Methods and systems for automatic detection of landmarks in digital images and annotation of those images are disclosed. A method for detecting and annotating landmarks in digital images includes the steps of automatically assigning a tag descriptive of a landmark to one or more images in a plurality of text-associated digital images to generate a set of landmark-tagged images, learning an appearance model for the landmark from the set of landmark-tagged images, and detecting the landmark in a new digital image using the appearance model. The method can also include a step of annotating the new image with the tag descriptive of the landmark.

An image pipeline execution method includes analyzing at least one attribute of an image and determining an image type of the image from a set of predetermined image types based on the analyzing the at least one attribute of the image. The image pipeline execution method also includes dynamically establishing an image pipeline to process the image based on the image type and executing the image pipeline to print the image.

Techniques describe apparatus and method for generating computed results based on sensor readings for detecting features, such as edges, corners etc. The sensor apparatus may include a sensor element array that includes a plurality of sensor elements. The sensor elements may be arranged in a 2-dimensional array, such as columns and rows. The sensor elements may be capable of generating sensor reading based on environmental conditions. The sensor apparatus may include a dedicated computer vision (CV) computation hardware in in-pixel circuitry, peripheral circuitry or dedicated microprocessor coupled to the sensor element array and configured to receive output from one or more of sensor elements. The dedicated CV computation hardware may include configurable blocks for detecting features using CV operations, wherein the configurable blocks may be configured to switch between multiple CV operations, such as linear binary pattern (LBP) and/or histogram of signed gradient (HSG) computer vision operations.

A three-dimensional (3D) image-based facial verification method and apparatus is provided. The facial verification method may include capturing a facial image of a 3D face of a user, determining an occluded region in the captured facial image by comparing the captured facial image and an average facial image, generating a synthetic image by synthesizing the captured facial image and the average facial image based on the occluded region, and verifying the user based on the synthetic image.

An image processing apparatus includes an image processing section that performs processing of a job relating to image processing, one or more information processing sections that perform processing on one or more information items as processing targets among information items generated by a device group, a permission acquisition section that acquires a collection permission for each of one or more information items used by the one or more information processing sections, from a manager of the device group or the image processing apparatus, and a collection section that collects the information items used by the one or more information processing sections from devices that generate the information items, under a condition in which acquisition of the collection permission for each of the information items from the manager is completed.

A display device and an operating method thereof are provided. The display device may include: a display; a camera; a memory configured to store one or more instructions; and a processor configured to execute the instructions to obtain an image captured by the camera, transform the image based on visual condition information of a user, the visual condition information including information about a type of visual impairment of the user, and display the transformed image on the display.

An approach is provided for providing a lower-power perception architecture. The approach involves, for example, determining that a device is equipped with a first perception system and a second perception system. The second perception system operates in a lower-power consumption mode than the first perception system to process image data for image recognition. The approach also involves determining a battery level of the device. The approach further involves switching from the first perception system to the second perception system based on determining that the battery level is below a threshold battery level.

Systems and methods for localization for mobile devices are described. Some implementations may include accessing motion data captured using one or more motion sensors; determining, based on the motion data, a coarse localization, wherein the coarse localization includes a first estimate of position; obtaining one or more feature point maps, wherein the feature point maps are associated with a position of the coarse localization; accessing images captured using one or more image sensors; determining, based on the images, a fine localization pose by localizing into a feature point map of the one or more feature point maps, wherein the fine localization pose includes a second estimate of position and an estimate of orientation; generating, based on the fine localization pose, a virtual object image including a view of a virtual object; and displaying the virtual object image.

A system for processing digital images comprising: at least one remote hardware processor; and at least one device, comprising at least one processing circuitry configured for: receiving from at least one image sensor, electrically coupled to the processing circuitry, at least one digital image captured by the at least one image sensor; partitioning at least one object, identified in the at least one digital image, into a plurality of object segments; replacing in the at least one digital image each of the plurality of object segments with a schematic segment, illustrating respective object segment, to produce at least one schematic image; and sending the at least one schematic image to the remote hardware processor; wherein the remote hardware processor is adapted to: receiving the at least one schematic image from the at least one device; analyzing the at least one schematic image to identify at least one behavioral pattern.

Apparatuses, methods, and systems are presented for sensing scene-based occurrences. Such an apparatus may comprise a vision sensor system comprising a first processing unit and dedicated computer vision (CV) computation hardware configured to receive sensor data from at least one sensor array comprising a plurality of sensor pixels and capable of computing one or more CV features using readings from neighboring sensor pixels. The vision sensor system may be configured to send an event to be received by a second processing unit in response to processing of the one or more computed CV features by the first processing unit. The event may indicate possible presence of one or more irises within a scene.