Image-based lens geometric distortion correction using minimization of average bicoherence index

In this paper, a lens geometric distortion correction method for low-cost digital cameras is described. The lens distortion coefficient is estimated by the value minimizing average bicoherence index which denotes image-dependent higher-order correlations in the frequency domain. In order to formulate the minimization of the average bicoherence index in terms of the lens distortion coefficient alone, the feasibility of a square-pixel assumption and the effect of the image center bias on the image quality are shown experimentally. At the same time, a computationally efficient method for calculating the bicoherence index is proposed, which comprises an approximation of coordinate resampling and development of a relationship between the lens distortion coefficients of the original and its reduced image. Depending on the spatial distribution of image features, the performance of the proposed method can be made comparable to that of precision camera calibration.     

A fast focus measure for video display inspection

In this paper, we propose a robust and computationally fast focus measure for cathode ray tubes (CRTs) found in TVs and computer and video monitors. The focus measure is represented by the area ratio of a predetermined object pattern in the test image of a fixed size, which increases in size as the degree of defocus increases. The area ratio is calculated using a simple, straightforward moment-preserving method. On-line, real-time application of focus adjustment can be realized with the proposed method. Received: 4 November 2001 / Accepted: 6 December 2002 Published online: 23 April 2003 Correspondence to. D.-M. Tsai (e-mail: iedmtsai@saturn.yzu.edu.tw)     

结合多分辨率与块分割的不同聚焦图像融合

提出了一种结合多分辨率与图像块分割的图像融合新方法。该方法除将多分辨率融合图像块作为可能位于源图像的模糊区域与清晰区域交界处位置的最终融合图像块之外,还将多分辨率图像融合方法获取的融合图像与源图像进行分块比较,选用与多分辨率融合图像块相似的源图像块作为最终融合图像块。实验结果表明,该方法能有效提高常用的多分辨率图像融合方法的融合效果。     

Shape from focus using fast discrete curvelet transform

A new method for focus measure computation is proposed to reconstruct 3D shape using image sequence acquired under varying focus plane. Adaptive histogram equalization is applied to enhance varying contrast across different image regions for better detection of sharp intensity variations. Fast discrete curvelet transform (FDCT) is employed for enhanced representation of singularities along curves in an input image followed by noise removal using bivariate shrinkage scheme based on locally estimated variance. The FDCT coefficients with high activity are exploited to detect high frequency variations of pixel intensities in a sequence of images. Finally, focus measure is computed utilizing neighborhood support of these coefficients to reconstruct the shape and a well-focused image of the scene being probed.     

On the use of a joint spatial-frequency representation for the fusion of multi-focus images

This paper shows practical examples of the application of a new image fusion paradigm for achieving a 2-D all in-focus image starting from a set of multi-focus images of a 3-D real object. The goal consists in providing an enhanced 2-D image showing the object entirely in focus. The fusion procedure shown here is based on the use of a focusing pixel-level measure. Such measure is defined in the space–frequency domain through a 1-D pseudo-Wigner distribution. The method is illustrated with different sets of images. Evaluation measures applied to artificially blurred cut and pasted regions have shown that the present scheme can provide equally or even better performance than other alternative image fusion algorithms.     

Multi-focal image fusion using degree of focus and fuzzy logic

The continuous advancement in the field of imaging sensor necessitates the development of an efficient image fusion technique. The multi-focal image fusion extracts the in-focus information from the source images to construct a single composite image with increased depth-of-field. Traditionally, the information in multi-focal images is divided into two categories: in-focus and out-of-focus data. Instead of using a binary focus map, in this work we calculate the degree of focus for each source image using fuzzy logic. The fused image is then generated based on the weighted sum of this information. An initial focus tri-state map is built for each input image by using spatial frequency and a proposed focus measure named as alternate sum modified Laplacian. For the cases where these measures indicate different source images to contain focused pixel or have equal strength, another focus measure based on sum of gradient is employed to calculate the degree of focus in a fuzzy inference system. Finally, the fused image is computed from the weights determined by the degree of focus map of each image. The proposed algorithm is designed to fuse two source images, whereas fusion of multiple input images can be performed by fusing a source image with the fusion output of the previous input group. The comparison of the proposed method with several transform and pixel domain based techniques are conducted in terms of both subjective visual assessment and objective quantitative evaluation. Experimental results demonstrate that our method can be competitive with or even outperforms the methods in comparison.     

