Transferring color between three-dimensional objects

A framework for transferring image-based color between three-dimensional objects by the use of a dichromatic reflection model is proposed. The framework addresses the following issues: (1) accurate recovery of an implicit geometric coefficient, (2) calculation of body color, (3) color transfer between different illuminants, and (4) segmentation of multicolored regions. The experimental results show that high color accuracy and photorealistic effects of the synthesized images can be achieved. The proposed technique has wide applications in image-based design and visualization of three-dimensional objects.     

Optical image recognition of three-dimensional objects

A three-dimensional (3-D) optical image-recognition technique is proposed and studied. The proposed technique is based on two-pupil optical heterodyne scanning and is capable of performing 3-D image recognition. A hologram of the 3-D reference object is first created and then is used to modulate spatially one of the pupils of the optical system; the other pupil is a point source. A 3-D target object to be recognized is then scanned in two dimensions by optical beams modulated by the two pupils. The result of the two-dimensional scan pattern effectively displays the correlation of the holographic information of the 3-D reference object and that of the 3-D target object. A strong correlation peak results if the two pieces of the holographic information are matched. We analyze the proposed technique and thereby lay a theoretical foundation for optical implementations of the idea. Finally, computer simulations are performed to verify the proposed idea.     

The presentation of three-dimensional objects with single image stereogram

A number of techniques offer us, at last, the possibility of viewing nature - the world of three dimensions - in a more realistic way with the deployment of three-dimensional display systems. Among these various methods, we chose the one based on the single image stereogram algorithm as the target of this paper, with respect to its simplicity, but efficiency in representing the vivid depth information of the real world. The goal of the paper is the improvement of the conventional implementation with our fixed-eyes-based calculation of constraints and with a new way to detect hidden surfaces in a three-dimensional scene. Going further than that, the paper extends the algorithm to moving objects, which is a new aspect, and, therefore, it has not yet been researched well. We also discuss the depth perception obtained by the algorithm and its visibility, a key question for the usability of the images of this category.     

Single-shot color fringe projection for three-dimensional shape measurement of objects with discontinuities

A simple but effective fringe projection profilometry is proposed to measure 3D shape by using one snapshot color sinusoidal fringe pattern. One color fringe pattern encoded with a sinusoidal fringe (as red component) and one uniform intensity pattern (as blue component) is projected by a digital video projector, and the deformed fringe pattern is recorded by a color CCD camera. The captured color fringe pattern is separated into its RGB components and division operation is applied to red and blue channels to reduce the variable reflection intensity. Shape information of the tested object is decoded by applying an arcsine algorithm on the normalized fringe pattern with subpixel resolution. In the case of fringe discontinuities caused by height steps, or spatially isolated surfaces, the separated blue component is binarized and used for correcting the phase demodulation. A simple and robust method is also introduced to compensate for nonlinear intensity response of the digital video projector. The experimental results demonstrate the validity of the proposed method.     

Recognition of polychromatic three-dimensional objects

We propose to use optical multichannel correlation in various chromatic systems to obtain a setup for recognition of polychromatic three-dimensional (3-D) objects based on Fourier-transform profilometry. Because red-green-blue color components are not able to split the luminance information of objects in a defined component, when the 3-D objects are brighter than the reference objects the correlation result gives false alarms. We demonstrate that it is possible to use different color spaces that can split luminance from chromatic information to yield adequate recognition of polychromatic 3-D objects. We show experimental results that prove the utility of the proposed method.     

An ontology-based hybrid methodology for image synthesis and identification with convex objects

ABSTRACT

One of the core challenges in developing a computer system for machine learning is to make the system learn efficiently and effectively like a real human by grasping the domain knowledge exemplified by human experts. In this challenge, we have introduced a hybrid image synthesis model that can simulate one of the human’s learning capabilities in the vision field – the ability to synthesize images of convex objects by identifying solid geometries and textures of specific objects using few photographs. We have incorporated an ontology-based, domain knowledge on solid geometries into our model to synthesize large number of training images with only a minimum number of input images. Our initial experiments have shown that our model has convincing improvements by demonstrating a substantially better FAR/FRR/EER results when it is compared with a smaller set of non-synthetic images.     

