Abstract: An object recognition system can be adapted to recognize subject matter having very few features or limited or no texture. A feature-sparse or texture-limited object can be recognized by complementing local features and/or texture features with color, region-based, shape-based, three-dimensional (3D), global, and/or composite features. Machine learning algorithms can be used to classify such objects, and image matching and verification can be adapted to the classification. Further, multiple modes of input can be integrated at various stages of the object recognition processing pipeline. These multi-modal inputs can include user feedback, additional images representing different perspectives of the object or specific regions of the object including a logo or text corresponding to the object, user behavior data, location, among others.

Abstract: The technology relates to controlling a vehicle based on a railroad light's activation status. In one example, one or more processors receive images of a railroad light. The one or more processors determine, based on the images of the railroad light, the illumination status of a pair of lights of the railroad light over a period of time as the vehicle approaches the railroad light. The one or more processors determine based on the illumination status of the pair of lights, a confidence level, wherein the confidence level indicates the likelihood the railroad light is active. The vehicle is controlled as it approaches the railroad light based on the confidence level.

Abstract: Aspects of the disclosure relate to training and using a model for determine states of lanes of interest. For instance, image data including an image and an associated label identifying at least one traffic light, a state of the at least one traffic light, and a lane controlled by the at least one traffic light may be received and used to train the mode such that the model is configured to, in response to receiving an image and a lane of interest included in the image, output a lane state for the lane of interest. This model may then be used by a vehicle in order to determine a state of a lane of interest. This state may then be used to control the vehicle in an autonomous driving mode based on the state of the lane of interest.

Abstract: Approaches provide for analyzing image data to determine and/or recognize text in the image data. The recognized text can be used to generate a search query that can be automatically submitted to a search engine without having to type the search query to identify a product (or related products) associated with the image. For example, a camera of a computing device can be used to capture a live camera view (or single images) an item. An application executing on the computing device (or at least in communication with the computing device) can analyze the image data of the live camera view to determine a set of keywords (e.g., identified text) based on visual features extracted from the image data. The keywords can be used to query an index of product titles, common search queries, among other indexed data to return a ranked list of search suggestions based on a relevance function.

Abstract: An image creation and editing tool can use the data produced from training a neural network to add stylized representations of an object to an image. An object classification will correspond to an object representation, and pixel values for the object representation can be added to, or blended with, the pixel values of an image in order to add a visualization of a type of object to the image. Such an approach can be used to add stylized representations of objects to existing images or create new images based on those representations. The visualizations can be used to create patterns and textures as well, as may be used to paint or fill various regions of an image. Such patterns can enable regions to be filled where image data has been deleted, such as to remove an undesired object, in a way that appears natural for the contents of the image.

Abstract: An image creation and editing tool can use the data produced from training a neural network to add stylized representations of an object to an image. An object classification will correspond to an object representation, and pixel values for the object representation can be added to, or blended with, the pixel values of an image in order to add a visualization of a type of object to the image. Such an approach can be used to add stylized representations of objects to existing images or create new images based on those representations. The visualizations can be used to create patterns and textures as well, as may be used to paint or fill various regions of an image. Such patterns can enable regions to be filled where image data has been deleted, such as to remove an undesired object, in a way that appears natural for the contents of the image.

Abstract: Approaches include using a machine learning-based approach to generating search strings and refinements based on a specific item represented in an image. For example, a classifier that is trained on descriptions of images can be provided. An image that includes a representation of an item of interest is obtained. The image is analyzed using the classifier algorithm to determine a first term representing a visual characteristic of the image. Then, the image is analyzed again to determine a second term representing another visual characteristic of the image based at least in part on the first term. Additional terms can be determined to generate a description of the image, including characteristics of the item of interest. Based on the determined characteristics of the item of interest, a search query and one or more refinements can be generated.

Abstract: An object recognition system can be adapted to recognize subject matter having very few features or limited or no texture. A feature-sparse or texture-limited object can be recognized by complementing local features and/or texture features with color, region-based, shape-based, three-dimensional (3D), global, and/or composite features. Machine learning algorithms can be used to classify such objects, and image matching and verification can be adapted to the classification. Further, multiple modes of input can be integrated at various stages of the object recognition processing pipeline. These multi-modal inputs can include user feedback, additional images representing different perspectives of the object or specific regions of the object including a logo or text corresponding to the object, user behavior data, location, among others.

