Image algebra techniques for binary image component labeling with local operators

Binary image component labeling is a fundamental process in image processing and computer vision. This paper presents several image component labeling algorithms with local operators expressed in the language of image algebra. These algorithms are then analyzed for their time and space complexities.     

Fast local image processing algorithms using recursive computation

Conclusions Most image and signal processing algorithms are local algorithms, regardless of the specific physical nature of the images and signals. Because of the strong correlation of operations required to determine two neighboring elements, image processing can be substantially accelerated by using fast algorithms. Some considerations regarding the construction of such algorithms on the basis of recursive computation have been examined in this paper. The development of specific algorithms for fast local image processing requires further work, because we still do not have the explicit form of the algorithm for the determination of a general local function. We have discussed some particular examples of fast algorithms, including linear and median filtering. Linear filtering and algorithms with linear function evaluation are often used for digital image and signal processing in various fields. The general form of linear filtering can be determined using piecewise-linear approximation, which also ensures independence of the number of operations on window size, albeit at a cost of a certain loss of accuracy. By sacrificing some accuracy of linear filtering, we accelerate the processing, whereas higher accuracy is achieved by reducing the processing speed. Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 146–157, January–February, 1994.     

Fast level set image and video segmentation using new evolution indicator operators

We propose an effective level set evolution method for robust object segmentation in real images. We construct an effective region indicator and an multiscale edge indicator, and use these two indicators to adaptively guide the evolution of the level set function. The multiscale edge indicator is defined in the gradient domain of the multiscale feature-preserving filtered image. The region indicator is built on the similarity map between image pixels and user specified interest regions, where the similarity map is computed using Gaussian Mixture Models (GMM). Then we combine these two methods to develop a new mixing edge stop function, which makes the level set method more robust to initial active contour setting, and forces the level set to evolve adaptively based on the image content. Furthermore, we apply an acceleration approach to speed up our evolution process, which yields real time segmentation performance. Finally, we extend the proposed approach to video segmentation for achieving effective target tracking results. As the results show, our approach is effective for image and video segmentation and works well to accurately detect the complex object boundaries in real-time.     

基于局部显著特征的快速图像配准方法

针对SIFT算法在进行图像配准时存在提取特征点数目大、无法精确控制、运算速度慢、配准点精度不高的问题,提出一种基于局部显著特征的快速图像配准方法。该方法首先对原始图像和待配准图像进行降采样,对降采样图像分别提取SIFT特征点,并对特征点运用改进的K-means聚类算法进行聚类;然后利用聚类结果筛选聚类区域,在各聚类区域提取显著特征点进行粗匹配;最后利用显著特征点在原始图像中定位显著区域,对所得显著区域进行精配准。实验结果表明,该方法减少了图像匹配时间,控制了特征点数量,在保证匹配准确度的同时,有效地提高了特征匹配的效率。     

Robust interactive image segmentation using structure-aware labeling

Interactive image segmentation has remained an active research topic in image processing and graphics, since the user intention can be incorporated to enhance the performance. It can be employed to mobile devices which now allow user interaction as an input, enabling various applications. Most interactive segmentation methods assume that the initial labels are correctly and carefully assigned to some parts of regions to segment. Inaccurate labels, such as foreground labels in background regions for example, lead to incorrect segments, even by a small number of inaccurate labels, which is not appropriate for practical usage such as mobile application. In this paper, we present an interactive segmentation method that is robust to inaccurate initial labels (scribbles). To address this problem, we propose a structure-aware labeling method using occurrence and co-occurrence probability (OCP) of color values for each initial label in a unified framework. Occurrence probability captures a global distribution of all color values within each label, while co-occurrence one encodes a local distribution of color values around the label. We show that nonlocal regularization together with the OCP enables robust image segmentation to inaccurately assigned labels and alleviates a small-cut problem. We analyze theoretic relations of our approach to other segmentation methods. Intensive experiments with synthetic and manual labels show that our approach outperforms the state of the art.     

Fast local Laplacian filtering based enhanced medical image fusion using parameter-adaptive PCNN and local features-based fuzzy weighted matrices

Generally, the anatomical CT/MRI modalities exhibit the brain tissue anatomy with a high spatial resolution, where PET/SPECT modalities show the metabolic features with low spatial resolution. Therefore, the integration of these two classes significantly improves several clinical applications and computer-aided diagnoses. In the proposed scheme, a fast local Laplacian filter (FLLF) is first applied to the source images to enhance the edge information and suppress the noise artifacts. Second, the RGB images are converted to YUV color space to separate the Y-component. Then to capture the spatial and spectral features of the source images, the NSST is applied to decompose the input (grayscale and/or Y-component) images into one low (LFS) and several high-frequency subbands (HFS). Third, an improved salience measure and matching factor (SMF) method by the local features-based fuzzy-weighted matrix (FW-SMF) is introduced to fuse the LFS coefficient. Due to the fast convergence with fewer iterations and robust pixels selection procedure, the PA-PCNN model is adopted to fuse the HFS coefficients. Fourth, the final fused image is obtained by applying inverse NSST and YUV format. Visual and statistical analysis performed on various experiments prove that the proposed scheme not only integrates the spatial and texture features details of the source images but also enhances the visual quality and contrast of the fused image compared to the existing state-of-arts.

