A farming machine moves through a field and includes an image sensor that captures an image of a plant in the field. A control system accesses the captured image and applies the image to a machine learned plant identification model. The plant identification model identifies pixels representing the plant and categorizes the plant into a plant group (e.g., plant species). The identified pixels are labeled as the plant group and a location of the pixels is determined. The control system actuates a treatment mechanism based on the identified plant group and location. Additionally, the images from the image sensor and the plant identification model may be used to generate a plant identification map. The plant identification map is a map of the field that indicates the locations of the plant groups identified by the plant identification model.

A method for controlling a robotic device is presented. The method includes positioning the robotic device within a task environment. The method also includes mapping descriptors of a task image of a scene in the task environment to a teaching image of a teaching environment. The method further includes defining a relative transform between the task image and the teaching image based on the mapping. Furthermore, the method includes updating parameters of a set of parameterized behaviors based on the relative transform to perform a task corresponding to the teaching image.

The information processing apparatus (2000) includes a feature point detection unit (2020), a determination unit (2040), an extraction unit (2060), and a comparison unit (2080). A feature point detection unit (2020) detects a plurality of feature points from the query image. The determination unit (2040) determines, for each feature point, one or more object images estimated to include the feature point. The extraction unit (2060) extracts an object region estimated to include the object in the query image in association with the object image of the object estimated to be included in the object region, on the basis of the result of the determination. The comparison unit (2080) cross-checks the object region with the object image associated with the object region and determines an object included in the object region.

In some aspects, a method includes performing optical character recognition (OCR) based on data corresponding to a document to generate text data, detecting one or more bounded regions from the data based on a predetermined boundary rule set, and matching one or more portions of the text data to the one or more bounded regions to generate matched text data. Each bounded region of the one or more bounded regions encloses a corresponding block of text. The method also includes extracting features from the matched text data to generate a plurality of feature vectors and providing the plurality of feature vectors to a trained machine-learning classifier to generate one or more labels associated with the one or more bounded regions. The method further includes outputting metadata indicating a hierarchical layout associated with the document based on the one or more labels and the matched text data.

Disclosed is an effective domain name defense solution in which a domain name string may be provided to or obtained by a computer embodying a visual domain analyzer. The domain name string may be rendered or otherwise converted to an image. An optical character recognition function may be applied to the image to read out a text string which can then be compared with a protected domain name to determine whether the text string generated by the optical character recognition function from the image converted from the domain name string is similar to or matches the protected domain name. This visual domain analysis can be dynamically applied in an online process or proactively applied in an offline process to hundreds of millions of domain names.

A method and device for testing product quality are disclosed. The method for testing product quality comprises: acquiring an image of a product to be tested; testing the image by using a pre-trained neural network model to obtain a testing result output by the neural network model; when the testing result indicates that the product to be tested is a defective product, performing a secondary judgment on the testing result according to position information of defective feature pixels in the image in the testing result, and determining whether the product to be tested is qualified according to a secondary judgment result. The method has high test accuracy, ensures the quality of product and facilitates reducing the labor cost of test.

In an example, a method includes, by one or more processors, receiving a greyscale image having a plurality of pixels, each pixel being associated with a grey level, and the greyscale image having a first number of grey levels. An order of the pixels may be determined based on the grey level. A second number of grey levels may be determined, wherein the second number of grey levels is greater than the first number, and an indication of a target number of pixels per grey level of the second number of grey levels may be further be determined. Taking the pixels in order, and based on the target number of pixels per grey level, a new grey level may be allocated to each pixel to provide the second number of grey levels. The new grey levels may be converted to a threshold of a threshold halftone screen.

A method may include generating views materializing tensors generated by a convolutional neural network operating on an image. Determining the outputs of the convolutional neural network operating on the image with a patch occluding various portions of the image. The outputs being determined by generating queries on the views that performs, based at least on the changes associated with occluding different portions of the image, partial re-computations of the views. A heatmap may be generated based on the outputs of the convolutional neural network. The heatmap may indicate the quantities to which the different portions of the image contribute to the output of the convolutional neural network operating on the image. Related systems and articles of manufacture, including computer program products, are also provided.

An electronic device is provided. The electronic device includes a memory, an image sensor including light receiving elements each including at least two sub light receiving elements, and an image signal processor. The image signal processor is configured to obtain images corresponding to light from outside by using the image sensor, the images including at least a raw image, a first sub image, and a second sub image, the first sub image being an image corresponding to light detected by at least one first sub light, the second sub image being an image corresponding to light detected by at least one second sub light, identify a luminance ratio between the first sub image and the second sub image, identify a defect in the raw image, based on the luminance ratio, and perform a function corresponding to a type of the defect.

Disclosed is a method for performing image convolution by considering a hierarchical relationship of hyperbolic feature vectors in a hyperbolic space. According to an embodiment of the present disclosure, an image convolution method in a hyperbolic space includes steps of embedding an image feature vector on a Euclidean space into a hyperbolic feature vector on a hyperbolic space, allocating a hierarchical weight on the hyperbolic feature vector based on a hierarchical property of the hyperbolic feature vector, and convolutioning the hyperbolic feature vector by applying the hierarchical weight.