Described herein is a mechanism for visual recognition of items or visual search using Optical Character Recognition (OCR) of text in images. Recognized OCR blocks in an image comprise position information and recognized text. The embodiments utilize a location-aware feature vector created using the position and recognized information in each recognized block. The location-aware features of the feature vector utilize position information associated with the block to calculate a weight for the block. The recognized text is used to construct a tri-character gram frequency, inverse document frequency (TGF-IDP) metric using tri-character grams extracted from the recognized text. Features in location-aware feature vector for the block are computed by multiplying the weight and the corresponding TGF-IDF metric. The location-aware feature vector for the image is the sum of the location-aware feature vectors for the individual blocks.

Described herein is a mechanism for visual recognition of items or visual search using Optical Character Recognition (OCR) of text in images. Recognized OCR blocks in an image comprise position information and recognized text. The embodiments utilize a location-aware feature vector created using the position and recognized information in each recognized block. The location-aware features of the feature vector utilize position information associated with the block to calculate a weight for the block. The recognized text is used to construct a tri-character gram frequency, inverse document frequency (TGF-IDP) metric using tri-character grams extracted from the recognized text. Features in location-aware feature vector for the block are computed by multiplying the weight and the corresponding TGF-IDF metric. The location-aware feature vector for the image is the sum of the location-aware feature vectors for the individual blocks.

A fingerprint sensing system for sensing a fingerprint pattern of a finger, comprising: a sensor array including a plurality of electrically conductive sensing structures; read-out circuitry connected to each of the sensing structures for providing sensing signals indicative of a capacitive coupling between the sensing structures and the finger; first signal providing circuitry for providing a first time-varying voltage signal to at least a portion of the sensor array; at least one electrically conductive edge-compensating structure arranged outside the sensor array; and second signal providing circuitry for providing a second time-varying voltage signal to the at least one edge-compensating structure.

In one embodiment, a method or system generates a high resolution 3-D point cloud to operate an autonomous driving vehicle (ADV) from a low resolution 3-D point cloud and camera-captured image(s). The system receives a first image captured by a camera for a driving environment. The system receives a second image representing a first depth map of a first point cloud corresponding to the driving environment. The system upsamples the second image by a predetermined scale factor to match an image scale of the first image. The system generates a second depth map by applying a convolutional neural network (CNN) model to the first image and the upsampled second image, the second depth map having a higher resolution than the first depth map such that the second depth map represents a second point cloud perceiving the driving environment surrounding the ADV.

A positioning and measuring system includes: an image capturing device performing image capturing operations on an object; a driving mechanism mechanically connected to one or both of the image capturing device and the object to cause a relative movement between the image capturing device and the object; and a processor electrically connected to the image capturing device and the driving mechanism and performing steps of: controlling the image capturing device to capture N portions of the object to generate N images before and after controlling the driving mechanism to cause M relative movements between the object and the image capturing device; and extracting feature points of each of the N images corresponding to unique surface morphology of the object and respectively performing cross-image feature point matching according to the feature points of every neighboring two of the N images to obtain information of the M relative movements.

In a reference position determination unit, for example, a plurality of feature points of eyes and a nose of a face are detected by rough search from an image area including a driver's face extracted by a face area extractor with a rectangular frame. Based on the feature points of the respective organs, a position between eyebrows of the driver's face is detected, and this is determined as a reference position of the face. Then, a face area re-extractor corrects the position of the rectangular frame with respect to image data so that the determined reference position of the face is the center of the rectangular frame, and an image area including the face is re-extracted from the image data by using the rectangular frame in the corrected position.

Devices and a method are provided for providing feedback to a user. In one implementation, the method comprises obtaining a plurality of images from an image sensor. The image sensor is configured to be positioned for movement with the user's head. The method further comprises monitoring the images, and determining whether relative motion occurs between a first portion of a scene captured in the plurality of images and other portions of the scene captured in the plurality of images. If the first portion of the scene moves less than at least one other portion of the scene, the method comprises obtaining contextual information from the first portion of the scene. The method further comprises providing the feedback to the user based on at least part of the contextual information.

Mipping systems and methods are disclosed. For example, a mipping system can include processing circuitry configured to receive combinations of a plurality of pixels N at a time, each pixel having material codes directed to respective materials of the pixels, where the material codes relate to infrared properties of the respective materials, and N is a positive integer greater than 1; and train an artificial neural network having a classification space by providing respective neurons for each unique combination of material codes, and condition the artificial neural network so that the respective neurons activate when presented with their unique of material code combinations in order to create a combined set of material code parameters for accurate rendering of the mipped pixels.

Described herein is a mechanism for visual recognition of items or visual search using Optical Character Recognition (OCR) of text in images. Recognized OCR blocks in an image comprise position information and recognized text. The embodiments utilize a location-aware feature vector created using the position and recognized information in each recognized block. The location-aware features of the feature vector utilize position information associated with the block to calculate a weight for the block. The recognized text is used to construct a tri-character gram frequency, inverse document frequency (TGF-IDF) metric using tri-character grams extracted from the recognized text. Features in location-aware feature vector for the block are computed by multiplying the weight and the corresponding TGF-IDF metric. The location-aware feature vector for the image is the sum of the location-aware feature vectors for the individual blocks.

Described herein is a mechanism for visual recognition of items or visual search using Optical Character Recognition (OCR) of text in images. Recognized OCR blocks in an image comprise position information and recognized text. The embodiments utilize a location-aware feature vector created using the position and recognized information in each recognized block. The location-aware features of the feature vector utilize position information associated with the block to calculate a weight for the block. The recognized text is used to construct a tri-character gram frequency, inverse document frequency (TGF-IDF) metric using tri-character grams extracted from the recognized text. Features in location-aware feature vector for the block are computed by multiplying the weight and the corresponding TGF-IDF metric. The location-aware feature vector for the image is the sum of the location-aware feature vectors for the individual blocks.