Rotation-invariant optical recognition of three-dimensional objects

An automatic method for rotation-invariant three-dimensional (3-D) object recognition is proposed. The method is based on the use of 3-D information contained in the deformed fringe pattern obtained when a grating is projected onto an object's surface. The proposed method was optically implemented by means of a two-cycle joint transform correlator. The rotation invariance is achieved by means of encoding with the fringe pattern a single component of the circular-harmonic expansion derived from the target. Thus the method is invariant for rotations around the line of sight. The whole experimental setup can be constructed with simple equipment. Experimental results show the utility of the proposed method.     

Volume-scanning three-dimensional display that uses an inclined image plane

A novel three-dimensional display based on a volume-scanning method that uses an inclined light-source array and a mirror scanner is proposed. With this technique it is possible to display three-dimensional images that satisfy all factors for human stereoscopic vision. Three-dimensional images of 8 x 8 x 8 pixels, 40 mm x 40 mm x 40 mm in size, with a frame rate of 12.7 Hz were obtained as real images through an experimental system that uses a galvanometer mirror and a LED array.     

Estimating three-dimensional vehicle motion in an outdoor scene using stereo image sequences

We propose a motion estimation system that uses stereo image pairs as the input data. To perform experimental work, we also obtain a sequence of outdoor stereo images taken by two metric cameras. The system consists of four main stages, which are (1) determination of point correspondences on the stereo images, (2) correction of distortions in image coordinates, (3) derivation of 3D point coordinates from 2D correspondences, and (4) estimation of motion parameters based on 3D point correspondences. For the first stage of the system, we use a four-way matching algorithm to obtain matched point on two stereo image pairs at two consecutive time instants (t_i and t_{i + 1}). Since the input data are stereo images taken by cameras, it has two types of distortions, which are (i) film distortion and (ii) lens distortion. These two distortions must be corrected before any process can be applied on the matched points. To accomplish this goal, we use (i) bilinear transform for film distortion correction and (ii) lens formulas for lens distortion correction. After correcting the distortions, the results are 2D coordinates of each matched point that can be used to derive 3D coordinates. However, due to data noise, the calculated 3D coordinates to not usually represent a consistent rigid structure that is suitable for motion estimation; therefore, we suggest a procedure to select good 3D point sets as the input for motion estimation. The procedure exploits two constraints, rigidity between different time instants and uniform point distribution across the object on the image. For the last stage, we use an algorithm to estimate the motion parameters. We also wish to know what is the effect of quantization error on the estimated results; therefore an error analysis based on quantization error is performed on the estimated motion parameters. In order to test our system, eight sets of stereo image pairs are extracted from an outdoor stereo image sequence and used as the input data. The experimental results indicate that the proposed system does provide reasonable estimated motion parameters.     

Volume-holographic filters for rotational sensing of three-dimensional objects

A highly precise rotational filter based on a volume-holographic optical element is proposed and demonstrated. We present a clear theoretical calculation of the rotation sensitivity of the volume-holographic filter used to sense the rotation of a spherical ground glass object. By introducing the longitudinal displacement of the scattering point across the sphere, the sensitivity of the filter is greatly improved to 350 times that of a general case for a planar ground glass.     

运用肤色信息和模板匹配的彩色人脸检测

人脸是一个复杂的模式,在图像中自动地对其进行定位和分割是进行人脸识别的第一步。本文提出一种运用肤色信息和模板匹配的人脸检测方法。该方法先进行肤色分割,然后对每一个人脸候选区域进行形状比例的分析,最后进行模板匹配。实验结果表明,该方法对任意背景下,任意姿态及任意数目的人脸检测非常有效。     

Digital color management in full-color holographic three-dimensional printer

We propose a new method of color management for a full-color holographic, three-dimensional (3D) printer, which produces a volume reflection holographic stereogram using red, green, and blue three-color lasers. For natural color management in the holographic 3D printer, we characterize its color reproduction characteristics based on the spectral measurement of reproduced light. Then the color conversion formula, which comprises a one-dimensional lookup table and a 3×3 matrix, was derived from the measurement data. The color reproducibility was evaluated by printing a color chart hologram, and the average CIELAB ΔE=13.19 is fairly small.     

