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This paper proposes a multiple features fusion method for moving shadows removal mainly based on optical reflection invariant. It assumed that the optical reflection of object surface changed little when casted by shadows. Two techniques are designed for reflection-invariant estimation. One is homomorphic filtering that obtains reflection component from input image directly; the other technique is the proposed maximum channel prior. Based on homomorphic filtering, scale-invariant local ternary pattern is utilized to discriminate shadows from the foreground mask. Based on maximum channel prior, an approximate illumination invariant is designed by local neighboring information calculation. These techniques are utilized for weak or strong shadow detection. For weak shadows, multiple descriptors including color constancy, texture consistency, and reflection invariance at the pixel-level or region-level are combined based on shadow assumptions. For strong shadows, a coarse-to-refine strategy is proposed using maximum channel prior technique. Experimental results on standard datasets consisting of 12 video sequences illustrate the proposed method’s effectiveness compared with some state-of-the-art approaches.

Li et al. and Yang et al. proposed (t,s,k,n)-essential secret image sharing methods to share a secret image into essential shadows and nonessential shadows. However, if the shadows have been distributed to the holders and extra shadows are required, the processes of regenerating and retransmitting shadows are necessary. In the proposed method, when receiving t essential shadows and k−t nonessential shadows, the total size of the adopted shadow is the same as the size of the secret image. Most importantly, the proposed method achieves less size of total required shadows (when using the minimal essential shadows) than what was proposed by Li et al. and achieves less size of essential shadow than the method contended by Yang et al. Moreover, we can have the additional shadows relate to other existing shadows without regenerating.

We present a practical photometric stereo method that works with general isotropic reflectances. Unlike previous approaches that use special hardware setups or dense measurements, our method only requires dozens of random yet known lighting directions. By spherically interpolating the light source directions to approximate the scene appearance under desired lighting directions, our method separately computes azimuth and elevation angles of the surface normal for each pixel. The effectiveness and accuracy of the proposed method are validated using a measured reflectance database with 100 isotropic materials and various real-world objects.

A method to estimate the environmental illumination distribution of a scene with gradient-based ray and candidate shadow maps is presented. In the shadow segmentation stage, we apply a Canny edge detector to the shadowed image by using a three-dimensional (3-D) augmented reality (AR) marker of a known size and shape. Then the hierarchical tree of the connected edge components representing the topological relation is constructed, and the connected components are merged, taking their hierarchical structures into consideration. A gradient-based ray that is perpendicular to the gradient of the edge pixel in the shadow image can be used to extract the shadow regions. In the light source detection stage, shadow regions with both a 3-D AR marker and the light sources are partitioned into candidate shadow maps. A simple logic operation between each candidate shadow map and the segmented shadow is used to efficiently compute the area ratio between them. The pro- posed method successively extracts the main light sources according to their relative contributions on the seg- mented shadows. The proposed method can reduce unwanted effects due to the sampling positions in the shadow region and the threshold values in the shadow edge detection. © The Authors. Published by SPIE under a

Moving cast shadow detection plays an exceedingly important role in content-based analysis in surveillance videos. A novel integrated moving cast shadow detection method is proposed for accurate moving objects detection. Based on the analysis for shadow model, we first exploit color information sufficiently in hue, saturation, and value color space and texture similarity in texture space to separate moving cast shadows from the foreground, respectively. Then we obtain the rough shadow detection result by means of synthesizing the above two results efficiently. Finally, post processing is put forward to modify misclassified pixels for acquiring the refined shadow detection result. Extensive experiments and comparisons of various scenes prove that the proposed method can detect cast shadows automatically and effectively and outperforms some well-known methods.

Removing shadows from single color images is an important problem in computer vision. In this paper, we propose a novel shadow removal approach, which could effectively remove shadows from textured surfaces, yielding high quality shadow-free images. Our approach aims at calculating scale factors to cancel the effect of shadows. Based on the regional gray edge hypothesis, which assumes the average of the reflectance differences in a region is achromatic, the scale factors can be computed without the restrictions that former algorithms need. The experimental results show that the proposed algorithm is effective.

