Abstract: The present disclosure provides an apparatus and method of guided neural network model for image processing. An apparatus may comprise a guidance map generator, a synthesis network and an accelerator. The guidance map generator may receive a first image as a content image and a second image as a style image, and generate a first plurality of guidance maps and a second plurality of guidance maps, respectively from the first image and the second image. The synthesis network may synthesize the first plurality of guidance maps and the second plurality of guidance maps to determine guidance information. The accelerator may generate an output image by applying the style of the second image to the first image based on the guidance information.

Abstract: Examples of methods for thermal mapping by an electronic device are described herein. In some examples, a map is obtained. In some examples, a first thermal image at a first resolution is obtained. In some examples, a neural network is used to determine a second thermal image at a second resolution based on the map and the first thermal image. The second resolution is greater than the first resolution in some examples.

Abstract: A dynamic facial expression recognition (FER) method based on a Dempster-Shafer (DS) theory improves a feature extraction effect of an expression video through multi-feature fusion, and deeply learns an imbalanced dynamic expression feature by using the DS theory, multi-branch convolution, and an attention mechanism. Compared with other methods, the dynamic FER method scientifically and effectively reduces an impact of sample imbalance on expression recognition, fully utilizes a spatio-temporal feature to mine potential semantic information of the video expression to perform expression classification, thereby improving reliability and accuracy and meeting a demand for the expression recognition.

Abstract: An information processing apparatus including: a classifier that is applied for medical treatment data; and a hardware processor that sets a property of the classifier, evaluates a sufficiency level of the property by using evaluation data for the classifier, and selects and sets one classifier as the classifier to be used in a facility by referring to an evaluation result.

Abstract: Various embodiments herein each include at least one of systems, methods, software, and data structures for context-aided machine vision. For example, one method embodiment includes identifying a customer in a shopping area and maintaining an item bin in a computing system of data identifying items the customer has picked up for purchase. This method further includes receiving an image of the customer holding an item and performing item identification processing on the image to identify the item the customer is holding. The item identification processing may be performed based in part on a stored shopping history of the customer indicating items the customer is more likely to purchase. The identified item is then added to the item bin of the customer.

Abstract: An apparatus acquires RAW image data captured by a first device and captured image data captured by a second device, obtains a parameter of the first device by using a learning model that has learned a correspondence relationship between a parameter used to develop RAW image data and an image developed using the parameter such that the characteristics of a developed image of the RAW image data are close to the characteristics of the captured image, and develops the RAW image data.

Abstract: Provided are a face image processing method and apparatus, an image device, and a storage medium. The face image processing method includes: acquiring first-key-point information of a first face image; performing position transformation on the first-key-point information to obtain second-key-point information conforming to a second facial geometric attribute, the second facial geometric attribute being different from a first facial geometric attribute corresponding to the first-key-point information; and performing facial texture coding processing by utilizing a neural network and the second-key-point information to obtain a second face image.

Abstract: Methods, systems, and computer readable storage media for using image processing to develop a library of facial expressions. The system can receive digital video of at least one speaker, then execute image processing on the video to identify landmarks within facial features of the speaker. The system can also identify vectors based on the landmarks, then assign each vector to an expression, resulting in a plurality of speaker expressions. The system then scores the expressions based on similarity to one another, and creates subsets based on the similarity scores.

Abstract: Provided herein is an apparatus for assessing a color characteristic of a gemstone. The apparatus comprises an optically opaque platform for supporting a sample gemstone to be assessed, a daylight-approximating light source to provide uniform illumination to the gemstone, an image capturing component, and a telecentric lens positioned to provide an image of the illuminated gemstone to the image capturing component. Also provided are methods of color analysis based on images collected using such an apparatus.

Abstract: This invention is related to a method to measure and calculate a point spread function PSF of an image detector system based on the analysis of quantum noise that is present in generic image acquisition data, and that is modulated by the transfer function of the digital imaging system. The method allows the calculation of the sharpness of a system without the need for image phantom recordings.

Abstract: Embodiments of the present disclosure relate to the field of image processing technologies and disclose an image processing method and apparatus, an electronic device, and a computer-readable storage medium. The image processing method includes: when an obtained first image includes a human face, performing a first transformation process on the first image to obtain a second image; determining, based on a first target face key point of the human face in the first image, a target position, in the first image, of a second target face key point of the human face in the second image; performing a first movement process on the second image based on the target position; and generating a target image based on the first image and the second image processed through the first movement process, and displaying the target image.

