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检索条件"机构=Inst. of Pattern Recognition and Image Processing"

共 118 条记录，以下是1-10 订阅

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Crowd Scene Analysis: Crowd Counting using MCNN based on Self-Supervised training with Attention Mechanism 25

Crowd Scene Analysis: Crowd Counting using MCNN based on Sel...

引用

25th International Multi Topic Conference, INMIC 2023

作者： Asif, Muhammad Junaid Asad, Mujtaba Imran, Shaheer University of Central Punjab Faculty of It & Cs Lahore Pakistan Inst. of Image Processing and Pattern Recognition Seiee Sjtu Shanghai China

ISBN: (纸本)9798350317701

Fully-supervised learning requires expensive and laborious annotations of labeled data for crowd-counting tasks. To alleviate this burden, it is desirable to explore methods that reduce the need for extensive labeling. Fortunately, there are a vast number of unlabeled images available in the world, making them easily accessible compared to labeled datasets. This paper proposes a self-supervised learning-based M-CNN framework with an attention mechanism that aims to leverage unlabeled data for pre-training the model. The framework consists of four sub-modules: a data augmentation framework, a self-supervised training network, a multi-column CNN, and an attention mechanism. These networks receive the images that undergo random processing using two defined augmentation transformations. Transformed images are then subjected to self-supervised learning and fed to a feature extraction network. FEN consists of M-CNN with five convolutional branches to extract features at a multi-scale level. These extracted features are then employed as an attention mechanism to focus on the head or shoulder location of people. To evaluate the effectiveness of our proposed model, experiments are conducted on two public datasets: ShanghaiTech Part A, Part B, and UCFQNRF. The experimental results demonstrate that our approach outperforms state-of-the-art semi-supervised methods, showcasing the effectiveness of our proposed approach in leveraging both unlabeled and limited labeled data for crowd counting tasks. © 2023 IEEE.

关键词： Deep learning

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Crowd Scene Analysis: Crowd Counting using MCNN based on Self-Supervised training with Attention Mechanism

Crowd Scene Analysis: Crowd Counting using MCNN based on Sel...

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IEEE International Conference on Multi Topic

作者： Muhammad Junaid Asif Mujtaba Asad Shaheer Imran Faculty of IT & CS University of Central Punjab Lahore Pakistan SEIEE SJTU Inst. of Image Processing and Pattern Recognition Shanghai China

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Multiscale Deep Convolutional Networks for Characterization and Detection of Alzheimer's Disease Using MR images 26

Multiscale Deep Convolutional Networks for Characterization ...

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26th IEEE International Conference on image processing, ICIP 2019

作者： Ge, Chenjie Qu, Qixun Gu, Irene Yu-Hua Store Jakola, Asgeir Dept. of Electrical Engineering Chalmers University of Technology Sweden Inst. of Neuroscience and Physiology Sahlgrenska Academy University of Gothenburg Sweden Inst. of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781538662496

This paper addresses the issues of Alzheimer's disease (AD) characterization and detection from Magnetic Resonance images (MRIs). Many existing AD detection methods use single-scale feature learning from brain scans. In this paper, we propose a multiscale deep learning architecture for learning AD features. The main contributions of the paper include: (a) propose a novel 3D multiscale CNN architecture for the dedicated task of AD detection;(b) propose a feature fusion and enhancement strategy for multiscale features;(c) empirical study on the impact of several settings, including two dataset partitioning approaches, and the use of multiscale and feature enhancement. Experiments were conducted on an open ADNI dataset (1198 brain scans from 337 subjects), test results have shown the effectiveness of the proposed method with test accuracy of 93.53%, 87.24% (best, average) on subject-separated dataset, and 99.44%, 98.80% (best, average) on random brain scan-partitioned dataset. Comparison with eight existing methods has provided further support to the proposed method. © 2019 IEEE.

