Automatic image orientation detection

We present an algorithm for automatic image orientation estimation using a Bayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a learning vector quantizer (LVQ) can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how principal component analysis (PCA) and linear discriminant analysis (LDA) can be used as a feature extraction mechanism to remove redundancies in the high-dimensional feature vectors used for classification. The proposed method is compared with four different commonly used classifiers, namely k-nearest neighbor, support vector machine (SVM), a mixture of Gaussians, and hierarchical discriminating regression (HDR) tree. Experiments on a database of 16 344 images have shown that our proposed algorithm achieves an accuracy of approximately 98% on the training set and over 97% on an independent test set. A slight improvement in classification accuracy is achieved by employing classifier combination techniques.     

图像方向性的条纹分析方法

在图像分析方法研究中,提出了用条纹概念来分析图像,并成功地把这一理论应用于图像的方向性检测。文章首先从直观的条纹图像三维对比中,归纳出图像条纹概念的两个基本属性,然后构造了区别于传统的方向滤波方法的三角分形基迭代函数系统的图像方向提取模型,最后定义了图像的方向统计表征量。实验证明了条纹分析方法的高效性和实时性要求的独特性能。     

Bayesian image segmentation using local iso-intensity structural orientation.

Image segmentation is a fundamental problem in early computer vision. In segmentation of flat shaded, nontextured objects in real-world images, objects are usually assumed to be piecewise homogeneous. This assumption, however, is not always valid with images such as medical images. As a result, any techniques based on this assumption may produce less-than-satisfactory image segmentation. In this work, we relax the piecewise homogeneous assumption. By assuming that the intensity nonuniformity is smooth in the imaged objects, a novel algorithm that exploits the coherence in the intensity profile to segment objects is proposed. The algorithm uses a novel smoothness prior to improve the quality of image segmentation. The formulation of the prior is based on the coherence of the local structural orientation in the image. The segmentation process is performed in a Bayesian framework. Local structural orientation estimation is obtained with an orientation tensor. Comparisons between the conventional Hessian matrix and the orientation tensor have been conducted. The experimental results on the synthetic images and the real-world images have indicated that our novel segmentation algorithm produces better segmentations than both the global thresholding with the maximum likelihood estimation and the algorithm with the multilevel logistic MRF model.     

An ON-OFF orientation selective address event representation image transceiver chip

This paper describes the electronic implementation of a four-layer cellular neural network architecture implementing two components of a functional model of neurons in the visual cortex: linear orientation selective filtering and half wave rectification. Separate ON and OFF layers represent the positive and negative outputs of two-phase quadrature Gabor-type filters, whose orientation and spatial-frequency tunings are electronically adjustable. To enable the construction of a multichip network to extract different orientations in parallel, the chip includes an address event representation (AER) transceiver that accepts and produces two-dimensional images that are rate encoded as spike trains. It also includes routing circuitry that facilitates point-to-point signal fan in and fan out. We present measured results from a 32/spl times/64 pixel prototype, which was fabricated in the TSMC0.25-/spl mu/m process on a 3.84 by 2.54 mm die. Quiescent power dissipation is 3 mW and is determined primarily by the spike activity on the AER bus. Settling times are on the order of a few milliseconds. In comparison with a two-layer network implementing the same filters, this network results in a more symmetric circuit design with lower quiescent power dissipation, albeit at the expense of twice as many transistors.     

SIH: segmented intensity histogram for orientation estimation in image matching

In this paper, we propose a fast and effective new method to reduce the overhead cost of orientation estimation. The proposed method uses the summation of intensity values from segments of image patches and forms a histogram based on those values. As a result, it is faster than SIFT-like algorithms because it does not require computation of gradient orientations and magnitudes. Also, it is as fast as other intensity-based algorithms with better image matching performance. Proposed method could be easily integrated to any image matching algorithms. Test results indicate that SIFT integrated with proposed orientation estimation method produces comparable results as the original multi-angle SIFT algorithm with less execution time.     

基于边缘方向投影的图像块修复方法

压缩图像在传输中,信道干扰常导致图像块数据丢失.给出一种基于边缘方向投影的图像损失块修复方法.按照周围邻域未损失像素的边缘方向信息将损失块分类,并根据类别用结合频域和空域信息的凸集投影算法自适应地修复损失块.能较好地修复图像边缘和复杂纹理.与RIBMAP方法相比,此方法在修复质量和算法顽健性上均有提高.     

