Finding corners

Many important image cues such as ‘T’, ‘X’ and ‘L’ junctions have a local two-dimensional (2D) structure. Conventional edge detectors are designed for one-dimensional ‘events’. Even the best edge operators cannot reliably detect these 2D features. The paper proposes a solution to the 2D problem. A mathematical proof is given to explain how the ‘corner’ detector algorithm of Harris¹ estimates image curvature. Although this algorithm will isolate image L junctions, its performance cannot be predicted for T junctions and other higher-order image structures. Instead, an image representation is proposed that exploits the local differential geometrical ‘topography’ of the intensity surface. Theoretical and experimental results are presented which demonstrate how idealized instances of 2D surface features such as junctions can be characterized by the differential geometry of a simple facet model.     

扩展相位拉伸变换的血管造影图像锐化

针对目前传统血管造影图像锐化增强后大量细小血管变得模糊不清或丢失,甚至增强图像中血管周围产生大量背景噪声,提出一种相位拉伸核函数,形成基于扩展相位拉伸变换的血管造影图像增强算法.该算法将"S"型群延迟相位滤波器推广到线性群延迟相位滤波器,并从理论上证明,这种线性相位拉伸的逆变换相位近似于原图的归一化二阶梯度,将高频特征传统的梯度极值表达转换为角度表达,从而更有利于凸显、增强图像中的高频特征.同时,该算法还结合相对总变分理论,将像素的邻域总变分测度与邻域内在变分测度用于增强过程,使算法更好地突出边缘轮廓与结构纹理,抑制细碎杂乱纹理与背景噪声,克服了目前方法存在的不足.利用Matlab软件平台对DeepLesion, OASIS等数据集中的部分图像数据进行实验,与传统相位拉伸变换增强算法、基于相位一致性的血管造影图像锐化算法等进行对照分析,结果表明,增强后图像上细小血管明显清晰,背景噪声得到有效的抑制,平均梯度和信息熵提高均在50%左右,证明了算法的优越性与实用性.     

Adaptive typhoon cloud image enhancement using genetic algorithm and non-linear gain operation in undecimated wavelet domain

By combining discrete undecimated wavelet transform (UWT) with genetic algorithm (GA) an efficient enhancement algorithm for typhoon cloud image is proposed. Having implemented UWT to a typhoon cloud mage, noise in a typhoon cloud image is reduced by modifying the undecimated wavelet coefficients by combining with generalization cross validation at fine resolution levels. GA and non-linear gain operation are used to modify the undecimated wavelet coefficients at coarse resolution levels in order to extrude the details of a typhoon cloud image. Experimental results show that the proposed algorithm can efficiently reduce the additive gauss white noise in a typhoon cloud image while well extruding the detail. In order to accurately assess an enhanced typhoon cloud image’s quality, an overall score index is proposed based on information entropy, contrast measure and peak signal-noise ratio. Finally, comparisons between the proposed algorithm and five other similar methods, are carried out.     

Algorithm to expand regions represented by linear quadtrees

An algorithm is presented that changes to ‘black’ those ‘white’ pixels within a specified distance of any ‘black’ pixel in an image represented by a linear quadtree. This function is useful for answering queries in a geographic information system such as ‘Find all wheat fields within five miles of a flood plain.’ The algorithm works by computing the chessboard distance to nearby black pixels for large white nodes, and either leaves them white, changes them to black or repeats the process on each subquadrant, as required. Small white nodes are a priori within the given radius and require no further calculation. Thus only a small percentage of the nodes of the quadtree need extensive processing. The algorithm is easily applied to multicoloured images by treating all nonwhite colours as ‘black’.     

Design of a high performance architecture for real-time enhancement of video stream captured in extremely low lighting environment

A high performance digital architecture for the implementation of a non-linear image enhancement technique is proposed in this paper. The image enhancement is based on a luminance dependent non-linear enhancement algorithm which achieves simultaneous dynamic range compression, colour consistency and lightness rendition. The algorithm provides better colour fidelity, enhances less noise, prevents the unwanted luminance drop at the uniform luminance areas, keeps the ‘bright’ background unaffected, and enhances the ‘dark’ objects in ‘bright’ background. The algorithm contains a large number of complex computations and thus it requires specialized hardware implementation for real-time applications. Systolic, pipelined and parallel design techniques are utilized effectively in the proposed FPGA-based architectural design to achieve real-time performance. Estimation techniques are also utilized in the hardware algorithmic design to achieve faster, simpler and more efficient architecture. The video enhancement system is implemented using Xilinx’s multimedia development board that contains a VirtexII-X2000 FPGA and it is capable of processing approximately 67 Mega-pixels (Mpixels) per second.     

Color image enhancement by fuzzy intensification

A Gaussian membership function to model image information in spatial domain has been proposed in this paper. We introduce a new contrast intensification operator, which involves a parameter t for enhancement of color images. By minimizing the fuzzy entropy of the image information, the parameter t is calculated globally. A visible improvement in the image quality for human contrast perception is observed, also demonstrated here by the reduction in ‘index of fuzziness’ and ‘entropy’ of the output image.     