关键词： Feature extraction Three-dimensional displays Training Alzheimer's disease Magnetic resonance imaging Testing

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Saliency Propagation from Simple to Difficult

Saliency Propagation from Simple to Difficult

引用

IEEE Conference on Computer Vision and pattern recognition

作者： Chen Gong Dacheng Tao Wei Liu S. J. Maybank Meng Fang Keren Fu Jie Yang Inst. of Image Process. & Pattern Recognition Shanghai Jiao Tong Univ. Shanghai China

ISBN: (纸本)9781467369657

Saliency propagation has been widely adopted for identifying the most attractive object in an image. The propagation sequence generated by existing saliency detection methods is governed by the spatial relationships of image regions, i.e., the saliency value is transmitted between two adjacent regions. However, for the inhomogeneous difficult adjacent regions, such a sequence may incur wrong propagations. In this paper, we attempt to manipulate the propagation sequence for optimizing the propagation quality. Intuitively, we postpone the propagations to difficult regions and meanwhile advance the propagations to less ambiguous simple regions. Inspired by the theoretical results in educational psychology, a novel propagation algorithm employing the teaching-to-learn and learning-to-teach strategies is proposed to explicitly improve the propagation quality. In the teaching-to-learn step, a teacher is designed to arrange the regions from simple to difficult and then assign the simplest regions to the learner. In the learning-to-teach step, the learner delivers its learning confidence to the teacher to assist the teacher to choose the subsequent simple regions. Due to the interactions between the teacher and learner, the uncertainty of original difficult regions is gradually reduced, yielding manifest salient objects with optimized background suppression. Extensive experimental results on benchmark saliency datasets demonstrate the superiority of the proposed algorithm over twelve representative saliency detectors.

关键词： Teachers region of an image propagation Propagation image invariant image dilation

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Small target detection using an optimization-based filter

Small target detection using an optimization-based filter

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： K. Xie K. Fu T. Zhou J. Yang Q. Wu X. He Inst. of Image Process. & Pattern Recognition Shanghai Jiao Tong Univ. Shanghai China

ISBN: (纸本)9781467369985

Small target detection is a critical problem in the Infrared Search And Track (IRST) system. Although it has been studied for years, there are some challenges remained, e.g. cloud edges and horizontal lines are likely to cause false alarms. This paper proposes a novel method using an optimization-based filter to detect infrared small target in heavy clutter. First, we design a certain pixel area as active area. Second, a weighted quadratic cost function is performed in the active area. Finally, a filter based on statistics of active area is derived from the cost function. Our method could preserve heterogeneous area, meanwhile, remove target region. Experimental results show our method achieves satisfied performance in heavy clutter.

关键词： active area area statistics heavy clutter optimization-based filter small target detection Object detection useful area Filters Cost functions clutter

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Video-based tracking and quantified assessment of spontaneous limb movements in neonates

Video-based tracking and quantified assessment of spontaneou...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Long Xu Irene Yu-Hua Gu Anders Flisberg Magnus Thordstein Inst. of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Dept. of Signals and Systems Chalmers University of Technology Gotheburg Sweden Dept. of Pediatrics Sahlgrenska University Hospital Gothenburg Sweden Dept. of Clinical Neurophysiology Sahlgrenska University Hospital Gothenburg Sweden

ISBN: (纸本)9781467383264

Central nervous system dysfunction in infants may be manifested through inconsistent, rigid and abnormal limb movements. Detection and quantification of these movements in infants from videos are hence desirable for providing useful information to clinicians. This could lead to computer-aided diagnosis of dysfunctions where early treatment may improve infant development. In this paper, we propose a scheme for detecting and quantifying qualitative aspects of limb movement through multiple tracking and state space motion modeling on videos. The main novelties of the paper include: (a) An enhanced detection method for effectively detection small weak marker points from video; (b) Bayesian estimation and nearest neighbor searching for selecting new observation in individual tracker and for tracking marker trajectories on limbs; (c) A criterion for anomaly detection based on the frequency and duration of abrupt changes in limb movement, using window averaged prominent residual powers. The proposed method has been tested on videos of neonates, results show that the proposed method is promising for tracking and quantifying the movement of neonate limbs for helping medical diagnostics.

关键词： Pediatrics Videos Trajectory Tracking Estimation Feature extraction Clutter

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Visual object tracking with online learning on Riemannian manifolds by one-class support vector machines

Visual object tracking with online learning on Riemannian ma...

