69,327 research outputs found
Topological model for machining of parts with complex shapes
Complex shapes are widely used to design products in several industries such
as aeronautics, automotive and domestic appliances. Several variations of their
curvatures and orientations generate difficulties during their manufacturing or
the machining of dies used in moulding, injection and forging. Analysis of
several parts highlights two levels of difficulties between three types of
shapes: prismatic parts with simple geometrical shapes, aeronautic structure
parts composed of several shallow pockets and forging dies composed of several
deep cavities which often contain protrusions. This paper mainly concerns High
Speed Machining (HSM) of these dies which represent the highest complexity
level because of the shapes' geometry and their topology. Five axes HSM is
generally required for such complex shaped parts but 3 axes machining can be
sufficient for dies. Evolutions in HSM CAM software and machine tools lead to
an important increase in time for machining preparation. Analysis stages of the
CAD model particularly induce this time increase which is required for a wise
choice of cutting tools and machining strategies. Assistance modules for
prismatic parts machining features identification in CAD models are widely
implemented in CAM software. In spite of the last CAM evolutions, these kinds
of CAM modules are undeveloped for aeronautical structure parts and forging
dies. Development of new CAM modules for the extraction of relevant machining
areas as well as the definition of the topological relations between these
areas must make it possible for the machining assistant to reduce the machining
preparation time. In this paper, a model developed for the description of
complex shape parts topology is presented. It is based on machining areas
extracted for the construction of geometrical features starting from CAD models
of the parts. As topology is described in order to assist machining assistant
during machining process generation, the difficulties associated with tasks he
carried out are analyzed at first. The topological model presented after is
based on the basic geometrical features extracted. Topological relations which
represent the framework of the model are defined between the basic geometrical
features which are gathered afterwards in macro-features. Approach used for the
identification of these macro-features is also presented in this paper.
Detailed application on the construction of the topological model of forging
dies is presented in the last part of the paper
Boundary and object detection in real world images
A solution to the problem of automatic location of objects in digital pictures by computer is presented. A self-scaling local edge detector which can be applied in parallel on a picture is described. Clustering algorithms and boundary following algorithms which are sequential in nature process the edge data to locate images of objects
Event detection in field sports video using audio-visual features and a support vector machine
In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable
Structure Preserving Large Imagery Reconstruction
With the explosive growth of web-based cameras and mobile devices, billions
of photographs are uploaded to the internet. We can trivially collect a huge
number of photo streams for various goals, such as image clustering, 3D scene
reconstruction, and other big data applications. However, such tasks are not
easy due to the fact the retrieved photos can have large variations in their
view perspectives, resolutions, lighting, noises, and distortions.
Fur-thermore, with the occlusion of unexpected objects like people, vehicles,
it is even more challenging to find feature correspondences and reconstruct
re-alistic scenes. In this paper, we propose a structure-based image completion
algorithm for object removal that produces visually plausible content with
consistent structure and scene texture. We use an edge matching technique to
infer the potential structure of the unknown region. Driven by the estimated
structure, texture synthesis is performed automatically along the estimated
curves. We evaluate the proposed method on different types of images: from
highly structured indoor environment to natural scenes. Our experimental
results demonstrate satisfactory performance that can be potentially used for
subsequent big data processing, such as image localization, object retrieval,
and scene reconstruction. Our experiments show that this approach achieves
favorable results that outperform existing state-of-the-art techniques
- …