Fractal analysis of tumor in brain MR images

Abstract. The purpose of this study is to discuss existing fractal-based algorithms and propose novel improvements of these algorithms to identify tumors in brain magnetic-response (MR) images. Considerable research has been pursued on fractal geometry in various aspects of image analysis and pattern recognition. Magnetic-resonance images typically have a degree of noise and randomness associated with the natural random nature of structure. Thus, fractal analysis is appropriate for MR image analysis. For tumor detection, we describe existing fractal-based techniques and propose three modified algorithms using fractal analysis models. For each new method, the brain MR images are divided into a number of pieces. The first method involves thresholding the pixel intensity values; hence, we call the technique piecewise-threshold-box-counting (PTBC) method. For the subsequent methods, the intensity is treated as the third dimension. We implement the improved piecewise-modified-box-counting (PMBC) and piecewise-triangular-prism-surface-area (PTPSA) methods, respectively. With the PTBC method, we find the differences in intensity histogram and fractal dimension between normal and tumor images. Using the PMBC and PTPSA methods, we may detect and locate the tumor in the brain MR images more accurately. Thus, the novel techniques proposed herein offer satisfactory tumor identification. Received: 13 October 2001 / Accepted: 28 May 2002 Correspondence to: K.M. Iftekharuddin     

Iterative model-based binarization algorithm for cheque images

Binarization of document images with poor contrast, strong noise, complex patterns, and variable modalities in the gray-scale histograms is a challenging problem. A new binarization algorithm has been developed to address this problem for personal cheque images. The main contribution of this approach is optimizing the binarization of a part of the document image that suffers from noise interference, referred to as the Target Sub-Image (TSI), using information easily extracted from another noise-free part of the same image, referred to as the Model Sub-Image (MSI). Simple spatial features extracted from MSI are used as a model for handwriting strokes. This model captures the underlying characteristics of the writing strokes, and is invariant to the handwriting style or content. This model is then utilized to guide the binarization in the TSI. Another contribution is a new technique for the structural analysis of document images, which we call “Wavelet Partial Reconstruction” (WPR). The algorithm was tested on 4,200 cheque images and the results show significant improvement in binarization quality in comparison with other well-established algorithms. Received: October 10, 2001 / Accepted: May 7, 2002 This research was supported in part by NCR and NSERC's industrial postgraduate scholarship No. 239464. A simplified version of this paper has been presented at ICDAR 2001 [3].     

Parameter-free based two-stage method for binarizing degraded document images

Binarization plays an important role in document image processing, especially in degraded documents. For degraded document images, adaptive binarization methods often incorporate local information to determine the binarization threshold for each individual pixel in the document image. We propose a two-stage parameter-free window-based method to binarize the degraded document images. In the first stage, an incremental scheme is used to determine a proper window size beyond which no substantial increase in the local variation of pixel intensities is observed. In the second stage, based on the determined window size, a noise-suppressing scheme delivers the final binarized image by contrasting two binarized images which are produced by two adaptive thresholding schemes which incorporate the local mean gray and gradient values. Empirical results demonstrate that the proposed method is competitive when compared to the existing adaptive binarization methods and achieves better performance in precision, accuracy, and F-measure.     

Range image segmentation using local approximation of scan lines with application to CAD model acquisition

Abstract. Automatic acquisition of CAD models from existing objects requires accurate extraction of geometric and topological information from the input data. This paper presents a range image segmentation method based on local approximation of scan lines. The method employs edge models that are capable of detecting noise pixels as well as position and orientation discontinuities of varying strengths. Region-based techniques are then used to achieve a complete segmentation. Finally, a geometric representation of the scene, in the form of a surface CAD model, is produced. Experimental results on a large number of real range images acquired by different range sensors demonstrate the efficiency and robustness of the method. Received: 1 August 2000 / Accepted: 23 January 2002 Correspondence to: I. Khalifa     

