Three-Dimensional Image Reconstruction Procedure for Food Microstructure Evaluation

Confocal laser scanning microscopy (CLSM) is a noninvasive technique for evaluating the microstructure of foods and other materials. CLSM provides several sequential subsurface layers of two-dimensional (2-D) images. An image processing algorithm was developed to reconstruct these 2-D layers into a three-dimensional (3-D) network. Microstructure of fat globules in cheese was used as an example application. The validity of the image reconstruction algorithm was evaluated by processing several layered digital images of known shape and size. Differences between the original and reconstructed images were 2–5% in terms of object size and 1–8% in terms of shape.     

Segmentation of volumetric tissue images using constrained active contour models

In this article we describe an application of active contour model for the segmentation of 3D histo-pathological images. The 3D images of a thick tissue specimen are obtained as a stack of optical sections using confocal laser beam scanning microscope (CLSM). We have applied noise reduction and feature enhancement methods so that a smooth and slowly varying potential surface is obtained for proper convergence. To increase the capture range of the potential surface, we use a combination of distance potential and the diffused gradient potential as external forces. It has been shown that the region-based information obtained from low-level segmentation can be applied to reduce the adverse influence of the neighbouring nucleus having a strong boundary feature. We have also shown that, by increasing the axial resolution of the image stack, we can automatically propagate the optimum active contour of one image slice to its neighbouring image slices as an appropriate initial model. Results on images of prostate tissue section are presented.     

Globally minimal surfaces by continuous maximal flows

In this paper, we address the computation of globally minimal curves and surfaces for image segmentation and stereo reconstruction. We present a solution, simulating a continuous maximal flow by a novel system of partial differential equations. Existing methods are either grid-biased (graph-based methods) or suboptimal (active contours and surfaces). The solution simulates the flow of an ideal fluid with isotropic velocity constraints. Velocity constraints are defined by a metric derived from image data. An auxiliary potential function is introduced to create a system of partial differential equations. It is proven that the algorithm produces a globally maximal continuous flow at convergence, and that the globally minimal surface may be obtained trivially from the auxiliary potential. The bias of minimal surface methods toward small objects is also addressed. An efficient implementation is given for the flow simulation. The globally minimal surface algorithm is applied to segmentation in 2D and 3D as well as to stereo matching. Results in 2D agree with an existing minimal contour algorithm for planar images. Results in 3D segmentation and stereo matching demonstrate that the new algorithm is robust and free from grid bias.     

Fusion and registration of THEOS multispectral and panchromatic images

This article presents a new method for the fusion and registration of THEOS (Thailand Earth Observation Satellite) multispectral and panchromatic images in a single step. In the usual procedure, fusion is an independent process separated from the registration process. However, both image registration and fusion can be formulated as estimation problems. Hence, the registration parameters can be automatically tuned so that both fusion and registration can be optimized simultaneously. Here, we concentrate on the relationship between low-resolution multispectral and high-resolution panchromatic imagery. The proposed technique is based on a statistical framework. It employs the maximum a posteriori (MAP) criterion to jointly solve the fusion and registration problem. Here, the MAP criterion selects the most likely fine resolution multispectral and mapping parameter based on observed coarse resolution multispectral and fine resolution panchromatic images. The Metropolis algorithm was employed as the optimization algorithm to jointly determine the optimum fine resolution multispectral image and mapping parameters. In this work, a closed-form solution that can find the fused multispectral image with correcting registration is also derived. In our experiment, a THEOS multispectral image with high spectral resolution and a THEOS panchromatic image with high spatial resolution are combined to produce a multispectral image with high spectral and spatial resolution. The results of our experiment show that the quality of fused images derived directly from misaligned image pairs without registration error correction can be very poor (blurred and containing few sharp edges). However, with the ability to jointly fuse and register an image pair, the quality of the resulting fused images derived from our proposed algorithm is significantly improved, and, in the simulated cases, the fused images are very similar to the original high resolution multispectral images, regardless of the initial registration errors.     

Fast Stereo Matching Using Rectangular Subregioning and 3D Maximum-Surface Techniques

This paper presents a fast and reliable stereo matching algorithm which produces a dense disparity map by using fast cross correlation, rectangular subregioning (RSR) and 3D maximum-surface techniques in a coarse-to-fine scheme. Fast correlation is achieved by using the box-filtering technique whose speed is invariant to the size of the correlation window and by segmenting the stereo images into rectangular subimages at different levels of the pyramid. By working with rectangular subimages, not only can the speed of the correlation be further increased, the intermediate memory storage requirement can also be reduced. The disparity map for the stereo images is found in the 3D correlation coefficient volume by obtaining the global 3D maximum-surface rather than simply choosing the position that gives the local maximum correlation coefficient value for each pixel. The 3D maximum-surface is obtained using our new two-stage dynamic programming (TSDP) technique. There are two original contributions in this paper: (1) development of the RSR technique for fast similarity measure; and (2) development of the TSDP technique for efficiently obtaining 3D maximum-surface in a 3D volume. Typical running time of our algorithm implemented in the C language on a 512 × 512 image is in the order of a few seconds on a 500 MHz PC. A variety of synthetic and real images have been tested, and good results have been obtained.     

