Abstract Feature curves are largely adopted to highlight shape features, such as sharp lines, or to divide surfaces into meaningful segments, like convex or concave regions. Extracting these curves is not sufficient to convey prominent and meaningful information about a shape. We have first to separate the curves belonging to features from those caused by noise and then to select the lines, which describe non-trivial portions of a surface. The automatic detection of such features is crucial for the identification and or annotation of relevant parts of a given shape. To do this, the Hough transform (HT) is a feature extraction technique widely used in image analysis, computer vision and digital image processing, while, for 3D shapes, the extraction of salient feature curves is still an open problem. Thanks to algebraic geometry concepts, the HT technique has been recently extended to include a vast class of algebraic curves, thus proving to be a competitive tool for yielding an explicit representation of the diverse feature lines equations. In the paper, for the first time we apply this novel extension of the HT technique to the realm of 3D shapes in order to identify and localize semantic features like patterns, decorations or anatomical details on 3D objects (both complete and fragments), even in the case of features partially damaged or incomplete. The method recognizes various features, possibly compound, and it selects the most suitable feature profiles among families of algebraic curves.
* Feature characterization Overviews on methods for extracting feature points are provided in @cite_77 @cite_31 . In the realm of feature characterization it is possible to distinguish between features that are view-dependent, like silhouettes, suggestive contours and principal highlights @cite_76 , mainly useful for rendering purposes @cite_35 , or those that are independent of the spatial embedding and, therefore, more suitable for feature recognition and classification processes. In the following we sketch some of the methods that are relevant for our approach, i.e., characterizations that do not depend on the spatial embedding of the surface.
@cite_77 @cite_31 @cite_76 @cite_35
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We present a new technique for extracting line-type features on point-sampled geometry. Given an unstructured point cloud as input, our method first applies principal component analysis on local neighborhoods to classify points according to the likelihood that they belong to a feature. Using hysteresis thresholding, we then compute a minimum spanning graph as an initial approximation of the feature lines. To smooth out the features while maintaining a close connection to the underlying surface, we use an adaptation of active contour models. Central to our method is a multi-scale classification operator that allows feature analysis at multiple scales, using the size of the local neighborhoods as a discrete scale parameter. This significantly improves the reliability of the detection phase and makes our method more robust in the presence of noise. To illustrate the usefulness of our method, we have implemented a non-photorealistic point renderer to visualize point-sampled surfaces as line drawings of their extracted feature curves. We propose a novel approach to both learning and detecting local contour-based representations for mid-level features. Our features, called sketch tokens, are learned using supervised mid-level information in the form of hand drawn contours in images. Patches of human generated contours are clustered to form sketch token classes and a random forest classifier is used for efficient detection in novel images. We demonstrate our approach on both top-down and bottom-up tasks. We show state-of-the-art results on the top-down task of contour detection while being over 200x faster than competing methods. We also achieve large improvements in detection accuracy for the bottom-up tasks of pedestrian and object detection as measured on INRIA and PASCAL, respectively. These gains are due to the complementary information provided by sketch tokens to low-level features such as gradient histograms. Estimating surface normals from just a single image is challenging. To simplify the problem, previous work focused on special cases, including directional lighting, known reflectance maps, etc., making shape from shading impractical outside the lab. To cope with more realistic settings, shading cues need to be combined and generalized to natural illumination. This significantly increases the complexity of the approach, as well as the number of parameters that require tuning. Enabled by a new large-scale dataset for training and analysis, we address this with a discriminative learning approach to shape from shading, which uses regression forests for efficient pixel-independent prediction and fast learning. Von Mises-Fisher distributions in the leaves of each tree enable the estimation of surface normals. To account for their expected spatial regularity, we introduce spatial features, including texton and silhouette features. The proposed silhouette features are computed from the occluding contours of the surface and provide scale-invariant context. Aside from computational efficiency, they enable good generalization to unseen data and importantly allow for a robust estimation of the reflectance map, extending our approach to the uncalibrated setting. Experiments show that our discriminative approach outperforms state-of-the-art methods on synthetic and real-world datasets. While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a Naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust. Furthermore, it scales well to handle large number of classes. To recognize the patches surrounding keypoints, our classifier uses hundreds of simple binary features and models class posterior probabilities. We make the problem computationally tractable by assuming independence between arbitrary sets of features. Even though this is not strictly true, we demonstrate that our classifier nevertheless performs remarkably well on image datasets containing very significant perspective changes.