A noise attribute thresholding method for document image binarization

A new thresholding method, called the noise attribute thresholding method (NAT), for document image binarization is presented in this paper. This method utilizes the noise attribute features extracted from the images to make the selection of threshold values for image thresholding. These features are based on the properties of noise in the images and are independent of the strength of the signals (objects and background) in the image. A simple noise model is given to explain these noise properties. The NAT method has been applied to the problem of removing text and figures printed on the back of the paper. Conventional global thresholding methods cannot solve this kind of problem satisfactorily. Experimental results show that the NAT method is very effective. Received July 05, 1999 / Revised July 07, 2000     

基于多维侧窗聚类分块的退化书法文档二值化

书法字文档图像在不良光照条件下的灰度值分布差异较大, 低光照区域图像对比度较低、笔画形态纹理特征出现退化, 传统方法通常仅考虑了局部信息的均值、平方差、熵等因素, 在形态纹理方面考虑较少, 从而对低对比度区域的特征信息不敏感. 针对此类问题, 本文提出了一种多维侧窗聚类分块的退化书法文档的二值化方法CS-SWF (clustering segmentation based SWF), 该方法首先利用SWF卷积核描述具有相似形态学特征的像素块, 之后提出多种修正规则利用下采样提取低纬度信息去修正特征区域. 最后, 对特征图中聚类块进行前后景分离, 得到二值化结果图. 本文使用FM、PSNR和DRD为指标, 将现有方法和本文方法进行对比, 实验结果表明, 在自建的100张手写退化文档图像数据集下, 本文方法在低对比度暗部区域的二值化效果较为稳定, 在精准度和鲁棒性上优于对比算法.     

Extracting characters of license plates from video sequences

In this paper, we present a new approach to extract characters on a license plate of a moving vehicle, given a sequence of perspective-distortion-corrected license plate images. Different from many existing single-frame approaches, our method simultaneously utilizes spatial and temporal information. We first model the extraction of characters as a Markov random field (MRF), where the randomness is used to describe the uncertainty in pixel label assignment. With the MRF modeling, the extraction of characters is formulated as the problem of maximizing a posteriori probability based on a given prior knowledge and observations. A genetic algorithm with local greedy mutation operator is employed to optimize the objective function. Experiments and comparison study were conducted and some of our experimental results are presented in the paper. It is shown that our approach provides better performance than other single frame methods. Received: 13 August 1997 / Accepted: 7 October 1997     

Adaptive binarization method for degraded document images based on surface contrast variation

Document binarization is an important technique in document image analysis and recognition. Generally, binarization methods are ineffective for degraded images. Several binarization methods have been proposed; however, none of them are effective for historical and degraded document images. In this paper, a new binarization method is proposed for degraded document images. The proposed method based on the variance between pixel contrast, it consists of four stages: pre-processing, geometrical feature extraction, feature selection, and post-processing. The proposed method was evaluated based on several visual and statistical experiments. The experiments were conducted using five International Document Image Binarization Contest benchmark datasets specialized for binarization testing. The results compared with five adaptive binarization methods: Niblack, Sauvola thresholding, Sauvola compound algorithm, NICK, and Bataineh. The results show that the proposed method performs better than other methods in all binarization cases.     

Yoda,an adaptive soft classification model: content-based similarity queries and beyond

Abstract. Providing a customized result set based upon a user preference is the ultimate objective of many content-based image retrieval systems. There are two main challenges in meeting this objective: First, there is a gap between the physical characteristics of digital images and the semantic meaning of the images. Secondly, different people may have different perceptions on the same set of images. To address both these challenges, we propose a model, named Yoda, that conceptualizes content-based querying as the task of soft classifying images into classes. These classes can overlap, and their members are different for different users. The “soft” classification is hence performed for each and every image feature, including both physical and semantic features. Subsequently, each image will be ranked based on the weighted aggregation of its classification memberships. The weights are user-dependent, and hence different users would obtain different result sets for the same query. Yoda employs a fuzzy-logic based aggregation function for ranking images. We show that, in addition to some performance benefits, fuzzy aggregation is less sensitive to noise and can support disjunctive queries as compared to weighted-average aggregation used by other content-based image retrieval systems. Finally, since Yoda heavily relies on user-dependent weights (i.e., user profiles) for the aggregation task, we utilize the users' relevance feedback to improve the profiles using genetic algorithms (GA). Our learning mechanism requires fewer user interactions, and results in a faster convergence to the user's preferences as compared to other learning techniques. Correspondence to: Y.-S. Chen (E-mail: yishinc@usc.edu) This research has been funded in part by NSF grants EEC-9529152 (IMSC ERC) and IIS-0082826, NIH-NLM R01-LM07061, DARPA and USAF under agreement nr. F30602-99-1-0524, and unrestricted cash gifts from NCR, Microsoft, and Okawa Foundation.     

