Reversible Data Hiding in Encrypted Image Based on Block Classification Permutation

Recently, reversible data hiding in encrypted image (RDHEI) has attracted extensive attention, which can be used in secure cloud computing and privacy protection effectively. In this paper, a novel RDHEI scheme based on block classification and permutation is proposed. Content owner first divides original image into non-overlapping blocks and then set a threshold to classify these blocks into smooth and non-smooth blocks respectively. After block classification, content owner utilizes a specific encryption method, including stream cipher encryption and block permutation to protect image content securely. For the encrypted image, data hider embeds additional secret information in the most significant bits (MSB) of the encrypted pixels in smooth blocks and the final marked image can be obtained. At the receiver side, secret data will be extracted correctly with data-hiding key. When receiver only has encryption key, after stream cipher decryption, block scrambling decryption and MSB error prediction with threshold, decrypted image will be achieved. When data hiding key and encryption key are both obtained, receiver can find the smooth and non-smooth blocks correctly and MSB in smooth blocks will be predicted correctly, hence, receiver can recover marked image losslessly. Experimental results demonstrate that our scheme can achieve better rate-distortion performance than some of state-of-the-art schemes.     

A Cryptograph Domain Image Retrieval Method Based on Paillier Homomorphic Block Encryption

With the rapid development of information network, the computing resources and storage capacity of ordinary users cannot meet their needs of data processing. The emergence of cloud computing solves this problem but brings data security problems. How to manage and retrieve ciphertext data effectively becomes a challenging problem. To these problems, a new image retrieval method in ciphertext domain by block image encrypting based on Paillier homomophic cryptosystem is proposed in this paper. This can be described as follows: According to the Paillier encryption technology, the image owner encrypts the original image in blocks, obtains the image in ciphertext domain, then passes it to the third party server. The server calculates the difference histogram of the image in ciphertext domain according to the public key and establishes the index database. The user passes the retrieved image to the server. The server computes the differential histogram of the retrieved image by public key. Then, compares the similarity of it with the histogram in index database and selects larger similarity images in ciphertext and send them to the user. The user obtains the target image with the private key. The experimental results show that the method is feasible and simple.     

Programmable Reversible Shape Transformation of Hydrogels Based on Transient Structural Anisotropy

Stimuli-responsive shape-transforming hydrogels have shown great potential toward various engineering applications including soft robotics and microfluidics. Despite significant progress in designing hydrogels with ever more sophisticated shape-morphing behaviors, an ultimate goal yet to be fulfilled is programmable reversible shape transformation. It is reported here that transient structural anisotropy can be programmed into copolymer hydrogels of N-isopropylacrylamide and stearyl acrylate. Structural anisotropy arises from the deformed hydrophobic domains of the stearyl groups after thermomechanical programming, which serves as a template for the reversible globule-to-coil transition of the poly(N-isopropylacrylamide) chains. The structural anisotropy is transient and can be erased upon cooling. This allows repeated programming for reversible shape transformation, an unknown feature for the current hydrogels. The programmable reversible transformation is expected to greatly extend the technical scope for hydrogel-based devices.     

序列图象目标的一种特殊数字跟踪方法

介绍了在数，模检测相结合的电视跟踪器的一项关键技术－指寅区域的位置和大小的序列图象的实时捕获和处理，并以ＡＤＳＰ２１８１为核心实现了一实际的系统，分析了其中的关键硬件技术介绍了可编程器件ＥＰＬＤ在其中的应用，给出了实时采集处理的结果。     

An Efficient Detection Approach of Content Aware Image Resizing

Content aware image resizing (CAIR) is an excellent technology used widely for image retarget. It can also be used to tamper with images and bring the trust crisis of image content to the public. Once an image is processed by CAIR, the correlation of local neighborhood pixels will be destructive. Although local binary patterns (LBP) can effectively describe the local texture, it however cannot describe the magnitude information of local neighborhood pixels and is also vulnerable to noise. Therefore, to deal with the detection of CAIR, a novel forensic method based on improved local ternary patterns (ILTP) feature and gradient energy feature (GEF) is proposed in this paper. Firstly, the adaptive threshold of the original local ternary patterns (LTP) operator is improved, and the ILTP operator is used to describe the change of correlation among local neighborhood pixels caused by CAIR. Secondly, the histogram features of ILTP and the gradient energy features are extracted from the candidate image for CAIR forgery detection. Then, the ILTP features and the gradient energy features are concatenated into the combined features, and the combined features are used to train classifier. Finally support vector machine (SVM) is exploited as a classifier to be trained and tested by the above features in order to distinguish whether an image is subjected to CAIR or not. The candidate images are extracted from uncompressed color image database (UCID), then the training and testing sets are created. The experimental results with many test images show that the proposed method can detect CAIR tampering effectively, and that its performance is improved compared with other methods. It can achieve a better performance than the state-of-the-art approaches.     