Event detection for video surveillance is a difficult task due to many challenges: cluttered background, illumination variations, scale variations, occlusions among people, etc. We propose an effective and efficient event detection scheme in such complex situations. Moving shadows due to illumination are tackled with a segmentation method with shadow detection, and scale variations are taken care of using the CamShift guided particle filter tracking algorithm. For event modeling, hidden Markov models are employed. The proposed scheme also reduces the overall computational cost by combing two human detection algorithms and using tracking information to aid human detection. Experimental results on TRECVid event detection evaluation demonstrate the efficacy of the proposed scheme. It is robust, especially to moving shadows and scale variations. Employing the scheme, we achieved the best run results for two events in the TRECVid benchmarking evaluation.

This paper proposes a polynomial-style region incremental multisecret image sharing (PBRIMSIS), for sharing multiple secrets in an image among n participants. The method enables the dealer to distribute the content of an image S to multiple regions, where each region is asso- ciated with a certain level of secrecy. In the proposed n-level PBRIMSIS scheme, input image S is encoded to n + 1 shadow images that exhibit the following features: a. each shadow image cannot reveal any region in S ,b . anyt (2 ≤ t ≤ n + 1) shadow images can be used to reveal these regions associated with up to t - 1 secret levels, and c. S can be com- pletely reconstructed when all of the n + 1 shadow images are available. A discrete cosine transform-based PBRIMSIS with a smaller shadow im- age scheme is designed to improve the transmission and storage of the generated shadow images. The property of incremental disclosure to the region-based secrets in an image is applicable to image sharing in diverse applications that require the sharing of multiple secrets with different se- crecy priorities, such as in cooperative working or in military secrets. C � 2011

We propose a sharing method to progressively reveal a given important image in the recovery phase. In the encoding phase, the distributor utilizes the three frequency bands (low, middle, and high) of the given image to generate shadows according to three prespecified thresholds. In the recovery phase, the secret image cannot be revealed if the number of shadows a team collects is less than the lowest threshold. However, when the number of collected shadows reaches the prespecified low (or middle- or high) threshold, the team can reconstruct a low- (or middle- or high-) quality version of the secret image. In other words, the quality of the reconstructed image depends only on the number of shadows being received, rather than on which of the generated shadows are received. Each noise-like shadow is so small that it can be hidden in an ordinary image that is still several times smaller than the original image.

We present a novel method to estimate the light source direction and relight a face texture image of a single 3-D model under arbitrary unknown illumination conditions. We create a virtual irradiance sphere to detect the light source direction from a given illuminated texture image using both normal vector mapping and weighted bilinear interpolation. We then induce a relighting equation with estimated ambient and diffuse intensity. We provide the result of a series of experiments on light source estimation, relighting, and face recognition to show the efficiency and accuracy of the proposed method in restoring the shading and shadow areas of a face texture image. Our approach for face relighting can be used for illuminant-invariant face recognition applications.

We propose a robust method to extract silhouettes of foreground objects from color-video sequences. To cope with various changes in the background, we model the background as a Laplace distribution and update it with a selective running average and static pixel observation. All pixels in the input video image are classified into four initial regions using background subtraction with multiple thresholds. Shadow regions are eliminated using color components, and the final foreground silhouette is extracted by smoothing the boundaries of the foreground and eliminating errors inside and outside of the regions. Experimental results show that the proposed algorithm works very well in various background and foreground situations.

Segmentation of moving objects in image sequences by change detection has been a very important topic in multimedia and surveillance applications. One popular approach is to model the background of the scene and then threshold the differences between the background and the input images to detect the change caused by the moving object. Although this idea is simple and effective, the selection of a proper threshold value faces a trade-off between false alarms and misdetection. In this paper, a new method of thresholding for moving object segmentation in scenes without dynamic background and rapid variation of illumination is proposed to avoid misdetection while reducing false alarms. This new approach adopts the concept of thresholding-with-hysteresis , which utilizes a multiresolution and variable thresholding (MRVT) scheme to achieve improvements on the segmentation performance. Combined with a module of shadow removal, MRVT can generate accurate moving object masks. Segmentation results are evaluated qualitatively and quantitatively for indoor scenes, and the effectiveness of MRVT is encouraging. Compared with two other state-of-the-art approaches, our proposed method can achieve more accurate object boundary, fewer false alarms, and reduced fragmentation of objects.

