scholarly journals Automatic Classification of Decorative Patterns in the Minoan Pottery of Kamares Style

2013 ◽  
pp. 1132-1150
Author(s):  
Filippo Stanco ◽  
Davide Tanasi ◽  
Giuseppe Claudio Guarnera ◽  
Giovanni Gallo
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Cultural Production ◽  
Contour Detection ◽  
Automatic Identification ◽  
Second Phase ◽  
Different Shapes ◽  
Motif Extraction ◽  
High Level

An important feature of the Minoan culture is the pottery of Kamares style, that documents the Cretan cultural production between the first half of the 2nd millennium BC. This high level painted production, characterized by the combination of several diverse motifs, presents an enormous decorative repertoire. The extraordinary variety of combinations between elementary motifs according to a complex visual syntax makes interesting the automatic identification of the motifs, particularly upon potsherds. A complete pipeline to accomplish this task is still a challenge to Computer Vision and Pattern Recognition. Starting from a digital image ROI identification, motif extraction, robust contour detection should be performed to obtain a bag of digital shapes. In a second phase each of the extracted shapes has to be classified according to prototypes in a database produced by an expert. The co-occurrence of the different shapes in a specimen will, in turn, be used to help the archaeologists in the cultural and even chronological setting.

scholarly journals Automatic Classification of Decorative Patterns in the Minoan Pottery of Kamares Style

2011 ◽  
pp. 186-211
Author(s):  
Filippo Stanco ◽  
Davide Tanasi ◽  
Giuseppe Claudio Guarnera ◽  
Giovanni Gallo
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Cultural Production ◽  
Contour Detection ◽  
Automatic Identification ◽  
Second Phase ◽  
Different Shapes ◽  
Motif Extraction ◽  
High Level

An important feature of the Minoan culture is the pottery of Kamares style, that documents the Cretan cultural production between the first half of the 2nd millennium BC. This high level painted production, characterized by the combination of several diverse motifs, presents an enormous decorative repertoire. The extraordinary variety of combinations between elementary motifs according to a complex visual syntax makes interesting the automatic identification of the motifs, particularly upon potsherds. A complete pipeline to accomplish this task is still a challenge to Computer Vision and Pattern Recognition. Starting from a digital image ROI identification, motif extraction, robust contour detection should be performed to obtain a bag of digital shapes. In a second phase each of the extracted shapes has to be classified according to prototypes in a database produced by an expert. The co-occurrence of the different shapes in a specimen will, in turn, be used to help the archaeologists in the cultural and even chronological setting.

Application of computer vision in the automatic identification and classification of woven fabric weave patterns

2010 ◽  
Vol 80 (20) ◽  
pp. 2144-2157 ◽  
Author(s):  
Chung-Feng Jeffrey Kuo ◽  
Chung-Yang Shih ◽  
Cheng-En Ho ◽  
Kai-Ching Peng
Keyword(s):  
Computer Vision ◽  
Woven Fabric ◽  
Automatic Identification

Unsupervised and Supervised Image Segmentation Using Graph Partitioning

2012 ◽  
pp. 72-94
Author(s):  
Charles-Edmond Bichot
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Image Segmentation ◽  
Graph Partitioning ◽  
Research Area ◽  
Important Research ◽  
Pattern Recognition Methods ◽  
Segmentation Approach ◽  
Important Research Area ◽  
High Level

Image segmentation is an important research area in computer vision and its applications in different disciplines, such as medicine, are of great importance. It is often one of the very first steps of computer vision or pattern recognition methods. This is because segmentation helps to locate objects and boundaries into images. The objective of segmenting an image is to partition it into disjoint and homogeneous sets of pixels. When segmenting an image it is natural to try to use graph partitioning, because segmentation and partitioning share the same high-level objective, to partition a set into disjoints subsets. However, when using graph partitioning for segmenting an image, several big questions remain: What is the best way to convert an image into a graph? Or to convert image segmentation objectives into graph partitioning objectives (not to mention what are image segmentation objectives)? What are the best graph partitioning methods and algorithms for segmenting an image? In this chapter, the author tries to answer these questions, both for unsupervised and supervised image segmentation approach, by presenting methods and algorithms and by comparing them.

