Volume 5, Number 3, 2008
Special Issue on Multi-dimensional and Multi-view Image Processing (pp.217-225)
This paper describes a multiple camera-based method to reconstruct the 3D shape of a human foot.From a foot database,an initial 3D model of the foot represented by a cloud of points is built.The shape parameters,which can characterize more than 92% of a foot,are defined by using the principal component analysis method.Then,using active shape models,the initial 3D model is adapted to the real foot captured in multiple images by applying some constraints(edge points distance and color variance).We insist here on the experiment part where we demonstrate the efficiency of the proposed method on a plastic foot model,and also on real human feet with various shapes.We propose and compare different ways of texturing the foot which is needed for reconstruction.We present an experiment performed on the plastic foot model and on human feet and propose two different ways to improve the final 3D shapes accuracy according to the previous experiments results.The first improvement proposed is the densification of the cloud of points used to represent the initial model and the foot database.The second improvement concerns the projected patterns used to texture the foot.We conclude by showing the obtained results for a human foot with the average computed shape error being only 1.06 mm.
This paper proposes a novel method for color restoration that can effectively apply accurate color based on spectral information to a segmented image using the normalized cut technique.Using the proposed method,we can obtain a digital still camera image and spectral information in different environments.Also,it is not necessary to estimate reflectance spectra using a spectral database such as other methods.The synthesized images are accurate and high resolution.The proposed method effectively works in making digital archive contents.Some experimental results are demonstrated in this paper.
Systems using numerous cameras are emerging in many fields due to their ease of production and reduced cost,and one of the fields where they are expected to be used more actively in the near future is in image-based rendering(IBR).Color correction between views is necessary to use multi-view systems in IBR to make audiences feel comfortable when views are switched or when a free viewpoint video is displayed.Color correction usually involves two steps:the first is to adjust camera parameters such as gain,brightness,and aperture before capture,and the second is to modify captured videos through image processing.This paper deals with the latter,which does not need a color pattern board.The proposed method uses scale invariant feature transform(SIFT) to detect correspondences,treats RGB channels independently,calculates lookup tables with an energy-minimization approach,and corrects captured video with these tables.The experimental results reveal that this approach works well.
This paper presents a method for segmenting a 3D point cloud into planar surfaces using recently obtained discrete-geometry results.In discrete geometry,a discrete plane is defined as a set of grid points lying between two parallel planes with a small distance,called thickness.In contrast to the continuous case,there exist a finite number of local geometric patterns(LGPs) appearing on discrete planes.Moreover,such an LGP does not possess the unique normal vector but a set of normal vectors.By using those LGP properties,we first reject non-linear points from a point cloud,and then classify non-rejected points whose LGPs have common normal vectors into a planar-surface-point set.From each segmented point set,we also estimate the values of parameters of a discrete plane by minimizing its thickness.
In recent years,many image-based rendering techniques have advanced from static to dynamic scenes and thus become video-based rendering(VBR) methods.But actually,only few of them can render new views on-line.We present a new VBR system that creates new views of a dynamic scene in live.This system provides high quality images and does not require any background subtraction.Our method follows a plane-sweep approach and reaches real-time rendering using consumer graphic hardware,graphics processing unit(GPU).Only one computer is used for both acquisition and rendering.The video stream acquisition is performed by at least 3 webcams.We propose an additional video stream management that extends the number of webcams to 10 or more.These considerations make our system low-cost and hence accessible for everyone.We also present an adaptation of our plane-sweep method to create simultaneously multiple views of the scene in real-time.Our system is especially designed for stereovision using autostereoscopic displays.The new views are computed from 4 webcams connected to a computer and are compressed in order to be transfered to the mobile phone.Due to GPU programming,our method provides up to 16 images of the scene in real-time.The use of both GPU and CPU makes this method work on only one consumer grade computer.
To measure the 3D shape of large objects,scanning by a moving range sensor is one of the most efficient methods.However,if we use moving range sensors,the obtained data have some distortions due to the movement of the sensor during the scanning process.In this paper,we propose a method for recovering correct 3D range data from a moving range sensor by using the multiple view geometry under projective projections in space-time.We assume that range sensor radiates laser beams in a raster scan order,and they are observed from two cameras.We first show that we can deal with range data as 2D images,and show that the extended multiple view geometry can be used for representing the relationship between the 2D image of range data and the 2D image of cameras.We next show that the extended multiple view geometry can be used for rectifying 3D data obtained by the moving range sensor.The method is implemented and tested in synthetic images and range data.The stability of the recovered 3D shape is also evaluated.