一种基于图像方向信息测度算法的自适应表格图像增强算法

图像增强是图像处理的重要任务.为了有效地增强对比度极低的表格图像,提出了基于图像方向信息测度算法的自适应图像增强算法.该方法首先在像素级利用图像方向信息侧度将图像像素分为平滑点和阶跃边缘点;然后针对表格图像特点,再对平滑点细分为平滑点和屋脊边缘点,同时,对不同类型的点设计不同的滤波方法,以便通过算法自适应来初步增强图像;最后,利用模糊增强算法来对图像进行对比度增强,以达到更好的增强效果.实验表明,该算法能有效地增强对比度极低的表格图像,并使得结果图像有很好的视觉效果.     

基于改进图像方向信息测度的表格图像增强算法

提出了基于图像方向信息测度的图像增强算法。算法利用图像方向信息测度,在像素级对图像像素进行分类,然后针对表格图像特性,利用改进的图像方向信息测度,对边缘点进行区域连接,使得边缘点能完全地覆盖图像中的字线和表格边缘;利用得到的边缘信息和平滑信息,对图像进行空域滤波处理,初步增强图像;利用模糊增强算法,对图像进行对比度增强,达到较好的增强效果。实验表明,该算法能有效增强对比度极低的表格图像,使得结果图像有很好的视觉效果。     

Concept-based indexing of annotated images using semantic DNA

One of the challenges in image retrieval is dealing with concepts which have no visual appearance in the images or are not used as keywords in their annotations. To address this problem, this paper proposes an unsupervised concept-based image indexing technique which uses a lexical ontology to extract semantic signatures called ‘semantic chromosomes’ from image annotations. A semantic chromosome is an information structure, which carries the semantic information of an image; it is the semantic signature of an image in a collection expressed through a set of semantic DNA (SDNA), each of them representing a concept. Central to the concept-based indexing technique discussed is the concept disambiguation algorithm developed, which identifies the most relevant ‘semantic DNA’ (SDNA) by measuring the semantic importance of each word/phrase in the annotation. The concept disambiguation algorithm is evaluated using crowdsourcing. The experiments show that the algorithm has better accuracy (79.4%) than the accuracy demonstrated by other unsupervised algorithms (73%) in the 2007 Semeval competition. It is also comparable with the accuracy achieved in the same competition by the supervised algorithms (82–83%) which contrary to the approach proposed in this paper have to be trained with large corpora. The approach is currently applied to the automated generation of mood boards used as an inspirational tool in concept design.     

基于对比度信息的彩色图像分割

该文提出了一种基于对比度信息的彩色图像分割算法。文中不用一般的导矢信息而用对比度信息对彩色图像进行边缘提取。为了符合人的视觉特性,使用了CIEL*a*b*彩色空间,并用色差ΔEab作为彩色对比度的测量方法。该文提出的彩色图像分割方法合理、可靠,仿真结果表明了该方法的优越性。     

基于相似度测量和模糊熵的矿井图像增强方法

为了提高煤矿井下图像的视觉效果,提出一种改进的基于相似度测量的增强算法和模糊熵理论相结合的方法.根据煤矿井下图像的特点,引入模糊熵,建立基于模糊熵测度的判别准则,尽量去除噪声干扰.改进隶属度函数的计算方法,充分提取图像中的分形维特征信息.通过改进对比度变换因子的计算方法,进一步增强图像局部对比度.实验结果表明,处理后的图像不会出现增强过度现象,具有较好地噪声抑制、明暗对比和保持图像细节,是一种有效地矿井图像增强方法.     

Unpopping: Solving the Image-Space Blend Problem for Smooth Discrete LOD Transitions

This paper presents a new, simple and practical algorithm to avoid artifacts when switching between discrete levels of detail (LOD) by smoothly blending LOD representations in image space. We analyse the alternatives of conventional alpha‐blending and so‐called late‐switching (the switching of LODs ‘far enough’ from the eye‐point), widely thought to solve the LOD switching discontinuity problem, and conclude that they either do not work in practice, or defeat the concept of LODs. In contrast we show that our algorithm produces visually pleasing blends for static and animated discrete LODs, for discrete LODs with different types of LOD representations (e.g. billboards and meshes) and even to some extent totally different objects with similar spatial extent, with a very small runtime overhead.     

Apple color grading based on organization feature parameters

This paper presents a system for apple color grading into four classes according to standards stipulated in China. To automatically grade apple fruit color, a laboratory machine vision system was developed, which consisted of a color CCD camera equipped with an image grab device, a bi-cone roller device controlled by a stepping motor, and a lighting source. Four images, one for every rotation of 90°, were taken from each apple. Seventeen color feature parameters (FP) were extracted from each apple in the image processing. Three hundred and eighteen “Fuji” apples were examined by the system, and were divided into two sets, with 200 in “Training set” and 118 in “Test set”. A method called organization feature parameter (OFP), based on formulae expression trees by using genetic algorithms (GA), was used in this paper. When the initial FP could not sensitively distinguish among different classes of apples, the FP were organized into one new OFP by using genetic algorithm. By applying the step decision tree algorithm in combination with the OFP method, high grade judgment ratios were achieved in the classification of two of four apple color grades, i.e., ‘Extra’, and ‘Reject’. However, the grade judgment ratio for ‘class I’ and ‘class II’ was relatively low. Compared with BP-ANN and SVM, the OFPs method was more accurate than BP-ANN, but a little lower than SVM for identification results.     

