The present disclosure relates to systems and methods for image processing. The system may obtain at least one image of an object. For each of the at least one image, the system may determine a recognition result of the image. The recognition result may include an image type of the image, a type of a lesion in the image, a region of the lesion in the image, and/or an image feature of the image. Further, the system may process the at least one image of the object based on at least one recognition result corresponding to the at least one image.

A method for performing registration plate detection includes: performing image processing on image data of a predetermined region in an image to generate an edge image; and performing registration plate detection according to the edge image and the image data of the predetermined region to determine a location of a registration plate image within the predetermined region, for performing a post-processing corresponding to the registration plate image. The step of performing the image processing includes: performing gradient calculations on image data of the predetermined region to generate a gradient image, where the gradient image includes gradient data of the predetermined region; performing edge threshold estimation on the gradient data of the predetermined region, to update an edge threshold through a set of iterative calculations; and generating the edge image according to the edge threshold and according to selective gradient data of the predetermined region.

The current document is directed to automated methods and systems, controlled by various constraints and parameters, that identify document sub-images within digital images. Certain of the parameters constrain contour identification and document-subimage-hypothesis generation. The currently described methods and systems identify contours within the digital image, partition the identified contours into four contour sets corresponding to four different regions of the original digital image, construct hypotheses based on these contours for the edges or boundaries of a digital sub-image, and evaluate the hypotheses in order to select a most highly scored hypotheses as a representation of the borders of a digital sub-image within the original received digital image.

An image processing device includes a feature point detecting section which detects a feature point from an image and which includes a ternarizing section and a determining section. The ternarizing section sets each pixel of the image as a target pixel and inputs pixel values of the target pixel and surrounding pixels. The ternarizing section changes each of the pixel values of the surrounding pixels to one of three signal values which represent that brightness of a surrounding pixel is brighter or darker than or similar to brightness of the target pixel. When the number of consecutive surrounding pixels having the same signal value is equal to or greater than a threshold, the determining section determines the target pixel as the feature point.

The present invention discloses a method for detecting whether cells are infected with human papillomavirus (HPV), and the method includes: (a) capturing a contour of a cell of a cell image, wherein the contour has a contour pixel thereon; (b) identifying a tangent of the contour pixel on the contour to define in relation to the tangent a first side and a second side opposite to the first side, wherein the first side indicates the intracellular region of the cell and the second side indicates the extracellular region of the cell; (c) calculating an optical parameter average of the plurality of pixels on both of the first side and the second side; and (d) determining whether the cell is infected with HPV based on whether the optical parameter average on the first side is smaller than that on the second side.

Described are systems and methods for determining an agent that performed an event within a materials handling facility. A series of overhead images that include representations of the event location and one or more agents are processed to determine a motion or movement of the agent over a period of time. For example, a motion model representative of a motion of the agent over a period of time is generated from the images. A distance between the motion model and the event location is also determined. An association between the agent and the event may be determined based on the motion model and the distance between the motion model and the event location.

A shopping interface for purchasing items on media, including a user system having a user interface displaying items for purchase that allows a user to engage in a shopping experience seamlessly while interacting with the media, the user system including software stored on non-transitory computer readable media that recognizes and tags purchasable items in the media, wherein the user system displays items for purchase when the media is paused and allows the user to browse, navigate through displayed items, and select a desired item without any disruption to the media, the user system including a one-click purchase function for securely storing payment and address information of the user on non-transitory computer readable media allowing for secure and efficient purchases. A method of shopping on media.

A method for characteristic extraction includes dividing an image into a plurality of blocks, each block including a plurality of cells. The method also includes performing sparse signal decomposition for each cell to obtain a sparse vector corresponding to each cell. The method further includes extracting characteristics of Histogram of Oriented Gradient (HOG) of the image according to the sparse vectors.

A method and a device for feature extraction are provided in the disclosure. The method may include: partitioning an image into a plurality of blocks, each of the blocks including a plurality of cells; performing a sparse signal decomposition on the cells using a predetermined dictionary to obtain sparse vectors respectively corresponding to the cells; and extracting an image Histogram of Oriented Gradient (HOG) feature of the image according to the sparse vectors.

Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.