Systems and methods for assessing text legibility in an electronic document are disclosed. According to certain aspects, the electronic document may include a text layer and a background layer, and an electronic device may generate a text mask comprising a set of glyphs at certain positions. The electronic device may analyze the text mask to generate an output comprising a set of bounding boxes that indicate legibility degrees of the respective glyphs included in the text mask. The electronic device may display the output for review and assessment by a user, who may use the electronic device to facilitate any modifications to the electronic document.

Included are an encoding section, a decoding section, and an image recognition section. The encoding section performs an encoding process for a video signal to be input based on a calculated encoding mode, and transmits an encoded stream. The decoding section performs a decoding process for the received encoded stream, and outputs a decoded image. The image recognition section performs an image recognition process for the decoded image. The encoding section adjusts the encoding mode based on recognition accuracy information representing the certainty of a recognition result in the image recognition section.

Aspects of the present disclosure provide an image-based face detection and recognition system that processes and/or analyzes portions of an image using image strips and cascading classifiers to detect faces and/or various facial features, such an eye, nose, mouth, cheekbone, jaw line, etc.

An apparatus and a method for controlling a stop of a vehicle may include an information collection system collecting at least one of road information, position information, and driving information; and a control system stopping the vehicle on a basis of the information collected by the information collection system in a case of emergency while the vehicle is running on a road.

A method for a multiple camera sensor suite mounted on an autonomous robot to be able to detect and recognize shelf labels using color Haar classifiers is described.

A method of determining a summation of pixel characteristics for a rectangular region of a digital image includes determining if a base address for a data element in an integral image buffer is aligned for an SIMD operation by a processor embedded in an electronic assembly configured to perform Haar-like feature calculations. The data element represents a corner of the rectangular region of an integral image. The integral image is a representation of the digital image. The integral image is formed by data elements stored in the integral image buffer. The data element is loaded from the integral image buffer to the processor when the base address is aligned for the SIMD operation. An offset data element of an offset integral image is loaded from an offset integral buffer when the base address is non-aligned for the SIMD operation. The offset data element represents the corner of the rectangular region.

To present a determination result with respect to input data and also a reason of the determination result to a user, an extraction unit configured to extract a plurality of feature amounts from an image including an inspection target object, a determination unit configured to determine an anomaly degree of the inspection target object on the basis of the extracted feature amounts, and an image generation unit configured to generate a defect display image representing a defect included in the inspection target object on the basis of contribution degrees of the respective feature amounts with respect to the determined anomaly degree are provided.

The present invention relates to an unmanned aerial vehicle having an automatic tracking function and a control method thereof, the unmanned aerial vehicle comprising: an image input unit for acquiring an image of a peripheral image of a subject to be photographed; an object recognition unit for extracting a region of interest using the image acquired through the image input unit, detecting a specific region located within the region of interest to measure coordinates, and recognizing the specific region as an object to be tracked; an object tracking unit for calculating and tracking a position of the object to be tracked recognized by the object recognition unit using a tracking learning detection (TLD) learning algorithm and generating a drive command for driving the unmanned aerial vehicle corresponding to the position; a motion recognition unit for recognizing a motion of the object to be tracked and generating a driving command corresponding to a photographing mode, a moving picture photographing mode, and a return mode; and a drive control unit for driving the unmanned aerial vehicle according to the drive command. Due to this feature, the present invention has an effect of enabling autonomous flight of an unmanned aerial vehicle by recognizing and automatically tracking an object to be tracked.

A method and device for real-time generation of a multiresolution representation of a digital image for real-time generation are disclosed. A sequence of main representations of the digital image is stored at successive different main resolutions in a main memory. A part of a current main representation is loaded from the main memory into a local memory via a bus. A current main representation is processed by determining a corresponding part of an intermediate representation of the image having an intermediate resolution lying between the resolution of the current main representation and the resolution of the subsequent main representation. The loading and processing steps are repeated for other parts of the current main representation until all parts of the current main representation have been successively loaded and processed.

Described is a novel method for feature extraction for automatic gait recognition. This method uses Multi-kernel Fuzzy-based Local Gabor Binary Pattern. From a captured gait video sequence, the gait period is determined then a gait energy image is constructed to represent the spatial-temporal variations during one motion cycle of the gait sequence. Then, each gait sequence is represented with a feature vector. The computation of this vector is conducted by first applying the 2D Gabor filter bank then encoding the variations in the Gabor magnitude using a multi-kernel fuzzy local binary pattern operator. Finally, gait classification is performed using a support vector machine.