Syntactic Pattern Recognition in Computer Vision

2021 ◽  
Vol 54 (3) ◽  
pp. 1-35
Author(s):  
Gilberto Astolfi ◽  
Fábio Prestes Cesar Rezende ◽  
João Vitor De Andrade Porto ◽  
Edson Takashi Matsubara ◽  
Hemerson Pistori
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Literature Review ◽  
Level Structure ◽  
Research Community ◽  
Semantic Relationship ◽  
Structural Pattern ◽  
Structural Pattern Recognition ◽  
Syntactic Pattern ◽  
High Level

Using techniques derived from the syntactic methods for visual pattern recognition is not new and was much explored in the area called syntactical or structural pattern recognition. Syntactic methods have been useful because they are intuitively simple to understand and have transparent, interpretable, and elegant representations. Their capacity to represent patterns in a semantic, hierarchical, compositional, spatial, and temporal way have made them very popular in the research community. In this article, we try to give an overview of how syntactic methods have been employed for computer vision tasks. We conduct a systematic literature review to survey the most relevant studies that use syntactic methods for pattern recognition tasks in images and videos. Our search returned 597 papers, of which 71 papers were selected for analysis. The results indicated that in most of the studies surveyed, the syntactic methods were used as a high-level structure that makes the hierarchical or semantic relationship among objects or actions to perform the most diverse tasks.

scholarly journals Fisher Motion Descriptor for Multiview Gait Recognition

2017 ◽  
Vol 31 (01) ◽  
pp. 1756002 ◽  
Author(s):  
F.M. Castro ◽  
M.J. Marín-Jiménez ◽  
N.Guil Mata ◽  
R. Muñoz-Salinas
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Large Scale ◽  
State Of The Art ◽  
Gait Recognition ◽  
Local Motion ◽  
Motion Descriptor ◽  
Spatial Configurations ◽  
Motion Features ◽  
High Level

The goal of this paper is to identify individuals by analyzing their gait. Instead of using binary silhouettes as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based on densely sampled short-term trajectories. We take advantage of state-of-the-art people detectors to define custom spatial configurations of the descriptors around the target person, obtaining a rich representation of the gait motion. The local motion features (described by the Divergence-Curl-Shear descriptor [M. Jain, H. Jegou and P. Bouthemy, Better exploiting motion for better action recognition, in Proc. IEEE Conf. Computer Vision Pattern Recognition (CVPR) (2013), pp. 2555–2562.]) extracted on the different spatial areas of the person are combined into a single high-level gait descriptor by using the Fisher Vector encoding [F. Perronnin, J. Sánchez and T. Mensink, Improving the Fisher kernel for large-scale image classification, in Proc. European Conf. Computer Vision (ECCV) (2010), pp. 143–156]. The proposed approach, coined Pyramidal Fisher Motion, is experimentally validated on ‘CASIA’ dataset [S. Yu, D. Tan and T. Tan, A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition, in Proc. Int. Conf. Pattern Recognition, Vol. 4 (2006), pp. 441–444]. (parts B and C), ‘TUM GAID’ dataset, [M. Hofmann, J. Geiger, S. Bachmann, B. Schuller and G. Rigoll, The TUM Gait from Audio, Image and Depth (GAID) database: Multimodal recognition of subjects and traits, J. Vis. Commun. Image Represent. 25(1) (2014) 195–206]. ‘CMU MoBo’ dataset [R. Gross and J. Shi, The CMU Motion of Body (MoBo) database, Technical Report CMU-RI-TR-01-18, Robotics Institute (2001)]. and the recent ‘AVA Multiview Gait’ dataset [D. López-Fernández, F. Madrid-Cuevas, A. Carmona-Poyato, M. Marín-Jiménez and R. Muñoz-Salinas, The AVA multi-view dataset for gait recognition, in Activity Monitoring by Multiple Distributed Sensing, Lecture Notes in Computer Science (Springer, 2014), pp. 26–39]. The results show that this new approach achieves state-of-the-art results in the problem of gait recognition, allowing to recognize walking people from diverse viewpoints on single and multiple camera setups, wearing different clothes, carrying bags, walking at diverse speeds and not limited to straight walking paths.

