Reduced reference stereoscopic image quality assessment using digital watermarking

In this paper, a novel reduced-reference stereoscopic image quality assessment (RR-SIQA) algorithm is proposed by means of an unconventional use of watermarking technique. Watermarking techniques are usually employed for authenticity verification and copyright protection. Here, watermarking technique is adopted to provide a new approach for RR-SIQA. Firstly, the features of image are extracted in reorganized discrete cosine transform domain, and then embedded into the stereoscopic image as invisible hidden information. In order to improve the reliability of the watermarking, some channel coding techniques are applied before the process of embedding watermark. At the receiver, the watermark can be decoded and used to measure the quality of the distorted stereoscopic image. The proposed algorithm overcomes the limitations of other existing methods that require an auxiliary channel. Experimental results illustrate that the proposed algorithm has a good consistency with subjective quality scores, and can reflect the visual perception of stereoscopic image effectively.     

Automatic selection of color reference image for panoramic stitching

Color correction is an important step in the generation of high-resolution stitched panoramas. Typically, color correction schemes try to match the color of each image in the panorama to an arbitrarily selected reference image. We provide a scheme that uses quantitative metrics such as image contrast, gradient-based structure similarity index measure (G-SSIM) and color clipping information to automatically select the best reference that results in visually pleasing output panoramas. Quantitative and qualitative evaluation of the scheme show encouraging results for panoramic images as well as for stitched videos. The scheme acts as a pre-processing step to color correction and its applicability to both parametric and non-parametric global color correction schemes has also been demonstrated.     

Progressive completion of a panoramic image

Multimedia Tools and Applications - This paper proposes an exemplar-based modified priority belief propagation (MP-BP) method to complete a stitched panorama. The result is an image with a...     

Edge detection of noisy digital image using optimization of threshold and self organized map neural network

The purpose of this research is to find a suitable method for detecting the edges of noisy digital images by eliminating the noise effects. The image will be partitioned into equal partitions and the initial threshold of that image partition will be calculated. By applying all these thresholds into the self-organized map (SOM) neural network input optimized for learning and training based optimization algorithm (TLBO), threshold clustering will be performed. The partitioned image will be edge detected by entropy method. Choosing the threshold for image segmentation is of great importance. The mean of the brightness of digital noise images is not a good representative of the initial threshold. Noise causes the mean intensity of the brightness to take distance from the main range of the intensity of the image so the resulting edge detected image will be severely noisy and truncated. By determining the highest frequency of brightness intensity instead of the mean brightness, the above-mentioned weaknesses will be eliminated. This method outperforms many current methods, such as Tsallis entropy, Singh and Kiani and even Canny Edge Detection which demonstrates the effectiveness of the proposed method, In the Table 1 the PSNR of image 5 of the proposed method is 61.4896, but Singh method which is 55.61, Tsallis method which is 53.9234, Kiani method which is 53.9315 the proposed method is less than the other methods.

     

A novel method for digital image steganography based on a new three-dimensional chaotic map

This paper, presents a novel chaos-based image steganography algorithm. Because of efficient property of chaos based security systems besides steganography applicability in providing secure communication, chaos based steganography algorithms served as a hot topic in recent researches. The proposed scheme possess novelties and advantageous such as: 1) Introducing a novel 3-dimensional chaotic map (LCA map) with strong chaotic characteristics and maximum Lyapunov exponent 20.58, which is used for generating three chaotic sequences, each of them represents the number of row, column, and colour component, respectively. 2) Utilizing random selection procedure for selecting subsequences with length of 2L, which L is the length of secret message 3) Specifying L pairs of triples host positions for embedding LSBs and MSBs of secret message by using three high level chaotic maps. 4) Entering some parameters dependent on elementary initial values, host image, and secret message features as a key point for adding additional layer of security alongside providing high sensitivity. 5) Providing high capacity for embedding secret message, which is equal to 50 % of whole image capacity (M?×?N?×?12). The proposed method could be applied in different criterion such as, confidential communication and data storing, protection of data alteration, and etc. Our experimental results guarantees that our scheme is not only robust against differential attacks, but also has promising results such as highly sensitive keys, Quality index, PSNR, MSE, and hiding capacity as shown in statistical security analysis.     

基于TD-ERCS混沌序列的数字图像置乱方法

TD-ERCS是专门为混沌加密理论而设计的混沌系统,不存在由短周期引发的弱密钥,具有良好的安全特质·置乱是大数据量级的数字图像保护措施之一.算法利用TD-ERCS离散混沌序列生成地址索引表针对像素地址进行单向置乱,由序列产生密钥对图像的直流分量进行数学变换.空域与频域相结合增强系统复杂度和安全性,同时避免了一密多用和多密钥的传输,增强了系统的强度.     

