An electronic device is provided. The electronic device includes a display, a processor functionally connected with the display, and a memory functionally connected with the processor. The memory stores instructions configured to, when executed, enable the processor to display a first image through the display, display one or more second images through the display while displaying the first image, select a third image from among the one or more second images, identify a value of at least one property of the third image, generate a filter for applying the value of the at least one property to an image, apply the value of the at least one property to the first image using the filter, display the first image, to which the value of the at least one property is applied, through the display, and store the filter in the memory.

Disclosed herein is a method for encoding a feature map. The method may include arranging multiple channels based on similarity therebetween for a feature map having the multiple channels, rearranging the arranged multiple channels so as to be adjacent to each other in a feature map channel having a matrix form, and generating an encoded feature map by converting a feature value corresponding to the feature map channel from a real number to an integer.

Systems and methods are provided for multi-schema analysis of patient specific anatomical features from medical images. The system may receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology, and automatically classify the medical images using a segmentation algorithm. The system may use an anatomical feature identification algorithm to identify one or more patient specific anatomical features within the medical images by exploring an anatomical knowledge dataset. A 3D surface mesh model may be generated representing the one or more classified patient specific anatomical features, such that information may be extracted from the 3D surface mesh model based on the selected pathology. Physiological information associated with the selected pathology for the 3D surface mesh model may be generated based on the extracted information.

An object pose and model estimation method includes acquiring a global feature of an input image, and a location code of an object including location information for a joint point of the object and location information for a model vertex in a template model; determining a local area feature of the object based on the global feature of the input image and based on the location code of the object in the template model; and acquiring location information for the joint point of the object in the input image and location information for the model vertex in the input image based on the local area feature of the object.

Provided are a learning data collection apparatus, a learning data collection method, and a program for collecting learning data to be used for efficient retraining. A learning data collection apparatus (10) includes an inspection image acquisition unit (11) that acquires an inspection image, a region detection result acquisition unit (damage detection result acquisition unit (13)) that acquires a region detection result the region detection result indicating a region detected by a region detector that is trained, a correction history acquisition unit (15) that acquires a correction history of the region detection result, a calculation unit (17) that calculates correction quantification information obtained by quantifying the correction history, a database that stores the inspection image, the region detection result, and the correction history in association with each other, an image extraction condition setting unit (19) that sets a threshold value of the correction quantification information as an extraction condition, the extraction condition being a condition for extracting the inspection image to be used for retraining from the database, and a first learning data extraction unit (21) that extracts, as learning data for retraining the region detector, the inspection image satisfying the extraction condition and the region detection result and the correction history that are associated with the inspection image.

A fast object detection method based on deformable part model (DPM) is provided. The method includes importing a trained classifier for object detection, receiving an image frame from a plurality of frames in a video captured by a camera, and identifying regions possibly containing at least one object via objectness measure based on Binarized Normed Gradients (BING). The method also includes calculating Histogram of Oriented Gradients (HOG) feature pyramid of the image frame, performing DPM detection for the identified regions possibly containing the at least one object, and labeling the at least one detected object using at least one rectangle box via non-maximum suppression (NMS). Further, the method includes processing a next frame from the plurality of frames in the captured video until the video ends and outputting object detection results.

A method for real-time video processing for changing features of an object in a video, the method comprises: providing an object in the video, the object being at least partially and at least occasionally presented in frames of the video; detecting the object in the video; generating a list of at least one element of the object, the list being based on the object's features to be changed according to a request for modification; detecting the at least one element of the object in the video; tracking the at least one element of the object in the video; and transforming the frames of the video such that the at least one element of the object is modified according to the request for modification.

A method and device for automatically classifying document hardcopy images by using document hardcopy image descriptors are provided. The method and device include providing a document hardcopy image, the document hardcopy image having image features, extracting image descriptors by a first set of image descriptor extractors, each image descriptor of the image descriptors being descriptive of the image features of the document hardcopy image, estimating class probabilities of the document hardcopy image by multiple trained classifiers based on the image descriptors, determining a most probable class of the document hardcopy image by a trained meta-classifier based on the class probabilities estimated by the multiple trained classifiers, inputting the document hardcopy image and the most probable class of the document hardcopy image to an assigner, and assigning, by the assigner, the most probable class determined by the trained meta-classifier to the document hardcopy image to obtain a classified document hardcopy image.

An apparatus for detecting multiple objects using adaptive block partitioning is disclosed. An object contour extracting unit configured to extract a contour information of an object using a local binary pattern LBP and difference image between adjacent images. An adaptive block partitioning unit configured to perform a block partitioning of an object not overlapped based on the extracted contour information. A motion quantization unit configured to calculate a motion orientation histogram MOH of the object by performing N-directional quantization about a motion vector. An object detection unit configured to detect the object using a block of the partitioned object, the contour information and the MOH, and estimate a moving direction of the object after performing labeling the detected object. The apparatus may process effectively data through eight-directional quantization of a motion vector of an object using motion information provided in advance from an ISP chip, detect proper area of the object in the unit of a block with minimizing motion error of the object through the block with adaptive size and orientation histogram, and estimate simultaneously moving direction of the object with the detection of the object.

A face detector training method, a face detection method, and apparatuses are provided. In the present invention, during a training phase, a flexible block based local binary pattern feature and a corresponding second classifier are constructed, appropriate second classifiers are searched for to generate multiple first classifiers, and multiple layers of first classifiers that are obtained by using a cascading method form a final face detector; and during a detection phase, face detection is performed on a to-be-detected image by using a first classifier or a face detector that is learned during a training process, so that a face is differentiated from a non-face, and a face detection result is combined and output.