On parametric approach of aerial robots' visual navigation

In aerial robots＇ visual navigation, it is essential yet very difficult to detect the attitude and position of the robots operated in real time. By introducing a new parametric model, the problem can be reduced from almost unmanageable to be partly solved, though not fully, as per the requirement. In this parametric approach, a multi-scale least square method is formulated first. By propagating as well as improving the parameters down from layer to layer of the image pyramid, a new global feature line can then be detected to parameterize the attitude of the robots. Furthermore, this approach paves the way for segmenting the image into distinct parts, which can be realized by deploying a Bayesian classifier on the picture cell level. Comparison with the Hough transform based method in terms of robustness and precision shows that this multi-scale least square algorithm is considerably more robust to noises. Some discussions are also given.

Milestones of the Modeling of Human Physiology

An overview of about 70-year research efforts in area of mathematical modeling of human physiology is provided.The overview has two goals:(1)to recognize the main advantages and causes of disadvantages or disappointments;(2)to distinguish the most promising approach for creating future models.Until recently,efforts in the modeling of quantitative physiology were concentrated on the solving of three main types of tasks:(1)how to establish the input-output dynamic characteristics of a given isolated organ or isolated anatomical-functional system(AFS);(2)how to create a computer-based simulator of physiological complex systems(PCM)containing many organs and AFSs;and(3)how to create multi-scale models capable of simulating and explaining causalities in organs,AFSs,PCMs,and in the entire organism in terms that will allow using such models for simulating pathological scenarios(the“Physiome”project)too.The critical analysis of the modeling experience and recent physiological concepts convinced us that the platform provided by the paradigm of physiological super-systems(PPS)looks like the most promising platform for further modeling.PPS causally combines activities of specific intracellular mechanisms(self-tunable but of limited capacities)with their extracellular enhancers.The enhancement appears due to the increase of nutrients incomes toward cells affected because of low energy and inadequate chemical composition of cytoplasm.Every enhancer has its activator chemicals released by the affected cells.In fact,PPS,indicating causal relationships between cellscale and upper-scales(in organs,AFSs,PCMs)physiological activities,is the single platform for future models.They must definitely describe when and how the bottom-to-up information flows do appear and how is the organism-scale adaptation activated against destructive trends in cells.

Exploiting Local and Global Characteristics for Contrast Based Visual Saliency Detection

Visual saliency is an important cue in human visual system to identify salient region in the image;it can be useful in many applications including image retrieval,object recognition,image segmentation,etc.Image contrast has been used as an effective feature to detect visual salient region.However,the conventional contrast measures either in spectral domain or in spatial domain fail to give sufficient consideration towards the local and global characteristics of the image.This paper presents a visual saliency detection algorithm based on a novel contrast measurement.This measurement extracts the spectral information of image block using the 2D discrete Fourier transform(DFT),and combines with the total variation(TV)of image block in spatial domain.The proposed algorithm is used to perform salient region detection in the image,and compared with state-of-the-art algorithms.The experimental results from the MSRA dataset validate the effectiveness of the proposed algorithm.