There is a tradeoff between effective and computational costs in traditional change detection methods. Reducing the computational cost of the processing time results in loss of quality of detection results. We propose a new change detection method based on the temporal difference principle. The new method combines the connected component labeling, closing operation of image morphology, and human structure analysis to remove shading and noise in consecutive frames. A closed object boundary and region can be extracted with only a small additional cost in processing times. Additionally, this new method recovers the disadvantages of traditional temporal difference methods. Comparative results, in terms of computational complexity, completeness of the object and the object's outline, robustness against noise and shadow, with currently popular methods and the proposed method are also displayed. The experimental results show that the proposed method is robust in varied illuminations and effective in several predefined environments for change detection.

The contrast of shadow moireat high-order Talbot distances is studied. An exact solution for the contrast of shadow moirefringes is developed from the diffraction phenomenon and its validity is corrobo- rated experimentally. The result is combined with an additional effect from the finite aperture of the camera to propose a complete expression for the contrast of shadow moirefringes. The results can guide the de- sign of shadow moireconfigurations with high-order Talbot distances for high-sensitivity measurements. © 2005 Society of Photo-Optical Instrumentation

The out-of-plane displacement field at the crack tip region of a compact tension specimen is measured by stereo vision and used to generate a shadow spot on the virtual screen placed behind the specimen by using the mapping equation of reflected caustics. The J values are then computed using the maximum transverse diameter of the generated shadow spot. In the experiment, the in-plane displacement data are also recorded to obtain J measurements along contours surrounding the crack tip. Results indicate that for crack extensions up to 0.275 mm, the J values evaluated using the generated shadow spot are in good agreement with the measurements from the in-plane displacement data and the Merkle-Corten formula. However, the J values obtained using in-plane displacement data are still close to the Merkle-Corten results, even when the crack growth of Δ a ≈4.0 mm is reached.

For an accurate scene analysis using monocular color traffic image sequences, a robust segmentation of moving vehicles from the stationary background is generally required. However, the presence of moving cast shadow may lead to an inaccurate vehicle segmentation, and as a result, may lead to further erroneous scene analysis. We propose an effective method for the detection of moving cast shadow. By observing the characteristics of cast shadow in the luminance, chrominance, gradient density, and geometry domains, a combined probability map, called a shadow confidence score (SCS), is obtained. From the edge map of the input image, each edge pixel is examined to determine whether it belongs to the vehicle region based on its neighboring SCSs. The cast shadow is identified as those regions with high SCSs, which are outside the convex hull of the selected vehicle edge pixels. The proposed method is tested on 100 vehicle images taken under different lighting conditions (sunny and cloudy), viewing angles (roadside and overhead), vehicle sizes (small, medium, and large), and colors (similar to the road and not). The results indicate that an average error rate of around 14% is obtained while the lowest error rate is around 3% for large vehicles.

Sang-Yoon LeeDong-Seon YoonKorea Advanced Institute of Science andTechnologyDepartment of Mechanical EngineeringYusong-gu, Taejon, 305-701KoreaAbstract. We present a machine vision inspection method that is spe-cially devised to detect defects on shadow masks. This method incorpo-rates Fourier optics to capture only irregular defects in real time by block-ing out the periodically repetitive pattern of normal mask holes using apinhole type spatial filter under coherent illumination. In addition, a fastdefect-detection image processing algorithm efficiently suppresses un-desirable background noisy images. Experimental results prove that thismethod provides a detection capability of 500 mm

A new method called four-map absolute distance contouring, based on the shadow moiretechnique, is described. In this system, the period of a sinusoidal grating is varied through its rotation, with the effect that the phase of the moirepattern is also changed. By selecting suitable rotation angles, four images at different grating positions are acquired, from which the absolute distance from the object to the grating can be determined. The theoretical analysis is presented for the method, which has been verified by suitable laboratory experimentation. The measur- able range is directly proportional to the period of the grating and in- versely proportional to the angles through which the grating is rotated. The results show that the method is fast and accurate. © 1997 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(97)01809-6)

