This machine-learning device is provided with: a detection unit which detects a loss of consistency with a lapse of time in a determination result for unit data, the determination result being output from a determination unit that generates a learning model to be used when performing prescribed determination for one or more pieces of the unit data that form time series data; and a selection unit which selects, on the basis of the result of detection by the detection unit, unit data to be used as teacher data when the determination unit updates the learning model, thereby efficiently raising the accuracy of the learning model when machine learning is performed on the basis of the time series data.

Systems and methods are disclosed that improve output quality of any neural network, particularly an image generative neural network. In the real world, details of different scale tend to transform hierarchically. For example, moving a person's head causes the nose to move, which in turn moves the skin pores on the nose. Conventional generative neural networks do not synthesize images in a natural hierarchical manner: the coarse features seem to mainly control the presence of finer features, but not the precise positions of the finer features. Instead, much of the fine detail appears to be fixed to pixel coordinates which is a manifestation of aliasing. Aliasing breaks the illusion of a solid and coherent object moving in space. A generative neural network with reduced aliasing provides an architecture that exhibits a more natural transformation hierarchy, where the exact sub-pixel position of each feature is inherited from underlying coarse features.

Systems and methods are disclosed that improve output quality of any neural network, particularly an image generative neural network. In the real world, details of different scale tend to transform hierarchically. For example, moving a person's head causes the nose to move, which in turn moves the skin pores on the nose. Conventional generative neural networks do not synthesize images in a natural hierarchical manner: the coarse features seem to mainly control the presence of finer features, but not the precise positions of the finer features. Instead, much of the fine detail appears to be fixed to pixel coordinates which is a manifestation of aliasing. Aliasing breaks the illusion of a solid and coherent object moving in space. A generative neural network with reduced aliasing provides an architecture that exhibits a more natural transformation hierarchy, where the exact sub-pixel position of each feature is inherited from underlying coarse features.

Disclosed is a medical imaging system, including a processor circuit configured for communication with an x-ray imaging device movable relative to a patient and an intravascular catheter or guidewire sized and shaped for positioning within a blood vessel of the patient, wherein the processor circuit is configured to receive a first angiographic image of a first length of the vessel and a second angiographic image of a second length of the vessel, wherein the first image is obtained at a first position and the second angiographic image is obtained at a second position. The processor is further configured to generate a roadmap image of a combined length of the blood vessel by combining the first image and the second image, and to receive intravascular data associated with the blood vessel, and to co-register the intravascular data to corresponding locations in the roadmap image; and output the roadmap image and a graphical representation of the intravascular data at the corresponding locations in the roadmap image.

Disclosed is a medical imaging system, including a processor circuit configured for communication with an x-ray imaging device movable relative to a patient and an intravascular catheter or guidewire sized and shaped for positioning within a blood vessel of the patient, wherein the processor circuit is configured to receive a first angiographic image of a first length of the vessel and a second angiographic image of a second length of the vessel, wherein the first image is obtained at a first position and the second angiographic image is obtained at a second position. The processor is further configured to generate a roadmap image of a combined length of the blood vessel by combining the first image and the second image, and to receive intravascular data associated with the blood vessel, and to co-register the intravascular data to corresponding locations in the roadmap image; and output the roadmap image and a graphical representation of the intravascular data at the corresponding locations in the roadmap image.

Disclosed is a transparency detecting method based on machine vision. The transparency detecting method includes 1) operating a Secchi disk to start the water transparency measurement, and turning on the camera for shooting; 2). determining a critical position of the Secchi disk; 3) identifying a water ruler and calculating a reading of the water ruler; 4) outputting and displaying the calculated reading.

Disclosed is a transparency detecting method based on machine vision. The transparency detecting method includes 1) operating a Secchi disk to start the water transparency measurement, and turning on the camera for shooting; 2). determining a critical position of the Secchi disk; 3) identifying a water ruler and calculating a reading of the water ruler; 4) outputting and displaying the calculated reading.

Systems and methods are disclosed to manipulate or normalize image of animals to a reference size and shape. Synthetically normalizing the image data to a reference size and shape allows machine learning models to automatically identify subject behaviors in a manner that is robust to changes in the size and shape of the subject. The systems and methods of the invention can be applied to drug or gene therapy classification, drug or gene therapy screening, disease study including early detection of the onset of a disease, toxicology research, side-effect study, learning and memory process study, anxiety study, and analysis in consumer behavior.

Systems and methods are disclosed to manipulate or normalize image of animals to a reference size and shape. Synthetically normalizing the image data to a reference size and shape allows machine learning models to automatically identify subject behaviors in a manner that is robust to changes in the size and shape of the subject. The systems and methods of the invention can be applied to drug or gene therapy classification, drug or gene therapy screening, disease study including early detection of the onset of a disease, toxicology research, side-effect study, learning and memory process study, anxiety study, and analysis in consumer behavior.

A detection system (10) includes: an acquisition unit (110) configured to acquire an image including a living body; and a detection unit (120) configured to detect, from the image, a feature figure corresponding to an appropriately circular first part on the living body, and feature points corresponding to a second part around the first part on the living body. According to such a detection system, the first part and the second part with different features in shape can be individually detected appropriately.