Abstract: The electronic device includes a camera, a non-volatile memory storing at least one instruction and a plurality of object recognition models, a volatile memory, and a processor, connected to the non-volatile memory, the volatile memory, and the camera, and configured to control the electronic device. The processor, by executing the at least one instruction, is configured to, based on an operation mode of the electronic device, load, to the volatile memory, a hierarchical object recognition model having a hierarchical structure corresponding to the operation mode, the hierarchical object recognition model including objection recognition models among the plurality of object recognition models, obtain information on an object by inputting an object image obtained through the camera to the hierarchical object recognition model, and determine an operation of the electronic device based on the information on the object.

Abstract: A method for searching and ranking personalized videos includes receiving a user request via a communication chat between a user and another user. The user request includes a phrase. The method performs, based on the phrase, a search in a first pool of pre-rendered videos to determine a first subset of relevant videos ranked according to first rankings. The pre-rendered videos include pre-rendered text messages. The method performs a search in a second pool of customized videos to determine a second subset of relevant videos ranked according to second rankings. The customized videos include text messages modified by the user. The method generates a set of videos. The set of videos includes a first number of pre-rendered videos from the first subset of relevant videos and a second number of customized videos from the second subset of relevant videos. The method presents the set of videos via the communication chat.

Abstract: A system to recognize objects in an image includes an object detection network outputs a first hierarchical-calculated feature for a detected object. A face alignment regression network determines a regression loss for alignment parameters based on the first hierarchical-calculated feature. A detection box regression network determines a regression loss for detected boxes based on the first hierarchical-calculated feature. The object detection network further includes a weighted loss generator to generate a weighted loss for the first hierarchical-calculated feature, the regression loss for the alignment parameters and the regression loss of the detected boxes. A backpropagator backpropagates the generated weighted loss.

Abstract: Embodiments of the present disclosure disclose an image processing method and apparatus, a terminal, and a computer-readable storage medium, that are generally related to the field of computer technologies. The method can include obtaining a trained pixel classification model. The pixel classification model can be used for determining a classification identifier of each pixel in any image with the classification identifier including a head classification identifier. The head classification identifier can be used for indicating that a corresponding pixel is located in a head region. The method can further include classifying each pixel in the target image based on the pixel classification model to obtain a classification identifier of the pixel in the target image, and determining a head region in the target image according to pixels whose classification identifiers are a head classification identifier and editing the head region.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include accessing rules that each relate one or more values of the feature vectors to a respective label of a plurality of labels. The actions further include, based on the rules, generating heuristics that each identify related values of the feature vectors. The actions further include, for each of the heuristics, generating a matrix that reflects a similarity of the feature vectors. The actions further include, based on the matrices that each reflects a respective similarity of the feature vectors, generating clusters that each include a subset of the feature vectors. The actions further include, for each cluster, determining a label of the plurality of labels.

Abstract: A method for enhancing facial/object recognition includes receiving a query image, and providing a database of object images, including images relevant to the query image, each image having a first attribute and a second attribute with each of the first attribute and the second attribute having a first state and a second state. The method also includes creating an augmented database by generating a plurality of artificial images for each image in the database, the artificial images cooperating with the image to define a set of images including every combination of the first attribute and the second attribute in each of the first state and the second state, and comparing the query image to the images in the augmented database to find one or more matches.

Abstract: The present disclosure provides a neural network model training method and apparatus, a living body detecting method and apparatus, a device and a storage medium. It is feasible to, with respect to different scenarios, respectively obtain picture samples under the scenarios, each picture sample having a tag, the tag including a living body or a non-living body and a corresponding scenario; and according to obtained picture samples, train to obtain a neural network model whose output is a feature vector which is discriminative for different scenarios. As such, when the living body detection is performed, it is possible to determine the feature vector of the to-be-detected picture according to the neural network model, then determine a picture sample matched with the to-be-detected picture, and regard the tag of the matched picture sample as the tag of the to-be-detected picture, thereby obtaining the detection result of the to-be-detected picture.

Abstract: 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.

Abstract: Apparatuses and methods associated with preserving authentication under item change are disclosed herein. In embodiments, acquiring digital image data of an image of at least a portion of a target physical object; extracting features from the image data to form a digital fingerprint; querying the database system to seek a matching record based on the digital fingerprint; based on an amount of difference between the digital fingerprint and a stored digital fingerprint of the database, update the database system to output a new indication of a new match to the physical object for any new samples that are not matchable to the stored digital fingerprint within a first predetermined similarity threshold provided the new samples are matchable to the digital fingerprint within a second predetermined similarity threshold. Other embodiments may be disclosed or claimed.

Abstract: A system includes a memory module configured to store image data captured by a camera and an electronic controller communicatively coupled to the memory module. The electronic controller is configured to receive image data captured by the camera, implement a neural network trained to predict a drivable portion in the image data of an environment. The neural network predicts the drivable portion in the image data of the environment. The electronic controller is configured to implement a support vector machine. The support vector machine determines whether the predicted drivable portion of the environment output by the neural network is classified as drivable based on a hyperplane of the support vector machine and output an indication of the drivable portion of the environment.

Abstract: Techniques are described herein for predicting disk drive failure using a machine learning model. The framework involves receiving disk drive sensor attributes as training data, preprocessing the training data to select a set of enhanced feature sequences, and using the enhanced feature sequences to train a machine learning model to predict disk drive failures from disk drive sensor monitoring data. Prior to the training phase, the RNN LSTM model is tuned using a set of predefined hyper-parameters. The preprocessing, which is performed during the training and evaluation phase as well as later during the prediction phase, involves using predefined values for a set of parameters to generate the set of enhanced sequences from raw sensor reading. The enhanced feature sequences are generated to maintain a desired healthy/failed disk ratio, and only use samples leading up to a last-valid-time sample in order to honor a pre-specified heads-up-period alert requirement.

