Distortion-Free Data Embedding Scheme for High Dynamic Range Images

Distortion-free data embedding is a technique which can assure that not only the secret data is correctly extracted but also the cover media is recovered without any distortion after secret data is extracted completely. Because of these advantages, this technique attracts the attention of many researchers. In this paper, a new distortion-free data embedding scheme for high dynamic range （HDR） images is proposed. By depending on Cartesian product, this scheme can obtain higher embedding capacity while maintaining the exactly identical cover image and stego image when using the tone mapping algorithms. In experimental results, the proposed scheme is superior to Yu et aL＇s scheme in regard to the embedding rate——an average embedding rate of 0.1355 bpp compared with Yn et aL＇s scheme （0.1270 bpp）.

Shape recovery using high dynamic range images

An effective method for object shape recovery using HDRIs （high dynamic range images） is proposed. The radiance values of each point on the reference sphere and target object are firstly calculated, thus the set of candidate normals of each target point are found by comparing its radiance to that of each reference sphere point. In single-image shape recovery, a smoothness operation is applied to the target normals to obtain a stable and reasonable result; while in photometric stereo, radiance vectors of reference and target objects formed due to illuminations under different fight source directions are directly compared to get the most suitable target normals. Finally, the height values can be recovered from the resulting normal field. Because diffuse and specular reflection are handled in an unified framework with radiance, our approach eliminates the limitation presented in most recovery strategies, i.e., only Lambertian model can be used. The experiment results from the real and synthesized images show the performance of our approach.

A Method for Range Image Registration via Neural Network and ICP Algorithm

In this paper,a new integrated registration algorithm based on neural network and ICP（iterative closest point） algorithm is presented.A coarse registration process is implemented with neural network,and then further optimized by ICP algorithm.The corresponding point pairs are found according to the target points＇ curvature and color information.Mahalanobis distance,which reflects the scattering degree of point data,is employed to define the closest distance and the closest points.The results of the experiment show that our algorithm has better feasibility,validity and efficiency than the traditional ICP method.

Registering multiple range images based on distance metric of surface-to-surface

In most conventional algorithms of registering multiple range images, the pose parameters are estimated by using the distance sum between closest point pairs as the objective function. These algorithms have the problems of inexact point correspondence, registration accuracy, and sensitivity to initial registration parameters. Due to the scanner settings, scanner distance, and surface slopes, two or more 3D data sets are unlikely to be acquired such that the 3D data points exactly correspond, and also each point in the data set may represent different surface areas. This paper proposes a novel registration algorithm based on a distance metric of surface-to-surface. The algorithm uses triangle meshes to represent the surfaces. Based on surface sampling and the point-to-surface distances, the integration calculation of the mean distance between surfaces is derived and reduced to a simple formula. The method was tested on synthetic and real range images.

3D Laser Imaging Reconstruction of Scenes Using New Photometric Gaussian Beam Range Equation

In this article the problem of three-dimensional （3-D） reconstruction of scenes from terrestrial laser rangefinder data is processed, we present the simulations results of radiometric range function of the laser range finder imaging system for better optimization of maximum range detection with a given SNR （Signal-to-noise ratio）. The study proposes a new reformulation of the radiometric Gaussian beam range equation on the basis of the photodetector active area. The adjustment of the optical and geometrical characteristics of the laser range finder so defined can be used to minimize the signal span to avoid the saturation of the detector cell and improve the ranging measurements （accuracy and range） for a 3D imaging application.

Human and Bicycle Detection System Using Range Sensor

This paper presents a full-scale solution to the detection of the traffic data using laser device.Range images,gathered by a particular laser camera,are used in the multi-threshold segmentation.The multi-threshold segmentation is based on the height of the moving objects.In order to get the precise height of the moving objects,mapping of the original terrain is performed on the first step.On each layer,the clustering algorithm called iteration-self organizing data analysis techniques algorithm(ISODATA) is conducted afterwards.Kalman filtering technique is applied to recognize and track the moving objects.Extensive experiments show that these algorithms are effective in object recognition and tracking,as well as robust in the applications.

A method of 3D modeling and codec

3D modeling and codec of real objects are hot issues in the field of virtual reality. In this paper, we propose an automatic registration two range images method and a cycle based automatic global registration algorithm for rapidly and automatically registering all range images and constructing a realistic 3D model. Besides, to meet the requirement of huge data transmission over Internet, we present a 3D mesh encoding/decoding method for encoding geometry, topology and attribute data with high compression ratio and supporting progressive transmission. The research results have already been applied successfully in digital museum.

A novel scheme for processing FMCW-ladar data acquired with low sampling rate

The frequency-modulated continuous wave(FMCW)ladar is a useful sensor for remote sensing applications and has been widely used in both the military and civilian fields for precise geospatial data acquisition.Typically,the FMCW-ladar signal is collected by use of the hetero-dyne detection through the dechirping so as to decrease the sampling rate commensurate with the bandwidth.In this manuscript,we proposed a novel scheme for processing the FMCW-ladar signal acquired through the simplified heterodyne detection,and the sampling rate was the same as that in the dechirp detection.Based on the time-frequency relation of the FMCW-ladar signal,two algorithms were proposed to post-process the echo acquired by the simplified heterodyne detection with the sub-Nyquist sampling rate.For the FMCW-ladar echo data acquired by the simplified heterodyne detection with the same sampling rate as in the traditional dechirp detection,the algorithms can achieve the unambiguous range image retaining the range resolution commensurate with the transmitted chirp bandwidth.The effectiveness of the scheme was validated by simulation and real data processing experiments.The capability of the proposed scheme provides an alternative for the FMCW-ladar system without use of the dechirp detection,which can benefit the future FMCW-ladar and microwave photonics radar applications in the remote sensing.