Comparing images using joint histograms

Color histograms are widely used for content-based image retrieval due to their efficiency and robustness. However, a color histogram only records an image's overall color composition, so images with very different appearances can have similar color histograms. This problem is especially critical in large image databases, where many images have similar color histograms. In this paper, we propose an alternative to color histograms called a joint histogram, which incorporates additional information without sacrificing the robustness of color histograms. We create a joint histogram by selecting a set of local pixel features and constructing a multidimensional histogram. Each entry in a joint histogram contains the number of pixels in the image that are described by a particular combination of feature values. We describe a number of different joint histograms, and evaluate their performance for image retrieval on a database with over 210,000 images. On our benchmarks, joint histograms outperform color histograms by an order of magnitude.     

空间可变有限混合模型

目的 有限混合模型是一种无监督学习方法,它被广泛的应用到数据分类任务中。然而,在图像分割过程中,由于有限混合模型没有引入邻域像素间的空间关系,导致了图像分割结果对噪声非常敏感。为了增强有限混合模型的抗噪性,提出一种新的空间可变有限混合模型。方法 该模型通过在像素的先验分布中引入一种新的空间关系来降低噪声对图像分割结果的干扰。在构建空间关系的过程中,利用形态学膨胀原理将空间邻域内特征值出现的概率而不是特征值本身进行膨胀操作,然后通过根据具有最大概率的分类标记在高斯混合模型迭代地计算过程中进行局部像素空间平滑,从而起到抑制噪声干扰的作用。结果 本文实验包含了人工合成图像和医学CT图像的图像分割实验。在人工合成图像分割实验中,对人工合成图像添加了不同程度的噪声来测试本文模型和对比模型对噪声抑制能力的高低;对医学CT图像进行图像分割实验,以是比较本文模型与对比模型之间在实际图像分割中的效果。结论 实验数据显示,本文提出的模型在噪声抑制能力上,图像分割精度和计算效率上均有更优的性能。     

Silhouette-based occluded object recognition through curvature scale space

A complete and practical system for occluded object recognition has been developed which is very robust with respect to noise and local deformations of shape (due to weak perspective distortion, segmentation errors and non-rigid material) as well as scale, position and orientation changes of the objects. The system has been tested on a wide variety of free-form 3D objects. An industrial application is envisaged where a fixed camera and a light-box are utilized to obtain images. Within the constraints of the system, every rigid 3D object can be modeled by a limited number of classes of 2D contours corresponding to the object's resting positions on the light-box. The contours in each class are related to each other by a 2D similarity transformation. The Curvature Scale Space technique [26, 28] is then used to obtain a novel multi-scale segmentation of the image and the model contours. Object indexing [16, 32, 36] is used to narrow down the search space. An efficient local matching algorithm is utilized to select the best matching models. Received: 5 August 1996 / Accepted: 19 March 1997     

Browsing and placement of multi-resolution images on parallel disks

Abstract. With rapid advances in computer and communication technologies, there is an increasing demand to build and maintain large image repositories. To reduce the demands on I/O and network resources, multi-resolution representations are being proposed for the storage organization of images. Image decomposition techniques such as wavelets can be used to provide these multi-resolution images. The original image is represented by several coefficients, one of them with visual similarity to the original image, but at a lower resolution. These visually similar coefficients can be thought of as thumbnails or icons of the original image. This paper addresses the problem of storing these multi-resolution coefficients on disks so that thumbnail browsing as well as image reconstruction can be performed efficiently. Several strategies are evaluated to store the image coefficients on parallel disks. These strategies can be classified into two broad classes, depending on whether the access pattern of the images is used in the placement. Disk simulation is used to evaluate the performance of these strategies. Simulation results are validated with results from experiments with real Disks, and are found to be in good qualitative agreement. The results indicate that significant performance improvements can be achieved with as few as four disks by placing image coefficients based upon browsing access patterns. Work supported by a research grant from NSF/ARPA/NASA IRI9411330 and NSF instrumentation grant CDA-9421978 and NSF Career grant No. IIS-9985019, and NSF grant 0010044-CCR.     

