102 results about "Active shape model" patented technology

The invention discloses a video-based 3D human face expression cartoon driving method, which comprises the following steps: (1) image preprocessing, namely improving the image quality through light compensation, Gaussian smoothing and morphology operation of a gray level image; (2) BP human face detection, namely detecting a human face in a video through a BP neural network algorithm, and returning the size of the position of the human face for providing a smaller search range for human face characteristic point positioning of the next step to ensure instantaneity; (3) ASM human face characteristic point positioning and tracking, namely precisely extracting characteristic point information of human face shape, eyes, eyebrows, mouth and nose through an enhanced active shape model algorithm and a predicted meanshift algorithm, and returning the definite position; and (4) data information conversion, namely converting the data information acquired in the human face characteristic point positioning and tracking step to acquire the motion information of the human face. The method can overcome the defects in the prior art, and can achieve live human face cartoon driving effect.

The invention relates to a rapid generation method for facial animation, belonging to the image processing technical field. The method comprises the following steps: firstly detecting coordinates of a plurality of feature points matched with a grid model face in the original face pictures by virtue of an improved active shape model algorithm; completing fast matching between a grid model and a facial photo according to information of the feature points; performing refinement treatment on the mouth area of the matched grid model; and describing a basic facial mouth shape and expression change via facial animation parameters and driving the grid model by a parameter flow, and deforming the refined grid model by a grid deformation method based on a thin plate spline interpolation so as to generate animation. The method can quickly realize replacement of animated characters to generate vivid and natural facial animation.

To make it possible to teach a grasping action for a work object whose shape and 3D position are unknown to a robot by an intuitive and simple input operation by an operator. a captured image of the working space is displayed on a display device; (b) an operation in which a recognition area including a part of a work object to be grasped by a hand is specified in two dimensions on an image of the work object displayed on the display device is received; (c) an operation in which a primitive shape model to be applied to the part to be grasped is specified from among a plurality of primitive shape models; (d) a parameter group to specify the shape, position, and posture of the primitive shape model is determined by fitting the specified primitive shape model onto 3D position data of a space corresponding to the recognition area; (e) a grasping pattern applicable to grasping of the work object is selected by searching a database in which grasping patterns applicable by a hand to primitive shape models are stored.

A method for modeling a vehicle, includes: receiving an image that includes a vehicle; and constructing a three-dimensional (3D) model of the vehicle, wherein the 3D model is constructed by: (a) taking a predetermined set of base shapes that are extracted from a subset of vehicles; (b) multiplying each of the base shapes by a parameter; (c) adding the resultant of each multiplication to form a vector that represents the vehicle's shape; (d) fitting the vector to the vehicle in the image; and (e) repeating steps (a)-(d) by modifying the parameters until a difference between a fit vector and the vehicle in the image is minimized.

The invention discloses a visual line estimation method based on a three-dimensional active shape model. The visual line estimation method comprises the steps of a first step, utilizing a user face front image which is acquired by two cameras as training data; a second step, performing two-dimensional key point positioning on the acquired image, wherein the two-dimensional key point positioning comprises pupil positioning and active shape model (ASM) positioning the user face in the image; a third step, performing three-dimensional coordinate reconstruction on the two-dimensional key point, obtaining three-dimensional coordinates of the pupil centers of a left eye and a right eye in a world coordinate system, and obtaining a face three-dimensional ASM shape; a fourth step, representing the visual line characteristics of the left eye and the right eye by means of relative position relationship between contours of double eyes and the pupil centers; and a fifth step, establishing a front viewpoint estimation model according to the obtained visual line characteristic; wherein the front viewpoint estimation model is used for performing human eye visual line estimation on a prediction sample. According to the visual line estimation method and the visual line estimation device provided by the technical solution of the invention, through establishing the user face three-dimensional ASM, the head gesture of man is estimated and displayed, thereby improving adaptability of visual line estimation to head motion.

