Sony Group Corporation

Sony Corporation

Sony Electronics Inc

Praggya Garg

Jong Hwa Lee

Gary Lyons

Seunghan Kim

Luis Vasconcelos

Moises Tafolla

Alexander Stoneham

Allison Langley

An electronic device and method for reverse image search is provided. The electronic device receives an image. The electronic device extracts, by a DNN model, a first set of image features associated with the image and generates a first feature vector based on the first set of image features. The electronic device extracts, by an image-feature detection model, a second set of image features associated with the image and generates a second feature vector based on the second set of image features. The electronic device generates a third feature vector based on combination of the first and second feature vectors. The electronic device determines a similarity metric between the third feature vector and a fourth feature vector of each of a set of pre-stored images and identifies a pre-stored image based on the similarity metric. The electronic device controls a display device to display information associated with the pre-stored image.

Reverse image search based on deep neural network (DNN) model and image-feature detection model

Implementations generally relate to evaluation of similar content-based images. In some implementations, a method includes receiving a first image, where the first image includes at least one first object. The method further includes receiving a second image, where the second image includes at least one second object. The method further includes computing a structural similarity index measure (SSIM) value based on the at least one first object and the at least one second object. The method further includes computing a scale invariant feature transform (SIFT) value based on the at least one first object and the at least one second object. The method further includes computing a histogram value based on the at least one first object and the at least one second object. The method further includes computing a similarity score based on the SSIM value, the SIFT value, and the histogram value.

Evaluation of similar content-based images

The scaled Peak Signal-to-Noise Ratio (PSNR) method scales PSNR to 1.00 or 100% from infinite decibel values. The PSNR describes the ratio between the maximum possible value (power) of a signal and the power of signal's noise. Noise affects the quality of its representation. Signals dynamically fluctuate from the smallest to largest possible quantity. The unsettled range to maximum from minimum values of the noise is unknown. Therefore, the PSNR usually forms on a logarithmic way (log) of describing a ratio, the decibel (dB). The dB is able to gauge the level of power, communication signal (intensity or voltage), sound pressure, and intensity of several other items including images.

Scaled PSNR for image quality assessment

A system and method for node-based image colorization is provided. The system receives a first user input via a Graphical User Interface (GUI) of an image/video editing application and controls a display device to display a node graph on the GUI based on the first user input. The node graph includes an input node to select grayscale images and a colorization node which may represent a workflow for colorization of at least a first object in the grayscale images. The system receives a second user input associated with a setting of the colorization node and selects an image colorization model based on the second user input. The system executes the workflow associated with the colorization node to transmit the grayscale images to a computing device that hosts the selected image colorization model, and to receive colorized images including at least the first object colorized based on a color effect.

Node-based image colorization on image/video editing applications

A computing system and method for neural network-based image colorization is provided. The computing system obtains a reference color image by selective application of a color effect on a region of interest of an input image and controls a display device to display a first node graph on a Graphical User Interface of an image/video editing application. The first node graph includes a colorization node representing a first workflow for colorization of at least a first object in grayscale images of a first image feed. The computing system selects the reference color image based on a user input and executes the first workflow associated with the colorization node by feeding the reference color image and the first image feed as an input to a neural network-based colorization model. The computing system receives a second image feed comprising colorized images as output of the neural network-based colorization model for the input.

Neural network-based image colorization on image/video editing applications

An apparatus comprising a connection hub having an input terminal for receiving a plurality of inputs from a plurality of component devices, and an output terminal for transferring a plurality of outputs from said connection hub to a display device. The apparatus further comprises a plurality of audio/video cables and a cable conduit coupled to the output terminal of the connection hub, wherein the audio/video cables traverse the cable conduit such that the visibility of the audio/video cables is obscured by the cable conduit.

Growing community of inventors

San Diego, CA, United States of America

Praggya Garg