Systems and methods for geometric calibration and image reconstruction in computed tomography (CT) scanning using iterative reconstruction algorithms are provided. An iterative reconstruction algorithm can be used to reconstruct an improved image, and then the improved image can be used to adjust inaccurate parameters by using a Locally Linear Embedding (LLE) method. Adjusted parameters can then be used to reconstruct new images, which can then be used to further adjust the parameters. The steps of this iterative process can be repeated until a quality threshold is met.

Textual content is analyzed to determine a tone of the content. A first color palette including a first plurality of colors is computed. The first plurality of colors corresponds to the tone. A first color of the first plurality of colors is selected, and a second color palette including a second plurality of colors is computed. The second plurality of colors corresponds to the first plurality of colors and satisfies a predetermined color-related accessibility requirement between the first selected color and each of the second plurality of colors. A second color of the second plurality of colors is selected, and the content is modified using the first selected color and the second selected color.

Embodiments relate a keypoint detection circuit for identifying keypoints in captured image frames. The keypoint detection circuit generates an image pyramid based upon a received image frame, and determine multiple sets of keypoints for each octave of the pyramid using different levels of blur. In some embodiments, the keypoint detection circuit includes multiple branches, each branch made up of one or more circuits for determining a different set of keypoints from the image, or for determining a subsampled image for a subsequent octave of the pyramid. By determining multiple sets of keypoints for each of a plurality of pyramid octaves, a larger, more varied set of keypoints can be obtained and used for object detection and matching between images.

An electronic circuitry of a spectrometer, configured to electrically connect with an optical sensor of the spectrometer, includes a memory unit configured to store a measurement setting, a trigger line configured to transmit at least one trigger signal, and a control unit electrically connected to the trigger line and the memory unit. The control unit is configured to receive the trigger signal from the trigger line so as to instruct the spectrometer to perform a plurality of exposure measurements continuously under the measurement setting, and to save a plurality of spectral data acquired from the exposure measurements into the memory unit. A spectrometer using the electronic circuitry for performing the exposure measurements and a measuring method of the spectrometer are also provided.

A method for calculating and reporting image quality properties of an image acquisition device after a subject or object has been scanned consists of a scan quality monitoring system with automated software for receiving scans and radiation dose data from scanners and automated algorithms for analyzing image quality metrics and radiation dose tradeoffs. Image quality assessment methods include algorithms for measuring fundamental imaging characteristics, level and type of image artifacts, and comparisons against large databases of historical data for the scanner and protocols. Image quality reports are further customized to report on expected clinical performance of image detection or measurement tasks.

The invention pertains to methods for monitoring the operational status of a home automation system through extrinsic visual and audible means. Initial training periods involve capturing image and audio data representative of nominal operation, which is then processed to identify operational indicators. Unsupervised machine learning models are trained with these indicators to construct a model of normalcy and identify expectation violations in the system's operational pattern. After meeting specific stopping criteria, real-time monitoring is initiated. When an expectation violation is detected, contrastive collages or sequences are generated comprising nominal and anomalous data. These are then transmitted to an end user, effectively conveying the context of the detected anomalies. Further features include providing deep links to smartphone applications for home automation configuration and the use of auditory scene analysis techniques. The invention provides a multi-modal approach to home automation monitoring, leveraging machine learning for robust anomaly detection.

The invention pertains to methods for monitoring the operational status of a home automation system through extrinsic visual and audible means. Initial training periods involve capturing image and audio data representative of nominal operation, which is then processed to identify operational indicators. Unsupervised machine learning models are trained with these indicators to construct a model of normalcy and identify expectation violations in the system's operational pattern. After meeting specific stopping criteria, real-time monitoring is initiated. When an expectation violation is detected, contrastive collages or sequences are generated comprising nominal and anomalous data. These are then transmitted to an end user, effectively conveying the context of the detected anomalies. Further features include providing deep links to smartphone applications for home automation configuration and the use of auditory scene analysis techniques. The invention provides a multi-modal approach to home automation monitoring, leveraging machine learning for robust anomaly detection.

The present disclosure provides a method for target detection, including: obtaining an image captured by a camera, and zoning the image based on a mounting angle of the camera to obtain at least one image block; determining target-detection algorithms for the at least one image block based on positions of the at least one image block in the image; and performing target detection on the at least one image block based on the target-detection algorithms.

The invention pertains to methods for monitoring the operational status of a home automation system through extrinsic visual and audible means. Initial training periods involve capturing image and audio data representative of nominal operation, which is then processed to identify operational indicators. Unsupervised machine learning models are trained with these indicators to construct a model of normalcy and identify expectation violations in the system's operational pattern. After meeting specific stopping criteria, real-time monitoring is initiated. When an expectation violation is detected, contrastive collages or sequences are generated comprising nominal and anomalous data. These are then transmitted to an end user, effectively conveying the context of the detected anomalies. Further features include providing deep links to smartphone applications for home automation configuration and the use of auditory scene analysis techniques. The invention provides a multi-modal approach to home automation monitoring, leveraging machine learning for robust anomaly detection.

A method is provided. The method includes activating an anti-shake mode for an electronic display. The method includes, in response to activating the anti-shake mode, identifying movement of a user relative to the electronic display at least based on processing of a plurality of captured images of at least part of the user, and transforming an image displayed on the electronic display based on the identified movement of the user. A computing device is also provided.