Contour Curve Research Articles

The role of local position in the detection of contour curvature

The role of local position in the detection of contour curvature

Do spatial factors influence the microgenesis of illusory contours?

Spatial factors such as contour curvature and support ratio influence the perceived strength of illusory contours. However, it is unknown whether these determinants of interpolative strength affect how quickly interpolated contours emerge during visual processing (microgenesis). We approached this issue using a dot localization method. Observers briefly viewed Kanizsa-type illusory shapes with varying support ratios and relative inducer orientations, as well as stimuli containing comparable spatial cues that did not give rise to illusory contours (non-IC stimuli). During the last 40 ms prior to masking, a small dot appeared somewhere between two inducers. Observers judged whether the dot appeared inside or outside of the illusory shape's perceived boundary. Dot position varied based on observer responses; two interleaved staircases produced the 83% threshold points for seeing the dot inside versus outside of the boundary. This objective paradigm revealed both the accuracy of the interpolation and the strength or precision with which the contour is perceived. Stimulus duration varied from 40 ms to 480 ms, allowing investigation of the microgenesis of contour formation. Our results indicated that observers localized a dot more accurately and precisely relative to illusory contours than relative to comparable non-IC stimuli; this difference diminished at longer durations, suggesting that the time course of illusory contour formation differs from the time course by which more cognitive strategies become useful. Second, consistent with previous data, our results suggest that all illusory contours emerge rapidly; precision reached asymptote within the first 120 ms following stimulus onset. Finally, preliminary data suggest that spatial factors affecting the subjective strength of illusory contours (curvature and support ratio) also influence the time course over which interpolation proceeds. These results will be discussed in the context of contour interpolation models.

The tuning of vision to natural contours: Straighter is better

Contour smoothness is a fundamental constraint in the visual interpretation of images. We aimed to quantify the human visual system's intrinsic model for contour curvature and to test whether this model corresponds to that determined from measurements of natural images. We did this by measuring how well observers could estimate contours in visual noise as a function of contour curvature. Contours were generated by appending line segments that could turn 45 deg left, remain straight, or turn 45 deg right. Contour models were generated by assigning various turning probabilities. Thus, for example, one model might generate contours that tended to zigzag, and another model contours that tended to be straight. Images were generated by assigning intensity values, y, to pixels according to probability distributions p(y|on) or p(y|off) depending on whether the pixel was on or off a contour. Observers were asked to estimate the pixels belonging to the contour by tracing a path through a sample image. In a given session, the contour probability model was fixed and known to the subject. To assess subjects' use of the prior information we measured efficiency by comparing human to ideal performance for several models, i.e. several sets of contour turning probabilities. There are two plausible predictions for efficiency. If an observer uses his or her knowledge of the turning probabilities, we would expect no difference in efficiency across turning probabilities. On the other hand, if the observer uses a fixed prior model, we predict a peak in efficiency for turning probabilities matched to the observer's prior. We found: 1) efficiencies were peaked and high (50–77%), indicating use of a fixed intrinsic prior model; 2) the intrinsic prior favored straight contours; and 3) the computed turning probabilities from a set of natural image contours were also biased toward straight contours.

Investigation on the Contour Curve of Straighter Roll by Conjugate Revolving Quadric Theory

Bar straightening is an important work before the bar is applied as qualified products. In this paper, the mechanics of the straightening process is analyzed in detail on the base of conjugate quadric theory. A simulation program is developed with the investigation of straightening process as well. The equations, which describe roll contour curve and the longitudinal section, are given and simulated in computer. And a brief method of designing and drawing the straightening roll contour curve is introduced.

Research of the Image-Based Fast Design for 3D Toys and Cartoons

This paper takes the 2D image of toys and cartoons as reference to assist the amateurs to design 3D models fast and conveniently. A contour deep tracing algorithm is proposed for acquiring the whole contour, and Ferguson interpolation curve is introduced to fit the contours. Taking the control points of Ferguson curves as reference, amateurs can easily sketch curves that fit to the image contours. Decomposing the extracted contours into several feature regions, the users can draw some enclosed component contour curves of the target toys or cartoons, and then adopt proper methods to generate the 3D mesh of components. Three modeling methods, namely local deformation, symmetric deformation, and 2D FFD deformation, are introduced for modeling the 3D surface fast. Ferguson curves are also used in 3D painting on triangular surface, and large regions enclosed by the painted colors can quickly be filled with a target color. The users select a region of the image and drag them onto 3D models, and then the details, such as eyes, nose, and artistic patterns can be easily painted.

