A list of images is received. The images were captured by a sensor of an ADV chronologically while driving through a driving environment. A first image of the images is identified that includes a first object in a first dimension (e.g., larger size) detected by an object detector using an object detection algorithm. In response to the detection of the first object, the images in the list are traversed backwardly in time from the first image to identify a second image that includes a second object in a second dimension (e.g., smaller size) based on a moving trail of the ADV represented by the list of images. The second object is then labeled or annotated in the second image equivalent to the first object in the first image. The list of images having the labeled second image can be utilized for subsequent object detection during autonomous driving.

A method for the computer-aided training of an artificial neural network (ANN) for recognizing structural features on objects, by means of which method identified structural features on objects are recognizable rapidly and reliable. That is achieved by virtue of the fact that a convolutional neural network (CNN) having a multiplicity of neurons is used for the training of an ANN for feature recognition on objects. Said network comprises a multiplicity of convolutional and/or pooling layers for the extraction of information from images of individual objects. In this case, the images of the objects are respectively scaled or scaled up and/or down from layer to layer. During the scaling of the images information about the structural features of the objects is maintained, specifically independently of the scaling of the images.

A system and method for identifying ground types from one or more interpolated covariates. The method proceeds by accessing soil composition information for plots of land, in which the soil composition information includes measured soil sample results, environmental results, soil conductivity results or any combination thereof. The method continues by identifying covariates from the soil composition information. Subsequently, the method interpolates covariates associated with different locations with an interpolation training model. Voxels are generated that are each associated with interpolated covariates having a corresponding geographical location. The method trains a random forest training model with the interpolated covariates. The voxels traverse the trained random forest model to identify clusters of voxels that are co-associated. The method identifies a ground type by combining the co-associated clusters. Each ground type is associated with a crop zone, a soil fertility, or a farm management recommendation.

Described herein is a computer-implemented method. The method includes: providing digital images and respective formulas for coating compositions with known pigments and/or pigment classes associated with the respective digital images, classifying, using an image annotation tool, for each digital image, each pixel, by visually reviewing the respective digital image pixel-wise, providing, for each digital image, an associated pixel-wise annotated image, training a first neural network with the provided digital images as input and the associated pixel-wise annotated images as output, making the trained first neural network available for applying the trained first neural network to at least one unknown input image of a target coating and for assigning a pigment label and/or a pigment class label to each pixel in the at least one unknown input image, and determining and/or outputting, for each unknown input image, a statistic of corresponding identified pigments and/or pigment classes, respectively.

The techniques described herein relate to computerized methods and apparatus for grouping images of objects based on semantic and visual information associated with the objects. The techniques described herein further relate to computerized methods and apparatus for training a machine learning model for object recognition.

Technology for the improved processing of search queries is provided. In one embodiment, methods may return semantically relevant search results for a search query. During a pre-computing offline processing, an inventory semantic index may be generated and may include inventory binary hashing signatures that are associated with inventory listings, such as goods or services for sell, and the index may be partitioned by categories and shards. When a search query is received, relevant categories are determined using a relevant category recognition service, and a search query binary hashing signature maybe generated for the search query. The relevant categories are searched to determine hamming distances between the inventory binary hashing signatures and the search query binary hashing signature, where the hamming distance indicates semantic relevance.

A system for predicting alimentary element ordering based on biological extraction, the system comprising a computing device configured to receive a biological extraction and alimentary element order chronicle of a user, retrieve an alimentary profile, identify, using the alimentary profile and a predictive machine-learning process, a predicted alimentary element and an alternative alimentary element, determine, using the predictive machine-learning process and the alimentary profile, the predicted alimentary element, select, using the predicted alimentary element, the alternative alimentary element, create a classifier, using a classification machine-learning process as a function of a plurality of alimentary element metrics, generate a plurality of related alimentary elements as a function of the classifier, rank the related alimentary elements as a function of the biological extraction, select the alternative alimentary element as a function of the ranking, and present the predicted alimentary element and the alternative alimentary element via a graphical user interface.

A computer implemented method is for determining railway vehicle movement profile type of a railway vehicle movement profile. The railway vehicle movement profile includes a sequence of measured transmitted currents of a transceiver of a track circuit with respect to the time. The method includes obtaining a railway vehicle movement profile, normalizing the railway vehicle movement profile, extracting one or more features from the normalized railway vehicle movement profile, determining the distance of the extracted features with respect to each centroid of a railway vehicle movement profile type determined in a classification process, and assigning the railway vehicle movement profile to the railway vehicle movement profile type with the closest centroid

A mechanism is described for facilitating embedding of human labeler influences in machine learning interfaces in computing environments, according to one embodiment. A method of embodiments, as described herein, includes detecting sensor data via one or more sensors of a computing device, and accessing human labeler data at one or more databases coupled to the computing device. The method may further include evaluating relevance between the sensor data and the human labeler data, where the relevance identifies meaning of the sensor data based on human behavior corresponding to the human labeler data, and associating, based on the relevance, human labeler data with the sensor data to classify the sensor data as labeled data. The method may further include training, based on the labeled data, a machine learning model to extract human influences from the labeled data, and embed one or more of the human influences in one or more environments representing one or more physical scenarios involving one or more humans.

Diagnostic and prognostic assays and a portable point of care system is provided that performs such assays using an automated, artificial intelligence (AI) based molecular analyses of a subject sample. The system provides for rapid, cost-efficient multiplexable assessment of biomarker panels in a cell sample and may be easily used for global and remote applications.