International Business Machines Corporation

JingChang Huang

Peng Ji

Jian Ding

Guo Qiang Hu

Fan Li

Ning Duan

Sheng Nan Zhu

Jian Xu

Yuan Yuan Ding

Guoqiang Hu

Jinfeng Li

Peng Gao

Wei Zhao

He Huang

A plurality of different images of a same region of interest in an object are input into a set of neural networks, wherein each image of the region has been captured under a different value of a variable condition. A classification for each image is generated by the set of neural networks, wherein each classification includes a confidence score in a prediction of whether a feature is present in the region. The image classifications are ensembled to generate a final classification for the region. By applying a loss function, a loss is computed based on comparing the final classification to a ground truth of whether the feature is present in the region. The parameters of the set of neural networks are adjusted based on the computed loss.

Feature detection based on neural networks

According to embodiments of the present invention, a method, a device and a computer program product for image processing is provided. A computing device obtains an image of a first object, the image presenting a defect of the first object. A computing device obtains defect distribution information indicating respective frequencies of a plurality of predetermined categories of defects presented at corresponding locations in a plurality of training images, the plurality of training images presenting second objects and being used for training a defect classifier. A computing device determines a target category of the defect of the first object by applying the image and the defect distribution information to the defect classifier. A computing device generates one or more correction notifications.

Object defect correction

The present invention relates to a method, system and computer program product for defect enhancement. According to the method, a plurality of proposed regions from a plurality of images taken for a display panel is obtained. Each of proposed region of the plurality of proposed regions include a suspected defect on the display panel. At least two proposed regions from the plurality of proposed regions that deserve to be superimposed based on a set of conditions is determined. The at least two proposed regions for acquiring an enhanced defect are superimposed.

Display panel defect enhancement

A method, a device and a computer program product for image processing are proposed. In the method, a first training image and region information are obtained. The region information indicates a region of a defect in the first training image. A second training image with the defect at least partially removed is generated using an image generator based on the first training image and the region information. The image generator is trained to recover the first training image by replacing pixels included in the region indicated by the region information. The image generator is updated based on the second training image. In this way, the image including the defect can be accurately and efficiently recovered.

Image recovery

Methods, systems and computer program products for providing improved visualization of inspection results are provided. Aspects include receiving an image from a connected camera by a computing device and processing the image by a graphics processing unit to divide the image into a plurality of image blocks. Responsive to inputting the processed image into a trained model, a plurality of image areas within the processed image is identified. Each image area is associated with an importance level and includes a unique set of image blocks. A sequence of image blocks is determined based on an importance level associated with each of the plurality of image blocks, wherein the sequence includes a list of image blocks in descending order by the importance level of each image block. The image blocks are stored in order of the sequence of image blocks by the server.

Visualization of inspection results

An apparatus or a method for detecting activity quantity of an object includes a sense unit, a count unit, a trigger unit, and a radio circuit. The sense unit senses movements of the object, and generates a sensing signal based on the sensed movements. The count unit receives the sensing signal from the sense unit, counts a number of the movements from the sensing signal, and generates a movement number signal including the number of the movements. The trigger unit generates a trigger signal based on whether one or more trigger conditions are satisfied. The radio circuit is triggered in response to the trigger signal, to generate an activity quantity signal based on the movement number signal and ID information corresponding to the object.

Sensor based activity monitor

A method of determining a remediation action includes detecting an acoustic event in a vehicle, detecting an environmental condition in the vehicle, detecting a respiratory disease symptom of a passenger in the vehicle based on the detected acoustic event, correlating the detected respiratory disease symptom with the environmental condition in the vehicle, and determining a remediation action for the detected respiratory disease symptom.

System, method and recording medium for determining a remediation action

A method for detecting the presence of an unattended child left in a vehicle includes receiving acoustic data obtained from inside a vehicle, determining if the vehicle is running or not in response to the acoustic data, determining if all windows in the vehicle are closed when the vehicle is not running, determining if sound volume inside the vehicle exceeds a predetermined threshold when all of the windows are closed, determining if a predetermined high-frequency word is spoken inside the vehicle when all of the windows are closed, and outputting an alert indicating that an unattended child is left in the vehicle when the sound volume inside the vehicle exceeds the predetermined threshold and when the predetermined high-frequency word is spoken inside the vehicle.

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Shanghai, China

JingChang Huang