B Description of gestural form features

2021 ◽  
pp. 203-210
Keyword(s):  
Form Features

FREE FORM FEATURES FOR AESTHETIC DESIGN

2000 ◽  
Vol 06 (02) ◽  
pp. 273-302 ◽  
Author(s):  
MARZIA FONTANA ◽  
FRANCA GIANNINI ◽  
MARIA MEIRANA
Keyword(s):  
Free Form ◽  
Aesthetic Design ◽  
Form Features

Geometry-Based Index for Predicting Sink Mark in Plastic Parts

1991 ◽  
Author(s):  
Kurt Beiter ◽  
Kosuke Ishii ◽  
Lee Hornberger
Keyword(s):  
Process Conditions ◽  
Plastic Part ◽  
Design Rules ◽  
Packing Pressure ◽  
Molded Parts ◽  
Injection Molded ◽  
Form Features ◽  
Plastic Parts ◽  
Sink Mark

Abstract This paper describes the development of geometry-based indices that predict sink mark depth in injection molded parts. Plastic part designers need such indices to incorporate manufacturability concerns at the conceptual stage of design. These indices apply to several form features so engineers do not have to check different design rules for each geometry element. First, we propose a geometry-based sink index that can be used to predict sink mark depth as a function of process conditions such as packing pressure. Next, we explain how this relationship is identified through experiments. We also describe HyperDesign/Plastics, a Macintosh-based design aid that incorporates the sink index.

Feature-Based Machining Precedence Reasoning and Sequence Planning

1998 ◽  
Author(s):  
Yong Se Kim ◽  
Eric Wang ◽  
Choong Soo Lee ◽  
Hyung Min Rho
Keyword(s):  
Feature Recognition ◽  
Search Space ◽  
Machining Process ◽  
Planning System ◽  
Process Information ◽  
Computer Aided Process Planning ◽  
Sequence Planning ◽  
Precedence Relations ◽  
Feature Based ◽  
Form Features

Abstract This paper presents a feature-based method to support machining sequence planning. Precedence relations among machining operations are systematically generated based on geometric information, tolerance specifications, and machining expertise. The feature recognition method using Alternating Sum of Volumes With Partitioning (ASVP) Decomposition is applied to obtain a Form Feature Decomposition (FFD) of a part model. Form features are classified into a taxonomy of atomic machining features, to which machining process information has been associated. Geometry-based precedence relations between features are systematically generated using the face dependency information obtained by ASVP Decomposition and the features’ associated machining process information. Multiple sets of precedence relations are generated as alternative precedence trees, based on the feature types and machining process considerations. These precedence trees are further enhanced with precedence relations from tolerance specifications and machining expertise. Machining sequence planning is performed for each of these precedence trees, applying a matrix-based method to reduce the search space while minimizing the number of tool changes. The precedence trees may then be evaluated based on machining cost and other criteria. The precedence reasoning module and operation sequence planning module are currently being implemented within a comprehensive Computer-Aided Process Planning system.

scholarly journals Object detection and tracking in video sequences: formalization, metrics and results

Informatics ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 43-60
Author(s):  
R. P. Bohush ◽  
S. V. Ablameyko
Keyword(s):  
Moving Objects ◽  
Human Movement ◽  
Video Sequences ◽  
Multiple Objects ◽  
Movement Trajectories ◽  
Detection And Tracking ◽  
Form Features ◽  
One And Many ◽  
Tracking Objects

One of the promising areas of development and implementation of artificial intelligence is the automatic detection and tracking of moving objects in video sequence. The paper presents a formalization of the detection and tracking of one and many objects in video. The following metrics are considered: the quality of detection of tracked objects, the accuracy of determining the location of the object in a frame, the trajectory of movement, the accuracy of tracking multiple objects. Based on the considered generalization, an algorithm for tracking people has been developed that uses the tracking through detection method and convolutional neural networks to detect people and form features. Neural network features are included in a composite descriptor that also contains geometric and color features to describe each detected person in the frame. The results of experiments based on the considered criteria are presented, and it is experimentally confirmed that the improvement of the detector operation makes it possible to increase the accuracy of tracking objects. Examples of frames of processed video sequences with visualization of human movement trajectories are presented.

