In a distributed system, a first computer system may require computationally verifiable assurances of the authenticity and integrity of computations (e.g., performed as part of the execution of a program) performed by a second computer system. Methods described herein may be utilized to enforce and/or ensure the correct execution of a program. The first computer system may delegate execution of a program to a second computer system and a protocol may be employed to constrain the second computer system to perform a correct execution of the program. The protocol may include mitigation and correction routines that mitigate and/or correct the incorrect execution of a program. In various systems and methods described herein, the protocol may utilize a blockchain network such as a Bitcoin-based blockchain network.

There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating image representations of categorical data. In one example, embodiments comprise receiving a categorical input feature, generating an image representation of the categorical input feature, generating an image-based prediction based at least in part on the image representation, and performing one or more prediction-based actions based at least in part on the image-based prediction.

In a distributed system, a first computer system may require computationally verifiable assurances of the authenticity and integrity of computations (e.g., performed as part of the execution of a program) performed by a second computer system. Methods described herein may be utilized to enforce and/or ensure the correct execution of a program. The first computer system may delegate execution of a program to a second computer system and a protocol may be employed to constrain the second computer system to perform a correct execution of the program. The protocol may include mitigation and correction routines that mitigate and/or correct the incorrect execution of a program. In various systems and methods described herein, the protocol may utilize a blockchain network such as a Bitcoin-based blockchain network.

In order to improve the learning performance of a neural network model, this image recognition model generating device is provided with an input image patch determining unit, a similar patch searching unit, a pixel value generating unit, and a convolution processing unit. The input image patch determining unit determines an input image patch containing a border region in contact with the outside of a boundary line of an input image. The similar patch searching unit searches for a similar patch that is similar to the input image patch. The pixel value generating unit generates a pixel value complementing the border region, on the basis of the similar patch. The convolution processing unit performs a convolution process from the generated pixel value and pixel values of the input image.

A system for adding persistence to SPAD imagery is configurable to capture, using a SPAD array, a plurality of image frames. The system is configurable to capture, using an IMU, pose data associated with the plurality of image frames. The pose data includes at least respective pose data associated with each of the plurality of image frames. The system is configurable to determine a persistence term based on the pose data. The system is also configurable to generate a composite image based on the plurality of image frames, the respective pose data associated with each of the plurality of image frames, and the persistence term. The persistence term defines a contribution of each of the plurality of image frames to the composite image.

There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating image representations of categorical data. In one example, embodiments comprise receiving a categorical input feature, generating an image representation of the categorical input feature, generating an image-based prediction based at least in part on the image representation, and performing one or more prediction-based actions based at least in part on the image-based prediction.

Systems and methods are provided for image classification using histograms of oriented gradients (HoG) in conjunction with a trainer. The efficiency of the process is greatly increased by first establishing a bitmap which identifies a subset of the pixels in the HoG window as including relevant foreground information, and limiting the HoG calculation and comparison process to only the pixels included in the bitmap.

A system for improved localization of image forgery. The system generates a variational information bottleneck objective function and works with input image patches to implement an encoder-decoder architecture. The encoder-decoder architecture controls an information flow between the input image patches and a representation layer. The system utilizes information bottleneck to learn useful residual noise patterns and ignore semantic content present in each input image patch. The system trains a neural network to learn a representation indicative of a statistical fingerprint of a source camera model from each input image patch while excluding semantic content thereof. The system can determine a splicing manipulation localization by the trained neural network.

The present disclosure describes systems and methods for image processing. Embodiments of the present disclosure include an image processing apparatus configured to generate modified images (e.g., synthetic faces) by conditionally changing attributes or landmarks of an input image. A machine learning model of the image processing apparatus encodes the input image to obtain a joint conditional vector that represents attributes and landmarks of the input image in a vector space. The joint conditional vector is then modified, according to the techniques described herein, to form a latent vector used to generate a modified image. In some cases, the machine learning model is trained using a generative adversarial network (GAN) with a normalization technique, followed by joint training of a landmark embedding and attribute embedding (e.g., to reduce inference time).

In scan data retrieval, a mesh is fit (32) to surface data of a current patient, such as data from an optical or depth sensor (18). Meshes are also fit (48) to medical scan data, such as fitting (48) to skin surface segments of computed tomography data. The meshes or parameters derived from the meshes may be more efficiently compared (34) to identify (36) a previous patient with similar body shape and/or size. The scan configuration (38) for that patient, or that patient as altered to account for differences from the current patient, is used. In some embodiments, the parameter vector used for searching (34) includes principle component analysis coefficients. In further embodiments, the principle component analysis coefficients may be projected to a more discriminative space using metric learning.