Coupling-and-decoupling: A hierarchical model for occlusion-free object detection

Handling occlusion is a very challenging problem in object detection. This paper presents a method of learning a hierarchical model for X-to-X occlusion-free object detection (e.g., car-to-car and person-to-person occlusions in our experiments). The proposed method is motivated by an intuitive coupling-and-decoupling strategy. In the learning stage, the pair of occluding X?s (e.g., car pairs or person pairs) is represented directly and jointly by a hierarchical And–Or directed acyclic graph (AOG) which accounts for the statistically significant co-occurrence (i.e., coupling). The structure and the parameters of the AOG are learned using the latent structural SVM (LSSVM) framework. In detection, a dynamic programming (DP) algorithm is utilized to find the best parse trees for all sliding windows with detection scores being greater than the learned threshold. Then, the two single X?s are decoupled from the declared detections of X-to-X occluding pairs together with some non-maximum suppression (NMS) post-processing. In experiments, our method is tested on both a roadside-car dataset collected by ourselves (which will be released with this paper) and two public person datasets, the MPII-2Person dataset and the TUD-Crossing dataset. Our method is compared with state-of-the-art deformable part-based methods, and obtains comparable or better detection performance.     

Target detection based on a dynamic subspace

For hyperspectral target detection, it is usually the case that only part of the targets pixels can be used as target signatures, so can we use them to construct the most proper background subspace for detecting all the probable targets? In this paper, a dynamic subspace detection (DSD) method which establishes a multiple detection framework is proposed. In each detection procedure, blocks of pixels are calculated by the random selection and the succeeding detection performance distribution analysis. Manifold analysis is further used to eliminate the probable anomalous pixels and purify the subspace datasets, and the remaining pixels construct the subspace for each detection procedure. The final detection results are then enhanced by the fusion of target occurrence frequencies in all the detection procedures. Experiments with both synthetic and real hyperspectral images (HSI) evaluate the validation of our proposed DSD method by using several different state-of-the-art methods as the basic detectors. With several other single detectors and multiple detection methods as comparable methods, improved receiver operating characteristic curves and better separability between targets and backgrounds by the DSD methods are illustrated. The DSD methods also perform well with the covariance-based detectors, showing their efficiency in selecting covariance information for detection.     

Phase Assemblages of Pr α-sialon Derived from SHS-ed Powders and TEM Study on the Nucleation of Pr α-sialon

The Pr α-sialon powders prepared by self-propagating high-temperature synthesis (SHS), consisting of 55 wt% Pr α-sialon and 45 wt% of β-sialon (abbreviated as α' and β'), were hot-pressed at 1800°C for 1 h. The results showed that Pr α' phase would transfer to β' with the appearance of JEM phase (Pr(Si_{6− z} Al _z )(N_{10− z} O _z )) after sintering, thus resulting in the increase of β' phase to 86 wt%. The addition of Y₂O₃ into SHS-ed Pr α' powders as the starting materials restrains the transformation of α' to β' and prevents the formation of JEM phase as well. The nucleation mechanism of Pr α' grain during hot-pressing was investigated in terms of transmission electron microscope and energy-dispersive spectrometer analysis. Two nucleation modes of Pr α' grains were found, i.e., nucleating on the undissolved Pr α' grains and on the nuclei of (Pr, Y) α' grains precipitated from liquid phase.     

Flow regime, hydrodynamics, floc size distribution and sludge properties in activated sludge bubble column, air-lift and aerated stirred reactors

This paper presents a comparative study how reactor configuration, sludge loading and air flowrate affect flow regimes, hydrodynamics, floc size distribution and sludge solids-liquid separation properties. Three reactor configurations were studied in bench scale activated sludge bubble column reactor (BCR), air-lift reactor (ALR) and aerated stirred reactor (ASR). The ASR demonstrated the highest capacity of gas holdup and resistance, and homogeneity in flow regimes and shearing forces, resulting in producing large numbers of small and compact flocs. The fluid dynamics in the ALR created regularly directed recirculation forces to enhance the gas holdup and sludge flocculation. The BCR distributed a high turbulent flow regime and non-homogeneity in gas holdup and mixing, and generated large numbers of larger and looser flocs. The sludge size distributions, compressibility and settleability were significantly influenced by the reactor configurations associated with the flow regimes and hydrodynamics.     