Abstract: An apparatus for learning an image of a vehicle camera and a method thereof are provided to apply a result of deep learning to all vehicles regardless of the color of a vehicle and the mounting angle (e.g., yaw, roll and pitch) of a camera. The apparatus includes an image input device that inputs an image photographed by a camera mounted on a vehicle, and a controller that masks a fixed area in the image input from the image input device with a pattern image, converts the masked image into a plurality of images having different views, and performs deep learning by using the masked image and the converted plurality of images.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include, for a subset of the feature vectors, accessing a first label. The actions further include generating a classifier that is configured to associate a given feature vector with a feature vector of the subset of the feature vectors. The actions further include applying the feature vectors that are not included in the subset of the feature vectors to the classifier. The actions further include generating a dissimilarity matrix. The actions further include, based on the dissimilarity matrix, generating a graph. The actions further include, for each node of the graph, determining a second label. The actions further include, based on the second labels and the first labels, determining a training label for each feature vector.

Abstract: An image acquisition system determines first and second sets of points defining an iris-pupil boundary and an iris-sclera boundary in an acquired image; determines respective ellipses fitting the first and second sets of points; determines a transformation to transform one of the ellipses into a circle on a corresponding plane; using the determined transformation, transforms the selected ellipse into a circle on the plane; using the determined transformation, transforms the other ellipse into a transformed ellipse on the plane; determines a plurality of ellipses on the plane for defining an iris grid, by interpolating a plurality of ellipses between the circle and the transformed ellipse; moves the determined grid ellipses onto the iris in the image using an inverse transformation of the determined transformation; and extracts an iris texture by unwrapping the iris and interpolating image pixel values at each grid point defined along each of the grid ellipses.

Abstract: Digital image ordering based on object position and aesthetics is leveraged in a digital medium environment. According to various implementations, an image analysis system is implemented to identify visual objects in digital images and determine aesthetics attributes of the digital images. The digital images can then be arranged in way that prioritizes digital images that include relevant visual objects and that exhibit optimum visual aesthetics.

Abstract: The disclosure relates, in part, to computer-based visualization of stent position within a blood vessel. A stent can be visualized using intravascular data and subsequently displayed as stent struts or portions of a stent as a part of a one or more graphic user interface(s) (GUI). In one embodiment, the method includes steps to distinguish stented region(s) from background noise using an amalgamation of angular stent strut information for a given neighborhood of frames. The GUI can include views of a blood vessel generated using distance measurements and demarcating the actual stented region(s), which provides visualization of the stented region. The disclosure also relates to display of intravascular diagnostic information such as indicators. An indicator can be generated and displayed with images generated using an intravascular data collection system. The indicators can include one or more viewable graphical elements suitable for indicating diagnostic information such as stent information.

Abstract: A computer-implemented method according to one embodiment includes analyzing an image stored on a device. In response to determining that a storage consumption of the device is greater than a first predetermined threshold, content of the image that is both non-focused and that is not of interest is selectively compressed. In response to determining that the storage consumption subsequent to selectively compressing content of the image that is both non-focused and that is not of interest, is greater than a second predetermined threshold, content of the image that is both focused and that is not of interest is selectively compressed. In response to determining that the storage consumption subsequent to selectively compressing content of the image that is both focused and that is not of interest, is greater than the second predetermined threshold, content of the image that is both focused and that is of interest is selectively compressed.

Abstract: A deepfake video detection system, including an input data detection module of a video recognition unit for setting a target video; a data pre-processing unit for detecting eye features from the face in the target video; a feature extraction module for extracting eye features and inputting the eye features to a long-term recurrent convolutional neural network (LRCN); and then using a sequence of long-term and short-term memory (LSTM) of a learning module; performing sequence learning; using a state prediction module to predict the output of each neuron, and then using a long and short-term memory model to output the quantized eye state, then connecting to a state quantification module, and comparing the original stored data from the normal video and the quantified eye state information of the target video, and outputting the recognition result by an output data recognition module.

Abstract: Disclosed is a detection method for quality grade of traditional Chinese medicine (TCM), including: detecting the levels of quality control index components of TCM and efficacy-related in vitro activity by establishing a correlation between principal components of TCM and in vitro activity; determining the state of sample cluster by principal component analysis; constructing a logistic regression model of quality grade versus index components and bioactivity and establishing corresponding grade detection formulas of Chinese medicinal materials by fitting a large number of sample data for Chinese medicinal materials from different places of origin and batches. The method of the present invention realizes the mathematical expression of a standard for quality difference of TCM, and provides a feasible solution for the industrialized evaluation of quality grades of Chinese medicinal materials or herbal slices finally.

Abstract: This disclosure relates generally to a system and method for monitoring and quality evaluation of perishable food items in quantitative terms. Current technology provides limited capability for controlling environmental conditions surrounding the food items in real-time or any quantitative measurement for the degree of freshness of the perishable food items. The disclosed systems and methods facilitate in quantitative determination of freshness of food items by utilizing sensor data and visual data obtained by monitoring the food item. In an embodiment, the system utilizes a pre-trained CNN model and a RNN model, where the pertained CNN model is further fine-tined while training the RNN model to provide robust quality monitoring of the food items. In another embodiment, a rate kinetic based model is utilized for determining reaction rate order of the food item at a particular post-harvest stage of the food item so as to determine the remaining shelf life thereof.