This paper presents an interactive technique that
produces static hairstyles by generating individual hair strands
of the desired shape and color, subject to the presence of gravity
and collisions. A variety of hairstyles can be generated by
adjusting the wisp parameters, while the deformation is solved
efficiently, accounting for the effects of gravity and collisions.
Wisps are generated employing statistical approaches. As for
hair deformation, we propose a method which is based on
physical simulation concepts but is simplified to efficiently
solve the static shape of hair. On top of the statistical wisp
model and the deformation solver, a constraint-based styler
is Proposed to model artificial features that oppose the natural
flow of hair under gravity and hair elasticity, such as a hairpin.
Our technique spans a wider range of human hairstyles than
previously Proposed methods, and the styles generated by this
technique are fairly realistic.
This paper introduces an affine invariant of trapezia, and the explicit constraint equation between the intrinsic matrix of a camera and the similarity invariants of a trapezium are established using the affine invariant. By this constraint, the inner parameters, motion parameters of the cameras and the similarity invariants of trapezia can be linearly determined using some prior knowledge on the cameras or the trapezia. The Proposed algorithms have wide applicability since parallel lines are not rare in many scenes. Experimental results validate the Proposed approaches. This work presents a unifying framework based on the parallelism constraint, and the previous methods based on the parallelograms or the parallelepipeds can be integrated into this framework.
Key words: invariant parallelism constraint camera calibration 3D reconstruction
A one-dimensional calibration object consists of three or more collinear points with known relative positions.
It is generally believed that a camera can be calibrated only when a 1D calibration object is in planar motion or rotates
around a ¯ xed point. In this paper, it is proved that when a multi-camera is observing a 1D object undergoing general
rigid motions synchronously, the camera set can be linearly calibrated. A linear algorithm for the camera set calibration
is Proposed，and then the linear estimation is further re¯ ned using the maximum likelihood criteria. The simulated and
real image experiments show that the Proposed algorithm is valid and robust.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is Proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is Proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is Proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is Proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This my phd thesis for the WDM optical network optimization, which employs convex optimization techniques to solve the Proposed integer problems. The computation complexity of my optimization framework is very low compared with other existing method and a performance bound is provided at the same time.
This paper examines the asymptotic (large sample) performance
of a family of non-data aided feedforward (NDA FF) nonlinear
least-squares (NLS) type carrier frequency estimators for burst-mode
phase shift keying (PSK) modulations transmitted through AWGN and
flat Ricean-fading channels. The asymptotic performance of these estimators
is established in closed-form expression and compared with the
modified Cram`er-Rao bound (MCRB). A best linear unbiased estimator
(BLUE), which exhibits the lowest asymptotic variance within the family
of NDA FF NLS-type estimators, is also Proposed.
In recent years large scientific interest has been
devoted to joint data decoding and parameter estimation
techniques. In this paper, iterative turbo decoding joint
to channel frequency and phase estimation is Proposed.
The phase and frequency estimator is embedded into the
structure of the turbo decoder itself, taking into consideration
both turbo interleaving and puncturing. Results
show that the Proposed technique outperforms conventional
approaches both in terms of detection capabilities and
implementation complexity.
