Abstract: A method for processing images of assembly line objects for quality or other inspection acquires at least one first image of an object from an image capturing device, and recognizes image irradiation areas of different monochromatic lights of the acquired first image. The method further includes dividing each of the first image according to the recognized irradiation areas, generating at least one second image by integrating and stitching the divided first images under same monochromatic light, and outputting the at least one second image.

Abstract: An identity authentication device includes a communication unit configured to establish communication with a plurality of online servers, a processor, and a storage configured to store a user profile database including a number of user profiles, wherein each of the user profiles corresponds to an online browsing habit. The processor receives an online browsing record of a user, compares the received online browsing record to the online browsing habits of the user profiles stored in the user profile database to determine whether the received online browsing record matches one of the online browsing habits, and determines whether the received online browsing record belongs to a same user of a matched one of the user profiles according to a result of comparison.

Abstract: A method for detecting defects in manufactured objects includes acquiring a high-resolution image of an object for test, dividing the image into a plurality of smaller sub-images and determining, by a first model, whether each of the small sub-images is similar to a preset template image. The test object is determined to be flawless, when each of the sub-images is found to be similar to a template image. When sub-images are not found sufficiently similar to template images, determining, by a second model, whether a defect is shown to exist within each sub-image, and if so the test object is declared defective. The longer application of the second model is only applied if testing for defects is not resolved by the application of the first model. An electronic device and a computer readable storage medium are also provided.

Abstract: A method for assisting a driver to drive a vehicle in a safer manner includes capturing images of road in front of the vehicle, and identifying a traffic sign in the images. A first image frame is captured at a first time and a second image frame is captured at a later second time when the images do comprise the traffic sign. A change in size or other apparent change of the traffic sign from the first image frame to the second image frame is determined, and conformity or non-conformity with a predetermined rule is then determined. The traffic sign can be analyzed and recognized to trigger the vehicle to perform an action accordingly when conformity is found. A device providing assistance with driving is also provided.

Abstract: A voice recognition system includes a computing device and at least one mobile terminal communicatively coupled to the computing device through a network. The computing device obtains an original sound from the at least one mobile terminal and converts the original sound into a digitized time-frequency map, performs compression segmentation on the time-frequency map to obtain a sound image corresponding to the time-frequency map, and uses an image recognition method to recognize the sound image, obtain an enhanced sound image, and search a preset database for sound information corresponding to the enhanced sound image.

Abstract: A cloud device, a terminal device, and an image classification system and method includes the terminal reading an image to be classified; transmitting an instruction of image classification to the cloud device; determining whether the instruction of image classification comprises an intermediate operation result of the first partial operation; performing the second partial operation to obtain an image classification result, when the instruction of image classification comprises intermediate operation result of the first partial operation; and transmitting the image classification result to the terminal device; performing the first partial operation to obtain the intermediate operation result, when the intermediate operation result is not contained in the instruction of image classification; transmitting the intermediate operation result to the terminal device; and performing the second partial operation to obtain an image classification result.

Abstract: A speech recognition device includes a speech acquiring unit, a speech outputting unit, a camera unit, and a processor. The processor obtains speech input acquired by the speech acquiring unit, obtains images acquired by the camera unit, chronologically links the obtained speech and the obtained images together, compares the obtained speech to a speech database to confirm matching speech and a confidence level of the matching speech, determines whether the confidence level of the matching speech exceeds a predetermined confidence level, and outputs the matching speech when the confidence level of the matching speech exceeds the predetermined confidence level.

Abstract: A marketing system includes at least one camera device detecting an image of a customer entering a designated area in real time, a computing device extracting appearance features of the customer from the image and matching the customer with associated commodities according to a database of appearance features and associated commodities, and at least one mobile terminal. The computing device, the at least one camera device, and the at least one mobile terminal are communicatively coupled through a network. The computing device obtains a navigation map of the designated area, determines location information of the customer entering the designated area, sends the location information to the mobile terminal, and labels the location information in the navigation map in the mobile terminal. The location information of the customer is displayed in a preset symbol in the navigation map according to a location of the associated commodity.

Abstract: A cloud device, a terminal device, and an image classification system and method includes the terminal reading an image to be classified; transmitting an instruction of image classification to the cloud device; determining whether the instruction of image classification comprises an intermediate operation result of the first partial operation; performing the second partial operation to obtain an image classification result, when the instruction of image classification comprises intermediate operation result of the first partial operation; and transmitting the image classification result to the terminal device; performing the first partial operation to obtain the intermediate operation result, when the intermediate operation result is not contained in the instruction of image classification; transmitting the intermediate operation result to the terminal device; and performing the second partial operation to obtain an image classification result.

