The storage of AI classified pathology image data and their exchange among multiple ends is one of the major sources of attention in the health care domain.Their cost (both maintenance and deployment) and storage capabilities raise a concern for information security shared over a network.We propose Ethereum blockchain distribution on IPFS, a decentralized network which is the best suitable for our domain.It works fuel induces phenomenon to provide privilege of sharing information and to enter as a block in the chain, an outsider must be authorized with an encrypted key.This makes the system private, filtering out any outsider who is not part of the hierarchy.The Ethereum blockchain is deployed on IPFS (Inter-Planetary File System) which works as a data center for all the users.Every task performed in the system or modifications to the data and every step involved is securely stored in IPFS to be used in the future.Images registered in the system are trained through transfer learning approach by applying ResNet34 deep learning.Ethereum blockchain and IPFS perfectly work together for our large file sizes distribution system as there is need for efficiently working biomedical image sharing system.
This paper presents a novel approach to estimate 3D head pose dynamically from a sequence of input images. The exact head pose estimation and facial motion tracking are critical problems to be solved in developing a vision based human computer interaction system. Given an initial reference template of head image and corresponding head pose, the full head motion is recovered by using a cylindrical head model. By updating the template dynamically in order to accommodate gradual changes in lighting, it is possible to recover head pose robustly regardless of light variation and self-occlusion. For this, we adopt optical flow along with iteratively re-weighted least square technique. From the experiments, we can show the proposed approach efficiently estimate 3D head pose
This paper presents a novel approach to estimate 3D head pose dynamically from a sequence of input images. The exact head pose estimation and facial motion tracking are critical problems to be solved in developing a vision based human computer interaction system. Given an initial reference template of head image and corresponding head pose, the full head motion is recovered by using a cylindrical head model. By updating the template dynamically in order to accommodate gradual changes in lighting, it is possible to recover head pose robustly regardless of light variation and self-occlusion. For this, we adopt optical flow along with iteratively re-weighted least square technique. From the experiments, we can show the proposed approach efficiently estimate 3D head pose.
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