Optimal entropy-constrained non-uniform scalar quantizer design for low bit-rate pixel domain DVC

In this paper, an optimal entropy-constrained non-uniform scalar quantizer is proposed for the pixel domain DVC. The uniform quantizer is efficient for the hybrid video coding since the residual signals conforming to a single-variance Laplacian distribution. However, the uniform quantizer is not optimal for pixel domain distributed video coding (DVC). This is because the uniform quantizer is not adaptive to the joint distribution of the source and the SI, especially for low level quantization. The signal distribution of pixel domain DVC conforms to the mixture model with multi-variance. The optimal non-uniform quantizer is designed according to the joint distribution, the error between the source and the SI can be decreased. As a result, the bit rate can be saved and the video quality won’t sacrifice too much. Accordingly, a better R-D trade-off can be achieved. First, the quantization level is fixed and the optimal RD trade-off is achieved by using a Lagrangian function J(Q). The rate and distortion components is designed based on P(Y|Q). The conditional probability density function of SI Y depend on quantization partitions Q, P(Y|Q), is approximated by a Guassian mixture model at encocder. Since the SI can not be accessed at encoder, an estimation of P(Y|Q) based on the distribution of the source is proposed. Next, J(Q) is optimized by an iterative Lloyd-Max algorithm with a novel quantization partition updating algorithm. To guarantee the convergence of J(Q), the monotonicity of the interval in which the endpoints of the quantizer lie must be satisfied. Then, a quantizer partition updating algorithm which considers the extreme points of the histogram of the source is proposed. Consequently, the entropy-constrained optimal non-uniform quantization partitions are derived and a better RD trade-off is achieved by applying them. Experiment results show that the proposed scheme can improve the performance by 0.5 dB averagely compared to the uniform scalar quantization.     

Prediction-based realistic 3D model compression

The benefit of using the geometry image to represent an arbitrary 3D mesh is that the 3D mesh can be re-sampled as a completely regular structure and coded efficiently by common image compression methods. For geometry image-based 3D mesh compression, we need to code the normal-map images while coding geometry images to improve the subjective quality and realistic effects of the reconstructed model. In traditional methods, a geometry image and a normal-map image are coded independently. However a strong correlation exists between these two kinds of images, because both of them are generated from the same 3D mesh and share the same parameterization. In this paper we propose a predictive coding framework, in which the normal-map image is predicted based on the geometric correlation between them. Additionally we utilize the strong geometric correlation among three components of normal-map image to improve the predicting accuracy. The experimental results show the proposed coding framework improves the coding efficiency of normal-map image, meanwhile the realistic effect of a 3D mesh is significantly enhanced.     

锦屏一级水电站左岸底层灌浆平洞深孔帷幕灌浆施工

锦屏一级水电站作为世界第一高的拱坝,在超高的水头和复杂的地质条件下,在底层灌浆洞中进行了超过170 m的深孔帷幕灌浆。施工中克服了各种不利条件,解决了多种非常规的施工技术难题,其经验可为其他类似工程提供参考。     

基于Java框架的银医一卡通系统设计

介绍了一种基于信息共享的医院--银行一卡通的信息系统设计方案。该系统使用Web服务实现接口通信,通过自助服务器连接医院系统和银行系统实现医院与银行的信息共享。基于该系统,用户可以使用中国银行借记卡代替医院门诊卡就医,提供了良好的就医环境,提高了医疗效率。     

A CMAC-PD compound torque controller with fast learning capacity and improved output smoothness for electric load simulator

The compound architecture of CMAC (Cerebellar Model Articulation Controller) and PD (Proportional-Derivative) can effectively reduce the loading error and restrain the surplus torque of electric load simulators. But due to its generalization ability, the CMAC controller has an unsmooth output, which leads to the motor vibration even the divergence of control system. The unsmooth problem of CMAC is analyzed in this paper and a novel scalar cost function of CMAC is proposed, which consists of an error item and a weight smoothing item to guarantee fast learning capacity and improved output smoothness of CMAC. With the novel scalar cost function, a compound torque controller of PD and smooth CMAC is derived by using the gradient descent algorithm. Both the simulation and experimental results demonstrate that the novel CMAC-PD compound controller can effectively improve the output smoothness of the electric load simulator, eliminate the surplus torque and assure the stability of system as well.     

Determination of the molecular weight distribution of cellulose on calibrated gel columns

CMC samples obtained by gel filtration on a preparative scale were used to calibrate an agarose column. The relation between molecular weight and the peak elution volume for monodisperse samples were calculated as well as the relation between band broadening and the peak elution volume. In the calculations of the molecular weight distribution curves of the studied CMC samples, consideration was given to the variable band broadening by proper transformations of the experimental data, thus making it possible to obtain numerical solutions free from oscillations. The method admits a rapid determination of the molecular weight distribution of cellulose and related materials.