Abstract: Systems and methods for detecting, counting and analyzing the blood content of a surgical textile are provided, utilizing an infrared or depth camera in conjunction with a color image.

Abstract: An autonomous system for generating and interpreting point clouds of a rail corridor along a survey path while moving on a railroad corridor assessment platform. The system includes two LiDAR sensors configured to scan along scan planes that intersect but not at all points. The LiDAR sensors are housed in autonomously controlled and temperature controlled protective enclosures.

Abstract: A hyperspectral remote sensing image classification method based on a self-attention context network is provided. The method constructs a spatial dependency between pixels in a hyperspectral remote sensing image by self-attention learning and context encoding, and learns global context features. For adversarial attacks in the hyperspectral remote sensing data, the proposed method has higher security and reliability to better meet the requirements of safe, reliable, and high-precision object recognition in Earth observation.

Abstract: Disclosed herein are methods and systems for automatically validating evidence of traffic violations. One instance of a method comprises receiving an evidence package comprising video frames showing a vehicle involved in a potential traffic violation. The video frames can be input into one or more deep learning models to obtain a plurality of classification results. The method can further comprise generating a score based in part on the classification results and evaluating the score against one or more thresholds to determine whether the evidence package is automatically approved, is automatically rejected, or requires further review.

Abstract: Disclosed is a computer program stored in a computer readable storage medium according to an exemplary embodiment of the present disclosure. When the computer program is executed by one or more processors of a computing device, the computer program may perform operations for managing a model, and the operations may include: generating an anomaly detection model including a plurality of anomaly detection sub models having a pre-learned network function through using a plurality of training data subsets included in a training data set; determining one or more anomaly detection sub models for calculating an input data among the generated anomaly detection sub models; and judging whether or not the anomaly is existed in the input data through using the one or more determined anomaly detection sub models.

Abstract: Machine learning is performed using synthetic data for neural network training using vectors. Facial images are obtained for a neural network training dataset. Facial elements from the facial images are encoded into vector representations of the facial elements. A generative adversarial network (GAN) generator is trained to provide one or more synthetic vectors based on the one or more vector representations, wherein the one or more synthetic vectors enable avoidance of discriminator detection in the GAN. The training a GAN further comprises determining a generator accuracy using the discriminator. The generator accuracy can enable a classifier, where the classifier comprises a multi-layer perceptron. Additional synthetic vectors are generated in the GAN, wherein the additional synthetic vectors avoid discriminator detection. A machine learning neural network is trained using the additional synthetic vectors.

Abstract: 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.

Abstract: A system includes: a sequential image string input unit configured to input a sequential image string having sequentiality; a reference image selection unit configured to select one or more images from the sequential image string as reference images; a variation calculation unit configured to select an adjacent reference image adjacent to the reference image from the sequential image string and calculate a variation between the reference image and the adjacent reference image; an image information regression unit configured to calculate class confidence by regression processing with the reference image as an input; a difference image information regression unit configured to calculate class confidence by regression processing with the variation as an input; a confidence integration unit configured to integrate class confidence calculated by the image information regression unit and class confidence calculated by the difference image information regression unit; and an output unit configured to output the inte

Abstract: A makeup evaluation system can include a mobile terminal for photographing an image and transmitting the photographed image to a makeup server; and a makeup server including a make-up DB management unit for storing at least one algorithm used for make-up evaluation, a region detection unit for detecting a face region in the photographed image, a makeup analysis unit for evaluating makeup by applying the stored algorithm to the detected face region, and a wireless communication unit for transmitting an evaluation result signal including information on the result of evaluating the makeup to the mobile terminal, in which the mobile terminal displays the evaluation result according to a received evaluation result signal, and the makeup server evaluates makeup by applying different algorithms for each face region.

Abstract: An object recognition ingestion system is presented. The object ingestion system captures image data of objects, possibly in an uncontrolled setting. The image data is analyzed to determine if one or more a priori know canonical shape objects match the object represented in the image data. The canonical shape object also includes one or more reference PoVs indicating perspectives from which to analyze objects having the corresponding shape. An object ingestion engine combines the canonical shape object along with the image data to create a model of the object. The engine generates a desirable set of model PoVs from the reference PoVs, and then generates recognition descriptors from each of the model PoVs. The descriptors, image data, model PoVs, or other contextually relevant information are combined into key frame bundles having sufficient information to allow other computing devices to recognize the object at a later time.

Abstract: Embodiments of this application disclose a video classification method, including: obtaining a to-be-processed video; obtaining a visual signal feature sequence corresponding to the to-be-processed video by using a video classification prediction model; obtaining an audio signal feature sequence corresponding to the visual signal feature sequence by using the video classification prediction model; generating a target signal feature sequence according to the visual signal feature sequence and the audio signal feature sequence; and obtaining a classification prediction result corresponding to the target signal feature sequence by using the video classification prediction model, the classification prediction result being used for predicting a video type of the to-be-processed video. The embodiments of this application further disclose a server.

