Systems and methods are disclosed to manipulate or normalize image of animals to a reference size and shape. Synthetically normalizing the image data to a reference size and shape allows machine learning models to automatically identify subject behaviors in a manner that is robust to changes in the size and shape of the subject. The systems and methods of the invention can be applied to drug or gene therapy classification, drug or gene therapy screening, disease study including early detection of the onset of a disease, toxicology research, side-effect study, learning and memory process study, anxiety study, and analysis in consumer behavior.

Techniques for padding audiovisual clips (for example, audiovisual clips of sporting events) for the purpose of causing the clip to have a predetermined duration so that the padded clip can be evaluated for viewer interest by a machine learning (ML) algorithm. The unpadded clip is padded with audiovisual segment(s) that will cause the padded clip to have a level of viewer interest that it would have if the unpadded clip had been longer. In some embodiments the padded segments are synthetic images generated by a generative adversarial network such that the synthetic images would have the same level of viewer interest (as adjudged by an ML algorithm) as if the unpadded clip had been shot to be longer.

The present invention discloses a method for recognizing a seawater polluted area based on a high-resolution remote sensing image and a device and belongs to the field of digital image processing. According to the method, firstly, automatic sea and land classification is performed on a remote sensing image by using a supervised learning algorithm, a classification result may reach a higher precision level by processized iterative clustering, and meanwhile, compared with an existing analysis and classification method for a sea and land boundary, the algorithm is less in calculation; and then, a chlorophyll-associated normalized difference vegetation index, a brightness-associated normalized difference water shadow index, a segmentation-based image interpretation thought and a human visual saliency based mechanism in remote sensing interpretation are combined by virtue of a chlorophyll concentration difference of a seawater polluted area and surrounding seawater and a brightness difference of pollutant shadows, and the seawater polluted area is extracted by threshold segmentation, an area where the water quality is good and a heavily polluted area are respectively extracted, and then, a pollution transition area is further extracted. The method disclosed by the present invention provides convenience and an accurate reference for prevention and control of marine pollution.

The present invention discloses a method for recognizing a seawater polluted area based on a high-resolution remote sensing image and a device and belongs to the field of digital image processing. According to the method, firstly, automatic sea and land classification is performed on a remote sensing image by using a supervised learning algorithm, a classification result may reach a higher precision level by processized iterative clustering, and meanwhile, compared with an existing analysis and classification method for a sea and land boundary, the algorithm is less in calculation; and then, a chlorophyll-associated normalized difference vegetation index, a brightness-associated normalized difference water shadow index, a segmentation-based image interpretation thought and a human visual saliency based mechanism in remote sensing interpretation are combined by virtue of a chlorophyll concentration difference of a seawater polluted area and surrounding seawater and a brightness difference of pollutant shadows, and the seawater polluted area is extracted by threshold segmentation, an area where the water quality is good and a heavily polluted area are respectively extracted, and then, a pollution transition area is further extracted. The method disclosed by the present invention provides convenience and an accurate reference for prevention and control of marine pollution.

The invention describes a generic framework for hand-eye calibration of camera-guided apparatuses, wherein the rigid 3D transformation between the apparatus and the camera must be determined. An example of such an apparatus is a camera-guided robot.

The invention describes a generic framework for hand-eye calibration of camera-guided apparatuses, wherein the rigid 3D transformation between the apparatus and the camera must be determined. An example of such an apparatus is a camera-guided robot.

A method may include obtaining an original image. The method may also include determining a plurality of decomposition coefficients of the original image by decomposing the original image. The method may also include determining at least one enhancement coefficient by performing enhancement to at least one of the plurality of decomposition coefficients using a coefficient enhancement model. The method may also include generating an enhanced image corresponding to the original image based on the at least one enhancement coefficient.

The present invention provides an apparatus and method for image classification and segmentation based on a feature-guided network, a device, and a medium, and belongs to the technical field of deep learning. A feature-guided classification network and feature-guided segmentation network of the present invention include basic unit blocks. A local feature is enhanced and a global feature is extracted among the basic unit blocks. This resolves a problem that features are not fully utilized in existing image classification and image segmentation network models. In this way, a trained feature-guided classification network and feature-guided segmentation network have better effects and are more robust. The present invention selects the feature-guided classification network or the feature-guided segmentation network based on a requirement of an input image and outputs a corresponding category or segmented image, to resolve a problem that the existing classification or segmentation network model has an unsatisfactory classification or segmentation effect.

The present invention provides an apparatus and method for image classification and segmentation based on a feature-guided network, a device, and a medium, and belongs to the technical field of deep learning. A feature-guided classification network and feature-guided segmentation network of the present invention include basic unit blocks. A local feature is enhanced and a global feature is extracted among the basic unit blocks. This resolves a problem that features are not fully utilized in existing image classification and image segmentation network models. In this way, a trained feature-guided classification network and feature-guided segmentation network have better effects and are more robust. The present invention selects the feature-guided classification network or the feature-guided segmentation network based on a requirement of an input image and outputs a corresponding category or segmented image, to resolve a problem that the existing classification or segmentation network model has an unsatisfactory classification or segmentation effect.

An image matching system includes a non-transitory computer-readable medium and a processor. The non-transitory computer-readable medium is configured to store information of a plurality of images. The processor is configured to identify an object area in an original image that illustrates an object. The processor is configured to normalize the object area, resulting in a normalized image. The processor is configured to calculate a shape vector and a color vector from the normalized image. The processor is configured to calculate a match score using the shape vector and the color vector. The processor is configured to determine if the non-transitory computer-readable medium stores an identical match for the original image based on the match score.