247 results about "Histogram matching" patented technology

The present disclosure provides systems and methods for virtual facial makeup simulation through virtual makeup removal and virtual makeup add-ons, virtual end effects and simulated textures. In one aspect, the present disclosure provides a method for virtually removing facial makeup, the method comprising providing a facial image of a user with makeups being applied thereto, locating facial landmarks from the facial image of the user in one or more regions, decomposing some regions into first channels which are fed to histogram matching to obtain a first image without makeup in that region and transferring other regions into color channels which are fed into histogram matching under different lighting conditions to obtain a second image without makeup in that region, and combining the images to form a resultant image with makeups removed in the facial regions. The disclosure also provides systems and methods for virtually generating output effects on an input image having a face, for creating dynamic texturing to a lip region of a facial image, for a virtual eye makeup add-on that may include multiple layers, a makeup recommendation system based on a trained neural network model, a method for providing a virtual makeup tutorial, a method for fast facial detection and landmark tracking which may also reduce lag associated with fast movement and to reduce shaking from lack of movement, a method of adjusting brightness and of calibrating a color and a method for advanced landmark location and feature detection using a Gaussian mixture model.

A method synthesizes virtual images from a sequence of texture images and a sequence of corresponding depth images, wherein each depth images stores depths d at pixel locations I(x, y). Each depth image, is preprocessed to produce a corresponding preprocessed depth image. A first reference image and a second reference image are from the sequence of texture images. Then, depth-based 3D warping, depth-based histogram matching, base plus assistant image blending, and depth-based in-painting are applied in order to synthesize a virtual image.

Apparatus and method for electronically estimating focusing distance between a camera (still and/or video camera) and a subject. Images at different focal positions of a calibration target are collected with distances between subject positions. In one aspect, histogram matching is performed to reduce noise error. A focus matching model is then generated in response to detected blur differences between successive images of the calibration target. The focus matching model is preferably converted to a polynomial equation of a desired order to smooth out image collection noise. The focus matching model is stored for access during operation. In use, the distance to subject is estimated in response to capturing images, detecting blur differences between the images and entering the blur difference information into the matching model.

A method for generating one or more geological models for oil field exploration. The method includes receiving one or more well facies logs, a vertical facies proportion curve, a lateral proportion map, a variogram model and a global target histogram. The method then includes generating a facies probability cube using a modified Sequential Gaussian Simulation (SGSIM) algorithm, the well facies logs, the vertical facies proportion curve, the lateral proportion map and the variogram model. After generating the facies probability cube, the method includes matching the facies probability cube to the global histogram and generating the geological models based on the matched facies probability cube.

The invention discloses a UAV-based electric power line patrol method and device and an UAV. The method comprises the steps of collecting a sample picture of a target tested electrical device in advance, calculating a standard grey histogram of the target tested electrical device in the sample picture, and storing the standard grey histogram in a template database; collecting a picture of a current tested electrical device in real time, and calculating a current grey histogram in the current tested electrical device in the picture; and when the current grey histogram is matched with the standard grey histogram, comparing the picture of the current tested electrical device with the sample picture of the target tested electrical device, recognizing each component of the current tested electrical device in the picture of the current tested electrical device and components which are in one-to-one correspondence in the target tested electrical device in the sample picture, calculating deviation information of components of the current tested electrical device from components which are in one-to-one correspondence in the sample picture respectively, and determining whether the current tested electrical device has a defect according to the deviation information.

An invasion monitor method based on computer vision and used for the field of intelligent video monitor is disclosed; the invention in advance collects video images of a plurality of frames through a monitor camera in order to establish a scene model on the basis of non-parameter statistics. For the new video images, the scene model is used to detect prospect points, and a motion target is obtained by further processing the prospect points through morphologic filter. Afterwards, weighting histogram characteristics are extracted from the motion target and a histogram matching method is applied to tracing the motion target to obtain the motion track of the target. The method judges whether the target track enters into the set warning region in order to decide whether to sends the alarm. The invasion monitor method adopts the computer vision technology and realizes the automatic detection and alarming by utilizing the video information acquired by the computer process monitor camera, therefore the invention is simple in structure and convenient in implementing as well as has the extensive market prospect and application value.

