Abstract: Aspects of the present disclosure relate to a computer-implemented method of processing data portion. The method comprises processing a first data portion in a convolutional neural network to generate a first input to an activation function in the convolutional neural network; providing a first output by applying the activation function to the first input; and storing an indicator, representative of the first input to the activation function, for the first data portion. The method further comprises determining whether to provide a second output by applying the activation function to a second input, generated from a second data portion, based at least in part on an evaluation of the indicator for the first data portion.

Abstract: A vehicle having one or more cameras, configured to record one or more images of a person approaching the vehicle. The camera(s) can be configured to send biometric data derived from the image(s). The vehicle can include a computing system configured to receive the biometric data and to determine a risk score of the person based on the received biometric data and an AI technique, such as an ANN or a decision tree. The received biometric data or a derivative thereof can be input for the AI technique. The computing system can also be configured to determine whether to notify a driver of the vehicle of the risk score based on the risk score exceeding a risk threshold. The vehicle can also include a user interface, configured to output the risk score to notify the driver when the computing system determines the risk score exceeds the risk threshold.

Abstract: Anti-spoofing of a deep learning neural network may include receiving, by an artificial neural network implemented in hardware, an image and multi-dimensional spatial frequency data for the image. The artificial neural network is trained using training images and multi-dimensional spatial frequency data for the training images. Using the artificial neural network, a classification for an object in an image is determined based on the image and the multi-dimensional spatial frequency data for the image.

Abstract: A method of analyzing cell populations includes receiving, by a transceiver of a computing device, an image of a tissue sample. The method also includes analyzing, by a processor of the computing device, the image of the tissue sample using image analysis. The image analysis parameters are determined by machine learning. The method also includes determining, by the processor and based on the analyzing, one or more cell features, such as shape, of a cell in the tissue sample. The method further includes identifying, by the processor, an interaction of the cell with an additional cell based at least in part on the shape of the cell.

Abstract: The exemplary embodiments relate to converting standard dynamic range (SDR) content to high dynamic range (HDR) content. An SDR image may be decomposed into a base layer of the SDR image that includes low frequency information from the SDR image and a detail layer of the SDR image that includes high frequency information from the SDR image. A base layer of an HDR image may be generated using the base layer of the SDR image and a detail layer of the HDR image may be generated using the detail layer of the SDR image. An HDR image is then generated using the base layer of the HDR image and the detail layer of the HDR image.

Abstract: A neural network system for detecting at least one object in at least one image, the system includes a plurality of object detectors. Each object detector receives respective image information thereto. Each object detector includes a respective neural network. Each the neural network including a plurality of layers. Layers in different object detectors are common layers when the layers receive the same input thereto and produce the same output therefrom. Common layers are computed only once during object detection for all the different object detectors.

Abstract: For registration of medical images with deep learning, a neural network is designed to include a diffeomorphic layer in the architecture. The network may be trained using supervised or unsupervised approaches. By enforcing the diffeomorphic characteristic in the architecture of the network, the training of the network and application of the learned network may provide for more regularized and realistic registration.

Abstract: A computer-implemented method for increasing the image resolution of a digital image is provided. The method includes performing bicubic upsampling of the digital image to generate a base high-resolution (HR) image. The digital image is converted from a red-green-blue (RGB) color space to a Luma (Y), Chroma Blue Difference (Cb), and Chroma Red Difference (Cr) (YCbCr) color space to generate a low-resolution (LR) residual image. A plurality of convolutional layers of a neural network model is applied to the LR residual image to convert it to a plurality of HR residual sub-images corresponding to the digital image. An HR image corresponding to the digital image is generated using the base HR image and the plurality of HR residual sub-images.

Abstract: A treatment efficacy of a treatment for treating a parkinsonian disease is determined. A first set of imaging information/data associated with a dMRI scan of an individual's brain captured at a first time and a second set of imaging information/data associated with a dMRI scan of the individual's brain captured at a second time are received. The individual underwent the treatment for a time period comprising at least part of the time between the first and second times. An expected change between the first and second times is determined based on a natural progression of the parkinsonian disease. The first and second sets of imaging information/data are analyzed to determine a first free-water pattern and a second free-water pattern. Based on the first and second free-water patterns, a disease progression score is determined. Based on the disease progression score and the expected change, a treatment efficacy for treating the individual with the treatment is determined.

Abstract: Provided is an image processing apparatus that includes an image processing section that executes filter processing using a filter coefficient. The filter coefficient is set at least on the basis of a detection result for details based on a first image and a detection result of detection of a disturbance performed on a second image.

Abstract: The present application provides a salt and pepper noise filtering method and device based on morphological component analysis. The method comprises: obtaining a to-be-filtered image containing salt and pepper noise; calculating the dimension of the to-be-filtered image, labeled as [n, m]; initializing an n*m-dimensional all-1 labeled matrix as a salt and pepper noise labeled map; obtaining a preset region centered on a pixel point with a pixel value of 0 or 255, and calculating a noise variance between the pixel points in the preset region; labeling the position of a salt and pepper noise point in the salt and pepper noise labeled map according to the noise variance between the pixel points in the preset region, and updating and determining the salt and pepper noise labeled map. The salt and pepper noise is filtered through the method based on morphological component analysis, which improves the quality of the image.