Nonlinear Numerical Optimization Design Applied to Wall-Thickness of Welding T Shaped Connecting HDPE Tube

There are two reasons to enlarge the thickness of welding HDPE tri-branch tube. Firstly, welding weakens the HDPE material properties. Secondly, stress concentration occurs at the joint of tri-branch tube. The material properties of HDPE and the tensile strength of welding HDPE were tested. Based on the test results, the wall-thickness of HDPE tri-branch tube was investigated by numerical optimization design with particle swarm optimization (PSO). Two material constitutive models of elasto-palstic and Ramberg-Osgood for HDPE are adopted in FEM. Some valuable conclusions of pipeline design are concluded by the distribution of stress contour curves and optimization curves of welding joint of HDPE tri-branch tube.

Generalizing edge detection to contour detection for image segmentation

One approach to image segmentation defines a function of image partitions whose maxima correspond to perceptually salient segments. We extend previous approaches following this framework by requiring that our image model sharply decreases in its power to organize the image as a segment’s boundary is perturbed from its true position. Instead of making segment boundaries prefer image edges, we add a term to the objective function that seeks a sharp change in fitness with respect to the entire contour’s position, generalizing from edge detection’s search for sharp changes in local image brightness. We also introduce a prior on the shape of a salient contour that expresses the observed multi-scale distribution of contour curvature for physical contours. We show that our new term correlates strongly with salient structure. We apply our method to real images and verify that the new term improves performance. Comparisons with other state-of-the-art approaches validate our method’s advantages.

Modeling and Control of 2D Grasping under Rolling Contact Constraints between Arbitrary Shapes: A Riemannian-Geometry Approach

Modeling, control, and stabilization of dynamics of two-dimensional object grasping by using a pair of multijoint robot fingers are investigated under rolling contact constraints and arbitrariness of the geometry of the object and fingertips. First, modeling of rolling motion between 2D rigid bodies with arbitrary shape is treated under the assumption that the two contour curves coincide at the contact point and share the same tangent. The rolling constraints induce the Euler equation of motion that is parameterized by a pair of arclength parameters and constrained onto the kernel space as an orthogonal complement to the image space spanned from all the constraint gradients. Furthermore, it is shown that all the Pfaffian forms of the rolling constraints are integrable in the sense of Frobenius and therefore the rolling contacts are regarded as a holonomic constraint. The Euler-Lagrange equation of motion of the overall fingers/object system is rederived together with a couple of first-order differential equations that express evolution of contact points in terms of quantities of the second fundamental form. A control signal called “blind grasping” is defined and shown to be effective in maintenance or stabilization of grasping without using the details of object shape and parameters or external sensing. An extension of the Dirichlet-Lagrange stability theorem to a system of DOF-redundancy under constraints is discussed by introducing a Morse-Bott function and deriving its Hessian, in a special case that the object to be grasped is a parallelepiped.

A Riemannian-Geometric Approach for Intelligent Control and Fingertip Design of Multi-fingered Hands

Based upon the analysis of modeling and control of two-dimensional (2-D) grasping and manipulation of arbitrary rigid objects under rolling contact constraints, geometrical conditions for the design of desired fingertip shapes of robot fingers are discussed from the Riemannian-geometric standpoint. A required condition is given as an inequality expressed in terms of quantities of the second fundamental form of fingertip contour curves, although the quantities do not enter into the Euler–Lagrange equation of motion of the overall fingers/object system. Satisfaction of the inequality is necessary for stabilization of grasping by using fingers–thumb opposable control signals without use of external sensings or knowledge of an object to be grasped. At the same time, asymptotic convergence of a solution to the closed-loop dynamics of 2-D precision prehension by a pair of multi-joint robot fingers is proved under rolling contact constraints and the existence of redundancy in the system's degrees of freedom. This is regarded as an extension of the Dirichlet–Lagrange stability theorem to a dynamical system with redundancy and geometric constraints.

Computer vision of the foot sole based on laser metrology and algorithms of artificial intelligence

An automatic technique for the 3-D vision of the foot sole is presented. This technique is performed by means of laser metrology and approximation networks. To retrieve the topography, the foot sole is scanned by a laser line through a glass window. The contouring of the foot sole is based on the behavior of the laser line. This 3-D modeling is performed by an approximation network. The structure of this network is built based on the line shift that is generated due to surface variation and the camera position. Also, the intrinsic and extrinsic parameters of the vision system are computed based on the network. In this manner, online setup modifications can be performed. Thus, the external measurements are not passed to the vision system. In this manner, the accuracy and the performance are improved because physical measurements are avoided. The approach of this vision system is to fit the shoe sole mold to the foot sole via contour curves. The results are evaluated by means of a root mean square of error using references from a contact method. Thus, a contribution in computer vision is achieved for profitable shoe design. The processing time is also described.