Kernel-based spectral color image segmentation

In this work, we propose a new algorithm for spectral color image segmentation based on the use of a kernel matrix. A cost function for spectral kernel clustering is introduced to measure the correlation between clusters. An efficient multiscale method is presented for accelerating spectral color image segmentation. The multiscale strategy uses the lattice geometry of images to construct an image pyramid whose hierarchy provides a framework for rapidly estimating eigenvectors of normalized kernel matrices. To prevent the boundaries from deteriorating, the image size on the top level of the pyramid is generally required to be around 75 x 75, where the eigenvectors of normalized kernel matrices would be approximately solved by the Nystr?m method. Within this hierarchical structure, the coarse solution is increasingly propagated to finer levels and is refined using subspace iteration. In addition, to make full use of the abundant color information contained in spectral color images, we propose using spectrum extension to incorporate the geometric features of spectra into similarity measures. Experimental results have shown that the proposed method can perform significantly well in spectral color image segmentation as well as speed up the approximation of the eigenvectors of normalized kernel matrices.     

Conoscopic holography: toward three-dimensional reconstructions of opaque objects

Conoscopic holography is an interferometric technique that permits the recording of three-dimensional objects. A two-step scheme is presented to recover an opaque object's shape from its conoscopic hologram, consisting of a reconstruction algorithm to give a first estimate of the shape and an iterative restoration procedure that uses the object's support information to make the reconstruction more robust. The existence, uniqueness, and stability of the solution, as well as the convergence of the restoration algorithm, are studied. A preliminary experimental result is presented.     

一种精确的医学细胞图像边缘检测法

细胞边缘检测是进行细胞形态分析的基础,其检测结果直接影响病情分析和诊断的结果。传统的边缘检测算子由于受噪声影响比较大,无法检测细胞可靠的边缘位置,因而不宜应用于细胞形态分析。提出一种用迭代算法求图像分割最佳阈值和运用数学形态学的腐蚀算法实现轮廓提取相结合的细胞图像边缘精确检测算法,并给出仿真实例。与传统的边缘检测算子Laplacian-Gauss算子、Sobel算子相比较,该算法具有检测精度高和抗干扰能力强的优点。     

Real-time detection of multiple moving objects in complex image sequences

In this article, a real-time multistage method for detecting multiple objects moving in real scenes is presented. At the first level, a rough focus-of-attention mechanism is used to individuate areas of the input image that show remarkable differences with a real-time updated background image. Binary statistical morphology (BMS) operators are applied to individuate image pixels, which can be associated with real objects moving into the scene. High stability to noise is obtained by tuning the smoothing effects of the BSM operators according to the noise level present in the original image sequence. Then, at the second level, a composition of BSM is applied to eliminate isolated points and to favor dense agglomerate of changed pixels, i.e., blobs. The last level attempts to describe changes in terms of motion of blobs by allowing blobs to merge, split, appear, and vanish. A blob-matching procedure is used for tracking blobs over consecutive frames. Experimental results on real scenes, which demonstrate the advantages of the proposed method with respect to existing change detection methods, are given. © 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol 10, 305–317, 1999     

Scale-invariant recognition of three-dimensional objects by use of a quasi-correlator

A method of scale-invariant recognition of three-dimensional (3-D) objects is presented. Several images of the observed scene are recorded under white-light illumination from several different points of view and compressed into a single complex two-dimensional matrix. After filtering with a single scale-invariant filter, the resultant function is then coded into a computer-generated hologram (CGH). When this CGH is coherently illuminated, a correlation space is reconstructed in which light peaks indicate the existence and location of true targets in the tested 3-D scene. The light peaks are detectable for different sizes of the true objects, as long as they are within the invariance range of the filter. Experimental results in a complete electro-optical system are presented, and comparisons with other systems are investigated by use of computer simulation.