Reversible Data Hiding in Classification-Scrambling Encrypted-Image Based on Iterative Recovery

To improve the security and quality of decrypted images, this work proposes a reversible data hiding in encrypted image based on iterative recovery. The encrypted image is firstly generated by the pixel classification scrambling and bit-wise exclusive-OR (XOR), which improves the security of encrypted images. And then, a pixel-type-mark generation method based on block-compression is designed to reduce the extra burden of key management and transfer. At last, an iterative recovery strategy is proposed to optimize the marked decrypted image, which allows the original image to be obtained only using the encryption key. The proposed reversible data hiding scheme in encrypted image is not vulnerable to the ciphertext-only attack due to the fact that the XOR-encrypted pixels are scrambled in the corresponding encrypted image. Experimental results demonstrate that the decrypted images obtained by the proposed method are the same as the original ones, and the maximum embedding rate of proposed method is higher than the previously reported reversible data hiding methods in encrypted image.     

基于智能像卡输入的手绘电气符号特征提取方法研究

提出了一种特定输入方式下脱机手绘电气符号的特征提取方法。采用一种新的书写载体和图像采样装置。在分析了以智能像卡输入的手绘电气符号的结构特点的基础上,给出了扩展网格构造方法,定义了扩展网格的比较特征,将其应用于手绘电气符号的特征提取。所提取的特征具备对不同书写笔画不规则变形的适应能力,有效地反映了手绘电气符号的局部和全局特征。识别实验表明,该特征提取方法是有效的。     

基于意象转化的重卡产品造型设计研究

目的探究意象转化在设计中的方法与流程,研究意象转化在重卡造型中的运用。方法以徐工重卡造型设计为例,对重型卡车造型要素进行对比分析与提炼,确定其意象主题和具象载体,并运用意象转化流程将其转化为意象元素,融合于设计之中。结论通过意象转化分析,建立了重卡产品的设计流程,并在其造型设计中加以运用,最终强化产品意象的表达,增强产品与用户的交流,降低产品开发的不确定性。     

A Recommendation Approach Based on Bayesian Networks for Clone Refactor

Reusing code fragments by copying and pasting them with or without minor adaptation is a common activity in software development. As a result, software systems often contain sections of code that are very similar, called code clones. Code clones are beneficial in reducing software development costs and development risks. However, recent studies have indicated some negative impacts as a result. In order to effectively manage and utilize the clones, we design an approach for recommending refactoring clones based on a Bayesian network. Firstly, clone codes are detected from the source code. Secondly, the clones that need to be refactored are identified, and the static and evolutions features are extracted to build the feature database. Finally, the Bayesian network classifier is used for training and evaluating the classification results. Based on more than 640 refactor examples of five open source software developed in C, we observe a considerable enhancement. The results show that the accuracy of the approach is larger than 90%. We believe our approach will provide a more accurate and reasonable code refactoring and maintenance advice for software developers.     

多聚焦宫颈细胞图像融合方法

由于显微镜物镜焦深范围小,随着放大倍数的增大,景深会相应减小,只有那些在聚焦平面或其附近的物体才是可见的.本文提出了一种基于小波分解的多聚焦宫颈细胞图像融合算法,利用小波变换获取原始图像的小波系数,利用不同的融合规则和融合算子构造融合图像的小波系数,利用一致性校验得到最终的融合结果,解决了宫颈细胞不能在一幅图像中完全聚焦清晰的问题.     

Hybrid Segmentation Scheme for Skin Features Extraction Using Dermoscopy Images

Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration. Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively, but they have unavoidable disadvantages when used to analyze skin features accurately. This study proposes a hybrid segmentation scheme consisting of Deeplab v3+ with an Inception-ResNet-v2 backbone, LightGBM, and morphological processing (MP) to overcome the shortcomings of handcraft-based approaches. First, we apply Deeplab v3+ with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells. Then, LightGBM and MP are used to enhance the pixel segmentation quality. Finally, we determine several skin features based on the results of wrinkle and cell segmentation. Our proposed segmentation scheme achieved a mean accuracy of 0.854, mean of intersection over union of 0.749, and mean boundary F1 score of 0.852, which achieved 1.1%, 6.7%, and 14.8% improvement over the panoptic-based semantic segmentation method, respectively.     

A New Multi Chaos-Based Compression Sensing Image Encryption

The advancements in technology have substantially grown the size of image data. Traditional image encryption algorithms have limited capabilities to deal with the emerging challenges in big data, including compression and noise toleration. An image encryption method that is based on chaotic maps and orthogonal matrix is proposed in this study. The proposed scheme is built on the intriguing characteristics of an orthogonal matrix. Gram Schmidt disperses the values of pixels in a plaintext image by generating a random orthogonal matrix using logistic chaotic map. Following the diffusion process, a block-wise random permutation of the data is performed using multi-chaos. The proposed scheme provides sufficient security and resilience to JPEG compression and channel noise through a series of experiments and security evaluations. It enables Partial Encryption (PE) for faster processing as well as complete encryption for increased security. The higher values of the number of pixels change rates and unified average change intensity confirm the security of the encryption scheme. In contrast to other schemes, the proposed approach can perform full and partial encryption depending on security requirements.     

