Interactive Feature Correspondence for Planar Shape Blending

Author(s):  
Wenwu Yang ◽  
Dingke Kong
Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4719
Author(s):  
Huei-Yung Lin ◽  
Yuan-Chi Chung ◽  
Ming-Liang Wang

This paper presents a novel self-localization technique for mobile robots using a central catadioptric camera. A unified sphere model for the image projection is derived by the catadioptric camera calibration. The geometric property of the camera projection model is utilized to obtain the intersections of the vertical lines and ground plane in the scene. Different from the conventional stereo vision techniques, the feature points are projected onto a known planar surface, and the plane equation is used for depth computation. The 3D coordinates of the base points on the ground are calculated using the consecutive image frames. The derivation of motion trajectory is then carried out based on the computation of rotation and translation between the robot positions. We develop an algorithm for feature correspondence matching based on the invariability of the structure in the 3D space. The experimental results obtained using the real scene images have demonstrated the feasibility of the proposed method for mobile robot localization applications.


2012 ◽  
Vol 11 (1) ◽  
pp. 25-32
Author(s):  
Yaqiong Liu ◽  
Seah Hock Soon ◽  
Ying He ◽  
Juncong Lin ◽  
Jiazhi Xia

The establishment of a good correspondence mapping is a key issue in planar animations such as image morphing and deformation. In this paper, we present a novel mapping framework for animation of complex shapes. We firstly let the user extract the outlines of the interested object and target interested area from the input images and specify some optional feature lines, and then we generate a sparse delaunay triangulation mesh taking the outlines and the feature lines of the source shape as constraints. Then we copy the topology from the source shape to the target shape to construct a valid triangulation in the target shape. After that, each triangle of this triangular mesh is further segmented into a dense mesh patch. Each mesh patch is parameterized onto a unit circle domain. With such parametrization, we can easily construct a correspondence mapping between the source patches and the corresponding target patches. Our framework can work well for various applications such as shape deformation and morphing. Pleasing results generated by our framework show that the framework works well.


2020 ◽  
Author(s):  
Neomi Mizrachi ◽  
Guy Nelinger ◽  
Ehud Ahissar ◽  
Amos Arieli

ABSTRACTHand movements are essential for tactile perception of objects. However, why different individuals converge on specific movement patterns is not yet clear. Focusing on planar shape perception, we tracked the hands of 11 participants while they practiced shape recognition. Our results show that planar shape perception is mediated by contour-following movements, either tangential to the contour or spatially-oscillating perpendicular to it, and by scanning movements, crossing between distant parts of the shapes’ contour. Both strategies exhibited non-uniform coverage of the shapes’ contours. We found that choice of strategy during the first experimental session was strongly correlated with two idiosyncratic parameters: participants with lower tactile resolution tended to move faster; and faster-adapting participants tended to employ oscillatory movements more often. In addition, practicing on isolated geometric features increased the tendency to use the contour-following strategy. These results provide insights into the processes of strategy selection in tactile perception.SIGNIFICANCE STATMENTHand movements are integral components of tactile perception. Yet, the specific motion strategies used to perceive specific objects and features, and their dependence on physiological features and on experience, are understudied. Focusing on planar shape perception and using high-speed hand tracking we show that human participants employ two basic palpation strategies: Contour-following and scanning. We further show that the strategy chosen by each participant and its kinematics depend strongly on the participant’s physiological thresholds – indicative of spatial resolution and temporal adaptation - and on their perceptual experience.


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