Accurate inverse-consistent symmetric optical flow for 4D CT lung registration

Humans interact socially with the help of facial expressions. Even health states or pains are reflected through facial expressions and hence can be useful in healthcare. Here, a facial expression recognition system is proposed. The bandlet transform is performed on face image to generate quadtree. Then on the output of bandlet transform centre symmetric - local binary pattern (CS-LBP) is applied. A feature vector of the image is generated by taking the histogram of CS-LBP. The support vector machine (SVM) is used to classify expressions in six categories. The experiments are performed using a publically available CK+ dataset. The initial results with LBP and CS-LBP are reported.

Texture is the surface property that is used to identify and recognise objects. This property is widely used in many applications including texture-based face recognition systems, surveillance, identity verification and so on. The Local binary pattern (LBP) texture method is most successful for face recognition. Owing to the great success of LBP, recently many models, which are variants of LBP have been proposed for texture analysis. Some of the derivatives of LBPs are multivariate local binary pattern, centre symmetric local binary pattern, local binary pattern variance, dominant local binary pattern, advanced local binary pattern, local texture pattern (LTP) and local derivative pattern (LDP). In this scenario, it is essential to review, whether LBP or their derivatives perform better for face recognition. The real-time challenges such as illumination changes, rotations, angle variations and facial expression variations are evaluated by different LBP-based models. Experiments were conducted on the Japanese female facial expression, YALE and FRGC version2 databases. The results show that LDP and LTP perform much better than the other LBP-based models.

A motion estimation algorithm using wavelet approximation as an optical flow model has been developed to estimate accurate dense optical flow from an image sequence. This wavelet motion model is particularly useful in estimating optical flows with large displacement. Traditional pyramid methods which use the coarse-to-fine image pyramid by image burring in estimating optical flow often produce incorrect results when the coarse-level estimates contain large errors that cannot be corrected at the subsequent finer levels. This happens when regions of low texture become flat or certain patterns result in spatial aliasing due to image blurring. Our method, in contrast, uses large-to-small full-resolution regions without blurring images, and simultaneously optimizes the coarser and finer parts of optical flow so that the large and small motion can be estimated correctly. We compare results obtained by using our method with those obtained by using one of the leading optical flow methods, the Szeliski pyramid spline-based method. The experiments include cases of small displacement (less than 4 pixels under 128/spl times/128 image size or equivalent displacement under other image sizes), and those of large displacement (10 pixels). While both methods produce comparable results when the displacements are small, our method outperforms pyramid spline-based method when the displacements are large.

Center-symmetric local binary pattern (CS-LBP) is a novel texture feature which utilizes texture to describe the local regions. It combines the good property of local binary pattern (LBP) and SIFT. It has been extended to a region descriptor and achieved promising performance in many applications. However, it is sensitive to noise and less efficient due to its high dimensional descriptor vector. Due to these, we propose a novel descriptor based on CS-LBP operator denoted as PCA-CS-LBP. Our proposed descriptor achieves better noise robustness using the difference of pixels instead of the rough comparing of pixels. Besides, PCA is employed and applied to generate a more compact representation. Comparisons between our descriptor and standard CS-LBP descriptor are given on a standard image matching dataset. Experimental results show that our descriptor is outperforms the standard CS-LBP descriptor in most cases.

In the realm of computer vision, image processing, and image retrieval is one of the most rapidly growing disciplines. The image can be retrieved using the annotation and the content - based. CBIR (Content-based image retrieval) is becoming more popular in a range of areas, as well as data mining, education, diagnostic imaging, preventing crime, weather prediction and remote sensing. We can browse, search and retrieve images using an image retrieval system. This paper presents the CBIR using an orthogonal combination (OC) the Local Binary pattern (LBP), center-symmetric local binary pattern (CS-LBP) and PCA (Principal Component Analysis) of LBP. By computing the Gray-level difference, this approach connects the referenced pixel to its surrounding neighbors. Choosing the most appropriate images that correspond to the search image from the stored database is the paper’s primary objective. The proposed model has a combination of OC-LBP, CS-LBP, and PCA-LBP.

Content based image retrieval is grievous need of present scenario in digital imaging world. This work presents a new multi-scale content based image retrieval system which leverages the multi-resolution property of discrete wavelet transform (DWT) and the local information attribute of local extrema patterns (LEPs). Two level DWT is applied on images and wavelet coefficients are obtained for images, further the LEPs are collected from wavelet coefficients to extract the feature vector. The proposed method abbreviated as DWT+LEP and tested on two benchmark databases for validation and compared with local extrema patterns (LEPs), discrete wavelet transform (DWT), center-symmetric local binary pattern (CS LBP), local edge pattern for image retrieval (LEPINV), local edge pattern for segmentation (LEPSEG), local binary pattern (LBP) and block based local binary pattern (BLK LBP).