激光测距雷达距离图障碍物实时检测算法研究及误差分析

本文在首先阐述陆自 主车中利用激光测距成像雷达获得取的距离图实时检测障碍的坐标变换法的基础上，着重深入研究了雷达垂直扫描中心角的误差与自主车３个姿态角的误差及激光测距成像雷达多义性间距对障碍物检测结果的影响。     

相关系数准则下的多光谱图像最优融合方法

证明了在相关系数准则下最优融合算法与线性最优融合算法是等价的，利用这一结果大大减少了计算的空间与时间复杂度；并通过设置加权系数和提供一种自适应计算权值的方法以突出原多光谱图像目标区域的频谱特征；对于相关性较弱的多光谱图像集提出了分组融合的方法．实验结果表明：该最优融合算法高效可行，能处理各种不同多光谱场景的融合．     

Smooth Image Sequences for Data‐driven Morphing

Smoothness is a quality that feels aesthetic and pleasing to the human eye. We present an algorithm for finding “as‐smooth‐as‐possible” sequences in image collections. In contrast to previous work, our method does not assume that the images show a common 3D scene, but instead may depict different object instances with varying deformations, and significant variation in lighting, texture, and color appearance. Our algorithm does not rely on a notion of camera pose, view direction, or 3D representation of an underlying scene, but instead directly optimizes the smoothness of the apparent motion of local point matches among the collection images. We increase the smoothness of our sequences by performing a global similarity transform alignment, as well as localized geometric wobble reduction and appearance stabilization. Our technique gives rise to a new kind of image morphing algorithm, in which the in‐between motion is derived in a data‐driven manner from a smooth sequence of real images without any user intervention. This new type of morph can go far beyond the ability of traditional techniques. We also demonstrate that our smooth sequences allow exploring large image collections in a stable manner.     

Systems and Experiment Paper: Construction of Panoramic Image Mosaics with Global and Local Alignment

This paper presents a complete system for constructing panoramic image mosaics from sequences of images. Our mosaic representation associates a transformation matrix with each input image, rather than explicitly projecting all of the images onto a common surface (e.g., a cylinder). In particular, to construct a full view panorama, we introduce a rotational mosaic representation that associates a rotation matrix (and optionally a focal length) with each input image. A patch-based alignment algorithm is developed to quickly align two images given motion models. Techniques for estimating and refining camera focal lengths are also presented.In order to reduce accumulated registration errors, we apply global alignment (block adjustment) to the whole sequence of images, which results in an optimally registered image mosaic. To compensate for small amounts of motion parallax introduced by translations of the camera and other unmodeled distortions, we use a local alignment (deghosting) technique which warps each image based on the results of pairwise local image registrations. By combining both global and local alignment, we significantly improve the quality of our image mosaics, thereby enabling the creation of full view panoramic mosaics with hand-held cameras.We also present an inverse texture mapping algorithm for efficiently extracting environment maps from our panoramic image mosaics. By mapping the mosaic onto an arbitrary texture-mapped polyhedron surrounding the origin, we can explore the virtual environment using standard 3D graphics viewers and hardware without requiring special-purpose players.An erratum to this article can be found at     

波段排序的高光谱影像3维混合树编码方法

目的 高光谱影像压缩的关键技术是对空间维和光谱维的去相关性。根据高光谱影像数据结构的特点,如何有效去除其空间相关性与谱间相关性是高光谱影像压缩中至关重要的问题。对高光谱影像进行编码时,3维小波变换是极为有效的去除冗余的方法。因此提出了一种通过波段排序并结合3维混合树型结构对高光谱影像3维小波变换系数进行编码的算法。方法 首先,将高光谱影像按照自然波段顺序进行波段分组,并对每组影像进行相邻影像的谱间相关性统计;其次,对相关性较弱的波段组,建立以影像波段序号为顶点、影像相关性系数为边的完全图,对这个完全图求其最大汉密尔顿回路。按照求得的最大汉密尔顿回路顺序对该波段组进行重新排序,从而提高波段组的谱间相关性;在此基础上,对重新排序后的波段组进行3维小波变换,并通过3维混合树结构对3维小波变换系数进行零树编码。结果 通过对大量AVIRIS型高光谱影像数据的仿真实验,验证了本文方法的有效性。对相关性较低的波段组,加入排序算法后,其解码影像与未排序时比,峰值信噪比有了一定的提高。通过实验统计,算法平均用时2.7579s。结论 由于采用了对弱相关性波段组的重新排序机制,使得基于混合树结构的3维零树编码出现了更多有效的零树,在一定程度上提高了编码效率。通过实验统计算法用时,表明该方法以较小的时间代价获得了解码效果的提升。     

