161 results about "Texture model" patented technology

A technique for determining a characteristic of a face or certain other object within a scene captured in a digital image including acquiring an image and applying a linear texture model that is constructed based on a training data set and that includes a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations. A fit of the model to the face or certain other object is obtained including adjusting one or more individual values of one or more of the model components of the linear texture model. Based on the obtained fit of the model to the face or certain other object in the scene, a characteristic of the face or certain other object is determined.

This apparatus calculates a direction of a light source (the sun) in a coordinate system having a 3D geometrical model of an object placed therein from geographic information on an object and a shooting time of image data, and detects a shadow region cast on the 3D geometrical model by a beam from a light source direction so as to identify the shadow region in the image data based on correspondence information. It uses a predetermined reflection model to estimate effects of shadings caused to the 3D geometrical model and determines a parameter of a reflection model suited to estimated shadings. And it performs calculation for removing the effects of the shadows and shadings by using the determined parameter from pixel values sampled from the image data so as to fit the calculated pixel values in the 3D geometrical model and generate a texture model.

An apparatus for generating 3D geographical models includes a processor and memory storing executable computer program code causing the apparatus to at least perform operations including removing points of a cloud depicting vertical structures in meshes of triangles detected in an area corresponding to a set of 3D points responsive to identifying triangles on vertical structures. The triangles include vertices corresponding to geocoordinates. The program code further causes the apparatus to interpolate non-vertical structures of triangles to generate a dense cloud of points. The program code further causes the apparatus to downward project delineated points of segmented rooftops to closest points of a ground to generate 3D polygonal models depicting geographical objects. The program code further causes the apparatus to generate texturized objects responsive to mapping triangle locations to the 3D polygonal models and assign texture to vertical structures and rooftops. Corresponding methods and computer program products are also provided.

The invention belongs to the application field of the computer vision technology, and provides a three-dimensional photorealistic reconstruction method and system for objects and an electronic device. In the method and the system, steps are as follows: after establishing a three-dimensional texture model, using a Markov algorithm to split the three-dimensional texture model to obtain an optimal split method with minimum texture seams between different regions; then processing edge triangles of different regions to achieve the purpose of eliminating the texture seams to reconstruct the photorealistic three-dimensional model of the object. With respect to a conventional method which directly processes the edge triangles after establishing the three-dimensional texture model or processes the textures of overlapped regions to realize texture fusion, the method provided by the invention can more effectively eliminate the texture seams, and realize smooth and natural transition between different image regions in the three-dimensional texture model with a better blending effect.

The invention discloses a method for identifying human face through double models and mainly solves the problem that the traditional identification method greatly depends on textures of a human face image. The method of the invention comprises the following steps: dividing a human face image sample set into a test image set and a train image set, and studying a train image to obtain a characteristic face subspace and an active apparent model; projecting test and train images to the characteristic face subspace to obtain texture models, and calculating the distance between the test and train image texture models; automatically searching test and train image characteristic points according to the active apparent model, constructing shape models, and taking an image edit distance as the distance between test and train image shape models; and determining identity information of the test image through weighted fusion of the distances. Compared with the texture-based or structural information-based identification method, the method of the invention has the advantage of higher identification rate to the human face image with changed expression, illumination and size, particularly to the human face image acquired under the condition of changed illumination, and can be used for authentication under the influence of a plurality of factors.

The invention discloses a nanocomposite diatom ooze material and a construction method thereof. The material consists of a diatom ooze powder material and nano-titanium dioxide dispersion liquid, wherein the diatom ooze powder material consists of calcined diatomite, quartz sand, gray calcium powder, bentonite, wollastonite, seaweed gel, cellulose ether and negative ion powder. The construction method comprises the following steps of: mixing the diatom ooze powder material and water according to the weight ratio of 1:0.9 in the field; after stirring for 15 minutes by using an electric stirrer, performing batch-scraping coating texture modeling construction; uniformly spraying the nano-titanium dioxide dispersion liquid, namely photocatalyst solution to a construction surface by using a spraying gun after the surface is dried; and drying for 48 hours. By using the construction method, the problems that nano-titanium dioxide powder is difficult to disperse and easy to agglomerate, and has large additive amount, the production cost is high, and a nano-titanium dioxide material is wasted in the preparation process of the diatom ooze are solved, the functionality of the diatom ooze for purifying and decomposing harmful gases is improved, and the overall cost of the product is reduced.