Abstract: An object recognition system can be adapted to recognize subject matter having very few features or limited or no texture. A feature-sparse or texture-limited object can be recognized by complementing local features and/or texture features with color, region-based, shape-based, three-dimensional (3D), global, and/or composite features. Machine learning algorithms can be used to classify such objects, and image matching and verification can be adapted to the classification. Further, multiple modes of input can be integrated at various stages of the object recognition processing pipeline. These multi-modal inputs can include user feedback, additional images representing different perspectives of the object or specific regions of the object including a logo or text corresponding to the object, user behavior data, location, among others.

Abstract: The claimed subject matter provides for systems and/or methods for identification of instances of an object of interest in 2D images by creating a database of 3D curve models of each desired instance and comparing an image of an object of interest against such 3D curve models of instances. The present application describes identifying and verifying the make and model of a car from a possibly single image—after the models have been populated with training data of test images of many makes and models of cars. In one embodiment, an identification system may be constructed by generating a 3D curve model by back-projecting edge points onto a visual hull reconstruction from silhouettes of an instance. The system and methods employ chamfer distance and orientation distance provides reasonable verification performance, as well as an appearance model for the taillights of the car to increase the robustness of the system.

Abstract: The purpose of the invention is to provide a password with lower cost but higher safety used in an authentication system, and users may choose one specific picture as a password to register or log in the internet. Because the data string of the picture is too big for general crackers to alter, break and steal the data string of the picture with currently available cracked methods. The present invention of the authentication system also includes a communication device and a cloud server to provide users to register or log in the system.

Abstract: The claimed subject matter provides for systems and/or methods for identification of instances of an object of interest in 2D images by creating a database of 3D curve models of each desired instance and comparing an image of an object of interest against such 3D curve models of instances. The present application describes identifying and verifying the make and model of a car from a possibly single image—after the models have been populated with training data of test images of many makes and models of cars. In one embodiment, an identification system may be constructed by generating a 3D curve model by back-projecting edge points onto a visual hull reconstruction from silhouettes of an instance. The system and methods employ chamfer distance and orientation distance provides reasonable verification performance, as well as an appearance model for the taillights of the car to increase the robustness of the system.

Abstract: The claimed subject matter provides for systems and/or methods for identification of instances of an object of interest in 2D images by creating a database of 3D curve models of each desired instance and comparing an image of an object of interest against such 3D curve models of instances. The present application describes identifying and verifying the make and model of a car from a possibly single image—after the models have been populated with training data of test images of many makes and models of cars. In one embodiment, an identification system may be constructed by generating a 3D curve model by back-projecting edge points onto a visual hull reconstruction from silhouettes of an instance. The system and methods employ chamfer distance and orientation distance provides reasonable verification performance, as well as an appearance model for the taillights of the car to increase the robustness of the system.

Abstract: The purpose of the invention is to provide a password with lower cost but higher safety used in an authentication system, and users may choose one specific picture as a password to register or log in the internet. Because the data string of the picture is too big for general crackers to alter, break and steal the data string of the picture with currently available cracked methods. The present invention of the authentication system also includes a communication device and a cloud server to provide users to register or log in the system.

Abstract: The claimed subject matter provides for systems and/or methods for identification of instances of an object of interest in 2D images by creating a database of 3D curve models of each desired instance and comparing an image of an object of interest against such 3D curve models of instances. The present application describes identifying and verifying the make and model of a car from a possibly single image—after the models have been populated with training data of test images of many makes and models of cars. In one embodiment, an identification system may be constructed by generating a 3D curve model by back-projecting edge points onto a visual hull reconstruction from silhouettes of an instance. The system and methods employ chamfer distance and orientation distance provides reasonable verification performance, as well as an appearance model for the taillights of the car to increase the robustness of the system.

Abstract: Growth differentiation factor-15 (GDF-15) polynucleotide sequence and amino acid sequence are provided herein. Also described are diagnostic and therapeutic methods of using GDF-15 polypeptide and polynucleotide sequences.

Abstract: Growth differentiation factor-15 (GDF-15) polynucleotide sequence and amino acid sequence are provided herein. Also described are diagnostic and therapeutic methods of using GDF-15 polypeptide and polynucleotide sequences.