Design of a three-dimensional near real-time image processing system

The airborne three-dimensional (3D) imager, which integrates global positioning system (GPS), attitude measurement unit (AMU), scanning laser rangefinder (SLR) and spectral scanner, has been developed successfully. The spectral scanner and SLR use the same optical system, which ensures that the laser point matches pixel seamlessly. The distinctive advantage of the airborne 3D imager is that it can produce geo-referenced imagery and DSM (digital surface models) without any ground control points (GCPs). It is no longer necessary to obtain GCPs, and the software can process the data to produce DSM and a geo-referenced image in near real-time, so the airborne 3D imager is many times faster than the traditional approaches. The processing procedure of the software involves decomposing and checking the raw data, processing GPS data, calculating the positions of laser sample points, producing a geo-referenced image, producing DSM, and mosaicking strips. In this paper, we introduce the principle of the airborne 3D imager first and then focus on the fast processing algorithm. The flight tests and processed results demonstrate that the processing technique is feasible, and can meet the requirement of near real-time applications.     

Multispectral processing for color particle image velocimetry

Since the adoption of digital video cameras and cross-correlation methods for particle image velocimetry (PIV), the use of color images has largely been abandoned. Recently, however, with the re-emergence of color-based stereo and volumetric techniques, and the extensive use of color microscopy, color imaging for PIV has again become relevant. In this work, we explore the potential advantages of color PIV processing by developing and proposing new methods for handling multi-color images. The first method uses cross-correlation of every color channel independently to build a color vector cross-correlation plane. The vector cross-correlation can then be searched for one or more peaks corresponding to either the average displacement of several flow components using a color ensemble operation, or for the individual motion of colored particles, each with a different behavior. In the latter case, linear unmixing is used on the correlation plane to separate each known particle type as captured by the different color channels. The second method introduces the use of quaternions to encode the color data, and the cross-correlation is carried out simultaneously on all colors. The resulting correlation plane can be searched either for a single peak, corresponding to the mean flow or for multiple peaks, with velocity phase separation to determine which velocity corresponds to which particle type. Each of these methods was tested using synthetic images simulating the color recording of noisy particle fields both with and without the use of a Bayer filter and demosaicing operation. It was determined that for single-phase flow, both color methods decreased random errors by approximately a factor of two due to the noise signal being uncorrelated between color channels, while maintaining similar bias errors as compared to traditional monochrome PIV processing. In multi-component flows, the color vector correlation technique was able to successfully resolve displacements of two distinct yet coupled flow components with errors similar to traditional grayscale PIV processing of a single phase. It should be noted that traditional PIV processing is bound to fail entirely under such processing conditions. In contrast, the quaternion methods frequently failed to properly identify the correct velocity and phase and showed significant cross talk in the measurements between particle types. Finally, the color vector method was applied to experimental color images of a microchannel designed for contactless dielectrophoresis particle separation, and good results were obtained for both instantaneous and ensemble PIV processing. However, in both the synthetic color images that were generated using a Bayer filter and the experimental data, a significant peak-locking effect with a period of two pixels was observed. This effect is attributed to the inherent architecture of the Bayer filter. In order to mitigate this detrimental artifact, it is suggested that improved image interpolation or demosaicing algorithms tuned for use in PIV be developed and applied on the color images before processing, or that cameras that do not use a Bayer filter and therefore do not require a demosaicing algorithm be used for color PIV.     

True multi-image alignment and its application to mosaicing andlens distortion correction

Multiple images of a scene are related through 2D/3D view transformations and linear and nonlinear camera transformations. We present an algorithm for true multi-image alignment that does not rely on the measurements of a reference image being distortion free. The algorithm is developed to specifically align and mosaic images using parametric transformations in the presence of lens distortion. When lens distortion is present, none of the images can be assumed to be ideal. In our formulation, all the images are modeled as intensity measurements represented in their respective coordinate systems, each of which is related to an ideal coordinate system through an interior camera transformation and an exterior view transformation. The goal of the accompanying algorithm is to compute an image in the ideal coordinate system while solving for the transformations that relate the ideal system with each of the data images. Key advantages of the technique presented in this paper are: (i) no reliance on one distortion free image, (ii) ability to register images and compute coordinate transformations even when the multiple images are of an extended scene with no overlap between the first and last frame of the sequence, and (iii) ability to handle linear and nonlinear transformations within the same framework. Results of applying the algorithm are presented for the correction of lens distortion, and creation of video mosaics     

