Electronics and Telecommunications Research Institute

National University of Sciences & Technology

Information Technology University (itu)

Rehan Hafiz

Yong Ju Cho

Ji Hun Cha

Arshad Ali

Jin Woong Kim

Yong Ju Lee

Seung Kwon Beack

Joo Myoung Seok

Seong Yong Lim

Myung Seok Ki

Muhammad Faisal

Mohsen Ali

Ashar Rasul

Muhammad Murtaza Khan

Jeong Il Seo

Jeong-Il Seo

Sang Woo Ahn

Yong-Ju Cho

Atif Ahmed

Haris Anis

Mutahir Latif

Muhammad Murtaza

Aman Irshad

Usama Sadiq

Saleh Saeed

Tabasher Arif

Muhammad Umer Kakli

Disclosed herein are an apparatus and method for detecting a keypoint based on deep learning robust to scale changes based on information change across receptive fields. The apparatus for detecting a keypoint based on deep learning robust to scale changes based on information change across receptive fields includes a feature extractor for extracting a feature from an input image based on a pre-trained deep learning neural network, an information accumulation pyramid module for outputting, from the feature, at least two filter responses corresponding to receptive fields having different scales, an information change detection module for calculating an information change between the at least two filter responses, a keypoint detection module for creating a score map having a keypoint probability of each pixel based on the information change, and a continuous scale estimation module for estimating a scale of a receptive field having a biggest information change for each pixel.

Apparatus and method for detecting keypoint based on deep learning using information change across receptive fields

Disclosed herein is an image deep learning model training method. The method includes sampling a twin negative comprising a first negative sample and a second negative sample by selecting the first negative sample with a highest similarity out of an anchor sample and a positive sample constituting a matching pair in each class and by selecting the second negative sample with a highest similarity to the first negative sample, and training the samples to minimize a loss of a loss function in each class by utilizing the anchor sample, the positive sample, the first and second negative samples for each class. The first negative sample is selected in a different class from a class comprising the matching pair, and the second negative sample is selected in a different class from classes comprising the matching pair and the first negative sample.

Method of training image deep learning model and device thereof

An apparatus and a method for displaying a panoramic image using a look-up table (LUT) are disclosed, including generating an LUT may include determining first geometric correction information to transform an input domain pixel coordinate system of an input image to a panorama domain pixel coordinate system of a panoramic image, determining second geometric correction information to transform an output domain pixel coordinate system of an output image of the panoramic image to the panorama domain pixel coordinate system of the panoramic image, determining third geometric correction information to transform the output domain pixel coordinate system of the output image to the input domain pixel coordinate system of the input image based on the first geometric correction information and second geometric correction information, and generating an LUT that maps the output domain pixel coordinate system of the output image to the input domain pixel coordinate system of the input image.

System and method for displaying panoramic image using single look-up table

Provided is a geometric correction apparatus and method based on a recursive Bezier patch sub-division. A geometric correction method may include: receiving, from a camera, a first image that is obtained by photographing a black screen that is projected by a projector onto a projection surface; receiving, from the camera, a second image that is obtained by photographing a predetermined pattern that is projected by the projector onto the projection screen; generating a third image by subtracting the first image from the second image; and performing geometric correction with respect to the predetermined pattern to correct a distortion between the predetermined pattern and the third image.

Geometric correction apparatus and method based on recursive bezier patch sub-division cross-reference to related application

An apparatus and a method for correcting colors of an image projection device are provided. The method includes: acquiring a photographed image by photographing a sample image projected on projection surface; generating input-output color information for n regions, based on color values of a block in the sample image and corresponding color values of the block in the photographed image; selecting one of the n regions of photographed images as a reference region; generating look-up tables (LUTs) for non-reference regions, based on the reference region and the input and output color information; and correcting colors of input images to be projected by the image projection device using the look-up tables, thereby minimizing color difference of the input images on the projection surface for both intra and inter projection device color correction while simplifying the correction procedure.

Apparatus and method for correcting color of image projection device

Growing community of inventors

Islamabad, Pakistan

Rehan Hafiz