摘要:
There is provided an image processing device including a movement section which scrolls a medical image on a screen, and a display control section which, in a case where the medical image is scrolled on the screen, controls a display section to display the medical image in a manner that an observation reference position of a diagnosis region of the medical image passes through a display reference position of a display region of the screen.
摘要:
There is provided an image processing device including a movement section which scrolls a medical image on a screen, and a display control section which, in a case where the medical image is scrolled on the screen, controls a display section to display the medical image in a manner that an observation reference position of a diagnosis region of the medical image passes through a display reference position of a display region of the screen.
摘要:
An information processing device includes: a recognizer configured to recognize a predetermined part of a body of a person from an input image including the person; an evaluator configured to evaluate a difference between a recognized input part and a reference part serving as a basis; and a notifying unit configured to notify information relating to the difference of the input part from the reference part based on an evaluation result.
摘要:
An information processing device includes: a recognizer configured to recognize a predetermined part of a body of a person from an input image including the person; an evaluator configured to evaluate a difference between a recognized input part and a reference part serving as a basis; and a notifying unit configured to notify information relating to the difference of the input part from the reference part based on an evaluation result.
摘要:
Methods and systems disclosed herein provide the capability to automatically process digital pathology images quickly and accurately. According to one embodiment, an digital pathology image segmentation task may be divided into at least two parts. An image segmentation task may be carried out utilizing both bottom-up analysis to capture local definition of features and top-down analysis to use global information to eliminate false positives. In some embodiments, an image segmentation task is carried out using a “pseudo-bootstrapping” iterative technique to produce superior segmentation results. In some embodiments, the superior segmentation results produced by the pseudo-bootstrapping method are used as input in a second segmentation task that uses a combination of bottom-up and top-down analysis.
摘要:
An integrated interactive segmentation with spatial constraint method utilizes a combination of several of the most popular online learning algorithms into one and implements a spatial constraint which defines a valid mask local to the user's given marks. Additionally, both supervised learning and statistical analysis are integrated, which are able to compensate each other. Once prediction and activation are obtained, pixel-wised multiplication is conducted to fully indicate how likely each pixel belongs to the foreground or background.
摘要:
Systems and methods for implementing a multi-step image recognition framework for classifying digital images are provided. The provided multi-step image recognition framework utilizes a gradual approach to model training and image classification tasks requiring multi-dimensional ground truths. A first step of the multi-step image recognition framework differentiates a first image region from a remainder image region. Each subsequent step operates on a remainder image region from the previous step. The provided multi-step image recognition framework permits model training and image classification tasks to be performed more accurately and in a less resource intensive fashion than conventional single-step image recognition frameworks.
摘要:
A robot apparatus (1) includes leg blocks (3A to 3D), head block (4), etc. as a moving part (16), a motion controller (102), learning unit (103), prediction unit (104) and a drive unit (105). When the moving part (106), any of the blocks, is operated from outside, the learning unit (103) learns a time-series signal generated due to the external operation. The motion controller (102) and drive unit (105) control together the moving part (106) based on a signal generated at the moving part (106) due to an external force applied to the robot apparatus (1) and a signal having already been learned by the learning unit (103) to make an action taught by the user. The prediction unit (105) predicts whether the moving part (106) makes the taught action according to the initial signal generated at the moving part (106) due to the applied external force. Thus, the robot apparatus (1) can learn an action taught by the user and determine an external force-caused signal to make the taught action.
摘要:
Systems and methods for implementing a superpixel boosted top-down image recognition framework are provided. The framework utilizes superpixels comprising contiguous pixel regions sharing similar characteristics. Feature extraction methods described herein provide non-redundant image feature vectors for classification model building. The provided framework differentiates a digitized image into a plurality of superpixels. The digitized image is characterized through image feature extraction methods based on the plurality of superpixels. Image classification models are generated from the extracted image features and ground truth labels and may then be used to classify other digitized images.
摘要:
Systems and methods for implementing a multi-label image recognition framework for classifying digital images are provided. The provided multi-label image recognition framework utilizes an iterative, multiple analysis path approach to model training and image classification tasks. A first iteration of the multi-label image recognition framework generates confidence maps for each label, which are shared by the multiple analysis paths to update the confidence maps in subsequent iterations. The provided multi-label image recognition framework permits model training and image classification tasks to be performed more accurately than conventional single-label image recognition frameworks.