Abstract: A wafer-scale layered two-dimensional material transfer method includes following steps of: providing a growth substrate with a layered two-dimensional material thereon; coating an adhesive layer on layered two-dimensional material; cold pressing a support layer to the adhesive layer; removing the growth substrate and transferring to a target substrate; removing the support layer; and removing the adhesive layer to obtain a stack comprising the layered two-dimensional material and the target substrate, wherein the layered two-dimensional material is attached on the target substrate.

Abstract: A method, an image processing device, and a display system for power-constrained image enhancement are proposed. The method is applicable to an image processing device and includes the following steps. First, an input image is received and inputted into a power-constrained sparse representation (PCSR) model, where the PCSR model is associated with a sparse representation model and a power-constraint model, where the sparse representation model is associated with an over-complete dictionary and sparse codes, and where the power-constrained model is associated with pixel intensities of the input image and a gamma correction value of a display Next, a reconstructed image outputted by the PCSR model is obtained and displayed on the display.

Abstract: A method, an image processing device, and a display system for power-constrained image enhancement are proposed. The method is applicable to an image processing device and includes the following steps. First, an input image is received and inputted into a power-constrained sparse representation (PCSR) model, where the PCSR model is associated with a sparse representation model and a power-constraint model, where the sparse representation model is associated with an over-complete dictionary and sparse codes, and where the power-constrained model is associated with pixel intensities of the input image and a gamma correction value of a display Next, a reconstructed image outputted by the PCSR model is obtained and displayed on the display.

Abstract: An augmented reality interactive system and a dynamic information interactive display method thereof are provided. The augmented reality interactive system suitable for being disposed in a transportation vehicle includes a transparent display, a motion detection unit, and a processing unit. The transparent display has a display panel pervious to light. The display panel is suitable for serving as a windshield of the vehicle, where the transparent display controls the image displayed by the display panel according to a display signal, so as to display an interactive information on the display panel. The motion detection unit detects an operation motion of a driver, so as to generate a control command. The processing unit coupled to the transparent display and the motion detection unit receives the control command, so as to generate the corresponding display signal based on the operation motion for controlling the operation of the transparent display.

Abstract: The invention discloses an image restoration method and an imager processing apparatus using the same. The method includes the following steps: receiving a haze image comprising a plurality of pixels; detecting whether a color cast phenomenon occurs on the haze image; if yes, calculating a plurality of Laplacian distribution values respectively corresponding to the color channels; determining a maximum distribution value and a minimum distribution value among the Laplacian distribution values, and generating a self-adaptive parameter by comparing the maximum distribution value and the minimum distribution value; adjusting the transmission map by the self-adaptive parameter so as to generate a adjusted transmission map; calculating a plurality of color parameters respectively corresponding to the color channels according to the Laplacian distribution values; and restoring the haze image by using the color parameters and the adjusted transmission map so as to obtain a restored image.

Abstract: A video editing method for a video editing device is provided. The method includes: obtaining an editing segment of a video, wherein the editing segment follows a first segment of the video and precedes a second segment of the video, the first segment includes a first picture, the second segment includes a second picture, and a short-term reference index of the second picture points to the first picture; setting a header of a third picture in the first segment, such that the first picture is moved from a short-term reference picture list to a long-term reference picture list when the header of the third picture is decoded; changing the short-term reference index of the second picture into a long-term reference index, wherein the long-term reference index points to the first picture in the long-term reference picture list; and encoding the video. Accordingly, the speed of editing the video is increased.

Abstract: A method and a system for image haze removal are provided. The method includes the following steps: receiving an input hazy image including input pixels; determining a hybrid dark channel for each of the input pixels according to a first minimum dark channel of a corresponding large local patch and a second minimum dark channel of a corresponding small local patch; determining a transmission map for each of the input pixels according to the hybrid dark channel prior, the corresponding hybrid dark channel, and atmospheric light associated with the input hazy image in each color channel; determining a color spectrum adjustment parameter corresponding to each of the color channels; recovering scene radiance for each of the input pixels in each of the color channels according to the corresponding color spectrum adjustment parameter, the transmission map, and the corresponding atmospheric light to produce and output a de-hazed image.