基于自动聚焦算法的多聚焦图像融合

在分析了多聚焦图像特点的基础上, 将镜头自动聚焦算法移植到多聚集图像融合问题的研究中, 并加以改进, 其主要思想是: 以高频分量作为图像清晰度的判据, 寻求多聚焦图像的聚焦区域, 将其直接移植到融合图像中; 而对于一个焦点到另一焦点的过渡区域, 对其用取最大特征的融合算子进行处理。实验表明, 该算法是原图像特征的最佳近似, 优于小波分解的融合算法。     

Image segmentation using a local extrema texture measure

An image segmentation technique is proposed which uses a texture measure that counts the number of local extrema in a window centered at each picture point. Four gray level pictures are derived, each of which represents a texture or gray level property of the original image. These intermediate pictures are used to derive the number of segments in which to divide the original image. The segmentation is then performed by assigning each pixel in the original image to a region by using a four-dimensional distance measure on the intermediate pictures, comparing each location to each selected segment. This process is then repeated in a hierarchical structure using decreasing window sizes so that smaller regions within the larger ones are defined. The computations required are amenable to real-time video implementation using state-of-the-art devices.     

基于SML和PCNN的NSCT域多聚焦图像融合

针对融合规则带来的虚假边缘、伪影等问题,提出了改进拉普拉斯能量和(Sum-modified Laplacian,SML)和脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)相结合的非下采样Contourlet变换(Non-Sampled Con-tourlet Transform,NSCT)域融合方法。首先,采用NSCT将每幅源图像分解成包含基本信息的低频子带图像和多幅包含细节信息的带通子带图像。然后,计算各尺度分解图像的SML值,根据值的大小对低频子带图像各像素点进行像素选择。对于带通子带部分,将计算的SML作为PCNN的输入激励,PCNN输出的点火映射图用来选择各子带图像的像素值。最后,将处理后的各子带系数进行NSCT重构得到融合图像。实验结果表明,此算法能很好地改善融合图像的聚焦清晰度,并且与现有的SIDWT,DTCWT,NSCT以及基于PCNN的融合方法相比,所提算法在互信息量、结构相似度以及边缘信息保留量等客观指标方面得到了提高。     

3-D analysis of functional instabilities of the ankle using digital still cameras

A technique to analyze the functional instability of an ankle is proposed. The technique is based on measurements using two digital still cameras. We carried out several experiments using this technique. Subjects are requested to stand on an experimental table whose angle of inclination is adjusted. By comparing the postures of both feet, we could tell that the subjects had experienced an ankle sprain and had functional instabilities in their ankles.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

Reliability measure for shape-from-focus

Shape-from-focus (SFF) is a passive technique widely used in image processing for obtaining depth-maps. This technique is attractive since it only requires a single monocular camera with focus control, thus avoiding correspondence problems typically found in stereo, as well as more expensive capturing devices. However, one of its main drawbacks is its poor performance when the change in the focus level is difficult to detect. Most research in SFF has focused on improving the accuracy of the depth estimation. Less attention has been paid to the problem of providing quality measures in order to predict the performance of SFF without prior knowledge of the recovered scene. This paper proposes a reliability measure aimed at assessing the quality of the depth-map obtained using SFF. The proposed reliability measure (the R-measure) analyzes the shape of the focus measure function and estimates the likelihood of obtaining an accurate depth estimation without any previous knowledge of the recovered scene. The proposed R-measure is then applied for determining the image regions where SFF will not perform correctly in order to discard them. Experiments with both synthetic and real scenes are presented.     

红外成像区域法自动调焦聚焦区域的研究

在红外成像区域法自动调焦系统中,聚焦区域选择很重要。为此,对一组红外图像进行实验仿真,选择相同大小、不同位置的聚焦区域观察其对调焦评价函数曲线的影响,以及选择同一聚焦位置、不同大小的聚焦区域观察其对调焦评价函数曲线的影响,通过分析得出聚焦区域选择的基本原则。     

运用距离测度进行多传感器目标级信息融合

针对多传感器目标级识别问题,提出了一种新的信息融合方法。定义了新的距离测度,通过求解各目标类别与待识别目标的综合距离测度之和最小的优化问题,客观地确定了指标的权重,根据综合距离给出目标识别方法。仿真实验验证了方法的有效性,非常适用于具有多个特征指标的多目标识别领域。     