Neural network model for completing occluded contours

When some parts of a pattern are occluded by other objects, the visual system can often estimate the shape of occluded contours from visible parts of the contours. This paper proposes a neural network model capable of such function, which is called amodal completion. The model is a hierarchical multi-layered network that has bottom-up and top-down signal paths. It contains cells of area V1, which respond selectively to edges of a particular orientation, and cells of area V2, which respond selectively to a particular angle of bend. Using the responses of bend-extracting cells, the model predicts the curvature and location of the occluded contours. Missing contours are gradually extrapolated and interpolated from the visible contours. Computer simulation demonstrates that the model performs amodal completion to various stimuli in a similar way as observed by psychological experiments.

Study on Modeling Technique about Complex Valve Body Based on Reverse Engineering

The reverse modeling solution and its key technologies about complex part with internal cavity are introduced in this paper, taken the casting valve body for an example. The melt silicon rubber is injected in the valve body to duplicate its internal cavity surface, which is transformed into the external surface of the concretionary silicon rubber entity. The technical hurdle of collecting internal cavity surface data is solved. The key modeling technologies are studied according to data of spot cloud collected by laser-scanning approach, data preprocessing of spot cloud based on synthetic method, respectively constructing contour curves of internal and external piping surfaces, building the sold models corresponding internal and external surfaces on basis of alignment of their coordinates. The final CAD model of valve body is created after Boolean calculation of two sold models. This technical process is be efficiently used to solve the bottleneck of the rapid design and lean development about part with internal cavity structure.

From 3D Point Clouds to Pose-Normalised Depth Maps

We consider the problem of generating either pairwise-aligned or pose-normalised depth maps from noisy 3D point clouds in a relatively unrestricted poses. Our system is deployed in a 3D face alignment application and consists of the following four stages: (i) data filtering, (ii) nose tip identification and sub-vertex localisation, (iii) computation of the (relative) face orientation, (iv) generation of either a pose aligned or a pose normalised depth map. We generate an implicit radial basis function (RBF) model of the facial surface and this is employed within all four stages of the process. For example, in stage (ii), construction of novel invariant features is based on sampling this RBF over a set of concentric spheres to give a spherically-sampled RBF (SSR) shape histogram. In stage (iii), a second novel descriptor, called an isoradius contour curvature signal, is defined, which allows rotational alignment to be determined using a simple process of 1D correlation. We test our system on both the University of York (UoY) 3D face dataset and the Face Recognition Grand Challenge (FRGC) 3D data. For the more challenging UoY data, our SSR descriptors significantly outperform three variants of spin images, successfully identifying nose vertices at a rate of 99.6%. Nose localisation performance on the higher quality FRGC data, which has only small pose variations, is 99.9%. Our best system successfully normalises the pose of 3D faces at rates of 99.1% (UoY data) and 99.6% (FRGC data).

Detection of (anti)symmetry and (anti)repetition: Perceptual mechanisms versus cognitive strategies

Symmetry and repetition are recognized as cues in perceptual organization, but there is disagreement on whether they are detected automatically. This disagreement is resolved by noting that some studies mixed up shape regularities and shape antiregularities (i.e., symmetries and repetitions with mismatches in contour curvature polarity). The results of two experiments indicate that a task-irrelevant regularity is automatically picked up by the visual system, whereas a task-irrelevant antiregularity is not. This suggests that detection of regularities is part of the visual system’s intrinsic encoding, whereas detection of antiregularities requires higher cognitive strategies involving selective attention.

Curvature coding in illusory contours

We have employed the shape frequency and shape-amplitude after-effects (SFAE and SAAE) to investigate: (i) whether the shapes of illusory and real curves are processed by the same or different mechanisms, and (ii) the carrier-tuning properties of illusory curvature mechanisms. The SFAE and SAAE are the phenomena in which adaptation to a sinusoidal-shaped contour results in a shift in, respectively, the perceived shape-frequency and perceived shape-amplitude of a test contour in a direction away from that of the adapting stimulus. Both after-effects are believed to be mediated by mechanisms sensitive to curvature ( Gheorghiu & Kingdom, 2007a, 2009; see also Hancock & Peirce, 2008). We observed both shape after-effects in sinusoidally-shaped illusory contours defined by phase-shifted line-grating carriers. We tested whether illusory and real contours were mediated by the same or different mechanisms by comparing same adaptor-and-test with different adaptor-and-test combinations of real and illusory contours. Real contour adaptors produced after-effects in illusory contour tests that were as great as, or even greater than those produced by illusory contour adaptors. However, illusory contour adaptors produced much weaker after-effects in real contour tests than did real contour adaptors. This asymmetry suggests that illusory contour curves are encoded by a sub-set of mechanisms sensitive to real contour curves. We also examined the carrier-tuning properties of illusory-contour curvature processing using adaptor and test illusory contours that differed in the luminance contrast-polarity, luminance scale and orientation of the carriers. We found no selectivity to any of these dimensions for either even-symmetric or odd-symmetric line-gratings carriers, even though selectivity to these dimensions was found for real contours.