Integrated position estimation using aerial image sequences

Presents an integrated system for navigation parameter estimation using sequential aerial images, where the navigation parameters represent the positional and velocity information of an aircraft for autonomous navigation. The proposed integrated system is composed of two parts: relative position estimation and absolute position estimation. Relative position estimation recursively computes the current position of an aircraft by accumulating relative displacement estimates extracted from two successive aerial images. Simple accumulation of parameter values reduces the reliability of the extracted parameter estimates as an aircraft goes on navigating, resulting in a large positional error. Therefore, absolute position estimation is required to compensate for the positional error generated by the relative position estimation. Absolute position estimation algorithms using image matching and digital elevation model (DEM) matching are presented. In the image matching, a robust-oriented Hausdorff measure (ROHM) is employed, whereas in the DEM matching, an algorithm using multiple image pairs is used. Experiments with four real aerial image sequences show the effectiveness of the proposed integrated position estimation algorithm     

Estimating the camera direction of a geotagged image using reference images

Millions of smart phones and GPS-equipped digital cameras sold each year, as well as photo-sharing websites such as Picasa and Panoramio have enabled personal photos to be associated with geographic information. It has been shown by recent research results that the additional global positioning system (GPS) information helps visual recognition for geotagged photos by providing valuable location context. However, the current GPS data only identifies the camera location, leaving the camera viewing direction uncertain within the possible scope of 360°. To produce more precise photo location information, i.e. the viewing direction for geotagged photos, we utilize both Google Street View and Google Earth satellite images. Our proposed system is two-pronged: (1) visual matching between a user photo and any available street views in the vicinity can determine the viewing direction, and (2) near-orthogonal view matching between a user photo taken on the ground and the overhead satellite view at the user geo-location can compute the viewing direction when only the satellite view is available. Experimental results have shown the effectiveness of the proposed framework.     

基于参考纹理与自身色彩的图像修复

传统的图像修复工作仅仅利用破损图像本身的信息完成,破损面积较大并且结构比较复杂时,破损图像不能提供足够的信息导致修复效果不理想。针对这个问题提出了基于参考图像纹理与破损图像自身颜色的修复算法。该算法在图像库中通过图像检索智能筛选相似参考图像,并选择最优区域填充破损图像区域,利用参考图像样块与自身未破损区域的纹理信息保证修复边界的平滑性,再结合颜色迁移与扩展算法使破损图像修复区域与完好区域的色彩协调一致。实验结果表明新提出的修复算法使得图像修复区域过渡更加自然,能在视觉上有较好的效果。     

Advanced digital image stabilization using similarity-constrained optimization

Multimedia Tools and Applications - As many people have portable video devices such as cameras on cell phones and camcorders, image stabilization technique is a crucial and challenging task in...     

Content-based image retrieval using growing hierarchical self-organizing quadtree map

In this paper, a growing hierarchical self-organizing quadtree map (GHSOQM) is proposed and used for a content-based image retrieval (CBIR) system. The incorporation of GHSOQM in a CBIR system organizes images in a hierarchical structure. The retrieval time by GHSOQM is less than that by using direct image comparison using a flat structure. Furthermore, the ability of incremental learning enables GHSOQM to be a prospective neural-network-based approach for CBIR systems. We also propose feature matrices, image distance and relevance feedback for region-based images in the GHSOQM-based CBIR system. Experimental results strongly demonstrate the effectiveness of the proposed system.     

Compression of chain codes using digital straight line sequences

Compression of chain codes is achieved by breaking the sequence into strings of digital straight lines whose representation is stored in a table. It is shown that the number of table entries is $\text{(}\text{1}\text{4π}{}^2\text{)N}{}^4\text{+}\text{O}\text{(N}{}^3\text{log}\text{N)}$. A computational method is given for an exact determination which shows that the asymptotic approximation is accurate.     

Similarity-based image organization and browsing using multi-resolution self-organizing map

An algorithm is presented in this paper to facilitate the exploration of large image collections based on visual similarities. Starting with an unordered and unannotated set of images, the algorithm first extracts the salient details into feature vectors using both color and gradient information. The feature vectors are then used to train a self-organizing map which maps high-dimensional feature vectors onto a 2D canvas so that images with similar feature vectors are grouped together. When users browse the image collection, an image collage is generated that selects and displays the most pertinent set of images based on which portion of the 2D canvas is currently in view. Flowing from an overview to details is a seamless operation controlled simply by pan and zoom, with representative images selected in a consistent and predictable way. To make organizing larger image collections practical in interactive time, the organization algorithm is designed to run in parallel on graphics processing units. Overall this paper presents an end-to-end solution that facilitates the surfing of image collections in a fresh way.     