Multi-exemplar inhomogeneous texture synthesis

This paper presents a new framework for the semi-automatic synthesis of an inhomogeneous texture from a theoretically infinite number of input exemplars. Our algorithm generates a result with natural multiway transitions, maintaining a reasonable synthesis time to keep interactivity. We introduce intermediate textures between multiple exemplars, called ‘transition textures’ which enable our algorithm to produce smooth transitions between input exemplar patches. We also propose ‘image index correction’ to achieve a synthesis speed that is independent of the number of input exemplars by smart placement of transition-texture samples. Our algorithm is semiautomatic, spatially deterministic and well suited to acceleration via parallel processors. Also, our algorithm can easily control the synthesis result to design a texture visual with user interactions.     

AMSR与SVM相结合的人脸识别方法

为解决在复杂光照条件下的人脸识别问题,提出一种自适应多尺度Retinex(AMSR)和支持向量机(SVM)相结合的人脸识别算法;首先,针对多尺度Retinex(MSR)只能处理光照均匀图像的缺点,提出了AMSR算法,该算法在MSR基础上增加了全局非线性对比度增强方法,使图像的灰度能够根据人脸图像的明暗度进行全局自适应调整,实现了各种光照条件下的人脸图像预处理;然后利用SVM多分类算法对人脸图像进行分类;在人脸库的实验结果证明了AMSR+SVM人脸识别算法的有效性。     

Fuzzy C-Means clustering through SSIM and patch for image segmentation

In this study, we propose a new robust Fuzzy C-Means (FCM) algorithm for image segmentation called the patch-based fuzzy local similarity c-means (PFLSCM). First of all, the weighted sum distance of image patch is employed to determine the distance of the image pixel and the cluster center, where the comprehensive image features are considered instead of a simple level of brightness (gray value). Second, the structural similarity (SSIM) index takes into account similar degrees of luminance, contrast, and structure of image. The DSSIM (distance for structural similarity) metric is developed on a basis of SSIM in order to characterize the distance between two pixels in the whole image. Next a new similarity measure is proposed. Furthermore, a new fuzzy coefficient is proposed via the new similarity measure together with the weighted sum distance of image patch, and then the PFLSCM algorithm is put forward based on the idea of image patch and this coefficient. Through a collection of experimental studies using synthetic and publicly available images, we demonstrate that the proposed PFLSCM algorithm achieves improved segmentation performance in comparison with the results produced by some related FCM-based algorithms.     

一种利用花粉算法优化的遥感图像分割方法

为提高遥感图像分割的准确性与抗噪性,以学生t分布混合模型为基础,结合K-means与花粉算法的特点,将K-means算法局部寻优能力强以及花粉算法全局寻优能力强的优点相结合,提出一种基于K-means的学生t分布混合模型,用于遥感图像分割.该方法中,根据学生t分布与高斯分布以及柯西分布比较接近的特点,对花粉算法的执行过...     

一种改进的直线检测算法

针对哈夫变换的不足,提出了一种改进的直线检测算法。该算法基于“多对一”的原理,降低了算法复杂度,利用从局部到全局的思想,在局部子区域中获取直线参数以减少量化误差的影响,然后据此参数扩展至全图范围搜索,完成直线检测,分析了算法的时间和空间复杂度。实验表明,该算法占用存储空间小、运算快速、且具有较强的鲁棒性。     

Optimization of Robust Loss Functions for Weakly-Labeled Image Taxonomies

The recently proposed ImageNet dataset consists of several million images, each annotated with a single object category. These annotations may be imperfect, in the sense that many images contain multiple objects belonging to the label vocabulary. In other words, we have a multi-label problem but the annotations include only a single label (which is not necessarily the most prominent). Such a setting motivates the use of a robust evaluation measure, which allows for a limited number of labels to be predicted and, so long as one of the predicted labels is correct, the overall prediction should be considered correct. This is indeed the type of evaluation measure used to assess algorithm performance in a recent competition on ImageNet data. Optimizing such types of performance measures presents several hurdles even with existing structured output learning methods. Indeed, many of the current state-of-the-art methods optimize the prediction of only a single output label, ignoring this ‘structure’ altogether. In this paper, we show how to directly optimize continuous surrogates of such performance measures using structured output learning techniques with latent variables. We use the output of existing binary classifiers as input features in a new learning stage which optimizes the structured loss corresponding to the robust performance measure. We present empirical evidence that this allows us to ‘boost’ the performance of binary classification on a variety of weakly-supervised labeling problems defined on image taxonomies.