A multistep approach for shape similarity search in image databases

Shape similarity searching is a crucial task in image databases, particularly in the presence of errors induced by segmentation or scanning images. The resulting slight displacements or rotations have not been considered so far in the literature. We present a new similarity model that flexibly addresses this problem. By specifying neighborhood influence weights, the user may adapt the similarity distance functions to his or her own requirements or preferences. Technically, the new similarity model is based on quadratic forms for which we present a multi-step query processing architecture, particularly for high dimensions as they occur in image databases. Our algorithm to reduce the dimensionality of quadratic form-based similarity queries results in a lower-bounding distance function that is proven to provide an optimal filter selectivity. Experiments on our test database of 10,000 images demonstrate the applicability and the performance of our approach, even in dimensions as high as 1,024     

A parallel stereo algorithm that produces dense depth maps and preserves image features

To compute reliable dense depth maps, a stereo algorithm must preserve depth discontinuities and avoid gross errors. In this paper, we show how simple and parallel techniques can be combined to achieve this goal and deal with complex real world scenes. Our algorithm relies on correlation followed by interpolation. During the correlation phase the two images play a symmetric role and we use a validity criterion for the matches that eliminate gross errors: at places where the images cannot be correlated reliably, due to lack of texture of occlusions for example, the algorithm does not produce wrong matches but a very sparse disparity map as opposed to a dense one when the correlation is successful. To generate a dense depth map, the information is then propagated across the featureless areas, but not across discontinuities, by an interpolation scheme that takes image grey levels into account to preserve image features. We show that our algorithm performs very well on difficult images such as faces and cluttered ground level scenes. Because all the algorithms described here are parallel and very regular they could be implemented in hardware and lead to extremely fast stereo systems.This research was supported in part under the Centre National d'Etudes Spatiales VAP contract and in part under a Defence Advanced Research Projects Agency contract at SRI     

2D/3D image registration on the GPU

We present a method that performs a rigid 2D/3D image registration efficiently on the Graphical Processing Unit (GPU). As one main contribution of this paper, we propose an efficient method for generating realistic DRRs that are visually similar to x-ray images. Therefore, we model some of the electronic post-processes of current x-ray C-arm-systems. As another main contribution, the GPU is used to compute eight intensity-based similarity measures between the DRR and the x-ray image in parallel. A combination of these eight similarity measures is used as a new similarity measure for the optimization. We evaluated the performance and the precision of our 2D/3D image registration algorithm using two phantom models. Compared to a CPU + GPU algorithm, which calculates the similarity measures on the CPU, our GPU algorithm is between three and six times faster. In contrast to single similarity measures, our new similarity measure achieved precise and robust registration results for both phantom models.     

A non-negative low-rank representation for hyperspectral band selection

Hyperspectral images are widely used in real applications due to their rich spectral information. However, the large volume brings a lot of inconvenience, such as storage and transmission. Hyperspectral band selection is an important technique to cope with this issue by selecting a few spectral bands to replace the original image. This article proposes a novel band selection algorithm that first estimates the redundancy through analysing relationships among spectral bands. After that, spectral bands are ranked according to their relative importance. Subsequently, in order to remove redundant spectral bands and preserve the original information, a maximal linearly independent subset is constructed as the optimal band combination. Contributions of this article are listed as follows: (1) A new strategy for band selection is proposed to preserve the original information mostly; (2) A non-negative low-rank representation algorithm is developed to discover intrinsic relationships among spectral bands; (3) A smart strategy is put forward to adaptively determine the optimal combination of spectral bands. To verify the effectiveness, experiments have been conducted on both hyperspectral unmixing and classification. For unmixing, the proposed algorithm decreases the average root mean square errors (RMSEs) by 0.05, 0.03, and 0.05 for the Urban, Cuprite, and Indian Pines data sets, respectively. With regard to classification, our algorithm achieves the overall accuracies of 77.07% and 89.19% for the Indian Pines and Pavia University data sets, respectively. These results are close to the performance with original images. Thus, comparative experiments not only illustrate the superiority of the proposed algorithm, but also prove the validity of band selection on hyperspectral image processing.     

Sketch-based aesthetic product form exploration from existing images using piecewise clothoid curves

We present a new sketch-based product form exploration technique that works from images and sketches of existing products. At the heart of our approach, is a multi-stroke curve beautification method and a curve-based image deformation algorithm. The proposed approach converts groups of strokes into piecewise clothoid curves in order to produce visually pleasing shapes. The deformation diffusion algorithm then spatially distributes the user specified deformations through out the image to produce smooth transformations from the original image to the resulting image. We demonstrate the technique on a variety of images including photo-realistic images, real product images, and sketches.