We present a novel feature extraction method named local patterns of gradients (LPOGs) for robust face recognition. LPOG uses block-wised elliptical local binary patterns (BELBP), a refined variant of ELBP, and local phase quantization (LPQ) operators directly on gradient images for capturing local texture patterns to build up a feature vector of a face image. From one input image, two directional gradient images are computed. A symmetric pair of BELBP and a LPQ operator are then separately applied upon each gradient image to generate local patterns images. Histogram sequences of local patterns images’ nonoverlapped subregions are finally concatenated to form the LPOG vector for the given image. Based on LPOG descriptor, we propose a novel face recognition system which exploits whitened principal component analysis (WPCA) for dimension reduction and weighted angle-based distance for classification. Experimental results on three large public databases (FERET, AR, and SCface) prove that LPOG WPCA system is robust against a wide range of challenges, such as illumination, expression, occlusion, pose, time-lapse variations, and low resolution. In addition, comparison with other systems shows that LPOG WPCA significantly outperforms the state-of-the-art methods. Computationally, timing benchmarks also demonstrate that our LPOG method is faster than many advanced feature extraction algorithms and can be applied in real-world applications.

Local Patterns of Gradients for Face Recognition

The invention relates to an on-line detection method for diameter size of a shaft disc type part, and belongs to the technical field of image measurement, which can detect variation in the diameter size during processing of the shaft disc type part in real time and guarantee higher detection efficiency and measurement accuracy. The method comprises the following steps of: calibrating internal parameters of a charge coupled device (CCD) video camera and correcting distortion of an image caused by lens distortion based on a CCD video camera plane calibration method; calibrating external parameters of the CCD video camera, acquiring the image of the shaft disc type part with a known diameter by using the CCD video camera, extracting two edges of the part from the image by a subpixel edge detection algorithm, and calibrating to acquire external parameters of the part relative to the CCD video camera by using coordinates of the subpixel edges and the acquired internal parameters of the camera video; and finally, measuring, acquiring the image of the detected shaft disc type part by using the CCD video camera, extracting the subpixel edges of the part and calibrating to acquire the internal parameters and the external parameters so as to solve the diameter size of the detected part.

On-line detection method for diameter size of shaft disc type part

Kernel Locality Preserving Symmetrical Weighted Fisher Discriminant Analysis based subspace approach for expression recognition

The presence of specular highlights can hide underlying features of a scene within an image and can be problematic in many application scenarios. In particular, this poses a significant challenge for applications where image stitching is used to create a single static image of a scene from inspection footage of pipes, gas tubes, train tracks and concrete structures. Furthermore, they can hide small defects in the images causing them to be missed during inspection. We present a method which exploits additional information in neighbouring frames from video footage to reduce specularity from each frame. The technique first automatically determines frames which contain overlapping regions before the relationship that exists between them is exploited in order to suppress the effects of specular reflections. This results in an image that is free from specular highlights provided there is at least one frame present in the sequence where a given pixel is present in a diffuse form. The method is shown to work well on greyscale as well as colour images and effectively reduces specularity and significantly improves the quality of the stitched image, even in the presence of noise. While applied to the challenge of reducing specularity in inspection videos, the method improves upon the state-of-the-art in specularity removal, and its applications are wide-ranging as a general purpose pre-processing tool.

Removal of specular reflections from image sequences using feature correspondences

Seismic noise suppression using weighted nuclear norm minimization method

The invention discloses a method for visualizing a three-dimensional anatomical tissue structure model of a human heart based on a ray cast volume rendering algorithm which, relates to the method for visualizing the three-dimensional anatomical model of the human heart and solves the problem of incapability of visualizing the anatomical tissue structure model of the heart in the prior art. The method comprises the following steps of: (1) obtaining a heart three-dimensional volume data set; (2) selecting a sampling point; (3) calculating a gradient of each volume element in a heart three-dimensional volume data set space; (4) obtaining brightness of each volume element in the heart three-dimensional volume data set space under light; (5) calculating an opacity value and a color value of each volume element; (6) obtaining the opacity value and the color value of each volume element; (7) obtaining pixel point color values corresponding to projection light beams on an image plane; and (8) rendering an image of the heart three-dimensional anatomical tissue structure model according to each pixel point color value on the image plane. The invention lays the foundation for exactly simulating the structure appearance and the behaving function of the heart.