scholarly journals Machine Learning for Diabetic Retinopathy Detection using Image Processing

2021 ◽  
Vol 9 (5) ◽  
pp. 209-215
Author(s):  
Ujwala W. Wasekar ◽  
R. K. Bathla
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Early Stage ◽  
Haar Wavelet ◽  
Feature Analysis ◽  
Median Filtering ◽  
Wavelet Filters ◽  
Knn Classifier ◽  
Form Features ◽  
Processing Techniques

he disorder of Diabetic Retinopathy (DR), a complication of Diabetes that may lead to blindness if not treated at an early stage, is diagnosed by evaluating the retina images of eye. However, the manual grading of images for identifying the seriousness of DR disease requires many resources and it also takes a lot of time. Automated systems give accurate results along with saving time. Ophthalmologists may find it useful in reducing their workload. Proposed work presents the method to correctly identify the lesions and classify DR images efficiently. Blood leaking out of veins form features such as exudates, microaneurysms and haemorrhages, on retina. Image processing techniques assist in DR detection. Median filtering is used on gray scale converted image to reduce noise. The features of the pre-processed images are extracted by textural feature analysis. Optic disc (OD) segmentation methodology is implemented for the removal of OD. Blood vessels are extracted using haar wavelet filters. KNN classifier is applied for classifying retinal image into diseased or healthy .The proposed algorithm is executed in MATLAB software and analyze results with regard to certain parameters such as accuracy, sensitivity, and specificity. The outcomes prove the superiority of the new method with sensitivity of 92.6%, specificity of 87.56% and accuracy of 95% on Diaretdb1 database.

A Generalized Method for the Classification and Extraction of Form Features

1996 ◽  
Author(s):  
A. Z. Qamhiyah ◽  
R. D. Venter ◽  
B. Benhabib
Keyword(s):  
Feature Extraction ◽  
Sequential Extraction ◽  
Feature Recognition ◽  
Boundary Elements ◽  
Geometric Reasoning ◽  
Extraction Techniques ◽  
Planar Surfaces ◽  
Cad Models ◽  
Extraction Stage ◽  
Form Features

Abstract Feature-extraction techniques address the primary limitation of feature-recognition approaches, namely their lack of generalization. This paper presents a boundary-based procedure for the classification and sequential extraction of form features from the CAD models of objects with planar surfaces. Form features are first classified based on their effect on the boundary elements of a basic shape. Geometric reasoning is then used to obtain generalized properties of the form-features’ classes. Finally, form-features’ classes are sequentially extracted based on the recognized properties. At the onset of each extraction stage, the object is viewed as an initial basic shape that has been iteratively altered through the introduction of form features.

Handling Blending Features in Form Feature Recognition Using Convex Decomposition

1994 ◽  
Author(s):  
Sreekumar Menon ◽  
Yong Se Kim
Keyword(s):  
Feature Recognition ◽  
Boundary Representation ◽  
Recognition Method ◽  
Geometric Information ◽  
Polyhedral Model ◽  
Curved Boundary ◽  
Convex Decomposition ◽  
Geometric Objects ◽  
Form Features ◽  
Form Feature

Abstract Form features intrinsic to the product shape can be recognized using a convex decomposition called Alternating Sum of Volumes with Partitioning (ASVP). However, the domain of geometric objects to which ASVP decomposition can be applied had been limited to polyhedral solids due to the difficulty of convex hull construction for solids with curved boundary faces. We develop an approach to extend the geometric domain to solids having cylindrical and blending features. Blending surfaces are identified and removed from the boundary representation of the solid, and a polyhedral model of the unblended solid is generated while storing the cylindrical geometric information. From the ASVP decomposition of the polyhedral model, polyhedral form features are recognized. Form feature decomposition of the original solid is then obtained by reattaching the stored blending and cylindrical information to the form feature components of its polyhedral model. In this way, a larger domain of solids can be covered by the feature recognition method using ASVP decomposition. In this paper, handling of blending features in this approach is described.

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