A method for developing an evolved structure by artificial evolution includes: obtaining one or more properties of a biological structure; computationally evolve the biological structure to obtain an evolved descriptor; inverse-mapping the evolved description to real space to form an evolved structure design; and constructing the evolved structure. The evolved structure comprises stronger performance across the properties than the biological structure. In an example aspect, a method for constructing an evolved structure includes: removing sericin from a cocoon; forming a first solution from the cocoon with removed sericin; forming a silk fibroin powder from the first solution; dissolving the silk fibroin powder to form a second solution; and electro spinning the second solution based on the evolved structure design.

Systems, apparatuses, and methods for converting data to a tiling format when implementing convolutional neural networks are disclosed. A system includes at least a memory, a cache, a processor, and a plurality of compute units. The memory stores a first buffer and a second buffer in a linear format, where the first buffer stores convolutional filter data and the second buffer stores image data. The processor converts the first and second buffers from the linear format to third and fourth buffers, respectively, in a tiling format. The plurality of compute units load the tiling-formatted data from the third and fourth buffers in memory to the cache and then perform a convolutional filter operation on the tiling-formatted data. The system generates a classification of a first dataset based on a result of the convolutional filter operation.

A data storage device and method for efficient image searching are provided. In one embodiment, a data storage device is provided comprising a memory and a controller. The controller is configured to store a plurality of images and a plurality of keys in the memory, wherein each key of the plurality of keys is generated from a respective image of the plurality of images; receive, from a host, a key generated from a target image desired by the host; and return, to the host, an image from the stored plurality of images that is associated with a key that matches the key received from the host. Other embodiments are provided.

An abnormality detection unit detects an abnormal object in target images repeatedly acquired. An abnormality type selection unit selects, for each of the target images, an abnormality type of the abnormal object from a plurality of specific abnormality types based on values of at least two basic feature amounts. A feature amount monitoring unit monitors the values of the basic feature amounts and a value of an auxiliary feature amount corresponding to the abnormality type currently selected by the abnormality type selection unit. An adjustment processing unit executes an adjustment process corresponding to the auxiliary feature amount being monitored by the feature amount monitoring unit. The abnormality type selection unit changes the abnormality type to be selected, in accordance with the change in the values of the basic feature amounts mentioned above.

A method for local approximation of a predictive model may include receiving unclassified data associated with a plurality of unclassified data items. The unclassified data may be classified based on a first predictive model to generate classified data. A first data item may be selected from the classified data. A plurality of generated data items associated with the first data item may be generated using a generative model. The plurality of generated data items may be classified based on the first predictive model to generate classified generated data. A second predictive model may be trained with the classified generated data. A system and computer program product are also disclosed.

A device with image processing includes: an image acquirer configured to acquire a plurality of images each having a different brightness; and one or more processors configured to extract an illumination map for an input image of the images and an illuminant color of the input image from the input image and temporal correlation information of the plurality of images, based on an illumination extraction model.

A method, apparatus, device, and storage medium for transforming a hairstyle are provided. The method may include: determining a face bounding box according to information on face key points of acquired face image; constructing grids according to the face bounding box; deforming, by using an acquired target hairstyle function, edge lines of at least a part of the constructed grids, which comprises the hairstyle, to obtain a deformed grid curve; determining a deformed hairstyle in the face image according to the deformed grid curve.

A method for stray light compensation is disclosed. The method comprising: acquiring a first image with a first imaging device covering a first field-of-view; acquiring a second image with a second imaging device covering a second field-of-view, wherein the second field-of-view is larger than the first field-of-view and wherein the first field-of-view is included in the second field-of-view; estimating stray light components in pixels of the first image from pixel data of pixels in the second image; and compensating for stray light in the first image by subtracting the estimated stray light components in pixels of the first image. Also, a system for stray light compensation is disclosed.

A method for restoring video data of a pipe based on computer vision is provided. The method includes: performing gray stretching on pipe image/video collected by a pipe robot; processing noise interference by smoothing filtering; extracting an iron chain from the center of a video image as a template for location; performing target recognition on the center of video data by an SIFT corner detection algorithm; detecting ropes on left and right sides of a target by Hough transform; performing gray covering on the iron chain at the center of the video image and the ropes on two sides; and restoring data by an FMM image restoration algorithm.

A method for detecting facial key points includes obtaining a face image to be detected, and extracting key point detection information of the face image to be detected; obtaining key point template information of the template face image; determining a facial key point mapping relationship between the face image to be detected and the template face image in combination with the key point detection information and the key point template information; and filtering the key point detection information according to the facial key point mapping relationship and the key point template information to generate target key point information of the face image to be detected, wherein target facial key points in the target key point information are facial key points of an un-block area in the face image to be detected.