Method for visualizing three-dimensional anatomical tissue structure model of human heart based on ray cast volume rendering algorithm

The invention relates to a protein secondary structure engineering prediction method based on large margin nearest central point, belonging to the protein secondary structure engineering prediction method field and solving the problems that the existing protein secondary structure prediction method has local minimum of data weight number and low prediction efficiency when adopting machine learning algorithm. The protein secondary prediction method of the invention includes that: firstly a non-redundant protein secondary structure training data set is constructed based on PDB database, then a multi-sequence comparison characteristic is constructed for a target protein chain based on NCBT nr database, and finally the large margin nearest central point algorithm is utilized to build a protein secondary structure prediction model. The large margin nearest central point algorithm utilizes Euclidean distance K-means clustering algorithm to determine the central point of each sample, and linear transformation of input space is learned by a minimization target loss function. The invention realizes fast, high-efficiency and high-precision protein secondary structure prediction and is applicable to protein secondary structure prediction.

Protein secondary structure engineering prediction method based on large margin nearest central point

Face recognition has been an important issue in computer vision and pattern recognition over the last several decades (Zhao et al., 2003). While human can recognize faces easily, automated face recognition remains a great challenge in computer-based automated recognition research. One difficulty in face recognition is how to handle the variations in expression, pose and illumination when only a limited number of training samples are available. Currently,face recognition methods can be grouped into three categories, feature-based, holistic-based, and hybrid approaches (Zhao et al., 2003). Feature-based approaches, which extract local features such as the locations and local statistics of the eyes, nose, and mouth, had been investigated in the beginning of the face recognition research (Kanade, 1973). Recently, with the introduction of elastic bunch graph matching (Wiskott, 1997) and local binary pattern (Timo, 2004), local feature-based approaches have shown promising results in face recognition. Holistic-based approaches extract a holistic representation of the whole face region, and have robust recognition performance under noise, blurring, and partial occlusion. After the introduction of Eigenfaces (Turk & Pentland, 1991) and Fisherfaces (Belhumeur et al., 1997), holistic-based approaches were extensively studied and widely applied to face recognition. Motivated by human perception system, hybrid approaches use both local feature and the whole face region for face recognition, and thus are expected to be potentially effective in improving recognition accuracy. In holistic-based face recognition, feature extraction is fundamental, which can be revealed from three aspects. First, the input facial image is high dimensional and most current recognition approaches suffer from the “curse of dimensionality” problem. Thus a feature extraction step is necessary. Second, facial image usually contains less discriminative or unfavorable information for recognition (e.g., illumination). By making use of feature extraction, this information can be efficiently suppressed while retaining discriminative information. Third, feature extraction can greatly reduce the dimensionality of facial image, and this reduces the system’s memory and computational requirements. Subspace method, which aims to reduce the dimension of the data while retaining the statistical separation property between distinct classes, has been a natural choice for facial feature extraction. Face images, however, are generally high dimensional and their withinclass variations is much larger than the between-class variations, which will cause the serious performance degradation of classical subspace methods. By far, various subspace methods have been proposed and applied to face recognition. O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

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Subspace Methods for Face Recognition: Singularity, Regularization, and Robustness

The invention discloses a method for calibrating a projector by a CCD (Charge Couple Device) camera and relates to a method for calibrating a projector. The invention solves the problem of lacking a method for calibrating a projector at present. The method comprises the following steps of: sequentially projecting G horizontal stripe pictures, a transverse center line picture, G longitudinal stripe pictures and a longitudinal center line picture on a target by utilizing a projector, capturing a target projecting image by a CCD camera and establishing the one-to-one mapping between the pixel of a CCD image and the pixel of a projector image by calculation; shooting a CCD image of the target by the CCD camera and mapping to obtain a corresponding projector image; converting the target position many times, obtaining a plurality of projector images corresponding to the CCD image of the target, calibrating the projector by utilizing a Matlab tool box camera calibrating tool and obtaining an internal parameter and an external parameter of the projector. The invention realizes the calibration on the projector by simulating the traditional camera calibrating technology and is suitable for calibrating various projectors.

Method for calibrating projector by CCD (Charge Couple Device) camera

In computational tongue diagnosis, specular reflection is generally inevitable in tongue image acquisition, which has adverse impact on the feature extraction and tends to degrade the diagnosis performance. In this paper, we proposed a two-stage (i.e., the detection and inpainting pipeline) approach to address this issue: (i) by considering both highlight reflection and subreflection areas, a superpixel-based segmentation method was adopted for the detection of the specular reflection areas; (ii) by extending the weighted nuclear norm minimization (WNNM) model, a nonlocal inpainting method is proposed for specular reflection removal. Experimental results on synthetic and real images show that the proposed method is accurate in detecting the specular reflection areas and is effective in restoring tongue image with more natural texture information of tongue body.

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Hongzhi Zhang

Papers

Method for visualizing three-dimensional anatomical tissue structure model of human heart based on ray cast volume rendering algorithm

Protein secondary structure engineering prediction method based on large margin nearest central point

Subspace Methods for Face Recognition: Singularity, Regularization, and Robustness

Method for calibrating projector by CCD (Charge Couple Device) camera

Weighted Nuclear Norm Minimization Based Tongue Specular Reflection Removal