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作者： Yun, Yixiao Fu, Keren Gu, Irene Yu-Hua Yang, Jie Dept. of Signals and Systems Chalmers Univ. of Technology Gothenburg41296 Sweden Inst. of Image Processing and Pattern Recognition Shanghai Jiao Tong Univ. Shanghai200240 China

ISBN: (纸本)9781479957514

This paper addresses issues in video object tracking. We propose a novel method where tracking is regarded as a one-class classification problem of domain-shift objects. The proposed tracker is inspired by the fact that the positive samples can be bounded by a closed hypersphere generated by one-class support vector machines (SVM), leading to a solution for robust learning of target model online. The main novelties of the paper include: (a) represent the target model by a set of positive samples as a cluster of points on Riemannian manifolds;(b) perform online learning of target model as a dynamic cluster of points flowing on the manifold, in an alternate manner with tracking;(c) formulate geodesic-based kernel function for one-class SVM on Riemannian manifolds under the log-Euclidean metric. Experiments are conducted on several videos, results have provided support to the proposed method. © 2014 IEEE.

关键词： Support vector machines

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Human activity recognition in images using SVMs and geodesics on smooth manifolds 14

Human activity recognition in images using SVMs and geodesic...

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8th ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2014

作者： Yun, Yixiao Fu, Keren Gu, Irene Yu-Hua Aghajan, Hamid Yang, Jie Dept. of Signals and Systems Chalmers University of Technology Sweden Inst. of Image Processing and Pattern Recognition Shanghai Jiao Tong University China IMinds Ghent University Belgium AIR Lab. Stanford University United States

ISBN: (纸本)9781450329255

This paper addresses the problem of human activity recognition in still images. We propose a novel method that focuses on human-object interaction for feature representation of activities on Riemannian manifolds, and exploits underlying Riemannian geometry for classification. The main contributions of the paper include: (a) represent human activity by appearance features from local patches centered at hands containing interacting objects, and by structural features formed from the detected human skeleton containing the head, torso axis and hands;(b) formulate SVM kernel function based on geodesics on Riemannian manifolds under the log-Euclidean metric;(c) apply multi-class SVM classifier on the manifold under the one-against.all strategy. Experiments were conducted on a dataset containing 17196 images in 12 classes of activities from 4 subjects. Test results, evaluations, and comparisons with state-of-the-art methods provide support to the effectiveness of the proposed scheme. Copyright 2014 ACM.

关键词： Support vector machines

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VISUAL OBJECT TRACKING WITH ONLINE LEARNING ON RIEMANNIAN MANIFOLDS BY ONE-CLASS SUPPORT VECTOR MACHINES

VISUAL OBJECT TRACKING WITH ONLINE LEARNING ON RIEMANNIAN MA...

引用

IEEE International Conference on image processing

作者： Yixiao Yun Keren Fu Irene Yu-Hua Gu Jie Yang Dept. of Signals and Systems Chalmers Univ. of Technology Inst. of Image Processing & Pattern Recognition Shanghai Jiao Tong Univ.

ISBN: (纸本)9781479957521

关键词： Visual object tracking Support vector machines One-class classification Online learning Riemannian manifold Covariance matrix

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3D deformable surfaces with locally self-adjusting parameters - A robust method to determine cell nucleus shapes

3D deformable surfaces with locally self-adjusting parameter...

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International Conference on pattern recognition

作者： Keuper, Margret Schmidt, Thorsten Padeken, Jan Heun, Patrick Palme, Klaus Burkhardt, Hans Ronneberger, Olaf Pattern Recognition and Image Processing Computer Science Department Freiburg University Germany Max-Planck Institute of Immunobiology Freiburg Germany University of Freiburg Germany Inst. of Biology II Freiburg Inst. for Advanced Studies - FRIAS University of Freiburg Germany

ISBN: (纸本)9780769541099

When using deformable models for the segmentation of biological data, the choice of the best weighting parameters for the internal and external forces is crucial. Especially when dealing with 3D fluorescence microscopic data and cells within dense tissue, object boundaries are sometimes not visible. In these cases, one weighting parameter set for the whole contour is not desirable. We are presenting a method for the dynamic adjustment of the weighting parameters, that is only depending on the underlying data and does not need any prior information. The method is especially apt to handle blurred, noisy, and deficient data, as it is often the case in biological microscopy. © 2010 IEEE.

关键词： Deformation

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