Introduction of Simulating the Motion of Rigid Ellipsoidal Objects in Ductile Shear Zone Based on the Jeffery's Theory

Rigid ellipsoidal objects(gravels and porphyroclasts)in ductile zone is an important factor to indicate the kinematics and dynamics.Jeffery’s theory(Jeffery G,1922),a quantitative research method,for the rotation ofthe rigid objects(no deformation)in the Newtonian fluid of the simple deformation field has been widely applied by geologists to the study of fabrics in rocks.The theory

Evaluating quality of motion for unsupervised video object segmentation

Current mainstream unsupervised video object segmentation(UVOS) approaches typically incorporate optical flow as motion information to locate the primary objects in coherent video frames. However, they fuse appearance and motion information without evaluating the quality of the optical flow. When poor-quality optical flow is used for the interaction with the appearance information, it introduces significant noise and leads to a decline in overall performance. To alleviate this issue, we first employ a quality evaluation module(QEM) to evaluate the optical flow. Then, we select high-quality optical flow as motion cues to fuse with the appearance information, which can prevent poor-quality optical flow from diverting the network's attention. Moreover, we design an appearance-guided fusion module(AGFM) to better integrate appearance and motion information. Extensive experiments on several widely utilized datasets, including DAVIS-16, FBMS-59, and You Tube-Objects, demonstrate that the proposed method outperforms existing methods.

Moving Behavior of an Object in Gas-Solid Fluidized Beds

The settling behavior of coarse particles in a gas-solid fluidized bed was experimentally studied by using magnetic tracer. It is well known that the calculation of terminal velocity is of interest in dense medium separation. However, this problem has not been completely solved up to now. In this work, the terminal velocity of an object mov-ing in a gas-solid fluidized bed was experimentally measured and theoretically calculated. The experimental results in-dicated that the plastic viscosity and yield stress of the bed increase as the size of fluidized particles increases, but it varies little when some coarser particles are mixed with the fluidized particles. The resistance to a rising object was an order magnitude greater than that to a settling object. The efficient buoyancy on a flaky object, which lies flatly on the gas distributor, was much less than that calculated by the Archimedes principle. The object does not always rise or set-tle with minimal projective area owing to radial motion of the fluidized particles. But in the lower part of the bed, the bar-shaped objects were likely with minimal projective area rising or settling.

Moving object detection method based on complementary multi resolution background models

A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models were used.The ghost and real static object could be classified by comparing the similarity of the edge images further.In each group,the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise.The computational color model was also used to depress illustration variations and light shadows.The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods.Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences.Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences,respectively.The proposed method shows a relatively good performance,especially for the intermittent object motion sequences.

Particle Filter Object Tracking Algorithm Based on Sparse Representation and Nonlinear Resampling

Object tracking with abrupt motion is an important research topic and has attracted wide attention.To obtain accurate tracking results,an improved particle filter tracking algorithm based on sparse representation and nonlinear resampling is proposed in this paper. First,the sparse representation is used to compute particle weights by considering the fact that the weights are sparse when the object moves abruptly,so the potential object region can be predicted more precisely. Then,a nonlinear resampling process is proposed by utilizing the nonlinear sorting strategy,which can solve the problem of particle diversity impoverishment caused by traditional resampling methods. Experimental results based on videos containing objects with various abrupt motions have demonstrated the effectiveness of the proposed algorithm.

A graph theory model using human nature structure

A graph theory model of the human nature structure( GMH) for machine vision and image/graphics processing is described in this paper. Independent from the motion and deformation of contours,the human nature structure( HNS) embodies the most basic movement characteristics of the body. The human body can be divided into basic units like head,torso,and limbs. Using these basic units,a graph theory model for the HNS can be constructed. GMH provides a basic model for human posture processing,and the outline in the perspective projection plane is the body contour of an image. In addition,the GMH can be applied to articulated motion and deformable objects,e. g.,in the design and analysis of body posture,by modifying mapping parameters of the GMH.