How to automatically predict people’s gaze has attracted attention in the field of computer vision and machine learning. Previous studies on this topic set many constraints, such as restricted scenarios and strict and complex inputs. To mitigate these constraints to predict the gaze of people in more general scenarios, we propose a three-pathway network (TPNet) to estimate gaze via the joint modeling of multiple cues. Specifically, we first design a human-centric relationship inference (HCRI) module to learn the object-level relationship between the target person and the surrounding persons/objects in a scene. To the best of our knowledge, this is the first time that the object-level relationship is introduced into the gaze estimation task. Then, we construct a novel deep network with three pathways to fuse multiple cues, including scene saliency, object-level relationships and head information, to predict the gaze target. In addition, to extract the multilevel features during network training, we build and embed a micropyramid module in TPNet. The performance of TPNet is evaluated on two gaze estimation datasets: GazeFollow and DLGaze. A large number of quantitative and qualitative experimental results verify that TPNet can obtain robust results and significantly outperform the existing state-of-the-art gaze estimation methods. The code of TPNet will be released later.

Recently, the use of information hiding techniques to protect biometric data has been an active topic. This paper proposes a novel image hiding approach based on correlation analysis to protect network-based transmitted biometric image for identification. Firstly, the correlation between the biometric image and the cover image is analyzed using principal component analysis (PCA) and genetic algorithm (GA). The purpose of correlation analysis is to enable the cover image to represent the secret image in content as much as possible, not just as a carrier of hidden information. Then, the unrepresented part of the biometric image, as the secret image, is encrypted and hidden into the middle-significant-bit plane (MSB) of the cover image redundantly. Extensive experimental results demonstrate that the proposed hiding approach not only gains good imperceptibility, but also resists some common attacks validated by the biometric identification accuracy.

Image segmentation is often required as a preliminary and indispensable stage in the computer aided medical image process, particularly during the clinical analysis of magnetic resonance (MR) brain images. The segmentation of magnetic resonance image (MRI) is a challenging problem that has received an enormous amount of attention lately. In this paper, we propose a simple and effective segmentation method combining watershed algorithm and normalized cuts (CWNC) for MR brain images. An initial partitioning of the MRI into primitive regions is set by applying the watershed transform. The latter process uses a region similarity graph representation of the image regions. And then the graph is segmented by normalized cuts algorithm. The efficacy of the proposed algorithm is demonstrated by extensive segmentation experiments using both simulated and real MR images and by comparison with other published algorithms.

A Modified FKCL (MFKCL) algorithm for automatic segmentation of MR brain images is proposed in this paper. This algorithm is an extension of traditional fuzzy Kohonen's competitive learning algorithm. In our method, a factor that can estimate the effect of the neighbor pixels to the central pixel is introduced into the objective function of the standard FKCL algorithm as the local information. The local information is applied to trail off the effect of noise to the result of MRI segmentation. Experiments with simulated MR data and real MR data show that our algorithm can resist not only the little, but also the heavy noise compared with standard FKCL segmentation and other reported methods.

The research aim is to use three clustering technologies for establishing molecular data model of large size sets by comparison between low energy samples (LES) and local molecular samples (LMS). Hierarchical cluster of multi-level tree distance relation, competitive learning network of similar inputs falling into the same cluster and topological SOM are used to analyze 6,242 LES and 5,000 LMS. Our experiments show that in SOM, there are 24 to 25 Davies-Boulding clustering index and color map cluster units in the LES more than 10 to 12 in the LMS, which is consistent with the results of hierarchical cluster and competitive learning network in the rough. The hierarchical cluster reflects the biggest inter-cluster distance about 30 for the LES is far larger than that of LMS about 10. The intra-cluster distance of LES about 15 is also far bigger than that of LMS about 3. In SOM, there are more cluster borders of high values (black) reflecting large distance and more clusters in the D-matrix and U-matrix of LES than that of LMS, due to the biggest standard deviation range from -8 to 10 of samples feature of the LES is bigger than that of LMS from -2.5 to 2.5.

Inspired by the idea of genetic algorithm, we propose two hybrid evolutionary algorithms based on PSO and GA methods through crossing over the PSO and GA algorithms. The main ideas of the two proposed methods are to integrate PSO and GA methods in parallel and series forms respectively. Simulations for a series of benchmark test functions show that both of the two proposed methods possess better ability to find the global optimum than that of the standard PSO algorithm.

The image fine co-registration whose accuracy has a direct influence on DEM accuracy, is more important for synthetic aperture radar interferometry (INSAR). The co-registration based on intensity values is easy to implement and computationally efficient but the least accurate. To get better accuracy, we must consider the characteristics of the complex images intensity, the geometrical characteristics of the distributed targets, and the correlative characteristics between the two characteristics. This paper presents the curvature method for fine co-registration of the images. It makes use of not only the intensity value of the pixel, but also the geometric distributing of the group of pixels. At the same time, the characteristic surface is built by combining the two characteristics. The curvatures of some curves in surface show the geometric characteristics of the surface, which have no relation with the surface flexibility and its coordinate position. The characteristic of the registration objects is obvious when using the curvature of surface as the fine co-registration object. This method is feasible and easy to be implemented. Detailed theoretical deduction and practical research is done in this paper. The experiment result shows that the registration accuracy is better than that of traditional methods.

Electromagnetic scattering (EMS) simulation is an important computationally intensive application waihin the field of eleciromagnetics.Advances in high performance computing and communication (HPCC) and data visualization environ ment(DVE) provide new opportunities to visualize real-time simulation problems such as EMS which requzre stgntficant computational resources.In this work, an integrated interactive visuahzatzon environment was created for an EMS simulation, coupling a graphtcal user tnterface(GUI) for runtime simulation parameters input and 3D rendering output on a graphical workstation, with computational modu!es running on a parallel supercomputer and two workstations.Application Visualization System(AVS) was used as integrating software to facilitate both networking and scientific data visualization.Using the EMS simulation as a case study in this paper, we explore the AVS datafiow methodology to naturally integrate data visualization, parallel systems and heterogeneous computing.Major issues in integrating this remote visualization system are discussed, including task decomposition, system integration, concurrent control, and a high level DVE-based distributed programming model.