Unmanned aerial vehicles (UAVs) have become an important part of today’s world. Micro Aerial Vehicles (MAVs) are small-sized UAVs that are suited for closed spaces, indoor or covert applications. Due to their small size and limited energy and load they can bear, there are several challenges in intelligent control of these vehicles. Also, due to their limitations, using energy-consuming sensors and complex control strategies which need high computational power, is not an option. To overcome MAV’s limitations, researchers are working on visual navigation systems. In this paper, we present an efficient strategy for the guidance and navigation of a 30gr MAV in indoor environments using a mixture of visual and inertial sensors. By taking inspiration from nature and flying insects, we combined optical flow and stereopsis methods to detect stationary obstacles and avoid collisions. Our control system uses two lateral cameras to avoid collision with lateral walls. To detect frontal walls and obstacles, instead of using optical flow methods, we use two frontal cameras with the same viewing direction and calculate the discrepancy between their images to detect walls and obstacles. For guidance, we designed a guidance system that functions well with our obstacle detection system and only needs destination and MAV’s current coordinates. By implementing our control system on a simulated coaxial helicopter, we were able to record more than 150 hours of collision-free flight and successful guidance to appointed destinations. Our control system is best suited for small sized UAVs in indoor environments and only needs inexpensive sensors and microcontrollers to implement

The Local Binary Pattern (LBP) and its variants are capable of extracting image texture and have been successfully applied to classification. However, LBP has not been used to extract and describe the texture of polarized images, and simple LBP cannot characterize the polarized texture information from different polarizations of angles. In order to solve these problems, we propose a new multi-angle orthogonal difference polarization image texture descriptor (MODP_ITD) by analyzing the relationship between the difference of orthogonal difference polarization images from different angles and the pixel intensity distribution in the local neighborhood of images from different angles. The MODP_ITD consists of three patterns: multi-angle polarization orthogonal difference local binary pattern (MODP_LBP), multi-angle polarization orthogonal difference local sampling point principal component sequence pattern (MODP_LPCSP) and multi-angle orthogonal difference polarization local difference binary pattern (MODP_LDBP). The MODP_LBP extracts local corresponding texture characteristics of polarized orthogonal difference images from multiple angles. The MODP_LPCSP sorts the principal component order of each angle orthogonal difference local sampling point. The MODP LDBP extracts the local difference characteristics between different angles by constructing a new polarized image. Then, the frequency histograms of MODP_LBP, MOD_LPCSP ,and MODP_LDBP are cascaded to generate MODP_ITD, so as to distinguish local neighborhoods. By using vertical and parallel polarization and unpolarized light active illumination, combined with the measurements at three different detection zenith angles, we constructed a polarization texture image database. A substantial number of experimental results on the self-built database show that our proposed MODP_ITD can represent the detailed information of polarization images texture. In addition, compared with the existing LBP methods, The MODP_ITD has a competitive advantage in classification accuracy.

This paper proposes a novel face representation approach, local Gabor binary mapping pattern (LGBMP), for multi-view gender classification. In this approach, a face image is first represented as a series of Gabor magnitude pictures (GMP) by applying multi-scale and multi-orientation Gabor filters. Each GMP is then encoded as a LGBP image where a uniform local binary pattern (LBP) operator is used. After that, each LGBP image is divided into non-overlapping rectangular regions, from which spatial histograms are extracted. Although an LGBP feature vector can be obtained by fitting together the regional histograms, it can not be employed in pattern classification due to its high dimension. We propose that each regional LGBP feature be mapped onto a one-dimensional subspace independently before they are concatenated as a whole feature vector. This is attractive since we reduce the feature dimension and also preserve the spatial information of LGBP image. Two ways have been proposed to map the regional LGBP feature in this paper. One is so-called LGBMP-LDA using linear discriminant analysis (LDA) for dimensionality reduction while the other is to project the regional LGBP feature onto the class center connecting line, namely, LGBMP-CCL. As a result, despite several decades of Gabor filters, the final feature dimension is even less than that of the feature extracted by using LBP directly on gray-scale images. The classification tasks in our work are performed by support vector machines (SVM). The experimental results on the CAS-PEAL face database indicate that the proposed approach achieves higher accuracy than the others such as SVMs+Gray-scale pixel, SVMs+Gabor and SVMs+LBP approach, more particularly, it has the lowest dimension of feature vector.

Existing smoky vehicle detection methods are vulnerable to false alarms because of the continuous interferences from common passed vehicles and the complex characteristics of smoke. This paper presents a video smoky vehicle detection method based on dynamic features. Three groups of features, including Multi-Sequence Integral Projection (MS-IP), Center-Symmetric Local Binary Patterns on Three Orthogonal Planes (CSLBP-TOP) and Histograms of Oriented Optical Flow (HOOF), are proposed or employed to characterize dynamic features of successive Region of Interest (ROIs). More specifically, the MS-IP characterizes the diffusion and distribution information based on multiple-sequence analysis and integral projection. The CSLBP-TOP characterizes the spatiotemporal texture information by (1) combining the strengths of Shift-Invariant Feature Transform (SIFT) and LBP and (2) extending the spatial features to three-dimensional (3D) space based on three orthogonal planes (TOP). The HOOF characterizes the motion information by inducing a very characteristic optical flow profile to distinguish smoky objects and non-smoky objects in successive ROIs based on the fact that the smoke is ejected from vehicle exhaust port and then gradually spreads around. The above three groups of features are complementary, and we fuse them to increase algorithm robustness. Experiment results show that our method achieves better performances than existing methods.

