National Taiwan University

Himax Technologies, Inc.

Shenzhen Goodix Technology Co., Ltd.

Chung-Te Li

Liang-Gee Chen

Ling-Hsiu Huang

Chao-Chung Cheng

Yen-Chieh Lai

Chieh-Wei Lo

Chien Wu

Hao-Jen Wang

Chia-Kai Liang

Yung-Lin Huang

Chih-Long Hsu

A background subtraction method includes: obtaining a first background image corresponding to a first object, wherein the first object has a first reflectivity; obtaining a second background image corresponding to a second object, wherein the second object has a second reflectivity, and the first reflectivity is different from the second reflectivity; calculating a plurality of relative values of the first background image relative to the second background image so as to obtain a mask image; obtaining a target image; subtracting the second background image from the target image so as to obtain a first background-removed image; and calculating and outputting a second background-removed image according to the first background image, the second background image, the first background-removed image and the mask image.

Background subtraction method, image module, and optical fingerprint identification system

A 3D image sensing system includes: a 3D image sensor (), including: a light-emitting module (), configured to emit a structured light to a target (), the structured light has a known pattern; and a light-sensing module () receives a reflected structured light of the structured light that is reflected by the target; and a processor (), configured to perform a time-of-flight distance measurement to obtain a first distance corresponding to the target based on time-of-flights of the structured light and the reflected structured light, the first distance has a global offset, the processor () establishes an unknown distance image based on the reflected structured light, estimates a disparity between the unknown distance image and a known distance image according to the known pattern, calculates a global compensation coefficient based on the disparity, and compensates the first distance to eliminate the global offset according to the global compensation coefficient.

3D image sensing system and associated electronic device and time-of-flight distance measurement method

The present application provides a fingerprint image processing method, applied in an optical fingerprint identification system of an electronic device. The electronic device comprises a display pixel array. The optical fingerprint identification system comprises an image sensing array. The image sensing array is disposed under the display pixel array. The fingerprint image processing method comprises obtaining a background image and obtaining at least an interfering frequency when the display panel is not pressed by a finger of a user; receiving a received image when the display panel is pressed by the finger of the user; performing a subtracting operation on the received image and the background image to obtain a difference image; and performing a filtering operation on the difference image at the at least an interfering frequency to obtain an operational result.

Fingerprint image processing method, optical fingerprint identification system and electronic device

A fingerprint image enhancement method, comprising: receiving a fingerprint image; computing a horizontal variation image and a vertical variation image of the fingerprint image (); computing a weighted image, wherein a weighted value of a first pixel corresponding to a finger ridge in the weighted image is greater than a weighted value of a second pixel corresponding to a finger valley (); multiplying the horizontal variation image with the weighted image to generate a weighted horizontal variation image, and multiplying the vertical variation image with the weighted image to generate a weighted vertical variation image (); computing a fingerprint orientation image according to the weighted horizontal variation image and the weighted vertical variation image (); and performing fingerprint image enhancement on the fingerprint image according to the fingerprint orientation image ().

Fingerprint image enhancement method and fingerprint image module

The present disclosure provides a vertical-cavity surface-emitting laser, structured light module, terminal comprising the structured light module, and method for projecting the structured light thereof. The structured light module includes a plurality of light sources and a single diffractive optical element (DOE); wherein the plurality of light sources simultaneously emit a plurality beams of invisible light to the single DOE, wherein the single DOE has a pseudo-random optical pattern groove, wherein the invisible light of each light source passes through the DOE and emits a beam of spectral encoded structured light, and the beam of structured light comprises a pattern corresponding to the pseudo-random optical pattern groove. The DOE projects an overall structured light that is formed by superimposing a plurality of beams of structured light, and there is an offset between patterns of different beams of structured light.

Vertical-cavity surface-emitting laser, structured light module and method for light projection and terminal

An example process to enhance a two-dimensional (2D) image may include generating a depth map for the 2D image based on edges detected from the 2D image, wherein the depth map can be used to convert the 2D image to a 3D image. The process may further include enhancing the 2D image using the depth map.

Real-time depth-aware image enhancement system

In accordance with at least some embodiments of the present disclosure, a processor for performing stereo matching of a first image and a second image is described. The processor may include a first pipeline stage configured to generate data costs associated with a first tile selected from the first image, wherein the data costs is generated based on pixels in the first tile and corresponding pixels in the second image. The processor may include a second pipeline stage configured to generate disparity values associated with the first tile and an outbound message from the first tile to one of neighboring tiles in the first image, wherein the disparity values and the outbound message are generated based on the data costs and inbound messages from the neighboring tiles to the first tile. The processor may further include a third pipeline stage configured to store the disparity values and the outbound message in a memory, wherein the outbound message is used by the second pipeline stage as one of the inbound messages during processing of a second tile selected from the first image.

Stereo-matching processor using belief propagation

A system and method of correcting a depth map for 3D image is disclosed. A spatial spectral transform unit extracts pixels of object boundaries according to an input image, wherein the spatial spectral transform unit adopts Hilbert-Huang transform (HHT). A correction unit corrects an input depth map corresponding to the input image according to the pixels of object boundaries, thereby resulting in an output depth map.

System and method of correcting a depth map for 3D image

A method of revising depth of a three-dimensional (3D) image is disclosed. The method comprises the following steps: firstly, at least one initial depth map associated with one image of the 3D image pair based on stereo matching technique is received, wherein the one image comprises a plurality of pixels, and the initial depth map carries an initial depth value of each pixel. Then, the inconsistence among the pixels of the one image of the 3D image pair is detected to estimate a reliable map. Finally, the initial depth value is interpolated according to the reliable map and the proximate pixels, so as to generate a revised depth map by revising the initial depth value.

System and method of revising depth of a 3D image pair

In accordance with at least some embodiments of the present disclosure, a process for generating a depth map for converting a two-dimensional (2D) image to a three-dimensional (3D) image is described. The process may include generating a depth gradient map from the 2D image, wherein the depth gradient map is configured to associate one or more edge counts with one or more depth values, extracting an image component from the 2D image, wherein the image component is associated with a color component in a color space, determining a set of gains to adjust the depth gradient map based on the image component, and generating the depth map by performing depth fusion based on the depth gradient map and the set of gains.

Depth map generation for a video conversion system

A depth-based image enhancement system is disclosed. A depth estimation unit generates three-dimensional (3D) depth information from a two-dimensional (2D) image. Subsequently, an image enhancement unit enhances the 2D image according to the 3D depth information.

Depth-based image enhancement

A system of generating three-dimensional (3D) depth information is disclosed. A color and object independent local blurriness estimation unit analyzes blurriness of each pixel of a two-dimensional (2D) image. Subsequently, a depth assignment unit assigns depth information to the 2D image according to the analyzed blurriness.

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Taipei, Taiwan

Chung-Te Li