According to one embodiment, an image processing apparatus includes processing circuitry. The processing circuitry is configured to acquire medical image data. The processing circuitry is configured to obtain spatial distribution of likelihood values representing a likelihood of corresponding to a textual pattern in a predetermined region of a medical image for each of a plurality of textual patterns based on the medical image data. The processing circuitry is configured to calculate feature values in the predetermined region of the medical image based on the spatial distribution obtained for the each of the plurality of textual patterns.

An image processing device which is capable of accurately detect pixels covered by cloud shadows and remove effects of the cloud shadows in an images are provided. The device includes: a cloud transmittance calculation unit that calculates transmittance of the one or more clouds in an input image, for each pixel; a cloud height estimation unit that determines estimation of a height from the ground to each cloud in the input image to detect position of corresponding one or more shadows; an attenuation factor estimation unit that calculates attenuation factors for the direct sun irradiance by applying an averaging filter to the cloud transmittance calculated; and a shadow removal unit that corrects pixels affected by the one or more shadows, based on a physical model of a cloud shadow formation by employing the attenuation factors calculated and the position, and outputs an image which includes the pixels corrected.

An image detection scanning method for object surface defects and an image detection scanning system thereof are provided. The method includes capturing a test image according to test light, determining whether the test image is normal, generating a coincidence signal if the test image is normal, coinciding the object with the detection position according to the coincidence signal, sequentially moving one of a plurality of areas on a surface of the object to the detection position, providing detection light facing the detection position, the detection light illuminating the detection position with a light incident angle of less than or equal to 90 degrees relative to a normal line of the area located at the detection position, and capturing a detection image of each of the areas sequentially located at the detection position according to the detection light after the object is coincided with the detection position.

An image detection scanning method for object surface defects and an image detection scanning system thereof are provided. The method includes capturing a test image according to test light, determining whether the test image is normal, generating a coincidence signal if the test image is normal, coinciding the object with the detection position according to the coincidence signal, sequentially moving one of a plurality of areas on a surface of the object to the detection position, providing detection light facing the detection position, the detection light illuminating the detection position with a light incident angle of less than or equal to 90 degrees relative to a normal line of the area located at the detection position, and capturing a detection image of each of the areas sequentially located at the detection position according to the detection light after the object is coincided with the detection position.

A non-destructive testing (NDT) system can provide a tree model of an inspection on a display of an NDT device and on a web page configured in a web browser on a computing device coupled to the NDT device. Inspection data acquired using the NDT device can be provided in real-time as the inspection data is associated with a node configured in the tree model. The NDT system can generate an inspection tree model based on an inspection template including a template tree model. Defect properties, inspection instructions, and/or image transforms can be applied to nodes of the template tree model such that the generated inspection tree model includes the applied defect properties, inspection instructions, and/or image transforms, which can then be applied to the inspection data acquired at the inspection point location corresponding to each node.

The present disclosure relates to a resonator network system comprising a set of resonator networks, each resonator network being configured to execute a resonator network, the resonator network being configured to receive an input hypervector representing a data structure and to perform an iterative process in order to factorize the input hypervector into individual hypervectors representing a set of concepts respectively, the set of N resonator networks being associated with N permutations respectively. The resonator network system being configured for applying the N permutations to N first hypervectors respectively, the N first hypervectors representing a set of N data structures respectively; and combining the N permuted hypervectors into a bundled hypervector. The resonator networks being configured for processing the bundled hypervector respectively, thereby factorizing the first hypervectors.

The present disclosure relates to a resonator network system comprising a set of resonator networks, each resonator network being configured to execute a resonator network, the resonator network being configured to receive an input hypervector representing a data structure and to perform an iterative process in order to factorize the input hypervector into individual hypervectors representing a set of concepts respectively, the set of N resonator networks being associated with N permutations respectively. The resonator network system being configured for applying the N permutations to N first hypervectors respectively, the N first hypervectors representing a set of N data structures respectively; and combining the N permuted hypervectors into a bundled hypervector. The resonator networks being configured for processing the bundled hypervector respectively, thereby factorizing the first hypervectors.

The disclosure discloses an online detection method of circular weft knitting stripe defects based on a gray gradient method, and belongs to the technical field of textile product detection. The method provides a defect detection and positioning method based on the gray gradient method. Before the detection on a new product, only a model needs to be trained to obtain stripe defect feature images with different stitch types and different stitch densities, and the detection is directly performed in a subsequent process. Defects can be fast and accurately recognized, and a cam position causing the stripe defects can be calculated according to a quantity of shot images between the feature images and a marked image, courses in which marks in the marked image are located, courses in which defects in a defect image are located, and machine operation parameters. Moreover, a defect detection device required by the disclosure can be modified on an original circular knitting machine, so the detection cost is reduced.

A method or system for assessing a patient response to a cancer treatment is provided. The method or system includes acquiring at least one base-line radiological image related to a patient immediately before a treatment, acquiring at least one follow-up radiological image during or after the treatment at a predetermined time interval, estimating a first number of specific tumor cells in a region of interest of the patient based on image features of the base-line radiological image using an algorithm or a model, estimating a second number of specific tumor cells in the region of interest of the patient based on image features of the follow-up radiological image using the algorithm or the model, obtaining a difference between the first number of specific tumor cells and the second number of specific tumor cells, and classifying a treatment response to a cancer based on the difference.

A method or system for assessing a patient response to a cancer treatment is provided. The method or system includes acquiring at least one base-line radiological image related to a patient immediately before a treatment, acquiring at least one follow-up radiological image during or after the treatment at a predetermined time interval, estimating a first number of specific tumor cells in a region of interest of the patient based on image features of the base-line radiological image using an algorithm or a model, estimating a second number of specific tumor cells in the region of interest of the patient based on image features of the follow-up radiological image using the algorithm or the model, obtaining a difference between the first number of specific tumor cells and the second number of specific tumor cells, and classifying a treatment response to a cancer based on the difference.