Samsung Electronics Co., Ltd.

Hana Lee

Byung In Yoo

Solae Lee

Youngjun Kwak

Jiho Choi

Yong-Il Lee

Sungheon Park

Kyuhong Kim

Jaejoon Han

Seon Min Rhee

Changkyu Choi

Seungju Han

Minsu Ko

Jiwon Baek

Chang Kyu Choi

Hui Zhang

Jingtao Xu

Yi Zhou

Changbeom Park

Juwoan Yoo

Sung Kwang Cho

Jiaqian Yu

Hyeongwook Yang

HyunJeong Lee

Pingjun Li

Qiang Wang

Jihye Kim

Byungin Yoo

Youngsung Kim

Sangil Jung

Insoo Kim

Seong-Jin Park

Yiwei Chen

Dohwan Lee

Hangkai Tan

Sungyoul Park

A processor-implemented method includes: determining a probability that a pixel of an input image belongs to each of a plurality of preset categories; and determining a category of the pixel to be a category corresponding to either one or both of a plurality of category areas and a category determined based on the probability that the pixel belongs to each of the preset categories, based on a result of comparing, to a preset threshold value, a probability that the pixel belongs to the category corresponding to the category areas.

Method and apparatus with optimization and prediction for image segmentation

A processor-implemented method with liveness detection includes: receiving a plurality of phase images of different phases; generating a plurality of preprocessed phase images by performing preprocessing, including edge enhancement processing, on the plurality of phase images of different phases; generating a plurality of differential images based on the preprocessed phase images; generating a plurality of low-resolution differential images having lower resolutions than the differential images, based on the differential images; generating a minimum map image based on the low-resolution differential images; and performing a liveness detection on an object in the phase images based on the minimum map image.

Method and apparatus with liveness detection

A liveness detection method and apparatus, and a facial verification method and apparatus are disclosed. The liveness detection method includes detecting a face region in an input image, measuring characteristic information of the face region, adjusting the measured characteristic information in response to the characteristic information not satisfying a condition, and performing a liveness detection on the face region with the adjusted characteristic information upon the measured characteristic information not satisfying the condition.

A processor-implemented method with video processing includes: determining a first image feature of a first image of video data and a second image feature of a second image that is previous to the first image; determining a time-domain information fusion processing result by performing time-domain information fusion processing on the first image feature and the second image feature; and determining a panoptic segmentation result of the first image based on the time-domain information fusion processing result.

Apparatus and method with multi-image panoptic segmentation

Disclosed is an object recognition method and object recognition apparatus. The object recognition method may include detecting an object region comprising an object in an input image, determining scores corresponding to regions of the object region, determining weights based on the scores and detection information of the object region, determining a final score based on the scores and the determined weights, and determining a result of recognizing the object based on the final score.

Object recognition method and object recognition apparatus

A device and method with object recognition is included. In one general aspect, an electronic device includes a camera sensor configured to capture a first image of a scene, the camera sensor is configured to perform at least one type of physical camera motion relative to the electronic device, the at least one type of physical camera motion includes rolling, panning, tilting, or zooming the camera sensor relative to the electronic device, and a processor configured to control the camera sensor to perform a physical motion of the physical camera motion type based on detecting an object in the first image, acquire a second image captured using the camera sensor as adjusted based on the performed physical motion, and recognize the object in the second image.

Device and method with object recognition

A method and apparatus for detecting a liveness based on a phase difference are provided. The method includes generating a first phase image based on first visual information of a first phase, generating a second phase image based on second visual information of a second phase, generating a minimum map based on a disparity between the first phase image and the second phase image, and detecting a liveness based on the minimum map.

Method and apparatus for detecting liveness based on phase difference

A fingerprint verification method and apparatus is disclosed. The fingerprint verification method may include obtaining an input fingerprint image, determining a matching region between the input fingerprint image and a registered fingerprint image, determining a similarity corresponding to the matching region, representing a determined indication of similarities between the input fingerprint image and the registered fingerprint image, relating the determined similarity to the matching region as represented in a matching region-based similarity, determining a result of a verification of the input fingerprint image based on the matching region-based similarity, and indicating the result of the verification.

Method and apparatus with fingerprint verification

A target tracking method and apparatus is provided. The target tracking apparatus includes a memory configured to store a neural network, and a processor configured to extract feature information of each of a target included in a target region in a first input image, a background included in the target region, and a searching region in a second input image, using the neural network, obtain similarity information of the target and the searching region and similarity information of the background and the searching region based on the extracted feature information, obtain a score matrix including activated feature values based on the obtained similarity information, and estimate a position of the target in the searching region from the score matrix.

Method and apparatus for tracking target

Disclosed is a method and apparatus for testing a liveness, where the liveness test method includes receiving a color image and a photodiode (PD) image of an object from an image sensor comprising a pixel formed of a plurality of PDs, preprocessing the color image and the PD image, and determining a liveness of the object by inputting a result of preprocessing the color image and a result of preprocessing the PD image into a neural network.

Method and apparatus for testing liveness

A depth estimation method and apparatus are provided. The depth estimation method includes obtaining an image from an image sensor comprising upper pixels, each comprising N sub-pixels, obtaining N sub-images respectively corresponding to the N sub-pixels from the image, obtaining a viewpoint difference between the N sub-images using a first neural network, and obtaining a depth map of the image based on the viewpoint difference using a second neural network.

Depth estimation method and apparatus

A processor-implemented method with blur estimation includes: acquiring size information of an input image; resizing the input image to generate a target image of a preset size; estimating a blur of the target image; and estimating a blur of the input image based on the size information of the input image.

Method and apparatus with blur estimation

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Suwon-si, South Korea

Hana Lee