Abstract: A motion detection method determines bit-rate changes of input pixels of a video frame by a grey relational analysis technique to establish a multi-quality background model, detects moving objects by two-stage block-based and pixel-based detection procedures to generate a binary motion mask, detects luminance changes of the video frame by entropy calculation to timely update the background model, provides a setting interface for a user to set a detection sensitivity, and examines false detections of the binary motion mask. Therefore, it can correctly interpret moving objects in VBR video streams, implement more accurate and complete motion detection, eliminate the influence of luminance changes, increase the detection accuracy, and decrease false detections.

Abstract: A method for moving object detection based on a Principal Component Analysis-based Radial Basis Function network (PCA-based RBF network) includes the following steps. A sequence of incoming frames of a fixed location delivered over a network are received. A plurality of Eigen-patterns are generated from the sequence of incoming frames based on a Principal Component Analysis (PCA) model. A background model is constructed from the sequence of incoming frames based on a Radial Basis Function (RBF) network model. A current incoming frame is received and divided into a plurality of current incoming blocks. Each of the current incoming blocks is classified as either a background block or a moving object block according to the Eigen-patterns. Whether a current incoming pixel of the moving object blocks among the current incoming blocks is a moving object pixel or a background pixel is determined according to the background model.

Abstract: A method and an image processing apparatus for image visibility restoration are provided. The method includes the following steps. After an incoming hazy image is received, each of the incoming pixels is classified as either belonging to a localized light region or a non-localized light region. The localized light region is partitioned into patches according to each patch size in associated with image sizes in a training data set. Localized light patches are determined based on a FLD model and a designated patch size is accordingly determined. Adaptive chromatic parameters and dual dark channel priors corresponding to the designated patch size and a small patch size are determined. The incoming hazy image is restored according to the adaptive chromatic parameters, atmospheric light and a transmission map determined based on the dual dark channel priors to produce and output a de-hazed image.

Abstract: The invention discloses an image restoration method and an imager processing apparatus using the same. The method includes the following steps: receiving a haze image comprising a plurality of pixels; detecting whether a color cast phenomenon occurs on the haze image; if yes, calculating a plurality of Laplacian distribution values respectively corresponding to the color channels; determining a maximum distribution value and a minimum distribution value among the Laplacian distribution values, and generating a self-adaptive parameter by comparing the maximum distribution value and the minimum distribution value; adjusting the transmission map by the self-adaptive parameter so as to generate a adjusted transmission map; calculating a plurality of color parameters respectively corresponding to the color channels according to the Laplacian distribution values; and restoring the haze image by using the color parameters and the adjusted transmission map so as to obtain a restored image.

Abstract: A method for moving object detection based on a Fisher's Linear Discriminant-based Radial Basis Function Network (FLD-based RBF network) includes the following steps. A sequence of incoming frames of a fixed location delivered over a network are received. A plurality of discriminant patterns are generated from the sequence of incoming frames based on a Fisher's Linear Discriminant (FLD) model. A background model is constructed from the sequence of incoming frames based on a Radial Basis Function (RBF) network model. A current incoming frame is received and divided into a plurality of current incoming blocks. Each of the current incoming blocks is classified as either a background block or a moving object block according to the discriminant patterns. Whether a current incoming pixel of the moving object blocks among the current incoming blocks is a moving object pixel or a background pixel is determined according to the background model.

Abstract: A method and a system for image haze removal are provided. The method includes the following steps: receiving an input hazy image including input pixels; determining a hybrid dark channel for each of the input pixels according to a first minimum dark channel of a corresponding large local patch and a second minimum dark channel of a corresponding small local patch; determining a transmission map for each of the input pixels according to the hybrid dark channel prior, the corresponding hybrid dark channel, and atmospheric light associated with the input hazy image in each color channel; determining a color spectrum adjustment parameter corresponding to each of the color channels; recovering scene radiance for each of the input pixels in each of the color channels according to the corresponding color spectrum adjustment parameter, the transmission map, and the corresponding atmospheric light to produce and output a de-hazed image.

