Techniques for quantifying accuracy of a prediction model that has been trained on a data set parameterized by multiple features are provided. The model performs in accordance with a theoretical performance manifold over an intractable input space in connection with the features. A determination is made as to which of the features are strongly correlated with performance of the model. Based on the features determined to be strongly correlated with performance of the model, parameterized sub-models are created such that, in aggregate, they approximate the intractable input space. Prototype exemplars are generated for each of the created sub-models, with the prototype exemplars for each created sub-model being objects to which the model can be applied to result in a match with the respective sub-model. The accuracy of the model is quantified using the generated prototype exemplars. A recommendation engine is provided for when there are particular areas of interest.

Techniques for recommending a prediction model from among a number of different prediction models are provided. Each of these prediction models has been trained based on a respective training data set, and each performs in accordance with a respective theoretical performance manifold. An indication of a region definable in relation to the theoretical performance manifolds of the different prediction models is received as input. For each of the different prediction models, the indication of the region is linked to features parameterizing the respective performance manifold; and one or more portions of the respective performance manifold is/are identified based on the features determined by the linking, the portion(s) having a volume and a shape that collectively denote an expected performance of the respective model for the input. The expected performance of the prediction models for the input is compared. Based on the comparison, one or more of the models is/are suggested.

Disclosed are methods, systems, and non-transitory computer-readable medium for color and pattern analysis of images including wearable items. For example, a method may include receiving an image depicting a wearable item, identifying the wearable item within the image by identifying a face of an individual wearing the wearable item or segmenting a foreground silhouette of the wearable item from background image portions of the image, determining a portion of the wearable item identified within the image as being a patch portion representative of the wearable item depicted within the image, deriving one or more patterns of the wearable item based on image analysis of the determined patch portion of the image, deriving one or more colors of the wearable item based on image analysis of the determined patch portion of the image, and transmitting information regarding the derived one or more colors and information regarding the derived one or more patterns.

We propose a neural network-based base caller that detects and accounts for stationary, kinetic, and mechanistic properties of the sequencing process, mapping what is observed at each sequence cycle in the assay data to the underlying sequence of nucleotides. The neural network-based base caller combines the tasks of feature engineering, dimension reduction, discretization, and kinetic modelling into a single end-to-end learning framework. In particular, the neural network-based base caller uses a combination of 3D convolutions, 1D convolutions, and pointwise convolutions to detect and account for assay biases such as phasing and prephasing effect, spatial crosstalk, emission overlap, and fading.

A first electronic device controls a second electronic device to measure a position of the first electronic device. The first electronic device includes a motion sensor, a network interface circuit, a processor, and a memory. The motion sensor senses motion of the first electronic device. The network interface circuit communicates with the second electronic device. The memory stores program code that is executed by the processor to perform operations that include, responsive to determining that the first electronic device has a level of motion that satisfies a defined rule, transmitting a request for the second electronic device to measure a position of the first electronic device. The position of the first electronic device is sensed and then stored in the memory. An acknowledgement is received from the second electronic device indicating that it has stored sensor data that can be used to measure the position of the first electronic device.

A first electronic device controls a second electronic device to measure a position of the first electronic device. The first electronic device includes a motion sensor, a network interface circuit, a processor, and a memory. The motion sensor senses motion of the first electronic device. The network interface circuit communicates with the second electronic device. The memory stores program code that is executed by the processor to perform operations that include, responsive to determining that the first electronic device has a level of motion that satisfies a defined rule, transmitting a request for the second electronic device to measure a position of the first electronic device. The position of the first electronic device is sensed and then stored in the memory. An acknowledgement is received from the second electronic device indicating that it has stored sensor data that can be used to measure the position of the first electronic device.

The invention relates to an exterior mirror simulation with image data recording and a display of the recorded and improved data for the driver of a motor vehicle. The display on a display device shows the data in a way chosen by the driver or the vehicle manufacturer.

Object/facial recognition systems, methods, architectures, mechanisms and/or apparatus for 3D face or object recognition using a 3D surface reconstructed from polarimetric thermal facial/object imager to form a 2D or 3D image which is rotated to conform with a pose of a reference image to allow comparison thereto.

Various embodiments of a facial recognition system are provided. In one embodiment, a processor determines a value for a lighting parameter associated with a captured facial image, determines whether any previously obtained images in a biometric database includes a similar value for the lighting parameter and, if not, stores the newly captured image in the database along with the lighting parameter value. In another embodiment, the processor calculates a score indicative of the likelihood that the face in the captured facial image is identical to the face of a previously obtained image in the database, determines whether the score exceeds a threshold value and, if so, generates a signal indicating a match. The processor adjusts the threshold based on one or more parameter values.

Disclosed are methods, systems, and non-transitory computer-readable medium for color and pattern analysis of images including wearable items. For example, a method may include receiving an image depicting a wearable item, identifying the wearable item within the image by identifying a face of an individual wearing the wearable item or segmenting a foreground silhouette of the wearable item from background image portions of the image, determining a portion of the wearable item identified within the image as being a patch portion representative of the wearable item depicted within the image, deriving one or more patterns of the wearable item based on image analysis of the determined patch portion of the image, deriving one or more colors of the wearable item based on image analysis of the determined patch portion of the image, and transmitting information regarding the derived one or more colors and information regarding the derived one or more patterns.