Methods and image processing systems are provided for determining a dominant gradient orientation for a target region within an image. A plurality of gradient samples are determined for the target region, wherein each of the gradient samples represents a variation in pixel values within the target region. The gradient samples are converted into double-angle gradient vectors, and the double-angle gradient vectors are combined so as to determine a dominant gradient orientation for the target region.

A method of label detection includes: obtaining, by an imaging controller, an image depicting a shelf; increasing an intensity of a foreground subset of image pixels exceeding an upper intensity threshold, and decreasing an intensity of a background subset of pixels below a lower intensity threshold; responsive to the increasing and the decreasing, (i) determining gradients for each of the pixels and (ii) selecting a candidate set of the pixels based on the gradients; overlaying a plurality of shelf candidate lines on the image derived from the candidate set of pixels; identifying a pair of the shelf candidate lines satisfying a predetermined sequence of intensity transitions; and generating and storing a shelf edge bounding box corresponding to the pair of shelf candidate lines.

The invention relates to method of classifying an object (10) in an image (9), the method comprising the steps of: defining an image area (11) located within the object (10) in the image (9), decomposing the image area (11) into an array of subareas (13), defining an array of aggregated pixel values by calculating, for each of the subareas (13), an aggregated pixel value of the respective subarea (13), calculating a gradient array depending on differences between the aggregated pixel values of adjacent subareas (13), analyzing the gradient array, identifying, depending on a result of the analyzing step, the object (10) as belonging to a class of a predefined set of classes. Furthermore, the invention relates to a device (4) for analyzing an object (10) contained in an image (9) and to a driver assistance system as well as to a vehicle (1) containing such a device (4).

A hardware accelerator for histogram of oriented gradients computation is provided that includes a gradient computation component configured to compute gradients G.sub.x and G.sub.y of a pixel, a bin identification component configured to determine a bin id of an angular bin for the pixel based on a plurality of representative orientation angles, G.sub.x, and signs of G.sub.x and G.sub.y, and a magnitude component configured to determine a magnitude of the gradients G.sub.mag based on the plurality of representative orientation angles and the bin id.

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.

A hardware accelerator for histogram of oriented gradients computation is provided that includes a gradient computation component configured to compute gradients G.sub.x and G.sub.y of a pixel, a bin identification component configured to determine a bin id of an angular bin for the pixel based on a plurality of representative orientation angles, G.sub.x, and signs of G.sub.x and G.sub.y, and a magnitude component configured to determine a magnitude of the gradients G.sub.mag based on the plurality of representative orientation angles and the bin id.

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.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collaboratively training an interaction prediction machine learning model using a plurality of user devices in a manner that respects user privacy. In one aspect, the machine learning model is configured to process an input comprising: (i) a search query, and (ii) a data element, to generate an output which characterizes a likelihood that a given user would interact with the data element if the data element were presented to the given user on a webpage identified by a search result responsive to the search query.

An image recognition apparatus 100 includes a gradient feature computation unit 120 configured to calculate, from an image divided into a plurality of blocks, gradient feature values for each of the plurality of blocks, a combination pattern storage unit 160 configured to store a plurality of combination patterns of the gradient feature values, and a co-occurrence feature computation unit 131 configured to calculate a co-occurrence feature value in a plurality of blocks for each of the plurality of combination patterns. Further, image recognition apparatus 100 includes an arithmetic computation unit 132 configured to calculate an addition value by adding the co-occurrence feature value calculated for each of the plurality of blocks for each of the plurality of combination patterns, a statistical data generation unit 140 configured to generate statistical data from the addition value. Further, image recognition apparatus 100 includes an image recognition computation unit configured to define a window having a predetermined size for the image and recognize whether or not a predetermined image is included in the window based on the statistical data within the window.

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.