38results about How to "Improve object recognition" patented technology

A device, apparatus, and method provide logic for processing information. In one implementation, a device may include an image acquisition unit configured to acquire an image, and a transmission unit configured to transmit information associated with the image to an information processing apparatus, such as a server. The server may be associated with a first feature quantity dictionary. The device also may include a receiving unit configured to receive a second feature quantity dictionary from the server in response to the transmission. The second feature quantity dictionary may include less information than the first feature quantity dictionary, and the server may generate the second feature quantity dictionary based on the image information and the first feature quantity dictionary. The device may include an identification unit configured to identify an object within the image using the second feature quantity dictionary.

Disclosed herein are an object recognition system and method which extract left and right feature vectors from a stereo image of an object, find feature vectors which are present in both the extracted left and right feature vectors, compare information about the left and right feature vectors and the feature vectors present in both the extracted left and right feature vectors with information stored in a database, extract information of the object, and recognize the object.

The invention discloses an object recognizing method and device of an intelligent robot. The method comprises the following steps of indication receiving: receiving a study indication from a user for recognizing a target object; information obtaining: responding the study indication, and obtaining a picture of the target object and recognition parameters, input by the user through voice, of the target object; tag storage: according to the recognition parameters, setting a tag for the picture, and storing the tag; object recognition: when the object recognition indication is received, recognizing the target object according to the tag. The method and the device have the advantages that the object recognition capability of the intelligent robot can be improved; the recognition on a large number of objects is realized at low cost; the recognition on objects with customized appearances can also be realized; the user experience is improved; the intellectuality and the humanoid performance of the intelligent robot object are improved.

A visual recognition support system includes a noise area determination function for determining a noise area where a visual noise is outputted to improve sensitivity of visual recognition of an object in need of attention of an operator of a vehicle. The intensity of the visual noise is determined based on a threshold of the visual perception.

A learning unit 4 generates a function table indicating the relationship between the class number and position information of an object and the probability of appearance of the object for each small area image pattern of a code book, calculates a sharing matrix indicating the commonality of a feature amount between the classes, makes a tree diagram in which the classes with a similar feature amount are clustered, and calculates the weight of each node in the tree diagram for each small area image pattern. The recognition processing unit 7 compares image data captured by a camera 2 with the code book, selects the closest small area image pattern among a plurality of small area image patterns, extracts the class related to the node with the smallest weight among the nodes with a weight equal to or greater than a threshold value for the selected small area image pattern, and votes the position information of the small area image pattern for the class, thereby recognizing the object.

A process for fixed camera and unmanned mobile device collaboration is disclosed in order to improve identification of an object of interest. A first point of view (POV) of a captured object is obtained and it is determined, with a first level of certainty, that the captured first POV of the object matches a stored object of interest. Then one or more camera-equipped unmanned mobile vehicles are identified in a determined direction of travel of the first captured object, and a dispatch instruction and intercept information is then transmitted to the one or more camera-equipped unmanned mobile vehicles. Subsequently, a captured second POV of the first captured object is received via the one or more camera-equipped unmanned mobile vehicles. The captured second POV of the captured object is used to determine, with a second level of certainty, that the captured object matches the stored object of interest.

A process for fixed camera and unmanned mobile device collaboration is disclosed in order to improve identification of an object of interest. A first point of view (POV) of a captured object is obtained and it is determined, with a first level of certainty, that the captured first POV of the object matches a stored object of interest. A dispatch instruction and intercept information is then wirelessly broadcast for receipt by camera-equipped unmanned mobile vehicles within a broadcast range for identifying and intercepting the object. Subsequently, a captured second POV of the first captured object is received via the one or more camera-equipped unmanned mobile vehicles. The captured second POV of the captured object is used to determine, with a second level of certainty, that the captured object matches the stored object of interest.

Provided is an apparatus for generating data based on generative adversarial networks (GANs), the apparatus including a first generator configured to receive input data and generate a first fake image and a first discriminator configured to receive the first fake image generated by the first generator and a first real image and verify whether an image is fake or real.

A training method and device for an object recognition model. An apparatus for optimizing a neural network model for object recognition, including a loss determination unit configured to determine loss data for features extracted from a training image set using the neural network model and a loss function with a weight function, and an updating unit configured to perform an updating operation on parameters of the neural network model based on the loss data and an updating function, wherein the updating function is derived based on the loss function with the weight function of the neural network model, and the weight function and the loss function change monotonically in a specific value interval in the same direction.