A non-destructive method used to estimate internal surface roughness of a small hole (0.2 mm in diameter and 0.7 mm in depth) is described. The first step of this method is to reconstruct a shadow pattern image of the internal surface from a series of optically sectioned images of the surface. The surface roughness parameters are then calculated from this shadow pattern image. The method is verified by measuring a known flat surface and comparing the results with stylus measurements.

Shadows can add significant edge and surface detail to imagery and thus substantially increase the performance of automated correlation guidance systems. A shadow-generation algorithm was implemented to increase the accuracy of synthetic imagery used to simulate visible, near-infrared, and far-infrared sensors. Initially, a data base was established in which all surfaces were represented by a list of vertices and material codes and arranged according to a scheme of a priori masking priority. Each surface was then clipped against updated clipping polygons representing the silhouette of all previous surfaces that had higher masking priorities as viewed from the position of the light source. The resulting hidden surface was inserted into the data base and flagged as a shadow for gray-scale prediction by the appropriate sensor model. Because each surface is compared to a union of polygons rather than to individual surfaces, this algorithm is computationally efficient for use with large data bases.

Shading correction of image sensors is a very important operation in visual inspection applications. The generation and application of shading correction with and without imaging system response linearization is discussed and demonstrated. The linearization is achieved by subtracting the dark current image and by applying a lookup table operation. The content of the lookup table is obtained by fitting an analytical function to the measured system response. The linearization enables the shading correction to be a linear operation. The quality of the linearization and the shading correction was evaluated using statistical parameters of the processed images. The results showed a substantial decrease in the standard deviation of the gray level distribution of uniform reference images.

Optical methods of shadow and projection moire are particularly suitable for mapping the absolute shape of a given surface or the difference between that surface and a master one or its accidental shape change. Essentially, product moire fringes are obtained on an image plane where a "filter" grating analyzes the image of the test surface which has been "coded" by projecting a master grating on it. The article resumes the theory in a unifying approach and presents a comprehensive list of applications.

A quantitative shadow method was developed and tested by measuring the one-dimensional refractive index distribution of a polished slab of gradient index glass. Tests made on the known glass sample showed that a one-dimensional index distribution can be measured by shadowgraphy to better than 1% accuracy. The method uses simple optics and can easily be auto-mated to perform rapid measurements. Extension of the shadow method to more general distributions requires making many traverses of the refractive index distribution with a narrow sheet of light.

Holography by shadow casting is a non-interferometric means for recording a hologram. Like ordinary (interferometric) holography, it leads to three-dimensional images of the object. Unlike ordinary holography, it works best at short wavelengths (y-rays and X-rays). The history, techniques, and prospects of shadow holography are reviewed.

The 19th century art of shadow imaging, as started by Wedgwood (1803), and as used by William Henry Fox-Talbot (1851) in England, has been revived by using a small-area electronic flash lamp and fine grain film. One immediate application has been the photographic recording of large samples of living plankton. The purpose of this paper is to re-examine the resolution of the shadow system, and to describe some of the strobe light sources that are presently available. Also, a modified method of shadow photography is described where enhanced color images give interesting and, perhaps, important color effects that may be useful in the study of plankton and other semitrans-parent subjects.

This paper presents a comprehensive approach to the design of machine vision software for roving robots and autonomous vehicles. Various techniques are proposed for solving the important problems of directional guidance, obstacle avoidance, and object identification. Artificial intelligence and knowledge-base concepts form the basis of the vision system design. The principle of texture invariance is introduced for shadow analysis and discrimination. The idea of scene layout footprints and 3-D maps for landmarks is proposed as a means of orientation determination for the guidance and navigation of roving robots and autonomous vehicles. The vision system performs three phases of visual processing: the initialization phase, the "walking" phase, and the warning phase. The visual processing and interpretation are monitored by the knowledge access and inference routine.