Feature recognition & tool path generation for 5 axis STEP-NC machining of free form / irregular contoured surfaces

This research paper presents a five step algorithm to generate tool paths for machining Free form / Irregular Contoured Surface(s) (FICS) by adopting STEP-NC (AP-238) format. In the first step, a parametrized CAD model with FICS is created or imported in UG-NX6.0 CAD package. The second step recognizes the features and calculates a Closeness Index (CI) by comparing them with the B-Splines / Bezier surfaces. The third step utilizes the CI and extracts the necessary data to formulate the blending functions for identified features. In the fourth step Z-level 5 axis tool paths are generated by adopting flat and ball end mill cutters. Finally, in the fifth step, tool paths are integrated with STEP-NC format and validated. All these steps are discussed and explained through a validated industrial component

Determination of machinable volume for finish cuts in CAPP

Identification of machinable volume for finish cut is a complex task as it involves the details not only of the final product but also the intermediate part obtained from rough machining of the blank. A feature recognition technique that adopts a rule-based methodology is required for calculating this small, complex shaped finish cut volume. This paper presents the feature recognition module in a CAPP system that calculates the intermediate finish cut volume by adopting a rule based syntactic pattern recognition approach. In this module, the interfacer uses STEP AP203/214, a CAD neutral format, to trace the coordinate point information and to calculate the machinable volume. Two illustrative examples are given to explain the proposed syntactic pattern approach for prismatic parts

Feature recognition in OCR text

This thesis investigates the recognition and extraction of special word sequences, representing concepts, from OCR text. Unlike general index terms, concepts can consist of one or more terms that combined, have higher retrieval value than the terms alone (i.e. acronyms, proper nouns, phrases). An algorithm to recognize acronyms and their definitions will be presented. An evaluation of the algorithm will also be presented

Geometric and form feature recognition tools applied to a design for assembly methodology

The paper presents geometric tools for an automated Design for Assembly (DFA) assessment system. For each component in an assembly a two step features search is performed: firstly (using the minimal bounding box) mass, dimensions and symmetries are identified allowing the part to be classified, according to DFA convention, as either rotational or prismatic; secondly form features are extracted allowing an effective method of mechanised orientation to be determined. Together these algorithms support the fuzzy decision support system, of an assembly-orientated CAD system known as FuzzyDFA

A characterization of visual feature recognition

technical reportNatural human interfaces are a key to realizing the dream of ubiquitous computing. This implies that embedded systems must be capable of sophisticated perception tasks. This paper analyzes the nature of a visual feature recognition workload. Visual feature recognition is a key component of a number of important applications, e.g. gesture based interfaces, lip tracking to augment speech recognition, smart cameras, automated surveillance systems, robotic vision, etc. Given the power sensitive nature of the embedded space and the natural conflict between low-power and high-performance implementations, a precise understanding of these algorithms is an important step developing efficient visual feature recognition applications for the embedded space. In particular, this work analyzes the performance characteristics of flesh toning, face detection and face recognition codes based on well known algorithms. We also show how the problem can be decomposed into a pipeline of filters that have efficient implementations as stream processors

Face Captioning Using Prominent Feature Recognition

Humans rely on prominent feature recognition to correctly identify and describe previously seen faces. Despite this fact, there is little existing work investigating how prominent facial features can be automatically recognized and used to create natural language face descriptions. Facial attribute prediction, a more commonly studied problem in computer vision, has previously been used for this task. However, the evaluation metrics and baseline models currently used to compare different attribute prediction methods are insufficient for determining which approaches are best at classifying highly imbalanced attributes. We also show that CelebA, the largest and most widely used facial attribute dataset, is too poorly labeled to be suitable for prominent feature recognition. To deal with these issues, we propose a method for generating weak prominent feature labels using semantic segmentation and show that we can use these labels to improve attribute-based face description

Design of a Feature Recognition System for CAD/CAM Integration

This paper presents a methodology for implementing the feature recognition system for achieving the Computer Aided Design/ Computer Aided Manufacturing (CAD/CAM) integration goals. The Featurebased modeling is being used to model the solid models. The features being considered in this paper is hole form feature. The input of the feature recognition system is the Standard for the Exchange of Product Model Data (STEP) files. The set of feature recognition rules is generated by using ruled based technique

Development of feature recognition system for CAD/CAM integration

This paper presents a methodology for implementing the feature recognition system for achieving the Computer Aided Design/ Computer Aided Manufacturing (CAD/CAM) integration goals. The Featurebased modeling is being used to model the solid models. The features being considered in this paper is hole form feature. The input of the feature recognition system is the Standard for the Exchange of Product Model Data (STEP) files. The set of feature recognition rules is generated by using ruled based technique

Ocean feature recognition using genetic algorithms with fuzzy fitness functions (GA/F3)

A model for genetic algorithms with semantic nets is derived for which the relationships between concepts is depicted as a semantic net. An organism represents the manner in which objects in a scene are attached to concepts in the net. Predicates between object pairs are continuous valued truth functions in the form of an inverse exponential function (e sub beta lxl). 1:n relationships are combined via the fuzzy OR (Max (...)). Finally, predicates between pairs of concepts are resolved by taking the average of the combined predicate values of the objects attached to the concept at the tail of the arc representing the predicate in the semantic net. The method is illustrated by applying it to the identification of oceanic features in the North Atlantic

Design Of Human Facial Feature Recognition System

Augmenting human computer interaction with automated analysis and synthesis of facial expressions is the goal towards which much research effort has been devoted to in the last few years. Facial feature recognition is one of the important aspects of natural human-machine interfaces; it has great applications such as in behavioral science, security systems and in clinical practice. Although humans recognize facial expressions virtually without effort or delay, reliable expression recognition by machine is still a challenging task. The face expression recognition problem is challenging because different individuals display the same expression differently. In this project we are trying to design a facial feature recognition system in real time using the concepts of Haar classifiers, contour concepts, template matching and studying some models related to it. We have tried to first extract face region from the video using above mentioned approach and had tried to extract some facial features and locate their position in the image