Unsupervised and Supervised Image Segmentation Using Graph Partitioning

2013 ◽  
pp. 322-344
Author(s):  
Charles-Edmond Bichot
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Image Segmentation ◽  
Graph Partitioning ◽  
Research Area ◽  
Important Research ◽  
Pattern Recognition Methods ◽  
Segmentation Approach ◽  
Important Research Area ◽  
High Level

Image segmentation is an important research area in computer vision and its applications in different disciplines, such as medicine, are of great importance. It is often one of the very first steps of computer vision or pattern recognition methods. This is because segmentation helps to locate objects and boundaries into images. The objective of segmenting an image is to partition it into disjoint and homogeneous sets of pixels. When segmenting an image it is natural to try to use graph partitioning, because segmentation and partitioning share the same high-level objective, to partition a set into disjoints subsets. However, when using graph partitioning for segmenting an image, several big questions remain: What is the best way to convert an image into a graph? Or to convert image segmentation objectives into graph partitioning objectives (not to mention what are image segmentation objectives)? What are the best graph partitioning methods and algorithms for segmenting an image? In this chapter, the author tries to answer these questions, both for unsupervised and supervised image segmentation approach, by presenting methods and algorithms and by comparing them.

scholarly journals Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Guillaume Lemaître ◽  
Mojdeh Rastgoo ◽  
Joan Massich ◽  
Carol Y. Cheung ◽  
Tien Y. Wong ◽  
...  
Keyword(s):  
Vision Loss ◽  
Normal Subjects ◽  
Local Binary Patterns ◽  
Extensive Study ◽  
Automatic Identification ◽  
Spectral Domain Oct ◽  
Classification Framework ◽  
High Level ◽  
Sd Oct

This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with DME versus normal subjects. Optical Coherence Tomography (OCT) has been a valuable diagnostic tool for DME, which is among the most common causes of irreversible vision loss in individuals with diabetes. Here, a classification framework with five distinctive steps is proposed and we present an extensive study of each step. Our method considers combination of various preprocessing steps in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and nonlinear classifiers, we tested the developed framework on a balanced cohort of 32 patients. Experimental results show that the proposed method outperforms the previous studies by achieving a Sensitivity (SE) and a Specificity (SP) of 81.2% and 93.7%, respectively. Our study concludes that the 3D features and high-level representation of 2D features using patches achieve the best results. However, the effects of preprocessing are inconsistent with different classifiers and feature configurations.

Real-time classification of shoulder girdle motions for multifunctional prosthetic hand control: A preliminary study

2019 ◽  
Vol 42 (9) ◽  
pp. 508-515
Author(s):  
Ghaith Kadhim Sharba ◽  
Mousa Kadhim Wali ◽  
Ali Hussein AI-Timemy
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Upper Limb ◽  
Classification Accuracy ◽  
Shoulder Girdle ◽  
Recognition System ◽  
Pattern Recognition System ◽  
High Level ◽  
Real Time Classification

In every country in the world, there are a number of amputees who have been exposed to some accidents that led to the loss of their upper limbs. The aim of this study is to suggest a system for real-time classification of five classes of shoulder girdle motions for high-level upper limb amputees using a pattern recognition system. In the suggested system, the wavelet transform was utilized for feature extraction, and the extreme learning machine was used as a classifier. The system was tested on four intact-limbed subjects and one amputee, with eight channels involving five electromyography channels and three-axis accelerometer sensor. The study shows that the suggested pattern recognition system has the ability to classify the shoulder girdle motions for high-level upper limb motions with 88.4% average classification accuracy for four intact-limbed subjects and 92.8% classification accuracy for one amputee by combining electromyography and accelerometer channels. The outcomes of this study may suggest that the proposed pattern recognition system can help to provide control signals to drive a prosthetic arm for high-level upper limb amputees.

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