Integrating Feature Direction Information with a Level Set Formulation for Image Segmentation

A feature-dependent variational level set formulation is proposed for image segmentation. Two second order directional derivatives act as the external constraint in the level set evolution, with the directional derivative across the image features direction playing a key role in contour extraction and another only slightly contributes. To overcome the local gradient limit, we integrate the information from the maximal (in magnitude) second-order directional derivative into a common variational framework. It naturally encourages the level set function to deform (up or down) in opposite directions on either side of the image edges, and thus automatically generates object contours. An additional benefit of this proposed model is that it does not require manual initial contours, and our method can capture weak objects in noisy or intensity-inhomogeneous images. Experiments on infrared and medical images demonstrate its advantages.     

基于影像融合技术的合成孔径雷达(SAR)图像目标识别方法的探讨

对美国密歇根大学电子工程系的研究人员提出的一种多源数据融合算法进行了介绍，对SAR图像与可见光图像融合的一系列相关技术及其主要步骤进行了探讨，简要概括了评价融合后图像效果的标准和方法，并介绍了目标的检测与识别。     

马氏体相变动力学及其立体图像模拟

提出了凸透镜片状马氏体离散成核连续长大的相变动力学模型,推导出了马氏体离散成核和连续长大的两个相变动力学公式。按相变动力学公式计算数据,通过Ansys分析软件,用立体图像演示了马氏体相变动力学,观察任意切割截面上的马氏体图像。得到呈竹叶状或片状的马氏体形貌,与试验中观察到的形貌一致。将截面上单位面积的马氏体含量百分率统计平均值AS与数学期望值AE比较,以及马氏体个数统计平均值NAS与数学期望值NAE比较,可见均有一定偏差,这与马氏体形态和取样太少有关。     

珠光体相变动力学及立体图像模拟

提出了珠光体离散成核相变动力学模型,导出了珠光体离散成核相变动力学表达式。随后利用Ansys分析软件,用立体图像演示了珠光体相变动力学,并任意切割截面,将得到的单位面积上珠光体含量百分率统计平均值As及珠光体个数的统计平均值NAs与单位体积内珠光体含量百分率V及珠光体个数Nv进行比较。结果表明:As接近于数学期望值V;NAs接近于数学期望值∑i Di,Di为单位体积内不同珠光体球的直径;与体视学理论所预期的一致。     

直方图累积器——一个图象处理硬件

本文介绍了一个直方图累积器的工作原理,设计方案,实验结果以及某些应用。     

点特征提取在锂电池丝印图像配准的应用

目的 为了提高锂电池丝印图像配准精度,从而解决产品质量检测中的漏检和误报问题,研究点特征提取算法在锂电池丝印图像配准中的应用.方法 对基于点特征的锂电池丝印图像配准进行综述,首先概述点特征提取算法的发展历程,然后着重围绕Harris,SIFT,SURF,ORB和AKAZE等5种经典的点特征提取算法进行分析,并介绍近几年的提升算法,最后对锂电池丝印图像进行配准测试,利用几种测评技术对实验效果进行分析,总结不同点特征提取算法在锂电池丝印图像配准中的优缺点和适用性.结果 实验结果表明,AKAZE算法提取的特征点具有较高的重复率和匹配准确率,经过配准后的定位误差也都控制在1个像素以内,但是该算法的尺度不变性较差.结论 相较于前4种算法,AKAZE算法具有较高的可靠性和稳定性,能够满足锂电池丝印图像配准的实时性和高效性需求.     

基于图像尺寸变换的角点检测算法研究

为了研究基于尺寸变换的角点检测技术的检测效果,基于图像缩小法的角点检测技术,提出了图像放大法的角点检测技术。详细介绍了两种检测技术的计算原理并进行了实验验证,结果表明这两种方法都是行之有效的。在此基础上,将这两种方法与不进行图像尺寸变换的角点检测技术进行了对比研究,结果表明,基于尺寸变换的角点检测技术与不进行图像尺寸变化的角点检测技术的检测结果精度基本相同,因此,在角点的实际检测计算中,无需对被检测图像进行尺寸变换处理。     

一种新的彩色图像碎片全自动拼接算法

目的为了实现横向和纵向分切的彩色图像碎片的拼接,提出针对同一张经过横向和纵向分切的彩色图像碎片建立关于相关系数的拼接复原模型和算法。方法通过获取彩色图像碎片的各单色图像,并提取单色图像边缘的灰度值,根据图像碎片边缘灰度值之间的相似程度自动拼接碎片。结果文中算法对彩色图像碎片的拼接效果优于常规算法,实验中采用100张彩色图,对每张彩图分切成64张300×300像素的彩图碎片进行顺序复原,综合拼接成功率达到100%,拼接平均耗时1.59 s。此外,文中算法实验性强,不仅能拼接仅纵切的图像,还能拼接横向和纵向分切的彩色图像。结论实验结果表明该算法对彩色图像碎片的拼接具有很好的适应性和可重复性,对图像碎片的大小和颜色无严格要求,是一套完整有效的针对规则彩色图像碎片的全自动拼接方案。