Abstract: A method, device, and system for evaluating geological and engineering sweet spots in an unconventional reservoir based on dual-energy computed tomography (CT) comprises acquiring and preprocessing a high-energy CT image and a low-energy CT image of a core from a core region, acquiring and preprocessing a high-energy CT image and a low-energy CT image of a core from a core reference sample, calculating a density and an effective atomic number of each pixel in the core region of a target reservoir, acquiring a geological sweet spot index and an engineering sweet spot index, acquiring evaluation results of geological and engineering sweet spots at different depths of the core region in the target reservoir, and matching the evaluation results to acquire reservoir types corresponding to the different depths of the core region in the target reservoir. The present disclosure achieves accurate and efficient reservoir evaluation and classification.

Abstract: An image decoding method, according to the present invention, includes the steps of: deriving an MPM candidate mode from neighboring blocks adjacent to a target block to be decoded; generating an MPM list using the MPM candidate mode derived from the neighboring blocks; and deriving an intra prediction mode for the target block to be decoded using the generated MPM list. According to the present invention, image compression efficiency can be improved.

Abstract: In some embodiments, a method can include executing a first model to extract a first region of interest (ROI) image and a second ROI image from an image that shows an item and an indication of information associated to the item. The first ROI image can include a portion of the image showing the item and the second ROI image can include a portion of the image showing the indication of information. The method can further include executing a second model to identify the item from the first ROI image and generate a representation of the item. The method can further include executing a third model to read the indication of information associated to the item from the second ROI image and generate a representation of information.

Abstract: An embodiment of an image processor for immersive video includes technology to re-order patches from a plurality of views based on one or more of relative position and orientation related information for a desired synthesized view, select a set of views to be used in each view synthesis pass, perform two or more view synthesis passes for the synthesized view to provide two or more intermediate view synthesis results, and mask and merge the two or more intermediate view synthesis results to provide a final view synthesis result. Other embodiments are disclosed and claimed.

Abstract: A differential recurrent neural network (RNN) is described that handles dependencies that go arbitrarily far in time by allowing the network system to store states using recurrent loops without adversely affecting training. The differential RNN includes a state component for storing states, and a trainable transition and differential non-linearity component which includes a neural network. The trainable transition and differential non-linearity component takes as input, an output of the previous stored states from the state component along with an input vector, and produces positive and negative contribution vectors which are employed to produce a state contribution vector. The state contribution vector is input into the state component to create a set of current states. In one implementation, the current states are simply output.

Abstract: A machine learning system to determine an identity of a user is trained using triplets of ad hoc synthetic data and actual data. The data may comprise multimodal images of a hand. Each triplet comprises an anchor, a positive, and a negative image. Synthetic triplets for different synthesized identities are generated on an ad hoc basis and provided as input during training of the machine learning system. The machine learning system uses a pairwise label-based loss function, such as a triplet loss function during training. Synthetic triplets may be generated to provide more challenging training data, to provide training data for categories that are underrepresented in the actual data, and so forth. The system uses substantially less memory during training, and the synthetic triplets need not be retained further reducing memory use. Ongoing training is supported as new actual triplets become available, and may be supplemented by additional synthetic triplets.

Abstract: In one embodiment, a method includes accessing a plurality of image frames captured by one or more cameras, classifying one or more first objects detected in one or more first image frames of the plurality of image frames as undesirable, applying a pixel filtering to the one or more first image frames to replace one or more first pixel sets associated with the one or more first objects with pixels from one or more second image frames of the plurality of image frames to generate a final image frame, providing the final image frame for display.

Abstract: The technology described herein obfuscates image content using a local neural network and a remote neural network. The local network runs on a local computer system and a remote classifier runs in a remote computing system. Together, the local network and the remote classifier are able to classify images, while the image never leaves the local computer system. In aspects of the technology, the local network receives a local image and creates a transformed object. The transformed object may be generated by processing the image with a local neural network to generate a multidimensional array and then randomly shuffling data locations within a multidimensional array. The transformed object is communicated to the remote classifier in the remote computing system for classification. The remote classifier may not have the seed used to deterministically scramble the spatial arrangement of data within the multidimensional array.

Abstract: This disclosure relates generally to visio-linguistic understanding. Conventional methods use contextual visio-linguistic reasoner for visio-linguistic understanding which requires more compute power and large amount of pre-training data. Embodiments of the present disclosure provide a method for visio-linguistic understanding using contextual language model reasoner. The method converts the visual information of an input image into a format that the contextual language model reasoner understands and accepts for a downstream task. The method utilizes the image captions and confidence score associated with the image captions along with a knowledge graph to obtain a combined input in a format compatible with the contextual language model reasoner. Contextual embeddings corresponding to the downstream task is obtained using the combined input. The disclosed method is used to solve several downstream tasks such as scene understanding, visual question answering, visual common-sense reasoning and so on.