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New approach to fast calculation of moments of 3-D gray level images

Parallel Computing 2000年第6期26卷 805-815页

作者： Liu, J.G. Chan, F.H.Y. Lam, F.K. Li, H.F. Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Computer Science University of Concordia Canada State Education Ministry Laboratory for Image Processing and Intelligent Control China

Fast computation of three-dimensional (3-D) moments is an important and pragmatic problem. In this paper, we propose a new algorithm together with suitable systolic structure for the fast computation of 3-D moments, extending our method for fast computation of 2-D moments. A scalable systolic structure, consisting of adders and latches, fully exploits parallelism of the network architecture and is very suitable for hardware implementation. Our new method, which also dispenses with the need for multiplication, is suitable for both binary images and gray level images. We also extend our method to efficient computation of k-dimensional (k-D) moments (k > 3).

关键词： Parallel processing systems

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Nonconvex penalties with analytical solutions for one-bit compressive sensing

arXiv

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arXiv 2017年

作者： Huang, Xiaolin Yan, Ming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing Shanghai200240 China Department of Computational Mathematics Science and Engineering Department of Mathematics Michigan State University East LansingMI48824 United States

One-bit measurements widely exist in the real world and can be used to recover sparse signals. This task is known as one-bit compressive sensing (1bit-CS). In this paper, we propose novel algorithms based on both convex and nonconvex sparsity-inducing penalties for robust 1bit-CS. We consider the dual problem, which has only one variable and provides a sufficient condition to verify whether a solution is globally optimal or not. For positive homogeneous penalties, a globally optimal solution can be obtained in two steps: a proximal operator and a normalization step. For other penalties, we solve the dual problem, and it needs to evaluate the proximal operators for many times. Then we provide fast algorithms for finding analytical solutions for three penalties: minimax concave penalty (MCP), 0norm, and sorted 1penalty. Specifically, our algorithm is more than 200 times faster than the existing algorithm for MCP. Its efficiency is comparable to the algorithm for the 1penalty in time, while its performance is much better than 1. Among these penalties, sorted 1is most robust to noise in different settings. Copyright © 2017, The Authors. All rights reserved.

关键词： Compressed sensing

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Modified lifting algorithm and VLSI architecture for the 9/7 wavelet filters

Modified lifting algorithm and VLSI architecture for the 9/7...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Chen-Gyi Xiong Zhi-Rong Gao Jin-Wen Tian Jian Liu The State Key Laboratory of Education Commission for Image Processing and Intelligent Control Institute of Pattern Recognition & Artificial Intelligence Huazhong University of Science and Technology Wuhan China Department of Computer Science Wuhan University of Science and Engineering Hebei China

ISBN: (纸本)0780384032

A new parallel-based lifting algorithm (PBLA) for the 9/7 filters, exploring the parallelism of arithmetic operations in each lifting step, was proposed in this paper. It shortened significantly the critical path of computation, and resulted in a fast VLSI implementation architecture. In comparison with the conventional lifting algorithm based implementation (CLABI), the latency is reduced by more than half from (4T/sub m/ + 8T/sub a/) to (T/sub m/ + 4T/sub a/), which is competitive to that of convolution based implementation CBI, and can be further reduced to Tm by inserting 3 stages of pipeline. The experimental results demonstrate that the proposed architecture has good performances in both speed and area.

关键词： Very large scale integration Discrete wavelet transforms Filters image coding Delay Parallel processing Arithmetic Convolution Hardware Transform coding

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Few-Shot Classification with Task-Adaptive Semantic Feature Learning

SSRN

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SSRN 2022年

作者： Pan, Mei-Hong Xin, Hong-Yi Xia, Chun-Qiu Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiaotong University Shanghai200240 China

Few-shot classification aims to learn a classifier that categorizes objects of unseen classes with limited samples. One general approach is to mine as much information as possible from limited samples. This can be achieved by incorporating data from multiple modals. However, existing multi-modality methods only use additional modality in support samples while adhering to a single modal in query samples. Such approach could lead to information imbalance between support and query samples, which confounds model generalization from support to query samples. Towards this problem, we propose a task-adaptive semantic feature learning mechanism to incorporate semantic features for both support and query samples. The semantic feature learner is trained episodic-wisely by regressing from the feature vectors of the support samples. It is utilized to predict semantic features for the query samples. Such method maintains a consistent training scheme between support and query samples and enables direct model transfer from support to query data, which significantly improves model generalization. We conduct extensive experiments on four benchmarks in both inductive and tranductive settings. Results show that the proposed TasNet outperforms state-of-the-art methods with an improvement of 1% to 5% in classification accuracy, demonstrating the superiority of our method. The exhaustive ablation studies further validate the effectiveness of our framework. © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Interactive object segmentation using iterative adjustable graph cut

Interactive object segmentation using iterative adjustable g...

