Embedding 3D Geometric Features for Rigid Object Part Segmentation

2017 ◽  
Author(s):  
Yafei Song ◽  
Xiaowu Chen ◽  
Jia Li ◽  
Qinping Zhao
Keyword(s):  
Geometric Features ◽  
Rigid Object ◽  
Object Part ◽  
Part Segmentation

scholarly journals AUTOMATED PART DECOMPOSITION FOR PRODUCT ARCHITECTURE MODELING

2020 ◽  
Vol 1 ◽  
pp. 2365-2374
Author(s):  
J. Redeker ◽  
P. Gebhardt ◽  
A.-K. Reichler ◽  
E. Türck ◽  
K. Dröder ◽  
...  
Keyword(s):  
Product Architecture ◽  
Hybrid Manufacturing ◽  
Geometric Features ◽  
The Novel ◽  
Product Model ◽  
Shape Segmentation ◽  
Part Segmentation ◽  
Part Decomposition ◽  
Architecture Modeling ◽  
Automatic Decomposition

AbstractThis paper presents an algorithm that contributes to an automatic decomposition of a mechanical part based on geometric features and methods of unsupervised machine learning. For the development of the algorithm, existing techniques of 3D shape segmentation, especially surface-based part segmentation procedures are reviewed and important areas of activities are revealed. The developed multi-step approach results in an abstract product model. This representation leads to a new way of designing and redesigning parts for the novel hybrid manufacturing concept Incremental Manufacturing (IM).

Pose-Guided Knowledge Transfer for Object Part Segmentation

2020 ◽  
Author(s):  
Shujon Naha ◽  
Qingyang Xiao ◽  
Prianka Banik ◽  
Md Alimoor Reza ◽  
David J. Crandall
Keyword(s):  
Knowledge Transfer ◽  
Object Part ◽  
Part Segmentation

scholarly journals Machine learning methods for 3D object classification and segmentation

2018 ◽  
Author(s):  
◽  
Truc Duc Le
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Fundamental Problem ◽  
Regular Structure ◽  
Learning Methods ◽  
3D Object ◽  
Data Resolution ◽  
Object Part ◽  
Part Segmentation ◽  
Art Performance

Object understanding is a fundamental problem in computer vision and it has been extensively researched in recent years thanks to the availability of powerful GPUs and labelled data, especially in the context of images. However, 3D object understanding is still not on par with its 2D domain and deep learning for 3D has not been fully explored yet. In this dissertation, I work on two approaches, both of which advances the state-of-the-art results in 3D classification and segmentation. The first approach, called MVRNN, is based multi-view paradigm. In contrast to MVCNN which does not generate consistent result across different views, by treating the multi-view images as a temporal sequence, our MVRNN correlates the features and generates coherent segmentation across different views. MVRNN demonstrated state-of-the-art performance on the Princeton Segmentation Benchmark dataset. The second approach, called PointGrid, is a hybrid method which combines points and regular grid structure. 3D points can retain fine details but irregular, which is challenge for deep learning methods. Volumetric grid is simple and has regular structure, but does not scale well with data resolution. Our PointGrid, which is simple, allows the fine details to be consumed by normal convolutions under a coarser resolution grid. PointGrid achieved state-of-the-art performance on ModelNet40 and ShapeNet datasets in 3D classification and object part segmentation.

Face Completion Using Semantic Segmentation and Geometric Features

2018 ◽  
Vol 11 (6) ◽  
pp. 304
Author(s):  
Javier Pinzon-Arenas ◽  
Robinson Jimenez-Moreno ◽  
Ruben Hernandez-Beleno
Keyword(s):  
Semantic Segmentation ◽  
Geometric Features

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

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