Rapid pedobarographic image registration based on contour curvature and optimization

Image registration, the process of optimally aligning homologous structures in multiple images, has recently been demonstrated to support automated pixel-level analysis of pedobarographic images and, subsequently, to extract unique and biomechanically relevant information from plantar pressure data. Recent registration methods have focused on robustness, with slow but globally powerful algorithms. In this paper, we present an alternative registration approach that affords both speed and accuracy, with the goal of making pedobarographic image registration more practical for near-real-time laboratory and clinical applications. The current algorithm first extracts centroid-based curvature trajectories from pressure image contours, and then optimally matches these curvature profiles using optimization based on dynamic programming. Special cases of disconnected images (that occur in high-arched subjects, for example) are dealt with by introducing an artificial spatially linear bridge between adjacent image clusters. Two registration algorithms were developed: a ‘geometric’ algorithm, which exclusively matched geometry, and a ‘hybrid’ algorithm, which performed subsequent pseudo-optimization. After testing the two algorithms on 30 control image pairs considered in a previous study, we found that, when compared with previously published results, the hybrid algorithm improved overlap ratio ( p=0.010), but both current algorithms had slightly higher mean-squared error, assumedly because they did not consider pixel intensity. Nonetheless, both algorithms greatly improved the computational efficiency (25±8 and 53±9 ms per image pair for geometric and hybrid registrations, respectively). These results imply that registration-based pixel-level pressure image analyses can, eventually, be implemented for practical clinical purposes.

Automatic Fragment Re-Assembly Method Based on DDTW Match

This paper presents an automatic re-assembly method for matching 3D fragments,which can compute the best match of a pair of broken pieces.To improve speed and accuracy,this paper gives a torsion estimation method based on anti-noise section and a fixed time cost 3D overlap test method.First,the contour curves of fragments are found,then the corner points.Contour curves can be divided into several sub-contours.Second,the torsion sequences are obtained for the DDTW based matching work.Thus,the scale and transformation matrixes can be computed to change size and position of one fragment.Best matching result will be finally chosen according to matching test and extra evaluations.Experiment results show that this method is simple and robust,and can get proper matching results quickly.

Automatic Fragment Re-Assembly Method Based on DDTW Match

This paper presents an automatic re-assembly method for matching 3D fragments,which can compute the best match of a pair of broken pieces.To improve speed and accuracy,this paper gives a torsion estimation method based on anti-noise section and a fixed time cost 3D overlap test method.First,the contour curves of fragments are found,then the corner points.Contour curves can be divided into several sub-contours.Second,the torsion sequences are obtained for the DDTW based matching work.Thus,the scale and transformation matrixes can be computed to change size and position of one fragment.Best matching result will be finally chosen according to matching test and extra evaluations.Experiment results show that this method is simple and robust,and can get proper matching results quickly.

Reduction of Consumed Energy and Control Input Variance in Machine Tool Feed Drives by Contouring Control

Contouring control has been widely studied to reduce contour error, defined as error components orthogonal to desired contour curves. Its effectiveness has, however, to the best of our knowledge, only been verified through comparative experiments using industrial non-contouring (independent axial) controllers or conventional contouring controllers such as the cross-coupling controller. Because control performance depends largely on controller gain, these comparisons have problems in showing the effectiveness of contouring control, meaning that similar control performance could be attained if non-contouring controller gain were appropriately assigned. This paper presents contouring controller design for biaxial feed drives that reduces controller gain, rather than contour error, better than conventional independent axial controllers. The contouring controller is shown in experiments to effectively reduce control input variance and electricity consumption on average by 4.5%.

Personalized Cranium Defects Restoration Technique Based on Reverse Engineering

The purpose of this paper is to overcome the limitations of the traditional cranial defects restoration technique and better satisfy the aesthetic and comfort demands of different patients. An arithmetic profile curve blending technique was used based on a well-proportioned points cloud data obtained by analyzing computer tomography (CT) images of the patients. This technique uses reverse engineering technique to reconstruct a model of the defective cranium, taking all the characteristics of the protruding cranium into consideration to check the form and appropriateness of the restoration and to adjust the surface in real time to obtain the ideal shape. Then, the model is transferred to a multiple-point forming (MPF) pressure machine to produce a titanium alloy restoration model. The system has greater flexibility, shorter production cycles, and lower cost through the use of digital production technology, guarantees the quality of the cranial defects restoration model, reduces the surgical risks, and alleviates the patients' pain. In addition, an improved contour curved bridge algorithm technique is used to repair any cranium defects on the contour curve to make the contour more complete and closed.