Disclosed are object recognizing method and apparatus. The method may comprise extracting a feature point of an object to be recognized in an input image; generating a plurality of feature images by extracting orientation information of a region adjacent to the feature point and normalizing the region based in the orientation information; deriving a covariance descriptor based on the feature images; and recognizing the object in the input image by comparing the covariance descriptor with a reference covariance descriptor stored in advance. Thus, the object can be recognized fast and robustly to changes on the size of the input image, rotations of the input image, and illumination, whereby the recognition performance can be enhanced.

A method of controlling a mobile apparatus includes acquiring a first original image and a second original image, extracting a first feature point of the first original image and a second feature point of the second original image, generating a first blurring image and a second blurring image by blurring the first original image and the second original image, respectively, calculating a similarity between at least two images of the first original image, the second original image, the first blurring image, and the second blurring image, determining a change in scale of the second original image based on the calculated similarity, and controlling at least one of an object recognition and a position recognition by matching the second feature point of the second original image to the first feature point of the first original image based on the change in scale.

Provided is an object recognition system including: a transmitting unit configured to include one or more light emitting unit to transmit laser light having different wavelengths to an object; a receiving unit configured to receive light reflected from the object to acquire information on the reflected light of a size, a speed, a number plate, a reflector, and material of the object; and a processing unit configured to store the feature information on objects of a vehicle, a two-wheeled vehicle, and a pedestrian and compare the information on the reflected light of the object received from the receiving unit with the feature information on the object to classify and recognize the objects into the vehicle, the two-wheeled vehicle, and the pedestrian.

A host vehicle is provided with external environment recognition sensors that acquire forward information to determine that a pedestrian is present in a forward position of the host vehicle using the external environment recognition sensors. In this pedestrian determination method, a front camera and a millimeter wave radar are provided as the external environment recognition sensors. When a pedestrian candidate is detected in a forward position of the host vehicle based on an image signal from the front camera, a matching area is set with the position of the detected pedestrian candidate as the center point. When the position of the object that is closest to the pedestrian candidate from among the plurality of objects detected by reflected waves from the millimeter wave radar is within the matching area thereby establishing a match, the pedestrian candidate is determined to be a pedestrian.

A process for fixed camera and unmanned mobile device collaboration is disclosed in order to improve identification of an object of interest. A first point of view (POV) of a captured object is obtained and it is determined, with a first level of certainty, that the captured first POV of the object matches a stored object of interest. Then one or more camera-equipped unmanned mobile vehicles are identified in a determined direction of travel of the first captured object, and a dispatch instruction and intercept information is then transmitted to the one or more camera-equipped unmanned mobile vehicles. Subsequently, a captured second POV of the first captured object is received via the one or more camera-equipped unmanned mobile vehicles. The captured second POV of the captured object is used to determine, with a second level of certainty, that the captured object matches the stored object of interest.

Disclosed is a working method using a sensor, which increases recognition of a component to increase mounting of a component and enhancing productivity. The working method includes: extracting an object to be picked from a pile of objects using the sensor; picking the extracted object to move the picked object to a predetermined place; and estimating an angle of the moved object in the current position using the sensor. Accordingly, the working method can perform precise component recognition and posture estimation by two steps: a component picking step and a component recognition step, and effectively apply to a manufacturing line, thereby improving mounting of a component and enhancing productivity of a product.

There is provided compounds of formula (I), wherein R1, R2, R3, R4, R5, R6, R7, R8, m, n, q and r have meanings given in the description, and pharmaceutically acceptable derivatives thereof, which compounds are useful in the treatment of diseases and conditions associated with inflammation.

An electronic device is provided. The electronic device includes a housing that includes a sensing surface, a light emitting unit that outputs a light through the sensing surface, a light receiving unit that collects a reflection light reflected from an external object in contact with the sensing surface, after the light is output from the light emitting unit, and a first Fresnel lens that is disposed between the light receiving unit and the sensing surface. A first surface of the first Fresnel lens total reflects the reflection light introduced in a direction perpendicular to the sensing surface, and a second surface of the first Fresnel lens refracts the total reflected light so as to be introduced to the light receiving unit.

