A device that includes a model training engine implemented by a processor. The model training engine is configured to select a first sub-string correlithm object and a second sub-string correlithm object from a set of sub-string correlithm objects. The model training engine is further configured to compute a Hamming distance between the first sub-string correlithm object and the second sub-string correlithm object and to compare the Hamming distance to a bit difference threshold value. The model training engine is further configured to determine that the Hamming distance is less than the bit difference threshold value and to compute an average of the first sub-string correlithm object and the second sub-string correlithm object in the n-dimensional space in response to the determination. The model training engine is further configured to train the machine learning model to define the average as a centroid for the first cluster.

A computer implemented method for automatic print analysis, the method comprising: receiving a first image wherein the first image shows one or more of: a latent print, a patent print, an impressed print, and an actual finger, palm, toe and/or foot; and wherein the first image includes characteristic features of at least one of a finger, a palm, a toe and a foot; creating an orientation field by estimating the orientation of one or more features in the first image, wherein the estimating comprises: applying an orientation operator to the first image, the orientation operator being based on a plurality of isotropic filters lying in quadrature.

Technologies for thermal enhanced semantic segmentation include a computing device having a visible light camera and an infrared camera. The computing device receives a visible light image of a scene from the visible light camera and an infrared image of the scene from the infrared camera. The computing device registers the infrared image to the visible light image to generate a registered image. Registering the infrared image may include increasing resolution of the infrared image. The computing device generates a thermal boundary saliency image based on the registered infrared image. The computing device may generate the thermal boundary saliency image by applying a Gabor jet convolution to the registered infrared image. The computing device performs semantic segmentation on the visible light image, the registered infrared image, and the thermal boundary saliency image to generate a pixelwise semantic classification of the scene. Other embodiments are described and claimed.

Apparatuses, methods, and systems are presented for sensing scene-based occurrences. Such an apparatus may comprise a vision sensor system comprising a first processing unit and dedicated computer vision (CV) computation hardware configured to receive sensor data from at least one sensor array comprising a plurality of sensor pixels and capable of computing one or more CV features using readings from neighboring sensor pixels. The vision sensor system may be configured to send an event to be received by a second processing unit in response to processing of the one or more computed CV features by the first processing unit. The event may indicate possible presence of one or more irises within a scene.

A process for operating a machine guided photo and video composition system involves generating processed image data. The process operates an object detection engine to identify objects and object locations in the processed image data. The process operates a computer vision analysis engine to identify geometric attributes of objects. The process operates an image cropping engine to select potential cropped image locations within the processed image data. The image cropping engine generates crop location scores for each of the potential cropped image locations and determine highest scored cropped image location. The image cropping engine communicates a highest crop location score to a score evaluator gate. The process generates object classifications from the object locations and the geometric attributes. The process receives device instructions at a user interface controller by way of the score evaluator gate. The method displays device positioning instructions through a display device.

An apparatus for responding to vehicle water splashing includes a processor determining the vehicle water splashing based on image data of a nearby vehicle and determining dangerousness caused by the vehicle water splashing to perform vehicle control and storage storing information determined by the processor and the image data of the nearby vehicle.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating numeric embeddings of images. One of the methods includes obtaining training images; generating a plurality of triplets of training images; and training a neural network on each of the triplets to determine trained values of a plurality of parameters of the neural network, wherein training the neural network comprises, for each of the triplets: processing the anchor image in the triplet using the neural network to generate a numeric embedding of the anchor image; processing the positive image in the triplet using the neural network to generate a numeric embedding of the positive image; processing the negative image in the triplet using the neural network to generate a numeric embedding of the negative image; computing a triplet loss; and adjusting the current values of the parameters of the neural network using the triplet loss.

An artificial neural network circuit includes a crossbar circuit, and a processing circuit. The crossbar circuit transmits a signal between layered neurons of an artificial neural network. The crossbar circuit includes input bars, output bars arranged intersecting the input bars, and memristors. The processing circuit calculates a sum of signals flowing into each of the output bars. The processing circuit calculates, as the sum of the signals, a sum of signals flowing into a plurality of separate output bars and conductance values of the corresponding memristors are set so as to cooperate to give a desired weight to the signal to be transmitted.

A method for local approximation of a predictive model may include receiving unclassified data associated with a plurality of unclassified data items. The unclassified data may be classified based on a first predictive model to generate classified data. A first data item may be selected from the classified data. A plurality of generated data items associated with the first data item may be generated using a generative model. The plurality of generated data items may be classified based on the first predictive model to generate classified generated data. A second predictive model may be trained with the classified generated data. A system and computer program product are also disclosed.

The invention provides a method for recognition of information in digital image data, said method comprising a learning phase on a data set of example digital images having known information, and characteristics of categories are computed automatically from each example digital image and compared to its known category, said method comprises training a convolutional neural network comprising network parameters using said data set, in which via deep learning each layer of said convolutional neural network is represented by a linear decomposition of all filters as learned in each layer into basis functions.