The invention discloses a visual saliency model based automatic detecting and tracking method. The visual saliency model based automatic detecting and tracking method comprises the steps of: calculating a color, brightness and direction saliency graph of an input video image by using a visual saliency model, and defining a simple scene and a complex scene based on the weighted saliency graph; establishing a rectangular frame to serve as a tracking target to be tracked by using a saliency region when the simple scene is detected; correcting a manually selected tracking frame based on different weights when the complex scene is detected; tracking the tracking frame by utilizing a tracking studying and detecting algorithm, and detecting that the tracking is failed; detecting the image of each frame after the failure by using the visual saliency model, performing histogram matching on each region in the saliency graph and the online model before the tracking failure, and tracking a region with the highest similarity; and sending multiple regions with similar similarity into a target detector at the same time for detection, repeating tracking detection for the image target of the next frame, using a histogram comparison step until a target is detected again and tracking.

The invention provides a remote-sensing image stripe noise removing method based on segmented correction. A method that an image can be divided into a uniform region and a non-uniform region according to distribution phenomena of different ground objects is designed, the other method that a region can be divided into areas with different gray levels according to different gray level values of the region scanned by a detecting element is designed, one image (scanning imaging in rows or columns) can be divided into different intervals in columns or rows by the two methods, stripe noises of the different intervals are sectionally removed by common spatial domain denoising methods of moment matching, histogram matching and the like, categories of the different ground objects are considered, gray level value influences caused by external radiation changes and the like are combined, ground object categories and spectral characteristics are simultaneously considered in the obtained result, the obtained result is close to authentic data, the computational efficiency is high, and the computation is steady. The method can be well applied to removing stripe noises in the remote-sensing images.

A method, apparatus, and article of manufacture provide the ability to generate a damage proxy map. A master coherence map and a slave coherence map, for an area prior and subsequent to (including) a damage event are obtained. The slave coherence map is registered to the master coherence map. Pixel values of the slave coherence map are modified using histogram matching to provide a first histogram of the master coherence map that exactly matches a second histogram of the slave coherence map. A coherence difference between the slave coherence map and the master coherence map is computed to produce a damage proxy map. The damage proxy map is displayed with the coherence difference displayed in a visually distinguishable manner.

The present invention is a multi-stage detection algorithm using a successive nodule candidate refinement approach. The detection algorithm involves four major steps. First, the lung region is segmented from a whole lung CT scan. This is followed by a hypothesis generation stage in which nodule candidate locations are identified from the lung region. In the third stage, nodule candidate sub-images pass through a streaking artifact removal process. The nodule candidates are then successively refined using a sequence of filters of increasing complexity. A first filter uses attachment area information to remove vessels and large vessel bifurcation points from the nodule candidate list. A second filter removes small bifurcation points. The invention also improves the consistency of nodule segmentations. This invention uses rigid-body registration, histogram-matching, and a rule-based adjustment system to remove missegmented voxels between two segmentations of the same nodule at different times.

The invention discloses a feasible road segmentation method based on an improved Unet network model, and the method comprises the steps: modifying a convolutional layer in a Unet training model into aResNet residual module, and carrying out the back propagation training through employing a cross entropy loss function and a Lovsz hinge loss function; Performing feasible region segmentation on theroad scene without the segmentation label by adopting the trained model; Carrying out scale, contrast and normalization processing on the training picture and the corresponding segmentation mask mark,carrying out enhancement processing on the training sample, carrying out histogram matching processing on the test data by taking an average histogram of the training data as a template, carrying outtesting, and outputting a road segmentation result. According to the method, on the basis of not increasing the model complexity, the road segmentation precision is greatly improved, and real-time accurate segmentation of the feasible region of the road can be basically realized.

The invention discloses an HDR reconstructing algorithm based on histogram normalization and superpixel segmentation. The method includes the steps of mapping a histogram of a referential image Ir onto another image Ii of an image sequence through a histogram matching algorithm, respectively generating a corresponding matching image I <M>, combining the pixels of the image into superpixels by comparing the consistency of each pixel point with adjacent pixels, obtaining the contours of moving objects in the image through a cutting algorithm, obtaining the absolute value of the difference between the matching image I <M> and the other image Ii, conducting superpixel segmentation, dividing the superpixels obtained through segmentation into static parts and dynamic parts, synthesizing the images Ii and I <M> into intermediate images I <L>, calculating the weight map of each intermediate image based on a gradient image quality evaluation system, and conducting weighted fusion to obtain a final image fusion result. Histogram matching of a referential image and other images in the input sequence is conducted instead of directly calculating the gradient information of the input image. In this way, more details in the image can be retained.