Accurate pelvic fracture detection is accomplished with automated X-ray and Computed Tomography (CT) images for diagnosis and recommended therapy. The system combines computational methods to process images from two different modalities, using Active Shape Model (ASM), spline interpolation, active contours, and wavelet transform. By processing both X-ray and CT images, features which may be visible under one modality and not under the other are extracted and validates and confirms information visible in both. The X-ray component uses hierarchical approach based on directed Hough Transform to detect pelvic structures, removing the need for manual initialization. The X-ray component uses cubic spline interpolation to regulate ASM deformation during X-ray image segmentation. Key regions of the pelvis are first segmented and identified, allowing detection methods to be specialized to each structure using anatomical knowledge. The CT processing component is able to distinguish bone from other non-bone objects with similar visual characteristics, such a blood and contrast fluid, permitting detection and quantification of soft tissue hemorrhage. The CT processing component draws attention to slices where irregularities are detected, reducing the time to fully examine a pelvic CT scan. The quantitative measurement of bone displacement and hemorrhage area are used as input for a trauma decision-support system, along with physiological signals, injury details and demographic information.

The invention discloses a three-dimension transform search method for extracting characteristic point in face image which uses ASM (Active Shape Models) method locating face image as a base, changes two-dimension transform shape search method in present ASM into three-dimension transform shape search method. The method includes steps as follows: firstly, constructing a standard face three-dimension model; secondly, constructing three-dimension coordinates of a two-dimension stat model (basic shape) of ASM train centralizing face characteristic point according with standard face three-dimension model; finally, processing three-dimension transform to the two-dimension stat model having three-dimension coordinate and projecting to the two-dimension plane for approaching the given face characteristic point shape after search, wherein the search process adopts the method of two-step conversion and iterated approximation. The method can reflect real change of face gesture and has more search precision; the test result shows: the method can more approach fact characteristic point compared with present two-dimension transform search method.

The invention discloses a method for automatically synthesizing a three-dimensional expression based on a single facial image. The method comprises the following steps of 1, performing facial shape positioning on a single input image by using an ASM (active shape model); 2, finishing facial shape modeling in a scattered interpolation way according to a positioned facial shape by virtue of a three-dimensional facial reference model, and performing texture mapping to obtain a three-dimensional facial model of a target face in the image; 3, calculating a facial expression movement matrix of an expression set relative to the three-dimensional facial reference model and the three-dimensional facial model of the target face; 4, calculating a linear movement model of each expression in the expression set according to the facial expression movement matrix, obtained in step 3, of the three-dimensional facial model of the target face; 5, obtaining a facial area division result of the face by virtue of a clustering method; 6, performing facial expression synthesis. The method is favorable for systematically synthesizing more flexible and richer facial expressions.

Active contour models and active shape models were developed for the detection of the kinematics landmarks on sequential back surface measurements. The anatomical landmarks correspond with the spinous process, the dimples of the posterior superior iliac spines (PSIS), the margo mediales and the elbow. Back surface curvatures are used as a basis to guide the ACM and ASM's towards interesting landmark features on the back surface. Geometrical bending and torsion costs, and the main modes of variation of the landmark points are added to the models in order to avoid unrealistic curve shapes from a biomechanical point of view. Reconstruction of the underlying skeletal structures is performed using the surface normals as approximations for skeletal rotations (e.g. axial vertebrae rotations, pelvic torsion, etc.) and anatomical formulas to estimate skeletal dimensions.

The invention discloses an X-ray chest radiography lung segmentation method so as to carry out accurate segmentation on lungs in an X-ray chest radiography. The method includes the following steps: S101: through horizontal and vertical projection, obtaining two rectangular areas which respectively surround left and right lung images in the X-ray chest radiography; S102: initializing the lungs in the two rectangular areas so as to obtain the initial shapes of the lungs; S103: according to a weighted grey local texture model, searching for optimal matching points of characteristic points in the lung images; S104: through adjustment of attitude parameters and a shape parameter b, enabling current shapes I<Xc> of the lungs to approximate I<X>+dI<X> in the largest degree; and repeating S103 and S104 until the change quantities are smaller than preset thresholds when obtained lung shapes are compared with lung shapes obtained through a previous adjustment next to a current adjustment. The method and device are capable of obtaining better initialization lung shapes and do not cause an over-segmentation phenomenon in a follow-up adjustment process and are capable of carrying out accurate segmentation on lung areas in the X-ray chest radiography under the restraint of an active shape model.

