for each image using a frequency framework. For each image, we also compute the corresponding camera response function. Additionally, we calculate a simple model for the reflectance properties of the 3D model. A robust non-linear optimization is proposed exploiting the high sparsity present in the problem.

EcoIP: An open source image analysis toolkit to identify different stages of plant phenology for multiple species with pan–tilt–zoom cameras

Because of the increased number of cameras employed in environmental sensing and the tremendous image output they produce, we have created a flexible, open-source software solution called EcoIP to help automatically determine different phenophases for different species from digital image sequences. Onset and ending dates are calculated through an iterative process: (1) training images are chosen and areas of interest identified, (2) separation of foreground and background is accomplished based on a naive Bayesian method, (3) a signal is created based on the separation model and (4) it is then fit to a sigmoid that contains the dates of interest. Results using different phenological events of different species indicate that estimated dates fall within a few days of the observed dates for most cases. Our experiments indicate that color separability and scene illumination are contributing factors to this error. EcoIP is implemented as an open platform that encourages anyone to execute, copy, distribute, study, change, and/or improve the application.

No Measured Effect of a Familiar Contextual Object on Color Constancy.

Some familiar objects have a typical color, such as the yellow of a banana. The presence of such objects in a scene is a potential cue to the scene illumination, since the light reflected from them should on average be consistent with their typical surface reflectance. Although there are many studies on how the identity of an object affects how its color is perceived, little is known about whether the presence of a familiar object in a scene helps the visual system stabilize the color appearance of other objects with respect to changes in illumination. We used a successive color matching procedure in three experiments designed to address this question. Across the experiments we studied a total of 6 subjects (2 in Experiment 1, 3 in Experiment 2, and 4 in Experiment 3) with partial overlap of subjects between experiments. We compared measured color constancy across conditions in which a familiar object cue to the illuminant was available with conditions in which such a cue was not present. Overall, our results do not reveal a reliable improvement in color constancy with the addition of a familiar object to a scene. An analysis of the experimental power of our data suggests that if there is such an effect, it is small: less than approximately a change of 0.09 in a constancy index where an absence of constancy corresponds to an index value of 0 and perfect constancy corresponds to an index value of 1.

Online illumination estimation of outdoor scenes based on videos containing no shadow area

Real-time estimation of outdoor illumination is one of the key issues for ensuring the illumination consistency of augmented reality. In this paper, we propose a novel framework to estimate the dynamic illumination of outdoor scenes based on an online video sequence captured by a fixed camera. All existing approaches are based on two assumptions, i.e. there exist some shadow areas in the scene and the distribution of the skylight is uniform over the sky. Both assumptions greatly simplify the problem of illumination estimation of outdoor scenes, but they also limit the applicability as well as the accuracy of these approaches. This paper presents a new approach that breaks these two hard constraints. It recovers the lighting parameters of outdoor scenes containing no shadow area through solving a constrained linear least squares problem. By representing the skylight as a parameterized model incorporating an occlusion coefficient, the proposed approach can handle the dynamic variation of non-uniform skylight distribution. Experimental results demonstrate the potential of our approach.

Mechatronic Design of a Fully Integrated Camera for Mini-Invasive Surgery

This paper describes the design features of an innovative fully integrated camera candidate for mini-invasive abdominal surgery with single port or transluminal access. The apparatus includes a CMOS imaging sensor, a light-emitting diode (LED)-based unit for scene illumination, a photodiode for luminance detection, an optical system designed according to the mechanical compensation paradigm, an actuation unit for enabling autofocus and optical zoom, and a control logics based on microcontroller. The bulk of the apparatus is characterized by a tubular shape with a diameter of 10 mm and a length of 35 mm. The optical system, composed of four lens groups, of which two are mobile, has a total length of 13.46 mm and an effective focal length ranging from 1.61 to 4.44 mm with a zoom factor of 2.75×, with a corresponding angular field of view ranging from 16° to 40°. The mechatronics unit, devoted to move the zoom and the focus lens groups, is implemented adopting miniature piezoelectric motors. The control logics implements a closed-loop mechanism, between the LEDs and photodiode, to attain automatic control light. Bottlenecks of the design and some potential issues of the realization are discussed. A potential clinical scenario is introduced.