方向相关与互信息加权组合多模图像配准方法

针对可见光与红外图像的配准问题,提出了一种基于边缘方向相关和局部优选区域互信息加权的配准方法。首先进行区域划分,建立区域优选测度,选取信息量丰富的局部区域;在优选区域的基础上,引入了图像的边缘方向信息,构造边缘方向图,以增加全局空间特征;然后,通过综合局部区域灰度信息和全局边缘方向信息,将互相关测度和互信息测度加权集成,构造了一种新的相似测度。实验结果表明,边缘方向相关测度的引入,提高了基于互信息的图像配准的精度,利用局部优选区域代替整幅图像提高了算法的速度。     

基于最小二乘互相关算法的图像定位匹配研究

提出了一种基于最小二乘互相关的图像定位匹配算法。该算法将图像互相关信息和最小二乘法结合实现图像定位匹配,匹配精度可以达到亚像素甚至1/100像素级,同时利用金字塔分层来提高定位匹配速度;通过本算法在印刷品质量自动化检测系统中的应用,验证了该算法的高精度与高速度特性。1     

超声相控阵PE检测图像缺陷定位算法研究

针对车轮无损检测图像中对缺陷评判的不足,提出一种能够自动定位检测图像中缺陷的算法。该算法是在相控阵技术的车轮超声探伤基础上,运用数字图像处理技术,对检测图像进行时域的处理。对算法的流程进行了详细介绍,并对每一步给出说明和处理结果。通过对比得知,这一算法对图像中缺陷的检测精度高于90%,大为提高识别效率,并减少人为因素引起的误判。     

Laplacian affine sparse coding with tilt and orientation consistency for image classification

Recently, sparse coding has become popular for image classification. However, images are often captured under different conditions such as varied poses, scales and different camera parameters. This means local features may not be discriminative enough to cope with these variations. To solve this problem, affine transformation along with sparse coding is proposed. Although proven effective, the affine sparse coding has no constraints on the tilt and orientations as well as the encoding parameter consistency of the transformed local features. To solve these problems, we propose a Laplacian affine sparse coding algorithm which combines the tilt and orientations of affine local features as well as the dependency among local features. We add tilt and orientation smooth constraints into the objective function of sparse coding. Besides, a Laplacian regularization term is also used to characterize the encoding parameter similarity. Experimental results on several public datasets demonstrate the effectiveness of the proposed method.     

No-reference stereoscopic image quality evaluator based on human visual characteristics and relative gradient orientation

Stereoscopic image quality assessment (SIQA) is of great significance to the development of modern three-dimensional (3D) display technology. In this work, by further mining the relationship between visual features and stereoscopic image quality perception, we build a new no-reference SIQA model, which combines the monocular and binocular features. Statistical quality-aware structural features from relative gradient orientation (RGO) map and texture features from the histogram of the weighted local binary pattern (LBP) in the texture image (TLBP) are not only extracted from both monocular view, but also extracted from binocular views to predict binocular quality perception. Meanwhile, the color statistical features ignored by most models and the binocularity feature is extracted to complement the monocular features and the above binocular features, respectively. Finally, all the extracted features and subjective scores are used to predict the objective quality score through the support vector regression (SVR) model. Experiments on four popular stereoscopic image databases show that the proposed model achieves high consistency with subjective assessment, and the performance of the model is very competitive with the latest models.     

A CBIR system based on saliency driven local image features and multi orientation texture features

In Content-based Image Retrieval (CBIR), the user provides the query image in which only a selective portion of the image carries the foremost vital information known as the object region of the image. However, the human visual system also focuses on a particular salient region of an image to instinctively understand its semantic meaning. Therefore, the human visual attention technique can be well imposed in the CBIR scheme. Inspired by these facts, we initially utilized the signature saliency map-based approach to decompose the image into its respective main object region (ObR) and non-object region (NObR). ObR possesses most of the vital image information, so block-level normalized singular value decomposition (SVD) has been used to extract salient features of the ObR. In most natural images, NObR plays a significant role in understanding the actual semantic meaning of the image. Accordingly, multi-directional texture features have been extracted from NObR using Gabor filter on different wavelengths. Since the importance of ObR and NObR features are not equal, a new homogeneity-based similarity matching approach has been devised to enhance retrieval accuracy. Finally, we have demonstrated retrieval performances using both the combined and distinct ObR and NObR features on seven standard coral, texture, object, and heterogeneous datasets. The experimental outcomes show that the proposed CBIR system has a promising retrieval efficiency and outperforms various existing systems substantially.     