A process for fixed camera and unmanned mobile device collaboration is disclosed in order to improve identification of an object of interest. A first point of view (POV) of a captured object is obtained and it is determined, with a first level of certainty, that the captured first POV of the object matches a stored object of interest. A dispatch instruction and intercept information is then wirelessly broadcast for receipt by camera-equipped unmanned mobile vehicles within a broadcast range for identifying and intercepting the object. Subsequently, a captured second POV of the first captured object is received via the one or more camera-equipped unmanned mobile vehicles. The captured second POV of the captured object is used to determine, with a second level of certainty, that the captured object matches the stored object of interest.

A method for training a machine learning system, in which image data are fed into a machine learning system with processing of at least a part of the image data by the machine learning system. The method includes synthetic generation of at least a part of at least one depth map that includes a plurality of depth information values. The at least one depth map is fed into the machine learning system with processing of at least a part of the depth information values of the at least one depth map. The machine learning system is then trained based on the processed image data and based on the processed depth information values of the at least one depth map, with adaptation of a parameter value of at least one parameter of the machine learning system, the adapted parameter value influencing an interpretation of input data by the machine learning system.

An electronic device is provided. The electronic device includes a housing that includes a sensing surface, a light emitting unit that outputs a light through the sensing surface, a light receiving unit that collects a reflection light reflected from an external object in contact with the sensing surface, after the light is output from the light emitting unit, and a first Fresnel lens that is disposed between the light receiving unit and the sensing surface. A first surface of the first Fresnel lens total reflects the reflection light introduced in a direction perpendicular to the sensing surface, and a second surface of the first Fresnel lens refracts the total reflected light so as to be introduced to the light receiving unit.

The invention relates to the technical field of large hydropower engineering low-altitude defense systems, in particular to a bit synchronization algorithm for maximum likelihood estimation, which comprises the following steps: S1, defining a system defense area; s2, setting a distributed and multi-node defense system; s3, formulating alarm levels of multiple stages; s4, setting a dual-guarantee working mode; s5, information is comprehensively obtained through the system, and comprehensive evaluation is carried out; and S6, the equipment and the system perform self-inspection, and the system defense area is divided into two parts, namely a core area and a detection early warning area: a, the equipment can form an invisible dome-type protection cover in the core area, and the hierarchical management of the whole area can be completed through the distributed and multi-node defense system; according to the invention, the working efficiency of the system can be effectively improved, the operation process of the system is simplified, the reliability of the system is improved, the detection accuracy and precision of the target can be improved, and the purposes of accurate identification and accurate strike of the low-altitude target are achieved.

A learning unit generates a function table indicating the relationship between the class number and position information of an object and the probability of appearance of the object for each small area image pattern of a code book, calculates a sharing matrix indicating the commonality of a feature amount between the classes, makes a tree diagram in which the classes with a similar feature amount are clustered, and calculates the weight of each node in the tree diagram for each small area image pattern. The recognition processing unit compares image data captured by a camera with the code book, selects the closest small area image pattern, extracts the class related to the node with the smallest weight among the nodes with a weight equal to or greater than a threshold value, and votes the position information of the small area image pattern for the class, thereby recognizing the object.

An apparatus for generating data based on generative adversarial networks (GANs) is provided. The apparatus includes a first generator configured to receive input data and generate a first fake image, a first discriminator configured to receive the first fake image generated by the first generator and a first real image and verify whether an image is fake or real, a second generator configured to receive the first fake image and generate a second fake image; and a second discriminator configured to receive the second fake image and a second real image and verify whether an image received by the second discriminator is fake or real. The apparatus improves an object recognition performance as compared with a typical object recognition algorithm by generating an image of a class of which a sufficient database is not secured so as to reduce an object misrecognition caused by an insufficient amount of data.

The present invention relates to a method for improving behaviour in a human subject, comprising administering a nutritional composition comprising 5-50 wt % vegetable lipids based on dry weight of the composition to the subject, the nutritional composition comprising lipid globules having (i) a volume-weighted mode diameter above 1.0 micrometer and / or (ii) a diameter of 2 to 12 micrometer in an amount of at least 45 volume % based on total lipid, wherein improving behaviour is selected from (a) increasing novel object recognition; (b) increasing the preference for novelty; (c) increasing the attention for surroundings; and (d) increasing exploratory behaviour and / or exploratory activity.