A computer implemented method of decoding a signal. The method includes receiving a signal (which may be an electromagnetic signal), sampling the received signal to generate an input waveform having magnitude and phase components, applying a transform operation to the input waveform to generate a first decoded signal, and outputting the first decoded signal. The transform operation includes pre-processing the input waveform to generate a mirrored inverted waveform and applying a continuous wavelet transform to the mirrored inverted waveform to generate the first decoded signal. This allows inversion of the frequency and temporal resolution of the continuous wavelet transform, thereby enabling improved temporal and frequency decoding of a signal. The method is particularly suitable for signal filters and filtering units.

An inspection apparatus (100) detects an inspection object (90) from first image data (10) in which the inspection object (90) is included. The inspection apparatus (100) generates second image data (20) by performing a geometric transform on the first image data (10) in such a way that a view of the detected inspection object (90) becomes a view satisfying a predetermined reference. In an inference phase, the inspection apparatus (100) detects, by using an identification model for detecting an abnormality of the inspection object (90), an abnormality of the inspection object (90) included in the second image data (20). Further, in a learning phase, the inspection apparatus (100) learns, by using the second image data (20), an identification model for detecting an abnormality of the inspection object (90).

An inspection apparatus (100) detects an inspection object (90) from first image data (10) in which the inspection object (90) is included. The inspection apparatus (100) generates second image data (20) by performing a geometric transform on the first image data (10) in such a way that a view of the detected inspection object (90) becomes a view satisfying a predetermined reference. In an inference phase, the inspection apparatus (100) detects, by using an identification model for detecting an abnormality of the inspection object (90), an abnormality of the inspection object (90) included in the second image data (20). Further, in a learning phase, the inspection apparatus (100) learns, by using the second image data (20), an identification model for detecting an abnormality of the inspection object (90).

A multi scale material segmentation method is provided that creates markers to identify unique particles, for small and large particles independently, and then separately processes those markers.

A multi scale material segmentation method is provided that creates markers to identify unique particles, for small and large particles independently, and then separately processes those markers.

An image processing apparatus and method are provided. The image processing apparatus acquires a target image including a depth image of a scene, determines three-dimensional (3D) point cloud data corresponding to the depth image based on the depth image, and extracts an object included in the scene to acquire an object extraction result based on the 3D point cloud data.

An image processing apparatus and method are provided. The image processing apparatus acquires a target image including a depth image of a scene, determines three-dimensional (3D) point cloud data corresponding to the depth image based on the depth image, and extracts an object included in the scene to acquire an object extraction result based on the 3D point cloud data.

The present disclosure relates to a method and apparatus for automatic head and neck organ segmentation. The method includes: receiving 3D images obtained by a CT system or an MRI system; processing the 3D images to transform to patient coordinate system and have the same spatial resolution and matrix size; building a deep learning framework using CNN models for organ segmentation; doubling the size of training dataset by emulating uncollected training data with mirrored images and their corresponding labels; improving the performance by predicting on original and mirrored images and averaging the output probabilities; and post-processing the output from the deep learning framework to obtain final organ segmentation.

The present disclosure relates to a method and apparatus for automatic head and neck organ segmentation. The method includes: receiving 3D images obtained by a CT system or an MRI system; processing the 3D images to transform to patient coordinate system and have the same spatial resolution and matrix size; building a deep learning framework using CNN models for organ segmentation; doubling the size of training dataset by emulating uncollected training data with mirrored images and their corresponding labels; improving the performance by predicting on original and mirrored images and averaging the output probabilities; and post-processing the output from the deep learning framework to obtain final organ segmentation.

Methods, devices, and systems associated with identifying data to transform are described. A method can include receiving, at a model stored on a computing device, data comprising a number of images, receiving, at the model, an input from a user, identifying, via the model, a number of attributes based on the input from the user, and identifying, via the model, a portion of an image of the number of images including at least one of the number of attributes to transform.