System/method identifying a defined object (e.g., hazard): a sensor detecting and defining a digital representation of an object; a processor (connected to the sensor) which executes two techniques to identify a signature of the defined object; a memory (connected to the processor) storing reference data relating to two signatures derived, respectively, by the two techniques; responsive to the processor receiving the digital representation from the sensor, the processor executes the two techniques, each technique assessing the digital representation to identify any signature candidate defined by the object, derive feature data from each identified signature candidate, compare the feature data to the reference data, and derive a likelihood value of the signature candidate corresponding with the respective signature; combining likelihood values to derive a composite likelihood value and thus determine whether the object in the digital representation is the defined object.

One aspect of the present invention includes artificial vision system. The system includes an image system comprising a video source that is configured to capture sequential frames of image data of non-visible light and at least one processor configured as an image processing system. The image processing system includes a wavelet enhancement component configured to normalize each pixel of each of the sequential frames of image data and to decompose the normalized image data into a plurality of wavelet frequency bands. The image processing system also includes a video processor configured to convert the plurality of wavelet frequency bands in the sequential frames into respective visible color images. The system also includes a video display system configured to display the visible color images.

An ensemble learning based image classification system contains multiple cellular neural networks (CNN) based integrated circuits (ICs) operatively coupling together as a set of base learners of an ensemble for an image classification task. Each CNN based IC is configured with at least one distinct deep learning model in form of filter coefficients. The ensemble learning based image classification system further contains a controller configured as a meta learner of the ensemble and a memory based data buffer for holding various data used in the ensemble by the controller and the CNN based ICs. Various data may include input imagery data to be classified. Various data may also include extracted feature vectors or image classification outputs out of the set of base learners. The extracted feature vectors or image classification outputs are then used by the meta learner to further perform the image classification task.

Defect classification includes acquiring one or more images of a specimen, receiving a manual classification of one or more training defects based on one or more attributes of the one or more training defects, generating an ensemble learning classifier based on the received manual classification and the attributes of the one or more training defects, generating a confidence threshold for each defect type of the one or more training defects based on a received classification purity requirement, acquiring one or more images including one or more test defects, classifying the one or more test defects with the generated ensemble learning classifier, calculating a confidence level for each of the one or more test defects with the generated ensemble learning classifier and reporting one or more test defects having a confidence level below the generated confidence threshold via the user interface device for manual classification.

A method for determining a property of a geological formation based on an optical image of rock samples taken from the formation is presented therein. The image comprises a plurality of pixels and the method comprises defining windows in the image, each window comprising predetermined number of pixels and being of a predetermined shape. The method also includes, for each window, extracting a rockprint value representative of the window. A rockprint comprises indicators for characterizing a texture of the window. The method also includes classifying the windows into categories of a predetermined set. Each category is representative of one type of rock and the classification is based on a comparison of the rockprint value of each window with rockprint values of images of reference rock samples for each category. Based on the classification, the method then includes determining the at least one property of the geological formation, ie the quantification of each type of rock in the sample.

In a method of determining the position of an object, raw image data of the sky is recorded using a celestial imaging unit. The last known position, orientation, date, and time data of the object are obtained, and the position of a celestial body is measured. A latitude and longitude of the object is determined by matching the measured celestial body position to the expected celestial body position based on the input parameters. A system for determining a new position of an object comprises a celestial imaging unit configured to record image data of the sky, a signal processing unit, and a signal processing unit configured to receive and store in memory the image data received from the celestial imaging unit. The signal processing unit filters the image to find the positions of celestial objects in the sky. The signal processing unit is further configured to use roll and pitch from an IMU, and date and time from a clock to determine the object's position (latitude and longitude).

An ensemble learning based image classification system contains multiple cellular neural networks (CNN) based integrated circuits (ICs) operatively coupling together as a set of base learners of an ensemble for an image classification task. Each CNN based IC is configured with at least one distinct deep learning model in form of filter coefficients. The ensemble learning based image classification system further contains a controller configured as a meta learner of the ensemble and a memory based data buffer for holding various data used in the ensemble by the controller and the CNN based ICs. Various data may include input imagery data to be classified. Various data may also include extracted feature vectors or image classification outputs out of the set of base learners. The extracted feature vectors or image classification outputs are then used by the meta learner to further perform the image classification task.

An image search device includes a processor that calculates a feature quantity of each divided region of a first image. The processor generates integrated regions constituted of adjacent divided regions that have feature quantities having differences within a first range. The processor determines, as first regions, the integrated regions and divided regions not included in the integrated regions. The processor extracts, for each first region, a second region in a second image. The second region has a same shape as a relevant first region and has a feature quantity different from a feature quantity of the relevant first region within a predetermined second range. The processor determines a relationship between a position of a first region in the first image and a position of a second region in the second image. The processor determines whether the first image and the second image have similar portions based on the relationship.

Apparatuses and methods of operating such apparatuses are disclosed. An apparatus comprises feature dataset input circuitry to receive a feature dataset comprising multiple feature data values indicative of a set of features, wherein each feature data value is represented by a set of bits. Class retrieval circuitry is responsive to reception of the feature dataset from the feature dataset input circuitry to retrieve from class indications storage a class indication for each feature data value received in the feature dataset, wherein class indications are predetermined and stored in the class indications storage for each permutation of the set of bits for each feature. Classification output circuitry is responsive to reception of class indications from the class retrieval circuitry to determine a classification in dependence on the class indications. A predicated class may thus be accurately generated from a simple apparatus.

The video-motion segmentation method using a randomized voting is provided which includes receiving the video, extracting a plurality of feature points from the video, and grouping the plurality of feature points by applying a randomized voting method using a score histogram on each of the at least some feature points of the plurality of feature points.