Extracting 3D parametric curves from 2D images of Helical objects

Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively

Improvement of mammographic mass characterization using spiculation measures and morphological features

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135119/1/mp1548.pd

Human hair and the impact of cosmetic procedures: a review on cleansing and shape-modulating cosmetics

Hair can be strategically divided into two distinct parts: the hair follicle, deeply buried in the skin, and the visible hair fiber. The study of the hair follicle is mainly addressed by biological sciences while the hair fiber is mainly studied from a physicochemical perspective by cosmetic sciences. This paper reviews the key topics in hair follicle biology and hair fiber biochemistry, in particular the ones associated with the genetically determined cosmetic attributes: hair texture and shape. The traditional and widespread hair care procedures that transiently or permanently affect these hair fiber features are then described in detail. When hair is often exposed to some particularly aggressive cosmetic treatments, hair fibers become damaged. The future of hair cosmetics, which are continuously evolving based on ongoing research, will be the development of more efficient and safer procedures according to consumers needs and concerns.Portuguese Foundation for Science and Technology (FCT) for providing Célia F. Cruz the grant for PhD studies (scholarship SFRH/BD/100927/2014) and Teresa Matamá the grant for post-doctoral research (SFRH/BPD/102153/2014). This work was also supported by FCT under the scope of the strategic funding of UID/BIO/04469/2013 and UID/BIA/04050/2013 units, COMPETE 2020 (POCI-01-0145-FEDER-006684andPOCI-01-0145-FEDER-007569) and under the Project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462).info:eu-repo/semantics/publishedVersio

Segmentation And Spatial Depth Ridge Detection Of Unorganized Point Cloud Data

Visual 3D data are of interest to a number of fields: medical professionals, game designers, graphic designers, and (in the interest of this paper) ichthyologists interested in the taxonomy of fish. Since the release of the Kinect for the Microsoft XBox, game designers have been interested in using the 3D data returned by the device to understand human movement and translate that movement into an interface with which to interact with game systems. In the medical field, researchers must use computer vision tools to navigate through the data found in CT scans and MRI scans. These tools must segment images into the parts that are relevant to researchers and account for noise related to the scanning process all while ignoring other types of noise such as foreign elements in the body that might indicate signs of illness. 3D point cloud data represents some unique challenges. Consider an object scanned with a laser scanner. The scanner returns the surface points of the object, but nothing more. Using the tool Qhull, a researcher can quickly compute the convex hull of an object (which is an interesting challenge in itself), but the convex hull (obviously) leaves out any description of an object\u27s concave features. Several algorithms have been proposed to illustrate an object\u27s complete features based on unorganized 3D point cloud data as accurately as possible, most notably Boissonnat\u27s tetrahedral culling algorithm and The Power Crust algorithm. We introduce a new approach to the area partitioning problem that takes into consideration these algorithms\u27 strengths and weaknesses. In this paper we propose a methodology for approximating a shape\u27s solid geometry using the unorganized 3D point cloud data of that shape primarily by utilizing localized principal component analysis information. Our model accounts for three comissues that arise in the scanning of 3D objects: noise in surface points, poorly sampled surface area, and narrow corners. We explore each of these areas of concern and outline our approach to each. Our technique uses a growing algorithm that labels points as it progresses and uses those labels with a simple priority queue. We found that our approach works especially well for approximating surfaces under the condition where a local surface is poorly sampled (i.e a significant hole is present in the point cloud). We then turn to study the medial axis of a shape for the purposes of `unfolding\u27 that structure. Our approach uses a ridge formulation based on the spatial depth statistic to create the medial axis. We conclude the paper with visual results of our technique

Resolution-Independent Meshes of Superpixels

The over-segmentation into superpixels is an important preprocessing step to smartly compress the input size and speed up higher level tasks. A superpixel was traditionally considered as a small cluster of square-based pixels that have similar color intensities and are closely located to each other. In this discrete model the boundaries of superpixels often have irregular zigzags consisting of horizontal or vertical edges from a given pixel grid. However digital images represent a continuous world, hence the following continuous model in the resolution-independent formulation can be more suitable for the reconstruction problem. Instead of uniting squares in a grid, a resolution-independent superpixel is defined as a polygon that has straight edges with any possible slope at subpixel resolution. The harder continuous version of the over-segmentation problem is to split an image into polygons and find a best (say, constant) color of each polygon so that the resulting colored mesh well approximates the given image. Such a mesh of polygons can be rendered at any higher resolution with all edges kept straight. We propose a fast conversion of any traditional superpixels into polygons and guarantees that their straight edges do not intersect. The meshes based on the superpixels SEEDS (Superpixels Extracted via Energy-Driven Sampling) and SLIC (Simple Linear Iterative Clustering) are compared with past meshes based on the Line Segment Detector. The experiments on the Berkeley Segmentation Database confirm that the new superpixels have more compact shapes than pixel-based superpixels