The invention provides a dense population estimation method and a system based on multi-feature fusion. The method comprises the following steps: partitioning an image into N equal sub-blocks; performing hierarchical background modeling on the image by using a method based on a CSLBP (Center-Symmetric Local Binary Pattern) histogram texture model and mixture Gaussian background modeling, extracting the foreground area of each sub-block subjected to perspective correction, detecting the edge density of each sub-block in combination with an improved Sobel edge detection operator, and extracting four important texture feature vectors in different directions for describing image texture features in combination with CSLBP transform and a gray-level co-occurrence matrix; performing dimension reduction processing on the extracted population foreground partition feature vectors and texture feature vectors through main component analysis; inputting the dimension-reduced feature vectors into an input layer of a nerve network model, and acquiring the population estimation of each sub-block through an output layer; adding to obtain the total population. The dense population estimation method and system have high accuracy and high robustness, and a good effect is achieved in the population counting experiment of subway station monitoring videos.

A method and apparatus removing redundant data of a simple texture model, using an extended depth image, and an image-based editing method and apparatus. The method involves calculating normal vectors of pixels projected into a three-dimensional (3D) space by using depth images among a plurality of simple texture images, the plurality of simple texture images rendering different aspects of a 3D model; calculating reliabilities of the pixels of each of the simple texture images by using the calculated normal vectors; and comparing the reliabilities of the pixels that originate from different simple texture images but render the same portion of the 3D model and removing the pixels having low reliabilities from the different simple texture images from which they respectively originate.

Systems and methods for capturing and recreating the feel of a surface are disclosed. A method for capturing a feel of a surface comprises contacting the surface with a handheld tool, recording data measured by the tool, and generating a texture model of the surface based on the recorded data. A method for recreating a feel of a surface comprises contacting a virtual surface with a handheld tool, determining an estimated contact force based on the data measured by the tool, generating a vibration waveform from a texture model based on the data measured by the tool; and actuating at least one actuator of the tool according to the vibration waveform. A system for capturing and recreating a feel of a surface comprises a handheld tool, a storage device, and a processor.

The invention discloses a three-dimensional modeling method of an object based on a depth sensor, comprising the following steps: S1, a data acquisition step of reading data from the depth sensor to obtain three-dimensional point cloud data; S2, a preprocessing step of separating the point cloud of the object from the whole scene, removing abnormal points, extracting the boundary of the point cloud of the object, and obtaining the point cloud of the object under each visual angle; S3, a point cloud registration step of registering an object point cloud under an adjacent perspective to obtain aconversion matrix between the object point clouds under the adjacent perspective; S4, a global alignment step of fusing the point clouds under each visual angle into the global point clouds by usinga conversion matrix to obtain a three-dimensional point cloud model; S5, meshing and texture mapping step, converting the three-dimensional point cloud model into a mesh model, and then mapping the color information to the surface of the mesh model to obtain a three-dimensional texture model. On the basis of guaranteeing the modeling accuracy, the method improves the modeling speed, and has high use and popularization value.

The invention discloses a face verification method and device. The method comprises: obtaining a textured face image and an initial face image needing verification; using a preset texture removing model to perform texture removing operation of the textured face image to obtain a de-textured face image; extracting features of the de-textured face image by means of a preset de-textured face verification model to obtain de-textured face features, and extracting features from the initial face image by means of the preset de-textured face verification model to obtain initial face features; and based on the de-textured face features and the initial face features, verifying the de-textured face image and the initial face image. According to the de-texture model and the de-textured face verification model trained in advance, the method conducts automatic processing and analysis of the textured face image and initial face image needing verification, with no need of manual operation by professionals. The method can automatically determine whether the textured face image and the initial face image are of the same person and improve the efficiency and accuracy of face verification.

The invention provides a method for carrying out Bayes judgment on modeling for a video image based on color-texture dual characteristic vectors so as to realize moving target detection. The method comprises the following steps: 1. obtaining a video frame; 2. preprocessing an image; 3. background modeling and carrying out motion detection by utilizing Bayes judgment, wherein the background modeling comprises image color-texture dual characteristic vectors, respectively adopting different characteristic vectors according to the variation of interframe difference of a pixel point, expanding the original Bayes judgment to be two-dimensional due to the independence of the characteristic vectors, setting model parameters and updating control parameters so as to adapt to the change of the state of a moving object; 4. shadow detection, using a texture model to detect a shadow candidate point and eliminating the shadow; 5. processing after the detection, using horizontal projection and vertical projection to determine a foreground area and perfecting the detection result in a divided area through block analysis; and 6. updating a background image, respectively updating according to a detection classification result and setting self-adaptive updating rate parameters so as to adapt to illumination change.