Image fusion meets deep learning: A survey and perspective

Image fusion, which refers to extracting and then combining the most meaningful information from different source images, aims to generate a single image that is more informative and beneficial for subsequent applications. The development of deep learning has promoted tremendous progress in image fusion, and the powerful feature extraction and reconstruction capabilities of neural networks make the fused results promising. Recently, several latest deep learning technologies have made image fusion explode, e.g., generative adversarial networks, autoencoder, etc. However, a comprehensive review and analysis of latest deep-learning methods in different fusion scenarios is lacking. To this end and in this survey, we first introduce the concept of image fusion, and classify the methods from the perspectives of the deep architectures adopted and fusion scenarios. Then, we review the state-of-the-art on the use of deep learning in various types of image fusion scenarios, including the digital photography image fusion, the multi-modal image fusion and the sharpening fusion. Subsequently, the evaluation for some representative methods in specific fusion tasks are performed qualitatively and quantitatively. Moreover, we briefly introduce several typical applications of image fusion, including photography visualization, RGBT object tracking, medical diagnosis, and remote sensing monitoring. Finally, we provide the conclusion, highlight the challenges in image fusion, and look forward to potential future research directions.     

路径识别和图像处理的CPLD实现

通过对传统智能车竞赛过程中道路信息采集模块的分析,提出了一种基于边缘检测原理来提取黑线的中心点和其他赛道信息的方法。该方法采用CPLD来处理CMOS数字摄像头拍摄到的黑白赛道信息,并将处理后的结果发送给CPU。最终得到的黑线中心点精度高、实时性好;且使得CPU将更多的资源放在复杂的控制算法和舵机控制上,有利于赛车速度的提高。     

Automatic data clustering using continuous action-set learning automata and its application in segmentation of images

Most of the proposed algorithms to solve the dynamic clustering problem are based on nature inspired meta-heuristic algorithms. In this paper a different reinforcement based optimization approach called continuous action-set learning automata (CALA) is used and a novel dynamic clustering approach called ACCALA is proposed. CALA is an optimization tool interacting with a random environment and learn the optimal action from the environment feedbacks. In this paper the dynamic clustering problem considered as a noisy optimization problem and the team of CALAs is used to solve this noisy optimization problem. To build such a team of CALAs this paper proposed a new representation of CALAs. Each automaton in this team uses its continuous action-set and defining a suitable action-set for each automaton has a great impact on the CALAs search behavior. In this paper we used the statistical property of data-sets and proposed a new method to automatically find an action-set for each automaton. The performance of ACCALA is evaluated and the results are compared with seven well-known automatic clustering techniques. Also ACCALA is used to perform automatic segmentation. The experimental results are promising and show that the proposed algorithm produced compact and well-separated clusters.     

Space and time sensor fusion using an active camera for mobile robot navigation

We propose a sensor-fusion technique where the data sets for previous moments are properly transformed and fused into the current data sets to allow accurate measurements, such as the distance to an obstacle or the location of the service robot itself. In conventional fusion schemes, measurements are dependent on the current data sets. As a result, more sensors are required to measure a certain physical parameter or to improve the accuracy of a measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequences of the data sets are stored and utilized to improve the measurements. The theoretical basis is illustrated by examples, and the effectiveness is proved through simulations. Finally, the new space and time sensor fusion (STSF) scheme is applied to the control of a mobile robot in an unstructured environment and a structured environment.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

Single-sensor imaging in consumer digital cameras: a survey of recent advances and future directions

This paper surveys in a systematic way recent advances in single-sensor imaging. In particular, the most distinctive elements of a single-sensor imaging pipeline, such as color filter array configurations, image interpolation solutions, and picture storage approaches are reviewed and commented upon. New perspectives in the field are proposed, suggesting that the demand for consumer digital camera solutions and digital color imaging applications will continue.     

The use of focus groups in complex and pressurised IS studies and evaluation using Klein & Myers principles for interpretive research

Studies of information systems (IS) often involve many complexities, uncertainties and restrictions. As a result, researchers have to adopt innovative research approaches or tailor and extend existing ones. This paper describes and evaluates an innovative focus group approach used to study three IS development teams, which had to cope with the application of a broad, polymorphous framework, untested in practice, seeking to elicit potentially highly sensitive opinions and judgements in a highly pressurised, time‐restricted environment. This paper makes two significant contributions. To conduct this evaluation, the researchers operationalised the Klein & Myers seven principles for interpretive study for the first time, creating a set of questions that other IS researchers can now use to evaluate their own interpretive studies. The paper also applies these to the design of the focus groups and develops a set of recommendations for future IS researchers to consider based on lessons learned in this study.