Abstract: A neural network training method includes: obtaining a weight value between each two connected nodes of a neural network, wherein the neural network comprises a plurality of nodes and each of the plurality of nodes represents an activation function, the nodes are distributed in a plurality of layers arranged in order of computation, and each of the nodes is connected to all the nodes of a followed neighboring layer; integrating an input value and a corresponding weight value of each of the nodes using an evolutionary computation to dynamically generate an output value; correcting the weight value between each two connected nodes; integrating a corrected weight value and the output value of each of the nodes iteratively to obtain the output value of a corresponding connected node of a followed neighboring layer. A neural network training device and a computer storage medium are also provided.

Abstract: A method for classifying images by applying probabilities in steps includes selecting image category corresponding to an image classifier with the first probability value top ranked from the first layer image classifier set when the first layer image classifier set cannot classify an image to be identified. A second layer image classifier set is established according to the selected image category. The method selects output of the image classifier with the largest second probability value from the second layer image classifier set as result of classification when the second probability value of at least one image classifier is greater than or equal to a second predetermined value.

Abstract: A method for identifying and associating information with particular individuals to render same sufficient for registration and login purposes by a specific individual, used in an identity information matching system, includes identifying individuals in scene, recording voices in the scene, identifying individual voices and words and semantics used, and determining an action from a target individual given in response to a trigger voice emanating from the individual voices. The trigger voice and words and semantics associated with the responding target individual are recorded. An identity information matching system and a user device processing the identity information matching method are also disclosed.

Abstract: A method for detecting an obstacle applicable in an electronic device includes detecting whether at least one object is within a line of sight of an image capturing device. The image capturing device is controlled to capture a first image of the object and the image capturing device is caused to move until a capturing angle for capturing another image of the object is changed. The image capturing device is controlled to capture a second image of the object and a determination is made as to whether the object in the first image is the same as the object in the second image. For such recognized objects, the object is determined to be a non-planar obstacle when the object in the first image is not the same as the object in the second image.

Abstract: An electronic device includes a display screen, a processor, and a memory. The processor is configured to obtain attributive information of a facial image, divide the facial image into different attributive portions according to the attributive information, calculate a surface area of each attributive area, determine whether any one of the attributive areas is a distorted area, arrange the attributive areas, when one of the attributive areas is a distorted area, according to a degree of distortion from a largest degree of distortion to a smallest degree of distortion, and process a preset number of the attributive areas having the largest degree of distortion.

Abstract: An autopilot control method is implemented in an electronic device of a host vehicle. The autopilot control method includes obtaining a first distance between the host vehicle and a first vehicle in front of the host vehicle, obtaining a second distance between the host vehicle and a second vehicle in front of the host vehicle, and controlling operation of the host vehicle according to the first distance and the second distance.

Abstract: An electronic device includes a display screen, a processor, and a memory. The processor is configured to obtain attributive information of a facial image, divide the facial image into different attributive portions according to the attributive information, calculate a surface area of each attributive area, determine whether any one of the attributive areas is a distorted area, arrange the attributive areas, when one of the attributive areas is a distorted area, according to a degree of distortion from a largest degree of distortion to a smallest degree of distortion, and process a preset number of the attributive areas having the largest degree of distortion.

Abstract: A method for filtering a television program includes capturing preview content of a television program, analyzing the preview content to generate a specific image model, determining, when the television program is displayed, whether the specific image model is displayed in the television program, and prompting a user to take a specific action when the specific image model is displayed in the television program. The preview content is captured within a preset time duration before the television program is displayed on the television.

Abstract: A speech recognition device includes a speech acquiring unit, a speech outputting unit, a camera unit, and a processor. The processor obtains speech input acquired by the speech acquiring unit, obtains images acquired by the camera unit, chronologically links the obtained speech and the obtained images together, compares the obtained speech to a speech database to confirm matching speech and a confidence level of the matching speech, determines whether the confidence level of the matching speech exceeds a predetermined confidence level, and outputs the matching speech when the confidence level of the matching speech exceeds the predetermined confidence level.

Abstract: A method for filtering a television program includes capturing preview content of a television program, analyzing the preview content to generate a specific image model, determining, when the television program is displayed, whether the specific image model is displayed in the television program, and prompting a user to take a specific action when the specific image model is displayed in the television program. The preview content is captured within a preset time duration before the television program is displayed on the television.

Abstract: An artificial neural network includes an input layer, a hidden layer, and an output layer which include first, second, and third neurons respectively. The hidden layer is divided into a plurality of planes arranged along a direction between the input layer and the output layer. The first neurons are connected to the second neurons positioned at one plane adjacent to the input layer. The third neurons are connected to the second neurons positioned at another plane adjacent to the output layer. The second neurons positioned at the same plane are connected to each other. The neurons positioned at two adjacent planes along the arranging direction of the planes are connected to each other. At least one of the neurons has at least six signal connections along the arranging direction of the planes and towards the other neurons on the same plane.