The invention provides a real-time three-dimensional reconstruction method for a human face based on an RGBD camera, which comprises the steps of S1, collecting a color image and a depth image; S2, obtaining a first point set; S3, solving the head attitude, an identity coefficient and an expression coefficient of the human, and constructing the data offset of a texture image; S4, calculating a current human face model and a human face three-dimensional geometric model; and S5, obtaining an updated human face three-dimensional geometric model. The beneficial effects are that the three-dimensional reconstruction method for the human face based on the RGBD camera further constructs the data offset of the texture image on the basis of a human face multilinear model and a human face hybrid shape model by using the collected color image and depth image so as to obtain the human face three-dimensional geometric model, and the method is small in calculation amount and easy to realize real-timecalculation; and precise geometric detail and texture modeling is achieved by adopting a small storage capacity, thereby being a real-time human face three-dimensional reconstruction method with a good expression effect.

The invention discloses a VR (Virtual Reality) system tactile glove device and an implementation method of the VR system tactile glove device. The device comprises a glove main body, a motor, a central control module, a motor driving module, a wireless communication module and a power supply component, wherein the motor is arranged in an interlayer, positioned in a finger movement joint part, of the glove main body, and is connected with the central control module through a conducting wire; the central control module is connected with the wireless communication module, the motor driving module and the power supply component through a connecting circuit; then the central control module is interconnected with a VR host through wireless communication. The method disclosed by the invention has the advantages that hand movement is detected through a gesture recognition device; detected information is input into the VR host, and a virtual hand is created by means of modeling through a game engine; a waveform signal with a certain frequency and amplitude is generated by means of performing virtual hand collision detection and object texture modeling, and a special motor on the glove is driven to vibrate by the waveform signal, therefore the shape and texture touch of a virtual object is represented; the tactile experience effect brought by VR equipment is greatly improved, and the effects of good maneuverability and high convenience degree are achieved.

An exemplary method for simplifying a texture of a three-dimensional model includes simplifying a first three-dimensional model to determine a second three-dimensional model. The first three-dimensional model has a higher resolution than the second three-dimensional model. The method also includes allocating a texture atlas for the second three-dimensional model. The method further includes filling in the texture atlas for the second three-dimensional model. Filling in the texture atlas may include determining a location on the second three-dimensional model corresponding to a pixel in the texture atlas for the second three-dimensional model, determining a location on the first three-dimensional model corresponding to the determined location on the second three-dimensional model, determining a color value texture mapped to the first three-dimensional model at the determined location on the first three-dimensional model, and setting the determined color value to the pixel in the texture atlas for the second three-dimensional model.

The invention discloses a method for positioning characteristic points in a facial image and belongs to the technical field of signal processing. The method for positioning the characteristic points in the facial image comprises a model training stage and an image searching stage. At the model training stage, a sufficient number of normal facial images and facial images under different light conditions are selected to serve as a training set, the positions of characteristic points of each training image are accurately marked manually, and a moving shape model and a local texture model are established. The model training stage just needs to be executed once. At the image searching stage, the accurate positions of pupils of a target image are firstly determined to initialize the initial position of the moving shape model, and then the model is fitted to the accurate target characteristic point positions through iteration.

The invention discloses a three-dimensional model simplification method suitable for a model with textures. The three-dimensional model simplification method comprises the following steps: 1. acquiring grid information of a three-dimensional model; 2. automatically marking triangles which can be combined and can not be combined; 3. calculating the combination operation cost of the triangles which can be combined; 4. determining the triangles with the lowest combination operation cost, executing the combination operation, and updating the combination operation cost of the influenced triangles; 5. if the amount of the residual triangles reaches the requirements or the model can not be continuously simplified, turning to the step 6, otherwise turning to the step 4; and 6. completing the simplification, and outputting the model. Texture mapping is a very important part in the model visual effect. Due to the adoption of the three-dimensional model simplification method, a triangle combination cost code suitable for the model with the textures is provided; as two errors of the combination of the triangles and texture variable quantity measurement are combined as the combination cost of the triangles, a geometrical error and the texture variation factors are both considered, the texture mapping effect of the existing model is kept as much as possible when the model is simplified, and variation of the model visual effect before and after the simplification is less.

