41 results about "Energy function minimization" patented technology

The invention discloses a height measurement method based on video multi-target tracking. The method includes the following steps that background modeling is conducted on video sequences collected by a camera, and foreground images are extracted through background subtraction; each frame image is mapped into an undirected network diagram G=<V, E>; an energy function is built; the built energy function is minimized, and label values of pixels of the current frame image are acquired, wherein the pixels belong to different targets and backgrounds,; different colors are given to the pixels which belong to the different targets, and a tracking frame of the multiple targets is determined; a vanishing point Vy where the camera is perpendicular to the horizontal plane and a vanishing line l of the horizontal plane are calculated; head feature points and foot feature points of the target to be detected in each frame image are extracted; the height of the targets to be detected in each frame image is calculated; the height measurement results of the multi-frame video sequences are merged, and the actual height of the targets to be detected is determined. According to the method, the camera does not need to be completely marked, only the vanishing point and the vanishing line of the horizontal plane need to be calculated, and therefore calculation complexity is reduced.

The invention relates to a human upper body detection and splitting method applied to a low-contrast video. The method mainly comprises two processes. In the first process, a communicated area representing a foreground object is extracted from a current frame by a background subtraction technology and a morphological method; and for each foreground area, the shape features of a polar-coordinate-based two-dimensional histogram corresponding to the foreground area are extracted as the input of a pre-trained support-vector-machine-based classifier, and a class tag corresponding to a human upper body class and a class tag corresponding to a non-human upper body class are output. In the second process, when an area which is identified as a human body area is misjudged as a non-human body area, the area is represented by an energy function, an inaccurate contour line is corrected by an energy function minimization process at the same time, and finally, a background frame is updated on the basis that an accurate foreground human body contour is obtained. By the method, a video with low contrast and resolution can be processed in real time, and both detection accuracy and a splitting result can meet the requirements of application.

The invention relates to a KNN (K-Nearest Neighbor) sorting algorithm based method for correcting and segmenting the grayscale nonuniformity of an MR (Magnetic Resonance) image, belonging to the field of image processing. The method comprises the following steps of: firstly constructing a grayscale nonuniform field model by utilizing surface fitting knowledge and using a group of orthonormalization basis functions, and establishing energy functions; and then solving model parameters according to an energy function minimization principle to realize grayscale nonuniformity correction and image segmentation, wherein subordinate functions are solved by adopting an iterative algorithm and the KNN algorithm in the model parameter solving process, therefore a partial volume effect is greatly reduced while a grayscale nonuniform field is eliminated, and the influence of noises on the correction and the segmentation of the grayscale nonuniformity of the MR image is reduced. The subordinate functions are solved with KNN through the following steps of: firstly acquiring an accurate smooth normalization histogram by using a kernel estimation algorithm; then respectively solving a threshold value TCG between cerebrospinal fluids and gray matters and a threshold value TGW between the gray matters and white matters by using a maximum between-cluster variance method; carrying out rough sorting on the KNN sorting algorithm by utilizing the two threshold values; and finally accurately sorting points to be fixed by adopting the traditional KNN sorting algorithm.

The invention belongs to the field of video processing technology and three-dimensional display, and discloses a time domain consistency depth video estimation method based on self-adaptive weight. The time domain consistency depth video estimation method aims to increase time domain continuity of depth video through minimization of an energy function, and increase quality of the depth video and drawn virtual viewpoints. Therefore, the invention adopts the technical scheme that the time domain consistency depth video estimation method based on self-adaptive weight comprises the steps of: 1, energy function construction; 2, inter-frame motion estimation; 3, self-adaptive weight calculation; 4, motion-based energy function updating; 5, and energy function minimization. The time domain consistency depth video estimation method is mainly used in video processing applications.