Abstract: Presented herein are systems and methods that provide for automated analysis of medical images to determine a predicted disease status (e.g., prostate cancer status) and/or a value corresponding to predicted risk of the disease status for a subject. The approaches described herein leverage artificial intelligence (AI) to analyze intensities of voxels in a functional image, such as a PET image, and determine a risk and/or likelihood that a subject's disease, e.g., cancer, is aggressive. The approaches described herein can provide predictions of whether a subject that presents a localized disease has and/or will develop aggressive disease, such as metastatic cancer. These predictions are generated in a fully automated fashion and can be used alone, or in combination with other cancer diagnostic metrics (e.g., to corroborate predictions and assessments or highlight potential errors). As such, they represent a valuable tool in support of improved cancer diagnosis and treatment.

Abstract: Systems and methods for tone mapping of high dynamic range (HDR) images for high-quality deep learning based processing are disclosed. In one embodiment, a graphics processor includes a media pipeline to generate media requests for processing images and an execution unit to receive media requests from the media pipeline. The execution unit is configured to compute an auto-exposure scale for an image to effectively tone map the image, to scale the image with the computed auto-exposure scale, and to apply a tone mapping operator including a log function to the image and scaling the log function to generate a tone mapped image.

Abstract: Systems and methods for color selection are provided and include a non-transitory computer readable medium storing computer executable instructions that, when executed by a processor, configure the computing device to receive an image and determine a dominant color for the image by dividing the image into pixel groups, analyzing pixels in each of the pixel groups at different sample rates based on how close the pixel group is to the center of the image with pixel groups closer to the center of the image having a higher sample rate, and determining the dominant color for the image based on the analyzed pixels in each of the pixel groups. The instructions further configure the computing device to determine a closest matching paint color for the dominant color for the image and display a color name or a color code associated with the closest matching paint color for the dominant color.

Abstract: Systems, methods, and computer media of processing a video are disclosed. An example method may include: receiving a plurality of video frames of a video; generating a plurality of first input features based on the plurality of video frames; generating a plurality of second input features based on reversing a temporal order of the plurality of first input features; generating a first set of joint attention features based on the plurality of first input features; generating a second set of joint attention features based on the plurality of second input features; and concatenating the first set of joint attention features and the second set of joint attention features to generate a final set of joint attention features.

Abstract: A production monitoring and analysis method based on an image data algorithm includes: obtaining a reconstructed gray histogram according to a gray image of an image where a product identification code is located; obtaining a segmentation threshold of an Otsu algorithm for segmenting the reconstructed gray histogram, obtaining the adjustment of the gray level in the sub-gray histogram according to the number of pixels corresponding to each gray level in the reconstructed gray histogram and the segmentation threshold, correcting the sub-gray histogram to obtain a corrected sub-gray histogram according to the adjustment of the gray level in the sub-gray histogram, the lateral segmentation threshold of the sub-gray histogram, and all the gray levels greater than the lateral segmentation threshold, and obtaining an enhanced gray image according to the corrected sub-gray histogram. The method avoids the loss of original information when using histogram equalization for image enhancement.

Abstract: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.

Abstract: An apparatus includes one or more processors that function as an image acquisition unit configured to acquire a training image and a correct answer image, a generation unit configured to input the training image to a neural network to generate an output image, an error acquisition unit configured to subject each of the correct answer image and the output image to processing for adjusting a color signal value, and acquire an error between the correct answer image and the output image that have been subjected to the processing, and an update unit configured to update parameters of the neural network based on the acquired error.

Abstract: A meme generation method, an electronic device, and a storage medium are provided. The method includes: determining a plurality of second expression images corresponding to a target face image based on a plurality of first expression images contained in a first meme; generating a second meme corresponding to the target face image based on the plurality of second expression images corresponding to the target face image; wherein, determining an affine transformation parameter between the target face image and an i-th first expression image in the plurality of first expression images according to a corresponding relation between a face key point in the target face image and a face key point in the i-th first expression image; and transforming the target face image based on the affine transformation parameter to obtain an i-th second expression image corresponding to the target face image.

Abstract: An example system includes a landmark engine to detect a facial landmark in an image of a face. The system includes a comparison engine to determine a difference between the facial landmark in the image and a facial landmark of a neutral face. The system also includes an action engine to determine whether a facial action unit occurred based on whether the difference satisfies a condition.