Classification of bone pathologies with finite discrete shearlet transform based shape descriptors

2015 ◽  
Author(s):  
Aysun Sezer ◽  
Hasan Basri Sezer ◽  
Songul Albayrak
Keyword(s):  
Shape Descriptors ◽  
Shearlet Transform

scholarly journals Shape Descriptors for Classification of Functional Data

Technometrics ◽  
2008 ◽  
Vol 50 (3) ◽  
pp. 284-294 ◽  
Author(s):  
Irene Epifanio
Keyword(s):  
Functional Data ◽  
Shape Descriptors

scholarly journals Detection and Classification of Haze Affected Images Using CNN Approach

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Senthil Kumar K Pa, Et. al.
Keyword(s):  
Data Transmission ◽  
Pattern Detection ◽  
Multimedia Data ◽  
Video Sequences ◽  
Classification Approach ◽  
Shearlet Transform ◽  
Multimedia Data Transmission ◽  
Indoor And Outdoor

Detection and classifications of the haze affected image is important for the real time multimedia data transmission and reception in remote mode in order to improve the quality of the received image or video sequences. In this paper, Convolutional Neural Networks (CNN) classification approach is used with Shearlet Transform for the detection and segmentation of haze affected images.The image to be tested for haze pattern detection is preprocessed and then it is decomposed with shearlet transform. The features are computed from the shearlet transform decomposed coefficients and then these computed features are classified by the deep learning CNN for identifying the haze affected images. This proposed haze classification method is tested on both indoor and outdoor environmental images.

scholarly journals Stamps Detection and Classification Using Simple Features Ensemble

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Paweł Forczmański ◽  
Andrzej Markiewicz
Keyword(s):  
Color Space ◽  
Shape Descriptors ◽  
Translation Invariant ◽  
Space Transformation ◽  
Rubber Stamp ◽  
Color Space Transformation ◽  
Extraction Stage ◽  
Simple Features ◽  
Scanned Documents

The paper addresses a problem of detection and classification of rubber stamp instances in scanned documents. A variety of methods from the field of image processing, pattern recognition, and some heuristic are utilized. Presented method works on typical stamps of different colors and shapes. For color images, color space transformation is applied in order to find potential color stamps. Monochrome stamps are detected through shape specific algorithms. Following feature extraction stage, identified candidates are subjected to classification task using a set of shape descriptors. Selected elementary properties form an ensemble of features which is rotation, scale, and translation invariant; hence this approach is document size and orientation independent. We perform two-tier classification in order to discriminate between stamps and no-stamps and then classify stamps in terms of their shape. The experiments carried out on a considerable set of real documents gathered from the Internet showed high potential of the proposed method.

Automated Radiographic Diagnosis via Feature Extraction and Classification of Cardiac Size and Shape Descriptors

1972 ◽  
Vol BME-19 (3) ◽  
pp. 174-186 ◽  
Author(s):  
Richard P. Kruger ◽  
James R. Townes ◽  
David Lee Hall ◽  
Samuel J. Dwyer ◽  
Gwilym S. Lodwick
Keyword(s):  
Feature Extraction ◽  
Shape Descriptors ◽  
Radiographic Diagnosis ◽  
Size And Shape

scholarly journals OBJECT CLASSIFICATION VIA PLANAR ABSTRACTION

2016 ◽  
Vol III-3 ◽  
pp. 225-231 ◽  
Author(s):  
Sven Oesau ◽  
Florent Lafarge ◽  
Pierre Alliez
Keyword(s):  
Main Idea ◽  
Classification Problem ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Shape Descriptors ◽  
Machine Learning Approach ◽  
Indoor Scenes ◽  
Planar Shapes ◽  
Point Data

We present a supervised machine learning approach for classification of objects from sampled point data. The main idea consists in first abstracting the input object into planar parts at several scales, then discriminate between the different classes of objects solely through features derived from these planar shapes. Abstracting into planar shapes provides a means to both reduce the computational complexity and improve robustness to defects inherent to the acquisition process. Measuring statistical properties and relationships between planar shapes offers invariance to scale and orientation. A random forest is then used for solving the multiclass classification problem. We demonstrate the potential of our approach on a set of indoor objects from the Princeton shape benchmark and on objects acquired from indoor scenes and compare the performance of our method with other point-based shape descriptors.

Using Global Shape Descriptors for Content Medical-Based Image Retrieval

2013 ◽  
pp. 492-502
Author(s):  
Saïd Mahmoudi ◽  
Mohammed Benjelloun
Keyword(s):  
Fourier Transform ◽  
Image Retrieval ◽  
Real Time ◽  
Search Engine ◽  
Shape Descriptors ◽  
Query Image ◽  
Global Shape ◽  
Aided Diagnosis ◽  
Preliminary Classification

In this chapter, the authors propose a new method belonging to content medical-based image retrieval approaches and that uses a set of region-based shape descriptors. The search engine discussed in this work allows the classification of newly acquired medical images into some well known categories and also to get the images that are more similar to a query image. The final goal is to help the medical staff to annotate these images. To achieve this task, the authors propose a set of three descriptors that are based on: (1) Hu, (2) Zernike moments, and (3) Fourier transform-based signature, which are considered as region descriptors. The advantage of using this kind of global descriptor is that they are very fast, real time, and they do not need any segmentation step. The authors propose also a comparative study between these three approaches. The search engines are tested by using a database composed of 75 images that have different sizes, and that are classified into five classes. The results provided by the proposed retrieval approaches are given with high precision. The comparison between the three approaches is achieved using classification matrices and the recall/precision curves. The three proposed retrieval approaches produce accurate results in real time. This proves the advantage of using global shape features as a preliminary classification step in an automated aided diagnosis system.

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