Correction to: 2D and 3D image localization,compression and reconstruction using new hybrid moments

Multidimensional Systems and Signal Processing -     

3D WLS hybrid and non hybrid localization using TOA,TDOA, azimuth and elevation

Telecommunication Systems - In this paper, we propose a Three Dimensional (3D) Weighted Least Square (WLS) estimation of mobile position using Time Of Arrival (TOA), Time Difference Of Arrival...     

3-D model localization using high-resolution reconstruction ofmonocular image sequences

In this paper, we present a complete system for the recognition and localization of a three-dimensional (3-D) model from a sequence of monocular images with known motion. The originality of this system is twofold. First, it uses a purely 3-D approach, starting from the 3-D reconstruction of the scene and ending by the 3-D matching of the model. Second, unlike most monocular systems, we do not use token tracking to match successive images. Rather, subpixel contour matching is used to recover more precisely complete 3-D contours. In contrast with the token tracking approaches, which yield a representation of the 3-D scene based on disconnected segments or points, this approach provides us with a denser and higher level representation of the scene. The reconstructed contours are fused along successive images using a simple result derived from the Kalman filter theory. The fusion process increases the localization precision and the robustness of the 3-D reconstruction. Finally, corners are extracted from the 3-D contours. They are used to generate hypotheses of the model position, using a hypothesize-and-verify algorithm that is described in detail. This algorithm yields a robust recognition and precise localization of the model in the scene. Results are presented on infrared image sequences with different resolutions, demonstrating the precision of the localization as well as the robustness and the low computational complexity of the algorithms.     

A new multi-resolution hybrid wavelet for analysis and image compression

Most of the current image- and video-related applications require higher resolution of images and higher data rates during transmission, better compression techniques are constantly being sought after. This paper proposes a new and unique hybrid wavelet technique which has been used for image analysis and compression. The proposed hybrid wavelet combines the properties of existing orthogonal transforms in the most desirable way and also provides for multi-resolution analysis. These wavelets have unique properties that they can be generated for various sizes and types by using different component transforms and varying the number of components at each level of resolution. These hybrid wavelets have been applied to various standard images like Lena (512 × 512), Cameraman (256 × 256) and the values of peak signal to noise ratio (PSNR) obtained are compared with those obtained using some standard existing compression techniques. Considerable improvement in the values of PSNR, as much as 5.95 dB higher than the standard methods, has been observed, which shows that hybrid wavelet gives better compression. Images of various sizes like Scenery (200 × 200), Fruit (375 × 375) and Barbara (112 × 224) have also been compressed using these wavelets to demonstrate their use for different sizes and shapes.     

3D facial model reconstruction, expressions synthesis and animation using single frontal face image

With better understanding of face anatomy and technical advances in computer graphics, 3D face synthesis has become one of the most active research fields for many human-machine applications, ranging from immersive telecommunication to the video games industry. In this paper we proposed a method that automatically extracts features like eyes, mouth, eyebrows and nose from the given frontal face image. Then a generic 3D face model is superimposed onto the face in accordance with the extracted facial features in order to fit the input face image by transforming the vertex topology of the generic face model. The 3D-specific face can finally be synthesized by texturing the individualized face model. Once the model is ready six basic facial expressions are generated with the help of MPEG-4 facial animation parameters. To generate transitions between these facial expressions we use 3D shape morphing between the corresponding face models and blend the corresponding textures. Novelty of our method is automatic generation of 3D model and synthesis face with different expressions from frontal neutral face image. Our method has the advantage that it is fully automatic, robust, fast and can generate various views of face by rotation of 3D model. It can be used in a variety of applications for which the accuracy of depth is not critical such as games, avatars, face recognition. We have tested and evaluated our system using standard database namely, BU-3DFE.     

Improved hybrid layered image compression using deep learning and traditional codecs

Recently deep learning-based methods have been applied in image compression and achieved many promising results. In this paper, we propose an improved hybrid layered image compression framework by combining deep learning and the traditional image codecs. At the encoder, we first use a convolutional neural network (CNN) to obtain a compact representation of the input image, which is losslessly encoded by the FLIF codec as the base layer of the bit stream. A coarse reconstruction of the input is obtained by another CNN from the reconstructed compact representation. The residual between the input and the coarse reconstruction is then obtained and encoded by the H.265/HEVC-based BPG codec as the enhancement layer of the bit stream. Experimental results using the Kodak and Tecnick datasets show that the proposed scheme outperforms the state-of-the-art deep learning-based layered coding scheme and traditional codecs including BPG in both PSNR and MS-SSIM metrics across a wide range of bit rates, when the images are coded in the RGB444 domain.     

A new LBG-based image compression method using DCT

In this letter, a new Linde-Buzo-Gray （LBG）-based image compression method using Discrete Cosine Transform （DCT） and Vector Quantization （VQ） is proposed. A gray-level image is firstly decomposed into blocks, then each block is subsequently encoded by a 2D DCT coding scheme. The dimension of vectors as the input of a generalized VQ scheme is reduced. The time of encoding by a generalized VQ is reduced with the introduction of DCT process. The experimental results demonstrate the efficiency of the proposed method.     