基于图像的轮式机器人位置与角度的检测方法

提出了一种新的轮式移动机器人位置与角度的检测方法。首先在机器人上方做一个黑色矩形标记,标记物的长边与机器人的正向平行,然后在图像平面内,用CAMShift算法对机器人进行跟踪。在跟踪的过程中,可以获得只包含目标体的子图片,即"感兴趣区域"。子图片的中心即为轮式运动机器人的几何中心。采用适当的方法检测出矩形标记物长边与图像坐标X轴之间的夹角,并以此角度作为机器人的方向角度。     

Quantification of collagen fiber orientation based on center line of second harmonic generation image for naturally aging skins

Quantification of fiber orientation is the key to characterizing the tissue mechanical properties and diagnosing diseases. A center line-based algorithm is presented for estimating the orientation distribution that first skeletonizes a binary image of fibers, followed by orientation estimation using a weight vector summation algorithm along the center line of image. Then we use the orientation at the skeleton to approximate the orientation of each pixel between the boundary and skeleton. The algorithm is applied for characterizing collagen fibers of mouse skins in second harmonic generation (SHG) image, and the circle standard deviation of orientation could be a biomarker to differentiate the naturally aging skins.     

基于多层级图像分解的图像融合算法

不同类型的探测器在成像机理上有不同的侧重点,使得成像图像表征的信息也有所不同,导致单幅图像不能完整地反映场景的有效信息。因此,提取多源图像的互补信息,并去除其中的冗余信息,合成一幅能准确、完整表达场景的复合图像的技术成为了图像处理领域中一项非常重要的技术,图像融合正是这类问题的一种有效解决方法。针对传统多尺度分解的图像融合方法易产生噪声和信息缺失的现象,文中提出了一种基于多层级图像分解的红外与可见光图像融合算法。首先,利用加权平均曲率滤波的边缘保持特性与高斯滤波的平滑特性,构建了多层级图像分解模型。在利用该模型将源图像分解为小尺度层、大尺度层和基层等3个不同层级。然后,针对基层,采用能量属性融合策略进行融合;针对大尺度层,采用复合融合策略进行融合;针对小尺度层,采用最大值融合策略。最后,将融合后的层级进行加和,以重构出最终的融合图像。实验结果表明:文中提出的基于多层级图像分解的图像融合算法能够有效降低噪声产生的概率,同时减少了融合后的信息缺失。     

Spatial-domain image hiding using image differencing

A method to embed a secret image into a cover image is proposed. The method is based on the similarity among the grey values of consecutive image pixels as well as the human visual system's variation insensitivity from smooth to contrastive. A stego-image is produced by replacing the grey values of a differencing result obtained from the cover image with those of a differencing result obtained from the secret image. The process preserves the secret image with no loss and produces the stego-image with low degradation. Moreover, a pseudorandom mechanism is used to achieve cryptography. It is found from experiment that the peak values of signal-to-noise ratios of the method are high and that the resulting stego-images are imperceptible. Even when the size of the secret image is about a half of the cover image     

基于图像增强和复原的图像去雾方法研究

雾霭等天气下获得的图像存在对比度低、颜色退化、景物模糊等一系列图像退化的问题,直接影响了对图像信息的有效利用。因此,对雾天图像进行有效的去雾处理,有效改善降质图像的质量,具有一定的实际意义。分析讨论基于图像增强的多尺度Retinex算法和利用图像复原原理的基于暗原色先验理论的去雾算法,并对具有不同特点的单幅有雾图像进行去雾仿真。实验结果表明,不同理论基础的两种去雾算法各有特点,基于暗原色理论处理得到的图像去雾效果更显著,算法运行速度更快。     

Points position weighted shape orientation

A new method for computing shape orientation is introduced. The advantage of the method is that it could control the impact of the shape points position (with respect to the shape centroid) to the computed shape orientation.     

Nonlinear diffusion for orientation estimation

This letter presents an image orientation estimation method which is based on a combination of two techniques： quadrature filtering and nonlinear diffusion. The quadrature filters are used to get the orientation tensors for edges, then the orientation tensors are smoothed through nonlinear diffusion. Experimental resuits and analysis show the robustness of the proposed method.