The present application concerns the visual identification of materials or documents for tracking or authentication purposes. It describes methods to automatically authenticate an object by comparing some object images with reference images, the object images being characterized by the fact that visual elements used for comparison are non-disturbing for the naked eye. In some described approaches it provides the operator with visible features to locate the area to be imaged. It also proposes ways for real-time implementation enabling user friendly detection using mobile devices like smart phones.

This disclosure describes one or more implementations of a digital image semantic layout manipulation system that generates refined digital images resembling the style of one or more input images while following the structure of an edited semantic layout. For example, in various implementations, the digital image semantic layout manipulation system builds and utilizes a sparse attention warped image neural network to generate high-resolution warped images and a digital image layout neural network to enhance and refine the high-resolution warped digital image into a realistic and accurate refined digital image.

An image processing apparatus configured to perform recognition processing for a target object in an input image, includes at least one memory storing a program, and at least one processor. The processor, by executing the program, causes the image processing apparatus to perform the recognition processing for the target object in a first image having a first resolution acquired based on the input image and acquire a first recognition result, determine whether or not a reliability of the first recognition result satisfies a predetermined standard, and perform the recognition processing for the target object in a second image which has a higher resolution than the first resolution acquired based on the input image, and acquire a second recognition result, in a case where it is determined that the reliability of the first recognition result does not satisfy the predetermined standard.

In an embodiment, autonomous vehicles with global positioning systems (GPS) are used for field inspection to reduce fuel and labor costs and improve reliability with increased consistency in field crop inspection. A vehicle may be programmed to traverse a field while using sensors to detect objects and operating in a first image capture mode, for example, capturing low-resolution images of objects in the field, typically crops. Under program control, machine vision techniques are used with the low-resolution images to recognize crops, non-crop plant material or undefined objects. Under program control, location data is used to correlate recognized objects with digitally stored field maps to resolve whether a particular object is in a location at which crop planting is expected or not expected. Under program control, depending on whether an object in a low-resolution digital image is recognized as a crop, and whether the object is in an expected geo-location for crops, the vehicle may cease traversing temporarily and switch to a second image capture mode, for example, capturing a high-resolution image of the object, for use in disease analysis or classification, weed analysis or classification, alert notifications or other messages, or other processing. In this manner, a field may be rapidly traversed and imaged using coarse-level, rapid techniques that require lower processing resources, storage or memory, while automatically switching to execute special processing only when necessary to resolve unexpected objects or to perform operations such as disease classification that benefit from high-resolution images and more intensive use of processing resources, storage or memory.

This invention applies dynamic weighting between a point-to-plane and point-to-edge metric on a per-edge basis in an acquired image using a vision system. This allows an applied ICP technique to be significantly more robust to a variety of object geometries and/or occlusions. A system and method herein provides an energy function that is minimized to generate candidate 3D poses for use in alignment of runtime 3D image data of an object with model 3D image data. Since normals are much more accurate than edges, the use of normal is desirable when possible. However, in some use cases, such as a plane, edges provide information in relative directions the normals do not. Hence the system and method defines a “normal information matrix”, which represents the directions in which sufficient information is present. Performing (e.g.) a principal component analysis (PCA) on this matrix provides a basis for the available information.

An object recognition device includes: a data holding unit that stores a reference image of an object of a recognition candidate, each feature point in the reference image, and a feature quantity at each feature point; an image acquisition unit acquires a scene image that is an image of a recognition processing target; a definition calculation unit detects definition indicating the degree of sharpness in each region of the scene image; and a feature acquisition unit and a matching calculation unit that detect a feature point in the scene image to perform a process of matching with the feature point. The matching calculation unit executes, by different methods, an extraction method of feature points in a first region of the scene image where the definition is a first range and in a second region of the scene image where the definition is a second range lower than the first range.

A method includes obtaining an image data set that depicts semiconductor components, and applying a hierarchical bricking to the image data set. In this case, the bricking includes a plurality of bricks on a plurality of hierarchical levels. The bricks on different hierarchical levels have different image element sizes of corresponding image elements.

Embodiments of the present disclosure disclose a method and apparatus for determining a target anchor, a device and a storage medium. The method may include: extracting a plurality of feature maps of an original image using a feature extraction network; inputting the plurality of feature maps into a feature pyramid network to perform feature fusion, to obtain a plurality of fused feature maps; and using a region proposal network to implement operations as follows: determining an initial anchor of a web header using the fused feature map, based on a size of each fused feature map, and determining an offset parameter of the initial anchor, based on a ratio of the size of the fused feature map to the original image, and generating a plurality of candidate anchors in different directions, based on the offset parameter of the initial anchor.

A method, computer readable medium, and system are disclosed for creating an image utilizing a map representing different classes of specific pixels within a scene. One or more computing systems use the map to create a preliminary image. This preliminary image is then compared to an original image that was used to create the map. A determination is made whether the preliminary image matches the original image, and results of the determination are used to adjust the computing systems that created the preliminary image, which improves a performance of such computing systems. The adjusted computing systems are then used to create images based on different input maps representing various object classes of specific pixels within a scene.

The purpose of the present invention is to obtain reliable recognition performance even in a lens which is capable of long-distance and close proximity photography. This object recognition device 1 comprises an image acquisition unit which acquires an image 1000 which includes a plurality of image regions 1001, 1002, 1003 which have differing resolutions, and a recognition processing unit which carries out a recognition process upon an object within the screen 1000. One image region 1003, among the plurality of image regions 1001, 1002, 1003 of the screen 1000, has a lower resolution than the other image regions 1001, 1002. The recognition processing unit carries out the recognition process of the object on the basis of assessment references which differ among the one image region 1003 and the other image regions 1001, 1002.