三维激光扫描测量系统的三维图像数据压缩

三维彩色激光扫描测量系统是利用计算机视觉的原理对物体进行三维测量和重建,以获取物体的三维图像。三维图像的数据量很大,文中研究了三维图像的压缩策略和算法,采用三角形条带的网格压缩方法,以及顶点数据的编码方法,可以把三维图像的存贮空间减小到压缩前数据的１／６,从而有效地解决了测量系统在实际应用环境下的图像存贮和传输问题。     

Fully-3D PET image reconstruction using scanner-independent, adaptive projection data and highly rotation-symmetric voxel assemblies

For iterative, fully 3D positron emission tomography (PET) image reconstruction intrinsic symmetries can be used to significantly reduce the size of the system matrix. The precalculation and beneficial memory-resident storage of all nonzero system matrix elements is possible where sufficient compression exists. Thus, reconstruction times can be minimized independently of the used projector and more elaborate weighting schemes, e.g., volume-of-intersection (VOI), are applicable. A novel organization of scanner-independent, adaptive 3D projection data is presented which can be advantageously combined with highly rotation-symmetric voxel assemblies. In this way, significant system matrix compression is achieved. Applications taking into account all physical lines-of-response (LORs) with individual VOI projectors are presented for the Siemens ECAT HR+ whole-body scanner and the Siemens BrainPET, the PET component of a novel hybrid-MR/PET imaging system. Measured and simulated data were reconstructed using the new method with ordered-subset-expectation-maximization (OSEM). Results are compared to those obtained by the sinogram-based OSEM reconstruction provided by the manufacturer. The higher computational effort due to the more accurate image space sampling provides significantly improved images in terms of resolution and noise.     

Two-layered image compression using 2G bandelets and wavelets

An image can be decomposed into the structural component and the geometric textural component.Based on this idea,an efficient two-layered compressing algorithm is proposed,which uses 2nd generation bandelets and wavelets.First,an original image is decomposed into the structural component and the textural component,and then these two components are compressed using wavelets and 2nd generation bandelets respectively.Numerical tests show that the proposed method works better than the bandelets and JPEG2000 in some specific SAR scene.     

基于VTK的医学图像三维重建研究

使用VTK工具包和MC重建算法开发了一套医学影像三维重建系统.由于重建后图像数据的处理时间和存储代价与三角形网格中三角形数量成正比, 过于复杂和细节化的网格会给图像数据的存储、传输、计算和实时绘制等带来负担, 故采用了顶点合并的三角形网格简化方法来减少三角形数量.     

3D Model compression using Connectivity-Guided Adaptive Wavelet Transform built into 2D SPIHT

Connectivity-Guided Adaptive Wavelet Transform based mesh compression framework is proposed. The transformation uses the connectivity information of the 3D model to exploit the inter-pixel correlations. Orthographic projection is used for converting the 3D mesh into a 2D image-like representation. The proposed conversion method does not change the connectivity among the vertices of the 3D model. There is a correlation between the pixels of the composed image due to the connectivity of the 3D mesh. The proposed wavelet transform uses an adaptive predictor that exploits the connectivity information of the 3D model. Known image compression tools cannot take advantage of the correlations between the samples. The wavelet transformed data is then encoded using a zero-tree wavelet based method. Since the encoder creates a hierarchical bitstream, the proposed technique is a progressive mesh compression technique. Experimental results show that the proposed method has a better rate distortion performance than MPEG-3DGC/MPEG-4 mesh coder.     

Direct 3D lossless image compression based on region growing

A segmentation-based 3D lossless compression method that exploits the dependencies in all three dimensions of volumetric images is proposed     

Two-band hybrid FIR-IIR filters for image compression

Two-band analysis-synthesis filters or wavelet filters are used pervasively for compressing natural images. Both FIR and IIR filters have been studied in this context, the former being the most popular. In this paper, we examine the compression performance of these two-band filters in a dyadic wavelet decomposition and attempt to isolate features that contribute most directly to the performance gain. Then, employing the general exact reconstruction condition, hybrid FIR-IIR analysis-synthesis filters are designed to maximize compression performance for natural images. Experimental results are presented that compare performance with the popular biorthogonal filters in terms of peak SNR, subjective quality, and computational complexity.     

基于3维SPIHT编码的超光谱图像压缩

提出一种针对超光谱图像压缩的3维SPIHT编码算法.通过对超光谱图像进行3维小波变换,同时去除像素数据间的空间冗余和谱间冗余.针对变换后得到的小波系数,构造一种3维空间方向树结构,并用经3维扩展后的SPIHT算法(3D SPIHT算法)对小波系数进行量化编码.实验证明,基于3维小波变换的3维SPIHT编码算法在对超光谱图像压缩时,表现出了优良的率失真性能.并且算法复杂度适中,具有嵌入式特性.     

Tomographic microwave diversity image reconstruction employingunitary compression

Data compression through a unitary transform is utilized in tomographic microwave diversity image reconstruction in order to reduce the dimensionality and to extract the features in the data space. The unitary compression is derived by minimizing the mean-square error (MSE) and the unitary transform is made of eigenvectors of the data's covariance, regarded as a Karhunen-Loeve transform. Tomographic microwave frequency-swept imaging was developed using a unique target-derived reference technique to access the three dimensional Fourier space of the scatterer and an image reconstruction algorithm based on the projection slice theorem. It is shown that centimeter resolution of a complex object can be preserved even when half of the data set is compressed and that the reconstructed image remains identifiable by a human observer even when 2/3 of the data set is compressed     

Watermarking 3D models using spectral mesh compression

We propose a robust and imperceptible spectral watermarking method for high rate embedding of a watermark into 3D polygonal meshes. Our approach consists of four main steps: (1) the mesh is partitioned into smaller sub-meshes, and then the watermark embedding and extraction algorithms are applied to each sub-mesh, (2) the mesh Laplacian spectral compression is applied to the sub-meshes, (3) the watermark data is distributed over the spectral coefficients of the compressed sub-meshes, (4) the modified spectral coefficients with some other basis functions are used to obtain uncompressed watermarked 3D mesh. The main attractive features of this approach are simplicity, flexibility in data embedding capacity, and fast implementation. Extensive experimental results show the improved performance of the proposed method, and also its robustness against the most common attacks including the geometric transformations, adaptive random noise, mesh smoothing, mesh cropping, and combinations of these attacks.