Geometrical Algorithms of Ear Contour Shape Representation and Feature Extraction

PurposeForensic dentistry is the application of dentistry in legal proceedings that arise from any facts relating to teeth. The ultimate goal of forensic odontology is to identify the individual when there are no other means of identification such as fingerprint, Deoxyribonucleic acid (DNA), iris, hand print and leg print. The purpose of selecting dental record is for the teeth to be able to withstand decomposition, heat degradation up to 1600 °C. Dental patterns are unique for every individual. This work aims to analyze the contour shape extraction and texture feature extraction of both radiographic and photographic dental images for person identification.Design/methodology/approachTo achieve an accurate identification of individuals, the missing tooth in the radiograph has to be identified before matching of ante-mortem (AM) and post-mortem (PM) radiographs. To identify whether the missing tooth is a molar or premolar, each tooth in the given radiograph has to be classified using a k-nearest neighbor (k-NN) classifier; then, it is matched with the universal tooth numbering system. In order to make exact person identification, this research work is mainly concentrate on contour shape extraction and texture feature extraction for person identification. This work aims to analyze the contour shape extraction and texture feature extraction of both radiographic and photographic images for individual identification. Then, shape matching of AM and PM images is performed by similarity and distance metric for accurate person identification.FindingsThe experimental results are analyzed for shape and feature extraction of both radiographic and photographic dental images. From this analysis, it is proved that the higher hit rate performance is observed for the active contour shape extraction model, and it is well suited for forensic odontologists to identify a person in mass disaster situations.Research limitations/implicationsForensic odontology is a branch of human identification that uses dental evidence to identify the victims. In mass disaster circumstances, contours and dental patterns are very useful to extract the shape in individual identification.Originality/valueThe experimental results are analyzed both the contour shape extraction and texture feature extraction of both radiographic and photographic images. From this analysis, it is proved that the higher hit rate performance is observed for the active contour shape extraction model and it is well suited for forensic odontologists to identify a person in mass disaster situations. The findings provide theoretical and practical implications for individual identification of both radiographic and photographic images with a view to accurate identification of the person.

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Haptic Display of Image Based on Multi-Feature Extraction

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001416550235 ◽

2016 ◽

Vol 30 (08) ◽

pp. 1655023 ◽

Cited By ~ 3

Author(s):

Lei Tian ◽

Aiguo Song ◽

Dapeng Chen ◽

Dejing Ni

Keyword(s):

Feature Extraction ◽

Virtual Environments ◽

Haptic Perception ◽

Differential Method ◽

Image Feature ◽

Surface Model ◽

Haptic Display ◽

Shape Extraction ◽

Contour Shape ◽

Texture Extraction

Image feature extraction is one of the key technologies of image haptic display. In this paper, multi-feature extraction method of the object in image is proposed to improve image-based haptic perception. The multi-feature extraction includes contour shape extraction, pattern extraction and detail texture extraction. Firstly, we use an intrinsic decomposition method to decompose an image into shading image and reflectance image. The reflectance image describes nonillumination affected color patterns spread on the surface. Then, the shading image is utilized in contour shape and detail texture extraction. Contour shape extraction is based on partial differential equation (PDE), to reconstruct three-dimensional (3D) surface model in virtual environments. Detailed texture extraction is based on fractional differential method simultaneously. Finally, the various features extracted above are haptic rendered by different methods. The experimental results show the effectiveness and potentiality of the proposed method for improving the ability of haptic perception and recognition of human in virtual environments.

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Local feature extraction of sheepskin based on structure contour shape description

MATEC Web of Conferences ◽

10.1051/matecconf/202133606026 ◽

2021 ◽

Vol 336 ◽

pp. 06026

Author(s):

Lianhua Hu ◽

Chengyi Xiang ◽

Feng Zhang

Keyword(s):

Computer Vision ◽

Feature Extraction ◽

Experimental Simulation ◽

Shape Description ◽

Contour Shape ◽

Edge Removal ◽

Starting Point ◽

Computer Vision Technology ◽

Local Feature Extraction ◽

Implementation Steps

Based on the precise sheepskin contour extracted by computer vision technology in the previous research of the team, this paper proposes the shape description technology based on the structure contour to extract the local features of the sheepskin, such as the head and hooves and the waste edge, which is the basis for the automatic edge removal of the sheepskin in the future. The algorithm uses Angle and position relation to segment the precise contour track of raw sheepskin into graph elements, and then uses geometric parameter shape description operator to describe and extract the edges that need to be removed, so as to obtain the starting point and end point of each local contour that needs to be removed. In this paper, the principle and implementation steps of this method are introduced in detail, and the experimental simulation verification shows that the extraction effect is good, which can meet the requirements of subsequent industrial production of automatic sheepskin cutting.

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An Improved Quantitative Image Analyser

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078663 ◽

1977 ◽

Vol 35 ◽

pp. 226-227

Author(s):

J.P. Fallon ◽

P.J. Gregory ◽

C.J. Taylor

Keyword(s):

Feature Extraction ◽

Quantitative Analysis ◽

Frequency Content ◽

General Sense ◽

Physical Measurements ◽

Quantitative Image ◽

Image Analyser ◽

Automatic Methods ◽

Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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