Gradient-based optical flow estimation methods such as LucasKanade method work well for scenes with small displacements but fail when objects move with large displacements. Hierarchical matching-based methods do not suffer from large displacements but are less accurate. By utilizing the high speed imaging capability of CMOS image sensors, the frame rate can be increased to obtain more accurate optical flow with wide range of scene velocities in real time. Further, by integrating the memory and processing with the sensor on the same chip, optical flow estimation using high frame rate sequences can be performed without unduly increasing the off-chip data rate. The paper describes a method for obtaining high accuracy optical flow at a standard frame rate using high frame rate sequences. The Lucas-Kanade method is used to obtain optical flow estimates at high frame rate, which are then accumulated and refined to obtain optical flow estimates at a standard frame rate. The method is tested on video sequences synthetically generated by perspective warping. Results demonstrate significant improvements in optical flow estimation accuracy with moderate memory and computational power requirements.

This paper describes the development of realtime human recognition system in video streams with the help of Local Binary Patterns (LBPs). The description of the system architecture, face detection process, additional methods for recognition accuracy increase and the method of image comparison based on center-symmetric LBPs are given.

In order to avoid crowd disaster in public gatherings, this paper aims to develop an efficient algorithm that works well in both indoor and outdoor scenes to give early warning message automatically. It also deals with high dense crowd and sudden illumination changing environment. To address this problem, first an XCS-LBP (eXtended Center Symmetric Local Binary Pattern) features are extracted which works well under sudden illumination changes. Subsequently, these features are trained using deep Convolutional Neural Network (CNN) for crowd count. Finally, a warning message is displayed to the authority, if the people count exceeds a certain limit in order to avoid the crowd disaster in advance. Benchmark datasets such as PETS2009, UCSD and UFC_CC_50 have been used for experimentation. The performance measures such as MSE (Mean Square Error), MESA (Maximum Excess over Sub Arrays) and MAE (Mean Absolute Error) have been calculated and the proposed approach provides high accuracy.

Facial expression which carries rich information of body behavior is the leading carrier of human affective and the symbol of intelligence. The main purpose of this paper is to recognize 3D human facial expression. The research in this paper includes the expression feature extraction algorithm and fusion with different kinds of feature. To contain more local texture feature information, we proposed a new feature of 3D facial expression named Local Threshold Binary Pattern (LTBP) which based on Local Binary Pattern (LBP). We calculate the difference of gray value standard between neighboring pixels and the center pixel as a threshold to binary instead of the traditional LBP operation which only comparison of size between neighboring pixels and the center pixel. After we get the LTBP feature, we fuse the LTBP and HOG (Histogram of Oriented Gradient) features to get multi-feature fusion for 3D facial expression recognition. Our algorithm of 3D facial expression recognition comprises three steps: (1) extracting two sets of feature vectors and establishing the correlation criterion function between the two sets of feature vectors; (2) solving the two sets canonical projective vectors and extracting their canonical correlation features by the framework of canonical correlation analysis algorithm; (3) doing feature fusion for classification by using proposed strategy. We have performed comprehensive experiments on the BU-3DFE database which is presently the largest available 3D face database. We have achieved verification rates of more than 90% for the 3D facial expression recognition.

The texture is an essential property of an image that tells about the arrangements of pixels of different intensities in an image matrix. Researchers have used many descriptors for texture classification of the images in computer vision and image processing. Local Binary Pattern (LBP) is one of the descriptors, which is computationally efficient and straightforward for texture classification. In recent years, researchers have made many enhancements to the basic LBP method to improve the quality of extracted features for an image. This paper presents a Local Roughness Binary Pattern (LRBP) descriptor for feature extraction of an image to increase LBP discriminative capability. This new descriptor extracts features from the local region of an image called a partial feature vector and concatenates these local features to obtain a feature vector of an image. This feature vector is used as input to a classifier to classify the class of an image. We tested the proposed descriptor for the images available on CUReT and KTHTIPS2b databases with Support Vector Machine (SVM) and k-nearest Neighbors (KNN) classifiers. We have presented the experimental results obtained from the analysis process regarding classification accuracy and confusion matrix. Finally, we have presented a performance comparison between the proposed method, basic LBP descriptor, and its variants. Results show that the proposed descriptor gives much better results than other descriptors on the KTHTIPS2b Database when we use the KNN classifier.KeywordsLBPLocal binary patternLRBPLocal roughness binary patternSVMSupport vector machineKNN K-nearest neighbors