A method for segmenting organs in digitized medical images includes providing a set of segmented training images of an organ, computing a surface mesh having a plurality of mesh cells that approximates a border of the organ, extracting positive examples of all mesh cells and negative examples in the neighborhood of each mesh cell which do not belong to the organ surface, training from the positive examples and negative examples a plurality of classifiers for outputting a probability of a point being a center of a particular mesh cell, computing an active shape model using a subset of center points in the mesh cells, generating a new shape by iteratively deforming the active shape model to fit a test image, and using the classifiers to calculate a probability of each center point of the new shape being a center of a mesh cell which the classifier was trained to recognize.

The invention provides a yawning action detection method for detecting fatigue driving. The yawning action detection method comprises the following steps: relative position relations of facial feature regions in a facial image region in a video image are respectively determined in a matched manner by virtue of feature region contours respectively corresponding to the facial feature regions of a face matching template, so as to well ensure the accuracy of mouth location, quick matching location of the mouth regions in the facial image region in the video image is carried out by adopting an active shape model matching algorithm, the data operation amount is small, the processing speed is high, and the real-time performance of the mouth location is ensured; then, the actual shapes of mouth feature region counters are determined by carrying out matching location on the mouth regions in the facial image region in the video image to recognize the mouth opening or closing state. The yawning action detection method realizes the detection of the yawning action, is high in detection accuracy, fast in speed, provides an effective and high-real-time-performance solution for the detection of the yawning action, and can provide an alerting signal with timeliness for the fatigue driving detection.

The invention discloses a human face fake photo automatic combining and modifying method based on a portrayal. The method comprises the following steps: firstly, automatically generating initial estimation of a fake photo by applying a partial intrinsic transform method; then, extracting hair and facial contour information from an input human face portrayal, and carrying out automatic enhancement on the initial estimation; finally, judging whether partial combining errors exist in the automatically-combined human face fake photo by a user, and if obvious combining errors exist, carrying out local deformation on a corresponding human face of the re-input fake photo by adopting a deformation method based on control points, then carrying out automatic combination again, and therefore modifying the partial combining errors. In a modifying process, an active shape model is used for obtaining the feature points of the human face portrayal, and rigid deformation based on the control points is carried out by using a least square method. The human face fake photo automatic combining and modifying method based on the portrayal can automatically combine a human face fake photo fast and effectively according to the input human face portrayal, and can provides assistance for manual and automatic identification of human face sketch portrayals in criminal investigation, anti-terrorist and other fields.

The invention discloses a method and a device for determining the position of a characteristic point in an image, which is used for solving the problem existing in the prior art that the positioning precision of the characteristic point is not high. The method for determining the position of the characteristic point in the image which is provided by the invention includes that: an active shape model ASM which consists of a shape model and a Gabor character model in a target area in the image is obtained in advance based on image sample trainings; an input image is detected, the initial position of the characteristic point in the target area of the input image is obtained, and the Gabor characteristic of the characteristic point in the target area in the input image is worked out according to the initial position; combined with the shape model in the target area, and a match is carried out between the Gabor characteristic of the characteristic point in the target area in the input image and the Gabor characteristic of the characteristic point in the Gabor characteristic model to determine the position of the characteristic point in the target area.

The invention discloses a mouse recognition method for gesture based on machine vision, comprising the following steps: (1) establishing an active shape model for gestures; (2) off-line training for gestures and getting a gesture features classifier; (3) collecting images; (4) extracting partial binary pattern characteristics for the images, searching the target gesture among the images through the gesture features classifier from step (2), then step (5); (5) positioning on the fingertips; (6) mapping of mouse. The invention is a natural and intuitive way of human-computer interaction, and has advantages of no need to carry other auxiliary equipments, finishing the mouse operation through natural hand and finger movement, small influence by light and background, and freely operation right switch when multi-users are operating the computer.

A method for segmenting a portion of a clip is provided. A first active-shape model of the portion is creating in a first state. A second active-shape model of the portion is created in a second state. A combined model for segmenting the portion is generated. The combined model is a linear combination of the first active-shape model and the second active-shape model. An apparatus for segmenting a portion of a clip is further provided. The apparatus includes a modeling means, a first linear combination means, a transformation means, a second linear combination means, and a segmentation means.

