We propose a needle-shape quality-control method. To this end, we have devised a new acquisition system that combines a camera and a backlight. Needle measurements are carried out at a micrometric scale using shadowgraphic image processing. Our method not only distinguishes good needles from bad ones, but also allows classifying flawed needles into various categories of defects. This classification is important because some categories of defects can affect the entire production, whereas others do not. The results of our needle-shape quality-control method are validated using real samples directly off the manufacturing line. Needles are correctly classified at >97%, and accurate measurements on global shape characteristics such as straightness and sharpness are obtained.
3D meshes are widely used in computer graphics applications for approximating 3D models. When representing
complex shapes in raw data format, meshes consume a large amount of space. Applications calling for compact
and fast processing of large 3D meshes have motivated a multitude of algorithms developped to process
these datasets efficiently. The concept of multiresolution analysis proposes an efficient and versatile tool for
digital geometric processing allowing for numerous applications. In this paper, we survey recent developments
in multiresolution methods for 3D triangle meshes. We also show some results of these methods through various
Recent advances in scanning and acquisition technologies allow the construction of complex models from real world scenes. However, the data of those models are generally corrupted by measurement errors. This paper describes an efficient single pass algorithm for denoising irregular meshes of scanned 3D model surfaces. In this algorithm, the frequency content of the model is assessed by a multiresolution analysis that requires only 1-ring neighbourhood without any particular parameterization of the model faces. Denoising is achieved by applying the soft thresholding method to the detail coefficients given by the multiresolution analysis. Our method is suitable for irregular meshes with appearance attributes such as normal vectors and colors. Some results of real world scene models denoised with the proposed algorithm are given to demonstrate its efficiency.
We present in this paper a new method for implementing geometric moment functions in a CMOS retina. It is based on the computation of the correlation value between the image under analysis and a second image since there is a similarity between the expression of the moment of an image and that of the correlation of two images. The second image which is stored in memory devices in the circuit is approximated by a binary image using a dithering algorithm in order to reduce hardware implementation cost. As a result the value of the moment is also an approximate one. Computer simulations using the COIL 100 Columbia image database on 128x128 pixel images show that the maximal relative error between the approximate and the exact value is less than 1% for moments of order less than 2, and less than 5% for moments of order less than 6. Finally, we have considered an object localization application and quantified the error in the localization due to the use of the approximate moment values instead of the exact values.
The concept of multiresolution analysis applied to irregular meshes has become more and more important. Previous contributions proposed a variety of methods using simplification and/or subdivision algorithms to build a mesh pyramid. In this paper, we propose a multiresolution analysis framework for irregular meshes with attributes. Our framework is based on simplification and subdivision algorithms to build a mesh pyramid. We introduce a surface relaxation operator that allows to build a non-uniform subdivision for a low computational cost. Furthermore, we generalize the relaxation operator to attributes such as color, texture, temperature, etc. The attribute analysis gives more information on the analysed models allowing more complete processing. We show the efficiency of our framework through a number of applications including filtering, denoising and adaptive simplification.
Traditionally, medical geneticists have employed visual inspection (anthroposcopy) to clinically evaluate dysmorphology. In the last 20 years, there has been an increasing trend towards quantitative assessment to render diagnosis of anomalies more objective and reliable. These methods have focused on direct anthropometry, using a combination of classical physical anthropology tools and new instruments tailor-made to describe craniofacial morphometry. These methods are painstaking and require that the patient remain still for extended periods of time. Most recently, semiautomated techniques (e.g., structured light scanning) have been developed to capture the geometry of the face in a matter of seconds. In this paper, we establish that direct anthropometry and structured light scanning yield reliable measurements, with remarkably high levels of inter-rater and intra-rater reliability, as well as validity (contrasting the two methods).