The invention discloses an SAR image noise suppression method based on a direction wave domain mixture Gaussian model, belonging to the field of image processing, mainly solving the problems of fuzzydistortion of an SAR image caused by the current denoising method and improvement of excessive zero values added by coset decomposition of direction wave, and comprising the following steps: (1) carrying out cartoon texture model decomposition on the SAR image; (2) carrying out improved direction wave transformation on the texture part; (3) carrying out fitting of the mixture Gaussian model on thehigh frequency subband coefficient of the coset; (4) estimating noise variance; (5) estimating noise-free coefficient by Bayesian Minimum Mean-Square Error (MMSE); (6) carrying out improved directionwave inverse transformation on the noise-free coefficient to obtain the denoised texture part; and (7) adding the cartoon part and the denoised texture part to obtain a denoised SAR image. The SAR image noise suppression method has the advantages of improved definition, better maintenance of detail information and smoother uniform region of the denoised SAR image and can be used in filtering speckle noise in the SAR image.

The invention provides a 3D texture model encryption method based on chaotic mapping, belongs to the field of information security cryptology, and in particular relates to a 3D texture model encryption method. The complexity of the encryption scheme can be effectively improved by extreme sensitivity of a chaotic system, and meanwhile a 3D texture model can be divided into three parts: a peak, polygons and a texture map; the reliability of the encryption method can be effectively improved by separately encrypting the three parts. According to the method, a design thought of a random reversibletransformation matrix is used, a secret key sequence can be generated by using a system parameter and an initial value, and thus a series of transformation matrixes are further generated for encryption. Therefore, a decrypting result can be greatly changed due to tiny change of the initial value or the system parameter. Experimental analysis shows that the encryption scheme can well resist violentstacks and count the attacks, and is high in secret key sensitivity and quick in encryption and decryption speeds. Furthermore, the encryption method is extremely easy to implement via software, andcan be widely applied and popularized to secure storage and transmission encryption of the texture model.

A method and apparatus in a preferred embodiment for generating anti-aliased layered fog which is textured manipulated as if in a geocentric virtual environment to thereby show horizon depression at high altitudes. Hardware is provided such that layer model data and texture model data is combined to generate fogged pixel color.

The invention discloses a dynamic target reconstruction method and device based on multiple RGBD cameras, the multiple RGBD cameras are installed at positions of different visual angles, and the method comprises the steps: obtaining RGB images and depth images collected by the multiple RGBD cameras; generating a point cloud of a three-dimensional space by using the depth image and a known camera internal reference; performing frame difference processing on the RGB images and corresponding pre-stored background RGB images in sequence to obtain moving target areas of the RGB images at differentviewing angles; denoising processing is performed on the point cloud corresponding to each moving target area; converting the denoised point cloud from a camera coordinate system to a world coordinatesystem; establishing a three-dimensional model according to the point cloud converted into the world coordinate system; mapping the textures of the RGB images collected from different visual angles to the three-dimensional model to obtain a dynamic target three-dimensional texture model, and completing dynamic target reconstruction. According to the method, the three-dimensional model of the dynamic target can be efficiently reconstructed, and the constructed three-dimensional model is rich in content and high in accuracy.

Various methods for local binary pattern based facial feature localization. One example method includes determining an eye state classification of an input image. The example method may also include selecting a texture model for a global shape and an associated mean shape based on eye center positions and the eye state classification, and adjusting locations of feature points defined by the mean shape based on the texture model for the global shape and an associated global shape model. Similar and related example methods and example apparatuses are also provided.

The invention discloses a line structured optical three-dimensional measurement system and a three-dimensional texture image construction algorithm. The system comprises a CCD camera, a two-dimensional shifting fixed support, a linear laser device and a two-dimensional adjusting platform. The CCD camera is fixed to one end of the two-dimensional shifting fixed support; the linear laser device is fixed to the other end of the two-dimensional shifting fixed support; the two-dimensional adjusting platform is arranged on the two-dimensional shifting fixed support and is used for containing a measured object. According to the three-dimensional texture image construction algorithm based on the system, a new color texture mapping method is adopted to construct a distortionless three-dimensional outline curved surface formed by real image outlines. According to the core concept, binarized contour lines without any color are made to have color information (or grey information) through color texture mapping. According to the line structured optical three-dimensional measurement system and the three-dimensional texture image construction algorithm, a three-dimensional texture model with the real surface textures can be constructed.

