Automatic skull-face overlay and mandible articulation in data science by AIRS-Genetic algorithm

Yuvaraj, N.; Kousik, N.V.; Raja, R. Arshath; Saravanan, M.

doi:10.1016/j.ijin.2020.05.003

Cited by 22 publications

(5 citation statements)

References 14 publications

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“…While craniofacial superimposition has previously been described as a straightforward, long-tested, and wellaccepted procedure that is accurate [3, 7-9, 40, 41], the method is in reality a much more finicky and technical undertaking that at present lacks key validation components. While several technological advances have provided substantial prior method improvements, see e.g., [3,9,11,32,[62][63][64][65][66][67], technology alone has not resolved the full suite of superimposition's underlying weaknesses. Perspective distortion is one such integral issue that (1) has long been recognized in the superimposition domain [12,42,[46][47][48],…”

Section: Discussionmentioning

confidence: 99%

“…(2) has been deemed too difficult to resolve [12,47,48], and (3) applies to all superimposition approaches whether they be traditional video approaches using real skulls [3,11,[62][63][64] or newer fully computerized approaches that use surface meshes of skulls [32,[65][66][67]. This paper provides the first comprehensive solution to the perspective distortion conundrum by extending the PerspectiveX algorithm to both frontal and profile views.…”

Section: Discussionmentioning

confidence: 99%

“…While several technological advances have provided substantial prior method improvements, see e.g., [ 3 , 9 , 11 , 32 , 62 – 67 ], technology alone has not resolved the full suite of superimposition’s underlying weaknesses. Perspective distortion is one such integral issue that (1) has long been recognized in the superimposition domain [ 12 , 42 , 46 – 48 ], (2) has been deemed too difficult to resolve [ 12 , 47 , 48 ], and (3) applies to all superimposition approaches whether they be traditional video approaches using real skulls [ 3 , 11 , 62 – 64 ] or newer fully computerized approaches that use surface meshes of skulls [ 32 , 65 – 67 ]. This paper provides the first comprehensive solution to the perspective distortion conundrum by extending the PerspectiveX algorithm to both frontal and profile views.…”

Section: Discussionmentioning

confidence: 99%

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Craniofacial superimposition: a review of focus distance estimation methods and an extension to profile view photographs

et al. 2022

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Craniofacial superimposition concerns the photographic overlay of skulls and faces, for skeletal identification. As a phased method that depends on photographic optics first and anatomical comparisons second, superimposition is strongly underpinned by the physics of light travel through glass lenses. So that the downstream (and dependent) anatomical evaluations are not thwarted or erroneous identification decisions risked, it is critical that the optical prerequisites for valid image comparisons are met. As focus distance sets the perspective, the focus distance used for skull photography must be matched to that used at face photography, so that anatomically comparable 1:1 images are obtained. In this paper, we review the pertinent camera optics that set these nonnegotiable fundamentals and review a recently proposed method for focus distance estimation. We go beyond the original method descriptions to explain the mathematical justification for the PerspectiveX algorithm and provide an extension to profile images. This enables the first scientifically grounded use of profile view (or partial profile view) photographs in craniofacial superimposition. Proof of concept is provided by multiple worked examples of the focus distance estimation for frontal and profile view images of three of the authors at known focus distances. This innovation (1) removes longstanding trial-and-error components of present-day superimposition methods, (2) provides the first systematic and complete optical basis for image comparison in craniofacial superimposition, and (3) will enable anatomical comparison standards to be established from a valid grassroots basis where complexities of camera vantage point are removed as interfering factors.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Craniofacial superimposition: a review of focus distance estimation methods and an extension to profile view photographs

et al. 2022

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“…The results showed that AC B-spline approaches are more robust, showing the highest accuracy index of 0.803. Yuvaraj et al (2020): An automatic skull-face overlay and mandible articulation using an AIRS-Genetic algorithm was performed using the Artificial Immune Recognition System (AIRS) model to calculate distances and obtain crisp points. The landmarks on the skull could be identified better.…”

Section: Computer-assisted Video Craniofacial Superimpositionmentioning

confidence: 99%

Development of Craniofacial Superimposition: A Review

et al. 2022

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Craniofacial superimposition is a method for identifying individuals by using secondary data in order to identify a target group of persons before a DNA process can be used, or to identify an individual instead of using primary data in cases where DNA, fingerprint or dental records are not found. Craniofacial superimposition has continued to evolve, with various techniques, including computer-assisted and photography techniques, to help the operation be more convenient, faster and reliable. The knowledge of forensic anthropology is applied, with a comparison between anatomical landmarks. The study of developments in craniofacial superimposition using computer-assistance has yielded satisfactory results.

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“…Our strategy is detailed based on patients with diabetes, and their associated parameters are not the only factors that may be analysed by our system. Therefore, we have concentrated on employing a variety of machine learning approaches to classify this data in order to save time [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Artificial Neural Network to Predict Diabetes with High-Quality Health System

Prakash¹,

Srihari²,

Karthik³

et al. 2022

Computational Intelligence and Neuroscience

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Patients with diabetes who are closely monitored have a higher overall quality of life than those who are not. Costs associated with healthcare can be decreased by utilising the Internet of Things (IoT), thanks to technological advancements. To satisfy the expectations of e-health applications, it is required for the development of the intelligent systems as well as increases the number of applications that are connected to the network. As a result, in order to achieve these goals, the cellular network should be capable of supporting intelligent healthcare applications that require high energy efficiency. In this paper, we model a neural network-based ensemble voting classifier to predict accurately the diabetes in the patients via online monitoring. The study consists of Internet of Things (IoT) devices to monitor the instances of the patients. While monitoring, the data are transferred from IoT devices to smartphones and then to the cloud, where the process of classification takes place. The simulation is conducted on the collected samples using the python tool. The results of the simulation show that the proposed method achieves a higher accuracy rate, higher precision, recall, and f-measure than existing state-of-art ensemble models.

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Automatic skull-face overlay and mandible articulation in data science by AIRS-Genetic algorithm

Cited by 22 publications

References 14 publications

Craniofacial superimposition: a review of focus distance estimation methods and an extension to profile view photographs

Craniofacial superimposition: a review of focus distance estimation methods and an extension to profile view photographs

Development of Craniofacial Superimposition: A Review

Implementation of Artificial Neural Network to Predict Diabetes with High-Quality Health System

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