Symmetry-driven accumulation of local features for human characterization and re-identification

This work proposes a method to characterize the appearance of individuals exploiting body visual cues. The method is based on a symmetry-driven appearance-based descriptor and a matching policy that allows to recognize an individual. The descriptor encodes three complementary visual characteristics of the human appearance: the overall chromatic content, the spatial arrangement of colors into stable regions, and the presence of recurrent local motifs with high entropy. The characteristics are extracted by following symmetry and asymmetry perceptual principles, that allow to segregate meaningful body parts and to focus on the human body only, pruning out the background clutter. The descriptor exploits the case where we have a single image of the individual, as so as the eventuality that multiple pictures of the same identity are available, as in a tracking scenario. The descriptor is dubbed Symmetry-Driven Accumulation of Local Features (SDALFs). Our approach is applied to two different scenarios: re-identification and multi-target tracking. In the former, we show the capabilities of SDALF in encoding peculiar aspects of an individual, focusing on its robustness properties across dramatic low resolution images, in presence of occlusions and pose changes, and variations of viewpoints and scene illumination. SDALF has been tested on various benchmark datasets, obtaining in general convincing performances, and setting the state of the art in some cases. The latter scenario shows the benefits of using SDALF as observation model for different trackers, boosting their performances under different respects on the CAVIAR dataset.

Dealing with Illumination in Visual Scenes: Effects of Ageing and Alzheimer's Disease

Various visual functions decline in ageing and even more so in patients with Alzheimer's disease (AD). Here we investigated whether the complex visual processes involved in ignoring illumination-related variability (specifically, cast shadows) in visual scenes may also be compromised. Participants searched for a discrepant target among items which appeared as posts with shadows cast by light-from-above when upright, but as angled objects when inverted. As in earlier reports, young participants gave slower responses with upright than inverted displays when the shadow-like part was dark but not white (control condition). This is consistent with visual processing mechanisms making shadows difficult to perceive, presumably to assist object recognition under varied illumination. Contrary to predictions, this interaction of “shadow” colour with item orientation was maintained in healthy older and AD groups. Thus, the processing mechanisms which assist complex light-independent object identification appear to be robust to the effects of both ageing and AD. Importantly, this means that the complexity of a function does not necessarily determine its vulnerability to age- or AD-related decline.We also report slower responses to dark than light “shadows” of either orientation in both ageing and AD, in keeping with increasing light scatter in the ageing eye. Rather curiously, AD patients showed further slowed responses to “shadows” of either colour at the bottom than the top of items as if they applied shadow-specific rules to non-shadow conditions. This suggests that in AD, shadow-processing mechanisms, while preserved, might be applied in a less selective way.

Illumination estimation via nonnegative matrix factorization

The problem of illumination estimation for color constancy and automatic white balancing of digital color imagery can be viewed as the separation of the image into illumination and reflectance com- ponents. We propose using nonnegative matrix factorization with sparseness constraints to separate these components. Since illumi- nation and reflectance are combined multiplicatively, the first step is to move to the logarithm domain so that the components are additive. The image data is then organized as a matrix to be factored into nonnegative components. Sparseness constraints imposed on the resulting factors help distinguish illumination from reflectance. The proposed approach provides a pixel-wise estimate of the illumination chromaticity throughout the entire image. This approach and its var- iations can also be used to provide an estimate of the overall scene illumination chromaticity. © 2012 SPIE and IS&T. (DOI: 10.1117/1.JEI .21.3.033022)

A hybrid motion and appearance prediction model for robust visual object tracking

In this paper a new video object tracking method is proposed. A hybrid model based on motion and appearance is constructed for the object and Kalman filter is applied to both components in order to reduce noise and provide a prediction for the next frame. Making available a prediction of the object appearance in the next frame contributes effectively in robust object tracking in spite of large changes in scene illumination. Experimental results using the proposed method and its counterparts without appearance prediction demonstrate the superiority of the novel hybrid prediction method under drastic changes in illumination.