Small storage space for photographs in formal documents is increasingly necessary in today s needs for huge amounts of data communication and storage.Traditional compression algorithms do not sufficiently utilize the distinctness of formal photographs.That is,the object is an image of the human head,and the background is in unicolor.Therefore,the compression is of low efficiency and the image after compression is still space-consuming.This paper presents an image compression algorithm based on object segmentation for practical high-efficiency applications.To achieve high coding efficiency,shape-adaptive discrete wavelet transforms are used to transformation arbitrarily shaped objects.The areas of the human head and its background are compressed separately to reduce the coding redundancy of the background.Two methods,lossless image contour coding based on differential chain,and modified set partitioning in hierarchical trees(SPIHT) algorithm of arbitrary shape,are discussed in detail.The results of experiments show that when bit per pixel(bpp)is equal to 0.078,peak signal-to-noise ratio(PSNR) of reconstructed photograph will exceed the standard of SPIHT by nearly 4dB.
A beamforming algorithm is introduced based on the general objective function that approximates the bit error rate for the wireless systems with binary phase shift keying and quadrature phase shift keying modulation schemes.The proposed minimum approximate bit error rate(ABER) beamforming approach does not rely on the Gaussian assumption of the channel noise.Therefore,this approach is also applicable when the channel noise is non-Gaussian.The simulation results show that the proposed minimum ABER solution improves the standard minimum mean squares error beamforming solution,in terms of a smaller achievable systems bit error rate.
A new definition of dissipativity for neural networks is presented in this paper.By constructing proper Lyapunov func-tionals and using some analytic techniques,sufficient conditions are given to ensure the dissipativity of neural networks with or without time-varying parametric uncertainties and the integro-differential neural networks in terms of linear matrix inequalities.Numerical examples are given to illustrate the effectiveness of the obtained results.
The minimal controller synthesis(MCS) is an extension of the hyperstable model reference adaptive control algorithm.The aim of minimal controller synthesis is to achieve excellent closed-loop control despite the presence of plant parameter variations,external disturbances,dynamic coupling within the plant and plant nonlinearities.The minimal controller synthesis algorithm was successfully applied to the problem of decentralized adaptive schemes.The decentralized minimal controller synthesis adaptive control strategy for controlling the attitude of a rigid body satellite is adopted in this paper.A model reference adaptive control strategy which uses one single three-axis slew is proposed for the purpose of controlling the attitude of a rigid body satellite.The simulation results are excellent and show that the controlled system is robust against disturbances.
The robust global stabilization problem of a class of uncertain nonlinear systems with input unmodeled dynamics is considered using output feedback,where the uncertain nonlinear terms satisfy a far more relaxed condition than the existing triangular-type condition.Under the assumption that the input unmodeled dynamics is minimum-phase and of relative degree zero,a dynamic output compensator is explicitly constructed based on the nonseparation principle.An example illustrates the usefulness of the proposed method.
Many physical processes have nonlinear behavior which can be well represented by a polynomial NARX or NARMAX model.The identification of such models has been widely explored in literature.The majority of these approaches are for the open-loop identification.However,for reasons such as safety and production restrictions,open-loop identification cannot always be done.In such cases,closed-loop identification is necessary.This paper presents a two-step approach to closed-loop identification of the polynomial NARX/NARMAX systems with variable structure control(VSC).First,a genetic algorithm(GA) is used to maximize the similarity of VSC signal to white noise by tuning the switching function parameters.Second,the system is simulated again and its parameters are estimated by an algorithm of the least square(LS) family.Finally,simulation examples are given to show the validity of the proposed approach.
This paper proposes a new type of control laws for free rigid bodies.The start point is the dual quaternion and its characteristics.The logarithm of a dual quaternion is defined,based on which kinematic control laws can be developed.Global exponential convergence is achieved using logarithmic feedback via a generalized proportional control law,and an appropriate Lyapunov function is constructed to prove the stability.Both the regulation and tracking problems are tackled.Omnidirectional control is discussed as a case study.As the control laws can handle the interconnection between the rotation and translation of a rigid body,they are shown to be more applicable than the conventional method.