Balance control of a biped robot using camera image of reference object

This paper presents a new balance control scheme for a biped robot. Instead of using dynamic sensors to measure the pose of a biped robot, this paper uses only the visual information of a specific reference object in the workspace. The zero moment point (ZMP) of the biped robot can be calculated from the robot’s pose, which is measured from the reference object image acquired by a CCD camera on the robot’s head. For balance control of the biped robot a servo controller uses an error between the reference ZMP and the current ZMP, estimated by Kalman filter. The efficiency of the proposed algorithm has been proven by the experiments performed on both flat and uneven floors with unknown thin obstacles. Recommended by Editorial Board member Dong Hwan Kim under the direction of Editor Jae-Bok Song. This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD). This research was supported by the MKE(The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA(Institute for Information Technology Advancement) (IITA-2008-C1090-0803-0006). Sangbum Park received the B.S. and M.S. degrees from Electronic Engineering of Soongsil University, Seoul, Korea, in 2004 and 2006 respectively. He has been with School of Electronic Engineering, Soongsil University since 2006, where he is currently pursuing a Ph.D. His current research interests include biped walking robot, robotics vision. Youngjoon Han received the B.S., M.S. and Ph.D. degrees in Electronic Engineering from Soongsil University, Seoul, Korea, in 1996, 1998, and 2003, respectively. He is currently an Assistant Professor in the School of Electornic Engineering at Soongsil University. His research interests include robot vision system, and visual servo control. Hernsoo Hahn received the B.S. and M.S. degrees in Electronic Engineering at Soongsil University and Younsei University, Korea in 1982 and 1983 respectively. He received the Ph.D. degree in Computer Engineering from University of Southern California in 1991, and became an Assistant Professor at the School Electroncis Engneering in Soongsil University in 1992. Currently, he is a Professor. His research interests include application of vision sensors to mobile robots and measurement systems.     

Object tracking in image sequences using point features

This paper presents an object tracking technique based on the Bayesian multiple hypothesis tracking (MHT) approach. Two algorithms, both based on the MHT technique are combined to generate an object tracker. The first MHT algorithm is employed for contour segmentation. The segmentation of contours is based on an edge map. The segmented contours are then merged to form recognisable objects. The second MHT algorithm is used in the temporal tracking of a selected object from the initial frame. An object is represented by key feature points that are extracted from it. The key points (mostly corner points) are detected using information obtained from the edge map. These key points are then tracked through the sequence. To confirm the correctness of the tracked key points, the location of the key points on the trajectory are verified against the segmented object identified in each frame. If an acceptable number of key-points lie on or near the contour of the object in a particular frame (n-th frame), we conclude that the selected object has been tracked (identified) successfully in frame n.     

基于SURF特征的航空序列图像位置估计算法

视觉传感器在航空无人机导航和定位任务中应用越来越广泛。针对无人机位置参数估计问题,提出了一种基于SURF特征的图像配准算法,该算法能够适应航空序列图像的旋转、尺度变换及噪声干扰,实现无人机位置的精确估计。构建了SURF尺度空间,运用快速Hessian矩阵定位极值点,计算出航空图像的64维SURF特征描述子;基于Hessian矩阵迹完成特征点匹配;使用RANSAC算法剔除出格点,实现位置参数的精确估计。通过航空图像序列实测数据位置估计实验,验证了该算法的有效性。     

Automatic machine interactions for content-based image retrieval using a self-organizing tree map architecture

In this paper, an unsupervised learning network is explored to incorporate a self-learning capability into image retrieval systems. Our proposal is a new attempt to automate recursive content-based image retrieval. The adoption of a self-organizing tree map (SOTM) is introduced, to minimize the user participation in an effort to automate interactive retrieval. The automatic learning mode has been applied to optimize the relevance feedback (RF) method and the single radial basis function-based RF method. In addition, a semiautomatic version is proposed to support retrieval with different user subjectivities. Image similarity is evaluated by a nonlinear model, which performs discrimination based on local analysis. Experimental results show robust and accurate performance by the proposed method, as compared with conventional noninteractive content-based image retrieval (CBIR) systems and user controlled interactive systems, when applied to image retrieval in compressed and uncompressed image databases.     

On the implementation of a copyright protection scheme using digital image watermarking

Multimedia Tools and Applications - In this paper, a spatial domain based digital image watermarking scheme has been developed to serve the purpose of copyright protection for digital images. This...     

Very-low-bit-rate coding of image sequences using the Gabor transform

Abstract— While the need for image-sequence compression has long been understood, recent applications have shown that existing methods are not always sufficient. A case in point is the class of applications referred to as “very low bit rate.” These applications require the transmission of sequences over very narrow channels, e.g., 9600 bits/s. The compression ratios required are substantially beyond those that can be accomplished (with reasonable image quality) using standard methods. The Gabor transform is a local frequency decomposition which has been proposed as a model of the early stages of human vision. It has found widespread use for a number of tasks in signal and image processing, including image compression. In this paper, we examine Gabor transform-based coding for image-sequence transmission at rates near 10 kbits/s.