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.

Crowd Light: Evaluating the Perceived Fidelity of Illuminated Dynamic Scenes

Rendering realistic illumination effects for complex animated scenes with many dynamic objects or characters is computationally expensive. Yet, it is not obvious how important such accurate lighting is for the overall perceived realism in these scenes. In this paper, we present a methodology to evaluate the perceived fidelity of illumination in scenes with dynamic aggregates, such as crowds, and explore several factors which may affect this perception. We focus in particular on evaluating how a popular spherical harmonics lighting method can be used to approximate realistic lighting of crowds. We conduct a series of psychophysical experiments to explore how a simple approach to approximating global illumination, using interpolation in the temporal domain, affects the perceived fidelity of dynamic scenes with high geometric, motion, and illumination complexity. We show that the complexity of the geometry and temporal properties of the crowd entities, the motion of the aggregate as a whole, the type of interpolation (i.e., of the direct and/or indirect illumination coefficients), and the presence or absence of colour all affect perceived fidelity. We show that high (i.e., above 75%) levels of perceived scene fidelity can be maintained while interpolating indirect illumination for intervals of up to 30 frames, resulting in a greater than three-fold rendering speed-up.

The color constancy of three-dimensional objects

Human color constancy has been studied for over 100 years, and there is extensive experimental data for the case where a spatially diffuse light source illuminates a set of flat matte surfaces. In natural viewing, however, three-dimensional objects are viewed in three-dimensional scenes. Little is known about color constancy for three-dimensional objects. We used a forced-choice task to measure the achromatic chromaticity of matte disks, matte spheres, and glossy spheres. In all cases, the test stimuli were viewed in the context of stereoscopically viewed graphics simulations of three-dimensional scenes, and we varied the scene illuminant. We studied conditions both where all cues were consistent with the simulated illuminant change (consistent-cue conditions) and where local contrast was silenced as a cue (reduced-cue conditions). We computed constancy indices from the achromatic chromaticities. To first order, constancy was similar for the three test object types. There was, however, a reliable interaction between test object type and cue condition. In the consistent-cue conditions, constancy tended to be best for the matte disks, while in the reduced-cue conditions constancy was best for the spheres. The presence of this interaction presents an important challenge for theorists who seek to generalize models that account for constancy for flat tests to the more general case of three-dimensional objects.

Scene-Motion Thresholds During Head Yaw for Immersive Virtual Environments.

In order to better understand how scene motion is perceived in immersive virtual environments, we measured scene-motion thresholds under different conditions across three experiments. Thresholds were measured during quasi-sinusoidal head yaw, single left-to-right or right-to-left head yaw, different phases of head yaw, slow to fast head yaw, scene motion relative to head yaw, and two scene illumination levels. We found that across various conditions 1) thresholds are greater when the scene moves with head yaw (corresponding to gain < 1:0) than when the scene moves against head yaw (corresponding to gain > 1:0), and 2) thresholds increase as head motion increases.

Estimating illuminant color based on luminance balance of surfaces

To accomplish color constancy the illuminant color needs to be discounted from the light reflected from surfaces. Some strategies for discounting the illuminant color use statistics of luminance and chromaticity distribution in natural scenes. In this study we showed whether color constancy exploits the potential cue that was provided by the luminance balance of differently colored surfaces. In our experiments we used six colors: bright and dim red, green, and blue, as surrounding stimulus colors. In most cases, bright colors were set to be optimal colors. They were arranged among 60 hexagonal elements in close-packed structure. The center element served as the test stimulus. The observer adjusted the chromaticity of the test stimulus to obtain a perceptually achromatic surface. We used simulated black body radiations of 3000 (or 4000), 6500, and 20000 K as test illuminants. The results showed that the luminance balance of surfaces with no chromaticity shift had clear effects on the observer's achromatic setting, which was consistent with our hypothesis on estimating the scene illuminant based on optimal colors.