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Salient region detection and segmentation for general object recognition and image understanding

science china(Information sciences) 2011年第12期54卷 2481-2490页

作者： huang tiejun 1 , tian yonghong 1 , li jia 2 & yu haonan 11 national engineering laboratory for video technology,{3., peking university, Beijing 100871, china 2 Key laboratory of Intelligent Information Processing, Institute of Computing technology, Chinese Academy of sciences, Beijing 100190, china 1. national engineering laboratory for {3. technology School of Electrical Engineering and Computer Science Peking University Beijing 100871 China2. Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

General object recognition and image understanding is recognized as a dramatic goal for computer vision and multimedia retrieval. In spite of the great efforts devoted in the last two decades, it still remains an open problem. In this paper, we propose a selective attention-driven model for general image understanding, named GORIUM (general object recognition and image understanding model). The key idea of our model is to discover recurring visual objects by selective attention modeling and pairwise local invariant features matching on a large image set in an unsupervised manner. Towards this end, it can be formulated as a four-layer bottom-up model, i.e., salient region detection, object segmentation, automatic object discovering and visual dictionary construction. By exploiting multi-task learning methods to model visual saliency simultaneously with the bottom-up and top-down factors, the lowest layer can effectively detect salient objects in an image. The second layer exploits a simple yet effective learning approach to generate two complementary maps from several raw saliency maps, which then can be utilized to segment the salient objects precisely from a complex scene. For the third layer, we have also implemented an unsupervised approach to automatically discover general objects from large image set by pairwise matching with local invariant features. Afterwards, visual dictionary construction can be implemented by using many state-of-the-art algorithms and tools available nowadays.

关键词： object recognition image understanding visual saliency salient object segmentation visual dictionary

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Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes

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engineering 2020年第4期6卷 449-461页

作者： Zhaofei yu jian K.liu Shanshan jia Yichen Zhang Yajing Zheng yonghong tian tiejun huang national engineering laboratory for {3. technology School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Peng Cheng laboratory Shenzhen 518055China Center for Systems Neuroscience Department of NeurosciencePsychology and BehaviourUniversity of LeicesterLeicester LE17RHUK

A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion,while simultaneously receiving stimuli from the environment and controlling some part of a human brain or *** visual information can be processed by the brain in millisecond *** retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further ***,the neuronal signal of interest for a retinal neuroprosthesis is the neuronal ***-loop computation in a neuroprosthesis includes two stages:encoding a stimulus as a neuronal signal,and decoding it back into a *** this paper,we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic *** hypothesize that in order to obtain a better understanding of the computational principles in the retina,a hypercircuit view of the retina is necessary,in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the *** different building blocks of the retina,which include a diversity of cell types and synaptic connections-both chemical synapses and {3. synapses(gap junctions)-make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual *** overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.

关键词： Visual coding Retina Neuroprosthesis Brain-machine interface Artificial intelligence Deep learning Spiking neural network Probabilistic graphical model

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