     

Efficient local transformation estimation using Lie operators

Conventional translation-only motion estimation algorithms cannot cope with transformations of objects such as scaling, rotations and deformations. Motion models characterizing non-translation motions are thus beneficial as they offer more accurate motion estimation and compensation. In this paper, we introduce low-complexity transformation estimation methods with four motion models based on Lie operators, which are linear operators that have found applications in optical character recognitions. We show that individual Lie operators are capable of capturing small degrees of object transformations. We propose an efficient local transformation estimation algorithm in order to further improve the accuracy of the translation-only estimation by integrating all four motion models. Simulations with an MPEG-2 video codec on two video sequences show that the proposed transformation estimation approach can noticeably improve the motion compensation performance of the translation-only method by achieving higher PSNR (peak signal-to-noise ratio) values for the predicted frames, with only a small fraction of the complexity required by the translation motion search.     

基于局部特征的遥感图像快速自动配准

针对图像处理领域中遥感图像的配准问题,提出一种基于图像局部特征的快速、自动配准方法。该方法选取具有良好尺度、旋转不变性以及精确特征点定位能力的SIFT局部特征,使用其特征向量间的欧氏距离作为相似性度量进行特征点匹配,并依据仿射变换误差准则去除奇异匹配特征点对,采用仿射变换的几何模型,实现了遥感图像的快速自动配准。实验结果表明,方法是高效、精确以及稳定的。     

局部区域拟合的快速水平集图像分割方法

目的 由于灰度不均匀图像在不同目标区域的灰度分布存在严重的重叠,对其进行分割仍然是一个难题;同时,图像中的噪声严重降低了图像分割的准确性。因此,传统水平集方法无法鲁棒、精确、快速地对具有灰度不均匀性和噪声的图像进行分割。针对这一问题,提出一种基于局部区域信息的快速水平集图像分割方法。方法 灰度不均匀图像通常被描述为一个分段常数图像乘以一个缓慢变化的偏移场。首先,通过一个经过微调的多尺度均值滤波器来估计图像的偏移场,并对图像进行预处理以减轻图像的不均匀性;然后,利用基于偏移场校正的方法和基于局部区域信息拟合的方法分别构建能量项,并利用演化曲线轮廓内外图像灰度分布的重叠程度,构建权重函数自适应调整两个能量项之间的权重;最后,引入全方差规则项对水平集进行约束,增强了数值计算的稳定性和对噪声的鲁棒性,并通过加性算子分裂策略实现水平集快速演化。结果 在具有不同灰度不均匀性和噪声图像上的分割结果表明,所提方法不但对初始轮廓的位置、灰度不均匀性和各种噪声具有较强的鲁棒性,而且具有高达94.5%的分割精度和较高的分割效率,与传统水平集方法相比分割精度至少提高了20.6%,分割效率是LIC（local intensity clustering）模型的9倍;结论 本文提出一种基于局部区域信息的快速水平集图像分割方法。实验结果表明,与传统水平集方法相比具有较高的分割精度和分割效率,可以很好地应用于具有灰度不均匀和噪声的医学、红外和自然图像等的分割。     

Fast image retrieval using color-spatial information

In this paper, we present an image retrieval system that employs both the color and spatial information of images to facilitate the retrieval process. The basic unit used in our technique is a single-colored cluster, which bounds a homogeneous region of that color in an image. Two clusters from two images are similar if they are of the same color and overlap in the image space. The number of clusters that can be extracted from an image can be very large, and it affects the accuracy of retrieval. We study the effect of the number of clusters on retrieval effectiveness to determine an appropriate value for “optimal' performance. To facilitate efficient retrieval, we also propose a multi-tier indexing mechanism called the Sequenced Multi-Attribute Tree (SMAT). We implemented a two-tier SMAT, where the first layer is used to prune away clusters that are of different colors, while the second layer discriminates clusters of different spatial locality. We conducted an experimental study on an image database consisting of 12,000 images. Our results show the effectiveness of the proposed color-spatial approach, and the efficiency of the proposed indexing mechanism. Received August 1, 1997 / Accepted December 9, 1997     

