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arXiv 2023年

作者： Hui, Wei Yu, Liping Wei, Yiran Laboratory of Algorithm for Cognitive Model School of Computer Science Shanghai Key Laboratory of Data Science Fudan University China Shanghai Pinghe School China

Understanding the shape and structure of objects is undoubtedly extremely important for object recognition, but the most common pattern recognition method currently used is machine learning, which often requires a large number of training data. The problem is that this kind of object-oriented learning lacks a priori knowledge. The amount of training data and the complexity of computations are very large, and it is hard to extract explicit knowledge after learning. This is typically called ‘knowing how without knowing why’. We adopted a method of inductive learning, hoping to derive conceptual knowledge of the shape of an object and its formal representation based on a small number of positive examples. It is clear that implementing object recognition is not based on simple physical features such as colors, edges, textures, etc., but on their common geometry, such as topologies, which are stable, persistent, and essential to recognition. In this paper, a formal representation of topological structure based on object’s skeleton (RTS) was proposed and the induction process of’seeking common ground’ is realized. This research helps promote the method of object recognition from empiricism to rationalism © 2023, CC BY-NC-ND.

关键词： Object recognition

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A shape-based object class detection model using local scale-invariant fragment feature

A shape-based object class detection model using local scale...

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作者： Wei, Hui Xiao, Jinwen Department of Computer Science Laboratory of Cognitive Model and Algorithm Fudan University China Shanghai Key Laboratory of Data Science Fudan University China

ISBN: (纸本)9781479957514

Detecting object in unseen images is an challenging task because of the strong clutter background, various scale of object and the deformation of class. In this paper, we present a shape-based object detection model using scale-invariant fragment feature which is approximated by conjunctive short straight segments. This is a novel shape descriptor for object detection by bypassing estimation of scale of object in natural scene. Utilizing those local and consistent segments, we improve the robustness of model to natural background and deformation of object. We experiment our model on two texture-less image datasets, INRIA horses dataset and Weizmann horses dataset. The results demonstrate our model outperform those state-of-the-art methods. © 2014 IEEE.

关键词： Object detection

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A SHAPE-BASED OBJECT CLASS DETECTION model USING LOCAL SCALE-INVARIANT FRAGMENT FEATURE

A SHAPE-BASED OBJECT CLASS DETECTION MODEL USING LOCAL SCALE...

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IEEE International Conference on Image Processing

作者： Hui Wei Jinwen Xiao Department of Computer Science Laboratory of Cognitive Model and Algorithm Fudan University

ISBN: (纸本)9781479957521

关键词： Shape matching Object detection Scale-invariant fragments Conjunctive lines

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A model of V4 neurons based on sparse coding

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8834卷 239-246页

作者： Wei, Hui Dong, Zheng Li, Qiang Department of Computer Science Laboratory of Cognitive Model and Algorithm Shanghai Key Laboratory of Data Science Fudan University Shanghai China

Area V4 lies in the middle of the ventral visual pathway in primate brains. It is an intermediate stage in the visual processing for object discrimination. V4 neurons exhibit selectivity to complex boundary conformation. In this paper, we propose a novel model of V4 neurons based on sparse coding. The model is a multi-layer neural network of which the output layer consists of laterally connected V4 units. We provide an informal proof for sparse coding with intra-layer inhibitory connections and show experimentally that this model successfully reproduces shape selectivity observed in V4 neurons. The model provides clues to the high level representation of visual stimuli in the brain © Springer International Publishing Switzerland 2014.

关键词： Neurons

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V4 Neural Network model for Visual Saliency and Discriminative Local Representation of Shapes

V4 Neural Network Model for Visual Saliency and Discriminati...

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International Joint Conference on Neural Networks

作者： Hui Wei Zheng Dong Department of Computer Science Laboratory of Cognitive Model and Algorithm Fudan University Shanghai Key Laboratory of Data Science Fudan University

ISBN: (纸本)9781479914821

Visual area V4 lies in the middle of the ventral visual pathway in the primate brain. It is an intermediate stage in the visual processing for object discrimination. It plays an important role in the neural mechanism of visual attention and shape recognition. V4 neurons exhibit selectivity for salient features of contour conformation. In this paper, we propose a novel model of V4 neurons based on a multilayer neural network inspired by recent studies on V4. Its low-level layers consist of computational units simulating simple cells and complex cells in the primary visual cortex. These layers extract preliminary visual features including edges and orientations. The V4 computational units calculate the entropy of the extracted features as a measure of visual saliency. The salient features are then selected and encoded with a layer of Restricted Boltzmann Machine to generate an intermediate representation of object shapes. The model was evaluated in shape distinction, handwritten digits classification, feature detection, and feature matching experiments. The results demonstrate that this model generates discriminative local representation of object shapes. It provides clues to understand the high level representation of visual stimuli in the brain.

关键词： Shape Neurons Visualization Entropy Computational modeling Brain modeling Gratings

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An object representation model based on the mechanism of visual perception

An object representation model based on the mechanism of vis...