Abstract: A method of removing noise from a depth image includes presenting real-world depth images in real-time to a first generative adversarial neural network (GAN), the first GAN being trained by synthetic images generated from computer assisted design (CAD) information of at least one object to be recognized in the real-world depth image. The first GAN subtracts the background in the real-world depth image and segments the foreground in the real-world depth image to produce a cleaned real-world depth image. Using the cleaned image, an object of interest in the real-world depth image can be identified via the first GAN trained with synthetic images and the cleaned real-world depth image. In an embodiment the cleaned real-world depth image from the first GAN is provided to a second GAN that provides additional noise cancellation and recovery of features removed by the first GAN.

Abstract: The system and method are configured to identify cars and obtain information about the identified cars. The system and method involves a software application installed on a computing device that includes a camera configured to capture images. After the software application is installed, the user can activate the camera through the software application and point the camera at a vehicle. The software application includes an object recognition function that can recognize the vehicle in the view of the camera. After recognizing the vehicle, the software application displays information about vehicle on a screen of the computing device. The software application can be used with an event such as an auto show. In addition to identifying the cars, the identified information can be used for other purposes such as a part of a scavenger hunt or other contests. Awards can be given to participants who complete the contest.

Abstract: The inventive concepts herein relate to performing block retrieval on a block to be processed of a urine sediment image. The method comprises: using a plurality of decision trees to perform block retrieval on the block to be processed, wherein each of the plurality of decision trees comprises a judgment node and a leaf node, and the judgment node judges the block to be processed to make it reach the leaf node by using a block retrieval feature in a block retrieval feature set to form a block retrieval result at the leaf node, and at least two decision trees in the plurality of decision trees are different in structures thereof and/or judgments performed by the judgment nodes thereof by using the block retrieval feature; and integrating the block retrieval results of the plurality of decision trees so as to form a final block retrieval result.

Abstract: A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. The encoder is configured to convert a point cloud into an image based representation. The encoder packs patch images into an image frame and fills empty spaces in the image frame with a padding, wherein pixel values for the padding are determined based on neighboring pixels values such that the padding is smoothed in the image frame. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud.

Abstract: The present invention discloses an image processing circuit, wherein the image processing circuit comprises a receiving circuit, a sharpness processing circuit, a luminance variation processing circuit and an output circuit. In the operations of the image processing circuit, the receiving circuit is configured to receive image data; the sharpness processing circuit is configured to perform a high-pass filtering operation on the image data to generate processed image data; the luminance variation processing is configured to determine a high frequency component of each pixel within the image data, and for each pixel, the luminance variation processing circuit is configured to calculate a difference between high frequency components of the pixel and neighboring pixel(s) to generate auxiliary image data; and the output circuit is configured to generate output image according to the processed image data and the auxiliary image data.

Abstract: An image processing method and apparatus, an electronic device, and a storage medium are provided. The method includes: obtaining an infrared image and a color image for the same object in a predetermined scenario, and a first depth map corresponding to the color image; obtaining a first optical flow between the color image and the infrared image; and performing first optimization processing on the first depth map by using the first optical flow to obtain an optimized second depth map corresponding to the color image.

Abstract: Provided is an image processing apparatus including a memory storing at least one instruction, and a processor configured to execute the at least one instruction stored in the memory to obtain first feature information by performing a convolution operation on a first image and a first kernel included in a first convolution layer among a plurality of convolution layers, obtain at least one piece of characteristic information, based on the first feature information; obtain second feature information, based on the first feature information and the at least one piece of characteristic information, obtain third feature information by performing a convolution operation on the obtained second feature information and a second kernel included in a second convolution layer that is a layer next to the first convolution layer among the plurality of convolution layers, and obtain an output image, based on the third feature information.

Abstract: Artistic styles extracted from source images may be applied to target images to generate stylized images and/or video sequences. The extracted artistic styles may be stored as a plurality of layers in one or more neural networks, which neural networks may be further optimized, e.g., via the fusion of various elements of the networks' architectures. The artistic style may be applied to the target images and/or video sequences using various optimization methods, such as the use of a first version of the neural network by a first processing device at a first resolution to generate one or more sets of parameters (e.g., scaling and/or biasing parameters), which parameters may then be mapped for use by a second version of the neural network by a second processing device at a second resolution. Analogous multi-processing device and/or multi-network solutions may also be applied to other complex image processing tasks for increased efficiency.

Abstract: Systems and methods of enhanced display and viewing of three dimensional (3D) tomographic data acquired in tomosynthesis or tomography. A set of projection data is acquired with an image acquisition system and used to reconstruct enhanced 3D volume renderings that are viewed with motion, advanced image processing or stereotactically to assist in medical diagnosis. Various enhancements are provided for further processing the images, thereby providing additional features and benefits during image viewing.