快速的局部二值拟合优化分割算法

针对灰度不均匀图像难以正确分割和分割结果依赖于初始轮廓的问题,提出一种快速稳定的分割算法,首先通过自适应距离保持水平集演化(ADPLS)算法进行初始分割以获取较好的初始轮廓,然后采用局部二值拟合(LBF)模型进行快速分割。实验表明,改进后的模型有良好的分割效果,较好地解决了分割速度、精度及稳定性之间的矛盾。      

Fast connected-component labeling

Labeling of connected components in a binary image is one of the most fundamental operations in pattern recognition: labeling is required whenever a computer needs to recognize objects (connected components) in a binary image. This paper presents a fast two-scan algorithm for labeling of connected components in binary images. We propose an efficient procedure for assigning provisional labels to object pixels and checking label equivalence. Our algorithm is very simple in principle, easy to implement, and suitable for hardware and parallel implementation. We show the correctness of our algorithm, analyze its complexity, and compare it with other labeling algorithms. Experimental results demonstrated that our algorithm is superior to conventional labeling algorithms.     

快速稳定的局部二元拟合分割算法

基于局部区域信息的局部二元拟合(LBF)模型在处理弱边界或灰度不均匀的图像分割方面有一定优势,但该方法非常依赖于初始轮廓,不当的初始轮廓不仅会导致分割时间较长,甚至分割失败。针对这一不足,提出一种快速稳定的LBF模型。首先通过添加带有变权系数面积项的LBF模型进行初始分类以获取较好的初始轮廓,然后采用传统的LBF模型对图像进行进一步的分割。实验证明,在保证良好分割效果的前提下,该方法对初始轮廓的选择更加灵活,分割速度明显快于传统的LBF模型。     

Fast image colourisation using the isolines concept

Multimedia Tools and Applications - Colourisation is the process of adding colour information to monochromatic images and video sequences. Generally, colourised images are visually more attractive...     

利用图像校正的快速目标识别算法

提出了一种基于图像校正的快速目标识别算法,特别适合于航拍照片中地面上面目标的识别。算法可以使目标模型单一化,在很大程度上克服了目标识别模型复杂、数据运算量大、计算实时性差等缺点,提高了目标识别的实时性和精确性。首先对图像进行感兴趣区域的检测;对检测出的区域进行图像的正视校正;然后对校正后的区域进行特征提取;最后进行目标识别,并输出目标信息,完成识别过程。实验表明,该算法用于大倾角航片目标识别是有发展潜力和前途的。     

Adaptive image compression using local pattern information

This paper describes a new adaptive coding technique to the coding of transform coefficients used in block based image compression schemes. The presence and orientation of the edge information in a sub-block are used to select different quantization tables and zigzag scan paths to cater for the local image pattern. Measures of the edge presence and edge orientation in a sub-block are calculated out of their DCT coefficients, and each sub-block can be classified into four different edge patterns. Experimental results show that compared to JPEG and the improved HVS-based coding, the new scheme has significantly increased the compression ratio without sacrificing the reconstructed image quality.     

Medical image fusion using multi-level local extrema

The fusion of data for medical imaging has become a central issue in such biomedical applications as image-guided surgery and radiotherapy. The multi-level local extrema (MLE) representation has been shown to have many advantages over conventional image representation methods. In this paper, we propose a new fusion algorithm for multi-modal medical images based on MLE. Our method enables the decomposition of input images into coarse and detailed layers in the MLE schema, and utilizes local energy and contrast fusion rules for coefficient selection in the different layers. This preserves more detail in the source images and further improves the quality of the fused image. The final fused image is obtained from the superposition of selected coefficients in the coarse and detailed layers. We illustrate the performance of the proposed method using three groups of medical images from different sources as our experimental subjects. We also compare our method with other techniques using cumulative mutual information, the objective image fusion performance measure, spatial frequency, and a blind quality index. Experimental results show that our method achieves a superior performance in both subjective and objective assessment criteria.     

Fast image dehazing using guided joint bilateral filter

In this paper, we propose a new fast dehazing method from single image based on filtering. The basic idea is to compute an accurate atmosphere veil that is not only smoother, but also respect with depth information of the underlying image. We firstly obtain an initial atmosphere scattering light through median filtering, then refine it by guided joint bilateral filtering to generate a new atmosphere veil which removes the abundant texture information and recovers the depth edge information. Finally, we solve the scene radiance using the atmosphere attenuation model. Compared with exiting state of the art dehazing methods, our method could get a better dehazing effect at distant scene and places where depth changes abruptly. Our method is fast with linear complexity in the number of pixels of the input image; furthermore, as our method can be performed in parallel, thus it can be further accelerated using GPU, which makes our method applicable for real-time requirement.