Abstract: A method and an image processing apparatus for image visibility restoration are provided. The method includes the following steps. After an incoming hazy image is received, each of the incoming pixels is classified as either belonging to a localized light region or a non-localized light region. The localized light region is partitioned into patches according to each patch size in associated with image sizes in a training data set. Localized light patches are determined based on a FLD model and a designated patch size is accordingly determined. Adaptive chromatic parameters and dual dark channel priors corresponding to the designated patch size and a small patch size are determined. The incoming hazy image is restored according to the adaptive chromatic parameters, atmospheric light and a transmission map determined based on the dual dark channel priors to produce and output a de-hazed image.

Abstract: The invention discloses an image processing method and an imager processing apparatus using the same. The method includes the following steps: receiving an training image; finding a minimum difference among the differences; determining whether the minimum difference is larger than a first threshold; if no, generating a first output value according to the first pixel, the background candidates and a plurality of weightings corresponding to the background candidates; updating a first background candidate corresponding to the minimum difference; updating a first weighting related to the first background candidate; if yes, adding the first pixel as a new background candidate to the background candidates and adding a new weighting corresponding to the new background candidate to the weightings; and detecting whether a moving object existing in an incoming image according to the background candidates and the weightings.

Abstract: A method and image processing apparatus for image visibility restoration are provided. The method includes the following steps: receiving an input hazy image including input pixels; obtaining edge information of each of the input pixels according to a median filtering operation and a dark channel; determining a transmission map according to each of the input pixels and atmospheric light associated with the input hazy image in each color channel; obtaining a refined transmission map according to the edge information and the transmission map; adjusting the refined transmission map by performing a gamma correction operation thereon to obtain an enhanced transmission map; determining a color difference value corresponding to each of the color channels; recovering scene radiance for each of the input pixels in each of the color channels according to the corresponding color difference value, the enhanced transmission map, and the atmospheric light to produce and output a de-hazed image.

Abstract: The present disclosure proposes a method of moving object detection in variable bit-rate video steams based on probabilistic neural networks, and the method features a background generation module and a moving object detection module. The background generation module produces a model of background images which express properties of variable bit-rate video streams. The moving object detection module distinguishes a moving object in both low and high bit-rate video steams in an efficient manner. The detection result is generated by calculating the output value of the probabilistic neural networks.

Abstract: A method for moving object detection based on a Principal Component Analysis-based Radial Basis Function network (PCA-based RBF network) includes the following steps. A sequence of incoming frames of a fixed location delivered over a network are received. A plurality of Eigen-patterns are generated from the sequence of incoming frames based on a Principal Component Analysis (PCA) model. A background model is constructed from the sequence of incoming frames based on a Radial Basis Function (RBF) network model. A current incoming frame is received and divided into a plurality of current incoming blocks. Each of the current incoming blocks is classified as either a background block or a moving object block according to the Eigen-patterns. Whether a current incoming pixel of the moving object blocks among the current incoming blocks is a moving object pixel or a background pixel is determined according to the background model.

Abstract: A method for moving object detection based on a Fisher's Linear Discriminant-based Radial Basis Function Network (FLD-based RBF network) includes the following steps. A sequence of incoming frames of a fixed location delivered over a network are received. A plurality of discriminant patterns are generated from the sequence of incoming frames based on a Fisher's Linear Discriminant (FLD) model. A background model is constructed from the sequence of incoming frames based on a Radial Basis Function (RBF) network model. A current incoming frame is received and divided into a plurality of current incoming blocks. Each of the current incoming blocks is classified as either a background block or a moving object block according to the discriminant patterns. Whether a current incoming pixel of the moving object blocks among the current incoming blocks is a moving object pixel or a background pixel is determined according to the background model.

Abstract: A method for moving object detection based on a Cerebellar Model Articulation Controller (CMAC) network includes the following steps. A time series of incoming frames of a fixed location delivered over a network is received. A CMAC network is constructed from the time series of incoming frames, where the CMAC network includes an input space, an association memory space, a weight memory space, and an output space. A current frame is received and divided into a plurality of current blocks. Each of the current blocks is classified as either a background block or a moving object block according to the CMAC network. Whether a target pixel of the moving object blocks is a moving object pixel or a background pixel is determined according to an output of the CMAC network in the output space.