Pattern matching is a technique where you test an expression to determine if it has certain characteristics. C# pattern matching provides more concise syntax for testing expressions and taking action when an expression matches. The "is expression" supports pattern matching to test an expression and conditionally declare a new variable to the result of that expression. The "switch expression" enables you to perform actions based on the first matching pattern for an expression. These two expressions support a rich vocabulary of patterns.
Edge Based Template Matching Code
This article provides an overview of scenarios where you can use pattern matching. These techniques can improve the readability and correctness of your code. For a full discussion of all the patterns you can apply, see the article on patterns in the language reference.
Another common use for pattern matching is to test a variable to see if it matches a given type. For example, the following code tests if a variable is non-null and implements the System.Collections.Generic.IList interface. If it does, it uses the ICollection.Count property on that list to find the middle index. The declaration pattern doesn't match a null value, regardless of the compile-time type of the variable. The code below guards against null, in addition to guarding against a type that doesn't implement IList.
The preceding code also demonstrates another important feature the compiler provides for pattern matching expressions: The compiler warns you if you don't handle every input value. The compiler also issues a warning if a switch arm is already handled by a previous switch arm. That gives you freedom to refactor and reorder switch expressions. Another way to write the same expression could be:
This article provided a tour of the kinds of code you can write with pattern matching in C#. The following articles show more examples of using patterns in scenarios, and the full vocabulary of patterns available to use.
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Widespread industrial products, which are usually texture-less, are mainly represented with 3D boundary representation (B-Rep) model for designing and manufacturing, hence the pose estimation of texture-less object based on B-Rep model is worthy of much studying in industrial inspection. In view of such facts that surfaces are much crucial both to construction of B-Rep model and to recognition of real object, the edges of the visible surfaces in each aspect view of B-Rep model are computed and the edges in a search image containing real B-Rep objects are extracted with modified Hough algorithm. Secondly, the two edge sets are converted into the metric space for comparison, where each edge is expressed with the tetrad of edge length, angle of middle point, angle of perpendicular axis, and length of perpendicular axis. In that way, the pose of real B-Rep object in a search image is estimated by comparing the edge set of every aspect view with the edge set of the search image with the bipartite graph matching algorithm. The corresponding experiment was taken with some products in national design reservoir (NDR), and it verified the effectiveness of the texture-less pose estimation approach based on B-Rep model.
From a neuropsychological point of view, surfaces are the primary factors of 3D object recognition. 3D shapes are spatial configurations of surface fragments encoded by IT neurons [4]. Ecological psychologist Gibson regarded that the composition and layout of surfaces to be perceived constitute what they afford [5]. Gestalt psychologists try to settle how local discontinuities in motion or depth are evaluated with respect to object boundaries and surfaces, and it hints that surfaces and their boundaries be the enhanced and ultimate cognitive elements of objects [6]. Similarly, Marr believed that 3D shape representation is to describe surface geometry [1]. The surface-based representation of 3D object is the intermediate stage between the image-based representation and 3D shape representation [7]. As a matter of fact, B-Rep model is just surface centered and usually converted into an adjacent attributed surface graph (AAG) in order to recognize and analyze, while the edges that make up the surface boundary are the most crucial visual attributes of surfaces. At mean time, it is mentioned that edges are the most fundamental image features, for instance, they are first located by Gabor filters in the current deep learning mechanism.
In this paper, we first set up the metric space about edges in order to compare the edges from aspect views of B-Rep model and the search image, and propose an algorithm about the pose estimation of texture-less object in the search image based on B-Rep model. In view that surfaces are as the crucial visual and functional features from ecological psychology and affordance theory [5], moreover, considering that surfaces are also the core elements in B-Rep model, surfaces are first extracted from the neutral STEP file of B-Rep model, and then the edges of surface boundary are picked up to constitute the feature set of B-Rep model. Secondly, when the B-Rep model is projected to generate a number of aspect views that make up an aspect graph, the edge sets from each aspect view that correspond to certain pose parameters are computed for next matching. In the same way, the edges in the search image containing real B-Rep object are detected and merged according to contiguity and continuity rule, and then the search image is characterized into the edge set. Now the object pose can be estimated by matching the bipartite graph of two edge sets from the search image and one of aspect views of B-Rep model.
In practice, some same problems like in [8] need to be solved. After placing the camera on one virtual sphere with constant radius centered at the B-Rep model center, we ensure the translation invariance and scale invariance by normalizing the two edge sets in the metric space, and the rotation invariance due to edge-pairwise comparison in a bipartite graph matching, because the origin of coordinate system is at the center, all B-Rep models are within the unit sphere, and the bipartite graph matching is the only best regardless of edge orientations.
One main contribution of this paper is that the aspect graph of B-Rep model can provide more accurate and fast alignment references because the edges are the inherent and direct geometry features of B-Rep model. It provides the simple edge comparison that the two edge sets are converted into the four-dimension metric space. Moreover, the bipartite graph matching is more complete and accurate in the comparison of the two edge sets than the template matching.
The rest of the paper is organized as follows. Section 2 gives the related literature. In Section 3, the metric space for edge comparison and bipartite graph matching algorithm are set up. The surface attributes of B-Rep model and the characteristics of projected surface edges in aspect views are analyzed in Section 4. In Section 5, the edges in the search image containing the real B-Rep object are detected and simplified. The object pose estimation based on the edge bipartite graph matching algorithm is described in Section 6. The experiment results are discussed in Section 7.
Approaches for 3D object recognition in a single image have been extensively studied. Reference [2] described the overviews of object recognition from the passive approaches and the active approaches, and alluded that titillating evidences from neuroscience motivated radically to rethink the solution to 3D object recognition. It indicated that detectors paid more attention to shape properties than to color or texture properties, for example, local shape features, medial axis or skeleton, Fourier descriptors, edge direction histograms, and so on. In addition, the chain of k-connected approximately straight boundary aimed at the calculated edges in outdoor images as it simulates the certain characteristics of human visual system [9]. Coarse and refined object recognition were performed by SIFT features of interesting points in images [10]. Other common shape features included bounding ellipses, curvature scale-space, elastic models, and edge direction histograms [11] in CBIR systems. Fergus detected all the curves by Canny edge operator, and each curve was split into independent segments at its bi-tangent points to obtain feature vector of the curve [12]. The singularities or shocks in medial axes of shape outlines were used to segment the skeleton of the object into a tree-like structure called shock graph [13]. There were other shape detector like a log-polar histogram of points in object boundary [14], the orientations and principle curvatures of visible patch [15]. Unlike above, the part-based approaches provide high level volumetric parts such as generalized cylinders and super-quadrics to reduce the search space [16, 17]. Though the methods based on descriptors of feature points can decrease run-time computational complexity, they were not suitable for shiny metal surfaces [18].
Nonetheless, the aforementioned methods were not specifically devised for the detection of texture-less objects. Current texture-less object detectors mainly involve edge/gradient-based template matching [19, 20], BOLD [21], gradient orientation [22], line [23], and curve [24]. Some other texture-less detectors also consider depth information from RGB-D data [25, 26]. Combining with the detectors, the search space of aspect views is reduced using prior knowledge [27] or scale-space hierarchical model [8]. A purely edge-based method was presented for real-time scalable detection of texture-less objects in 2D images [28]. A regularized, auto-context regression framework iteratively reduces uncertainty in object coordinate and detects multiple objects by a single RGB image [29]. 3D object was detected and pose was estimated only from color images [30]. 2ff7e9595c
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