A golden-block-based self-refining scheme for repetitive patterned wafer inspections

Abstract. This paper presents a novel technique for detecting possible defects in two-dimensional wafer images with repetitive patterns using prior knowledge. The technique has a learning ability that can create a golden-block database from the wafer image itself, then modify and refine its content when used in further inspections. The extracted building block is stored as a golden block for the detected pattern. When new wafer images with the same periodical pattern arrive, we do not have to recalculate their periods and building blocks. A new building block can be derived directly from the existing golden block after eliminating alignment differences. If the newly derived building block has better quality than the stored golden block, then the golden block is replaced with the new building block. With the proposed algorithm, our implementation shows that a significant amount of processing time is saved. Also, the storage overhead of golden templates is reduced significantly by storing golden blocks only. Received: 21 February 2001 / Accepted: 21 April 2002 Correspondence to: S.-U. Guan     

Motion detection with nonstationary background

Abstract. This paper proposes a new background subtraction method for detecting moving foreground objects from a nonstationary background. While background subtraction has traditionally worked well for a stationary background, the same cannot be implied for a nonstationary viewing sensor. To a limited extent, motion compensation for the nonstationary background can be applied. However, in practice, it is difficult to realize the motion compensation to sufficient pixel accuracy, and the traditional background subtraction algorithm will fail for a moving scene. The problem is further complicated when the moving target to be detected/tracked is small, since the pixel error in motion that is compensating the background will subsume the small target. A spatial distribution of Gaussians (SDG) model is proposed to deal with moving object detection having motion compensation that is only approximately extracted. The distribution of each background pixel is temporally and spatially modeled. Based on this statistical model, a pixel in the current frame is then classified as belonging to the foreground or background. For this system to perform under lighting and environmental changes over an extended period of time, the background distribution must be updated with each incoming frame. A new background restoration and adaptation algorithm is developed for the nonstationary background. Test cases involving the detection of small moving objects within a highly textured background and with a pan-tilt tracking system are demonstrated successfully. Received: 30 July 2001 / Accepted: 20 April 2002 Correspondence to: Chin-Seng Chau     

PessimalPrint: a reverse Turing test

Abstract. We exploit the gap in ability between human and machine vision systems to craft a family of automatic challenges that tell human and machine users apart via graphical interfaces including Internet browsers. Turing proposed [Tur50] a method whereby human judges might validate “artificial intelligence” by failing to distinguish between human and machine interlocutors. Stimulated by the “chat room problem” posed by Udi Manber of Yahoo!, and influenced by the CAPTCHA project [BAL00] of Manuel Blum et al. of Carnegie-Mellon Univ., we propose a variant of the Turing test using pessimal print: that is, low-quality images of machine-printed text synthesized pseudo-randomly over certain ranges of words, typefaces, and image degradations. We show experimentally that judicious choice of these ranges can ensure that the images are legible to human readers but illegible to several of the best present-day optical character recognition (OCR) machines. Our approach is motivated by a decade of research on performance evaluation of OCR machines [RJN96,RNN99] and on quantitative stochastic models of document image quality [Bai92,Kan96]. The slow pace of evolution of OCR and other species of machine vision over many decades [NS96,Pav00] suggests that pessimal print will defy automated attack for many years. Applications include `bot' barriers and database rationing. Received: February 14, 2002 / Accepted: March 28, 2002 An expanded version of: A.L. Coates, H.S. Baird, R.J. Fateman (2001) Pessimal Print: a reverse Turing Test. In: {\it Proc. 6th Int. Conf. on Document Analysis and Recognition}, Seattle, Wash., USA, September 10–13, pp. 1154–1158 Correspondence to: H. S. Baird     