The invention discloses a method for positioning facial features based on an improved ASM (Active Shape Model) algorithm, belonging to the technical field of visual and image treatment of a computer. The method comprises the following steps of: firstly, manually calibrating feature points; secondly, establishing a statistical shape model and a local gray model of an upper model and a lower model; thirdly, separately searching and matching the feature points in the upper model and the lower model; and finally, generating an example of a comprehensive model restrained by an energy function. To solve the problem that the traditional ASM method is difficult to position features under the facial expression conditions of a human face, the method for positioning facial features based on the improved ASM algorithm, disclosed by the invention, comprises the following steps of: carrying out the regional division of the facial features into an upper shape region and a lower shape region according to the change correlation, separately modeling the statistical shape model and the local gray model, generating examples of comprehensive shapes for the upper model and the lower model to restrain errors by introducing an energy function to the feature matching and searching process, and finally, obtaining an accurate feature positioning result. Due to the method for positioning facial features based on the improved ASM algorithm, the feature positioning accuracy of the ASM algorithm to the existing facial expression conditions are further improved.

The invention relates to a classifying and processing method based on an active shape model and a K nearest neighbor algorithm for facial forms of human faces; the method comprises the following steps of: (1) a sample database in the K nearest neighbor algorithm is established; (2) a user uploads images to be measured to a server through a network multi-media terminal, and the server extracts characteristic points of the human faces from the image to be measured by adopting an ASM (Automated Storage Management) algorithm and determines facial contours by selecting the characteristic points of the faces and lower jaws; (3) the server carries out normalization processing on point sets of the images to be measured according to a sample normalization method and integrates the point sets of the images to be measured and point sets of samples in a coordinate system; (4) the server classifies the images to be measured based on the Hausdorff distance and the K nearest neighbor algorithm to obtain a classifying result; and (5) the server automatically sends the classifying result to a network multi-medium terminal; and the network multi-medium terminal displays the classifying result. Compared with the prior art, the classifying and processing method has the advantages of high recognition rate, fast speed, easy implementation and the like.

A system and method for extracting an object of interest from an image using a robust active shape model are provided. A method for extracting an object of interest from an image comprises: generating an active shape model of the object; extracting feature points from the image; and determining an affine transformation and shape parameters of the active shape model to minimize an energy function of a distance between a transformed and deformed model of the object and the feature points.

The invention provides a method for human face recognition based on face geometrical features. The method includes human face feature points detection, geometrical feature construction, human face database classification and human face recognition. According to the invention, the method, based on human face detection and human face recognition technology, improves geometrical features used in the human face feature point detection method and human face recognition, and guarantees in-time and accuracy of a human face recognition system based on feature point detection which combines an active shape model and the elastic bunch graph matching algorithm and also simple and accurate face geometrical feature technology. According to the invention, the method decomposes data in a large database into a plurality of sub-databases with labels which are determined by selected classification geometrical features. As for the input image to be tested, matching data can be reduced by simply comparing classification geometrical features of the image to be tested with each sub-database label in order to greatly increase efficiency of human face recognition.

The invention belongs to the technical field of computer digital image processing and mode identification, mainly relates to a pantograph on-line automatic identification method used for an electric traction locomotive, and specifically relates to a pantograph on-line automatic identification method based on AdaBoost and an active shape model. The basic process comprises the following steps: acquiring a plurality of pantograph images through a pantograph on-line shooting system, and forming a pantograph image learning sample set; based on a sample, training to learn to generate the pantograph active shape model and an AdaBoost cascade classifier; utilizing the AdaBoost cascade classifier to perform pantograph detection on newly acquired to-be-identified images; through combination with a pantograph detection result and the pantograph active shape model constructed through learning, accurately matching and identifying pantograph shapes; and finally, performing quantitative detection and analysis on the basis of an accurate matching result. The method provided by the invention can effectively perform on-line automatic quantitative detection on the thickness of a pantograph carbon slide plate, can rapidly perform vehicle maintenance and can save detection cost of pantograph detection.