The invention discloses an improved CT image aorta segmentation method based on an active shape model. A segmentation and extraction method combining a Support vector machine (SVM) and an active shapemodel (ASM) are used to segment and extract the aortic region from the CT images of patients with aortic dissection accurately, which solves the problem of error caused by the large difference between the model and the actual segmentation target in the existing algorithm. The process is as follows: (1) selecting samples from aortic patient CT images to construct a training set and marking the characteristic points of an aortic region in the training set; (2) constructing a shape vector from the feature points of the sample marks and performing normalized registration; (3) reducing the dimension of the vector to determine the main sample components and constructing a statistical shape model; (4) performing gray-scale sampling ofa square matrix centered on feature points to establish a texture model; (5) constructing a support vector machine classifier in the training process of the model; (6) calculating the probability of the marked point set to the contour of the target, and findingthe best matching position.

The invention relates to a head posture estimation method based on a multi-feature-point set active shape model (ASM). The method includes the steps that firstly, face samples are trained to obtain the global ASM and a local texture model; secondarily, according to the models obtained through training, face feature point fitting is performed on an obtained face image sequence, feature point coordinates are stored, and reference coordinates are updated periodically; then, the displacement of all feature points is calculated, and the number of the feature points exceeding a displacement threshold value is counted; finally, according to the counted number of the feature points and the displacement direction, head postures are estimated. Influences, caused by inaccurate positioning on a small number of feature points, on head posture estimation can be reduced, meanwhile, the head posture estimation method has a high robustness effect on illumination, various head postures, such as the front face, left turning, right turning, head raising and head lowering, can be estimated, and the head posture estimation method has great application prospects in the fields of intelligent video monitoring, virtual reality, mode recognition, man-machine interaction and others.

The invention discloses a method for detecting and positioning key characteristic points in face recognition. The method comprises the steps of preprocessing a face image; presetting 22 key characteristic points; according to the calibration sequence of the preset key characteristic points, artificially calibrating the key characteristic points in the training samples for forming a set of shape vector training samples; constructing a global shape model and a local texture model; performing searching positioning on the key characteristic points; optimizing positioning of the key characteristic points; automatically calibrating the key characteristic points and performing normalized outputting of the key characteristic points. Because the method reduces a sampling range, sampling efficiency in subsequent extracting the key characteristic points can be effectively improved.

The invention discloses a remote sensing image classification method based on textons. The remote sensing image classification method based on the textons comprises the following steps: selecting the remote sensing images of typical surface features as a first training set and a second training set; extracting the neighborhood feature vectors of similar surface feature images in the first training set, clustering the neighborhood feature vectors to form a texton, and forming a texton dictionary by the textons of different surface features; marking the neighborhood feature vectors of images in the second training set by using the texton dictionary, binning center pixels, and counting the two-dimensional joint distribution of the center pixel-texton of each image to form a texture model base; and dividing images to be classified into superpixels, counting the two-dimensional joint distribution of the center pixel-texton of each superpixel after Laplace calibration is carried out, and comparing texture models of the superpixels with models in the texture model base to classify the superpixels so as to realize image classification. By taking advantages of the high homogeneity of the superpixels and the spatial distribution regularities of textures, the method has high classification accuracy, and exhibits high adaptability and interference immunity.

The invention discloses a tire inner wall texture identification method based on a locating block. The tire inner wall texture identification method based on the locating block includes steps: obtaining a coordinate of the locating block of an image frame, and transforming an original image at any angle into a standard front view by employing a transformation model; obtaining a static texture description by employing a static texture model through image enhancement; and performing texture identification on sampled images. According to the method, the standard front views can be obtained by transformation of the images with different angles by employing a transformation method, the fact that static texture presents the same characteristic in time and space is researched, a dynamic problem is changed into a static problem, a group of static texture models are obtained for the static texture, matching and identification with a database are performed so that rapid texture matching is realized, and the effect is obvious by employing voting and marking with the combination of various methods.