曲线笔触渲染的图像风格迁移

目的 针对GANILLA、Paint Transformer、StrokeNet等已有的风格迁移算法存在生成图像笔触丢失、线条灵活度低以及训练时间长等问题,提出一种基于曲线笔触渲染的图像风格迁移算法。方法 首先按照自定义的超像素数量将图像前景分割为小区域的子图像,保留更多图像细节,背景分割为较大区域的子图像,再对分割后的每个子区域选取控制点,采用Bezier方程对控制点进行多尺度笔触生成,最后采用风格迁移算法将渲染后的图像与风格图像进行风格迁移。结果 与AST （arbitrary style transfer）方法相比,本文方法在欺骗率指标上提升了0.13,测试者欺骗率提升了0.13。与Paint Transformer等基于笔触渲染的算法对比,本文能够在纹理丰富的前景区域生成细粒度笔触,在背景区域生成粗粒度笔触,保存更多的图像细节。结论 与GANILLA、AdaIN （adaptive instance normalization）等风格迁移算法相比,本文采用图像分割算法取点生成笔触参数,无需训练,不仅提高了算法效率,而且生成的多风格图像保留风格化图像的笔触绘制痕迹,图像色彩鲜明。     

自适应对称自回归模型的压缩图像内插方法

目的 大多数图像内插方法只考虑低分辨率图像的下采样降质过程,忽略编码噪声的影响。提出一种新的自适应对称自回归模型的压缩图像内插方法。方法 假设局部图像相似的图像块具有相同的图像内插模型。方法分为训练和重建两个阶段。在训练阶段,首先对训练图像采用主成分分析提取图像块的局部梯度主方向,根据方向进行一次分类,分别建立各个方向的对称自回归模型和训练集;其次对每个方向的训练集,根据图像基元特征,利用K均值聚类方法进行二次分类;最后对每个二次分类训练子集,选择其所属方向类的模型,使用有约束的最小二乘法估计对应于该子集的模型系数。在重建阶段,首先根据测试图像块的局部梯度主方向,确定方向类别,再计算测试块基元特征和该方向类中所有聚类中心的欧氏距离,选择具有最小欧氏距离的聚类中心的自回归模型用于内插。结果 采用8种不同的测试图像在JPEG的2种量化方式条件下进行测试,与7种典型的图像内插相比,结果表明本文方法能够有效地克服编码噪声的影响,峰值信噪比(PSNR)和结构相似度(SSIM)均优于其他方法。结论 本文方法具有较低的复杂度,可以适用于图像通信中增强图像的分辨率。     

Local Adaptivity to Variable Smoothness for Exemplar-Based Image Regularization and Representation

A novel adaptive and exemplar-based approach is proposed for image restoration (denoising) and representation. The method is based on a pointwise selection of similar image patches of fixed size in the variable neighborhood of each pixel. The main idea is to associate with each pixel the weighted sum of data points within an adaptive neighborhood. We use small image patches (e.g. 7×7 or 9×9 patches) to compute these weights since they are able to capture local geometric patterns and texels seen in images. In this paper, we mainly focus on the problem of adaptive neighborhood selection in a manner that balances the accuracy of approximation and the stochastic error, at each spatial position. The proposed pointwise estimator is then iterative and automatically adapts to the degree of underlying smoothness with minimal a priori assumptions on the function to be recovered. The method is applied to artificially corrupted real images and the performance is very close, and in some cases even surpasses, to that of the already published denoising methods. The proposed algorithm is demonstrated on real images corrupted by non-Gaussian noise and is used for applications in bio-imaging.     

Document skew estimation without angle range restriction

The existing skew estimation techniques usually assume that the input image is of high resolution and that the detectable angle range is limited. We present a more generic solution for this task that overcomes these restrictions. Our method is based on determination of the first eigenvector of the data covariance matrix. The solution comprises image resolution reduction, connected component analysis, component classification using a fuzzy approach, and skew estimation. Experiments on a large set of various document images and performance comparison with two Hough transform-based methods show a good accuracy and robustness for our method. Received October 10, 1998 / Revised version September 9, 1999