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26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013

作者： Wei, Hui Wang, Ziyan Laboratory of Cognitive Model and Algorithm Department of Computer Science Fudan University Shanghai China

ISBN: (纸本)9783642385766

In areas of artificial intelligence and computer vision, object representation and recognition is an important topic, and lots of methods have been developed for it. However, analysis and obtain the knowledge of object's structure at higher levels are still very difficult now. We draw on the experience of pattern recognition theories of cognitive psychology to construct a compact, abstract and symbolic representing model of object's contour based on components fitting which is more consistent with human's cognition process. In addition, we design an algorithm to match components between different images of the same object in order to make the feature extraction at higher levels possible. © 2013 Springer-Verlag.

关键词： Pattern recognition

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Discovering the multi-neuronal firing patterns based on a new binless spike trains measure

Discovering the multi-neuronal firing patterns based on a ne...

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10th International Symposium on Neural Networks, ISNN 2013

作者： Lu, Hu Wei, Hui Laboratory of Cognitive Model Algorithm School of Computer Science Fudan University Shanghai 200433 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai 200433 China

ISBN: (纸本)9783642390647

In this paper, we proposed a method which presented a new definition of different multi-step interval ISI-distance distribution of single neuronal spike trains and formed a new feature vector to represent the original spike trains. It is a binless spike train's measure method. We used spectral clustering algorithm on new multi-dimensional feature vectors to detect the multiple neuronal firing patterns. We tested this method on standard data set in machine learning, neuronal surrogate data set and in vivo multi-electrode recordings respectively. Results shown that the method proposed in this paper can effectively improve the clustering accuracy in standard data set and detect the firing patterns in neuronal spike trains. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Clustering algorithms

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Aa wavelet model of ganglion cells array and its application in image representation

Aa wavelet model of ganglion cells array and its application...

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14th Pacific-Rim Conference on Multimedia, PCM 2013

作者： Weng, Dawei Wang, Yunhong Wei, Hui Huang, Di IRIP Lab Beihang Univ. Beijing China Department of Computer Science Laboratory of Cognitive Model and Algorithm Fudan Univ. Shanghai China

ISBN: (纸本)9783319037301

In this paper, we explore a new local image descriptor based on the modeling of ganglion cells array at the retina. We first introduce the mathematical model of a single ganglion cell and detailed distribution characteristics of ganglion cells array. From above evidence, we find out that the features of ganglion cells array are similar with wavelets in nature. Hence, a set of novel wavelet basis functions is constructed, which well fit these features. Furthermore, we discover that the modeling wavelets are in possession of tight frame property in its corresponding spatial domain expression, that is, multiple scales, shifts, phases of the spatial wavelet basis functions form a tight frame of the L2 Hilbert space, which is helpful to extract strongly distinctive and slightly redundant information in the image matching task. Finally, we obtain an efficient descriptor, state-of-the-art ones. Additionally, the proposed descriptor is easier to construct and much faster to compute. Evaluated in the image matching task on the Multi-view Stereo Correspondence Data set, the results demonstrate its effectiveness. © Springer International Publishing Switzerland 2013.

关键词： Image matching

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A CSP-based orientation detection model

A CSP-based orientation detection model

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5th International Conference on Advances in Brain Inspired cognitive Systems, BICS 2012

作者： Wei, Hui Dong, Zheng Department of Computer Science Laboratory of Cognitive Model and Algorithm Fudan University Shanghai 200433 China

ISBN: (纸本)9783642315602

Hubel and Wiesel's hypothesis on the emergence of orientation selectivity of simple cells meets some difficulties. It requires the receptive fields of GC and LGN to be highly similar in size and sub-structure while arranged in perfect order. The strict regularities make the model uneconomical in both evolution and neural computation. Varying from the classical model, we propose a new model based on an algebraic method, which estimates orientation by solving constraint satisfaction problems (CSP). The algebraic model needs not to obey the constraints of Hubel and Wiesel's hypothesis and it is easily implemented as neural network. We also prove that both precision and efficiency of the model are practicable in mathematics. This study is significant in the aspect of explaining the neural mechanism of orientation detection, as well as of finding the circuit structure and computational route in neural network. © 2012 Springer-Verlag.

关键词： Constraint satisfaction problems

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A multiple sub-regions design of non-classical receptive field 1

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9th International Symposium on Neural Networks, ISNN 2012

作者： Wei, Hui Wu, Heng School of Computer Science Laboratory of Cognitive Model and Algorithm Fudan University Shanghai 200433 China

ISBN: (数字)9783642313622

ISBN: (纸本)9783642313615

The non-classical receptive field (nCRF) is a large area outside the classical receptive field (CRF). Stimulating such area alone fails to elicit neural responses but can modulate the neural response to CRF stimulation. The receptive field (RF) of retinal ganglion cell (GC) also has such a property and can vary with the different visual stimuli. Previous nCRF models are mainly based on fixed RF whose dynamic characteristics are overlooked. In this paper, we establish a multilayer neural computation model with feedback for the basic structure of nCRF, and use it to simulate the mechanisms of fixation eye movements to ascertain the properties of the stimuli within adjacent areas. In our model, GC's RF can dynamically and self-adaptively adjust its size according to stimulus properties. RF becomes smaller in areas where the image details need distinguishing and larger where the image information has no obvious difference. The experimental results fully reflect these dynamic characteristics. © 2012 Springer-Verlag.

关键词： Eye movements

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