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IEEE Visual Communications and image processing (VCIP)

作者： Ran Shi Zhi Liu Yinzhu Xue Xiang Zhang School of Communication and Information Engineering Shanghai University Shanghai China Key Laboratory of Advanced Display and System Application Ministry of Education Shanghai University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

Interactive object segmentation is widely used for extracting any user-interested objects from natural images. A common problem with many interactive segmentation approaches is that the object segmentation quality is degraded due to inaccurate object/background seeds provided by the user. This paper proposes an iterative adjustable graph cut to efficiently solve this problem. First, object/background seeds are initialized based on the object segmentation result obtained with the user-specified scribbles as the interactive input. Then, an iterative seed adjustment scheme is exploited to correct inaccurate seeds and extract new suitable seeds via graph cut, in which the balancing weight between energy terms are adaptively updated to protect stable seeds and speedup the iteration process. Finally, suitable seeds are obtained and graph cut is used to segment the objects. Experimental results demonstrate the better segmentation performance of our approach even if user provides rather rough seeds.

关键词： Object segmentation image segmentation Reliability Iterative methods Educational institutions image color analysis Error analysis

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Online Lidar-Camera Extrinsic Parameters Self-Checking

SSRN

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SSRN 2023年

作者： Wei, Pengjin Yan, Guohang You, Xin Fang, Kun Ma, Tao Yang, Jie Liu, Wei Department of Automation Shanghai Jiao Tong University 800 Dongchuan RD Minhang District China Autonomous Driving Group Shanghai AI Laboratory 129 Longwen RD Xuhui District China Institute of Image Processing and Pattern Recognition China

With the development of neural networks and the increasing popularity of automatic driving, the calibration of the LiDAR and the camera has attracted more and more attention. This calibration task is multi-modal, where the rich color and texture information captured by the camera and the accurate three-dimensional spatial information from the LiDAR is incredibly significant for downstream tasks. In real-world applications, as smart cars roll off the production line, their LiDAR and camera systems are meticulously calibrated, while in the rest of the car life period, the poses of the LiDARs and cameras no longer get continually supervised to ensure the security. To this end, this paper proposes a self-checking algorithm to judge whether the extrinsic parameters are well-calibrated during the car life period by introducing a binary classification network based on the fused information from the camera and the LiDAR. Moreover, since there is no such dataset for the task in this work, we further generate a new dataset branch from the KITTI dataset tailored for the task. Our experiments on the proposed dataset branch demonstrate the performance of our method. To the best of our knowledge, this is the first work to address the significance of continually checking the calibrated extrinsic parameters for autonomous driving. The dataset and code are available at https://***/OpenCalib/LiDAR2camera_self-check © 2023, The Authors. All rights reserved.

关键词： Cameras

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Detection of Venous Thromboembolism Using Recurrent Neural Networks with Time-Series Data 24

Detection of Venous Thromboembolism Using Recurrent Neural N...

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3rd Asia Conference on Algorithms, Computing and Machine Learning, CACML 2024

作者： Xu, Can Huang, Yaqin Xiang, Xinni Lei, Haike Yang, Jie Shanghai Jiao Tong University Shanghai China Chongqing University Cancer Hospital Chongqing China West China Hospital Sichuan University Chengdu China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition China Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment China West China School of Medicine China Chongqing Cancer Multi-omics Big Data Application Engineering Research Center China

ISBN: (纸本)9798400716416

Machine Learning (ML) has been widely applied to medical science for decades. As common knowledge, the progress of many diseases is often chronic and dynamic. Longitudinal data, or time-series data, has better descriptions of certain diseases, as it naturally captures the dynamic over time. Venous thromboembolism (VTE), as a regular complication of cancer tumour treatments, has been emphasized in many studies. Recent research has shown that ML algorithms incredibly contribute to VTE risk estimation and prediction. However, existing methods rarely consider the dynamic changes during the development of VTE, and often make decisions based on time-point data, such as the latest blood test results of patients. In this paper, we propose a VTE detection model that takes advantage of capturing dynamic information using time-series data. The model consists of a Recurrent Neural Network (RNN) based framework with time embedding and Attention mechanism to encode longitudinal features of patients. We test our proposed model on a real-world dataset that contains multiple times of blood test results of patients together with other clinical features. The network is compared with several commonly used ML models in previous VTE studies. The results show that our model outperforms all baseline models, and achieved a state-of-art performance with 80.0% accuracy and 0.881 AUC. © 2024 ACM.

关键词： Risk assessment

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A fast VLSI architecture for two-dimensional discrete wavelet transform based on lifting scheme [image compression applications]

A fast VLSI architecture for two-dimensional discrete wavele...

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International Conference on Solid-State and Integrated Circuit Technology

作者： Chengyi Xiong Jinwen Tian Jian Liu College of Electronic Information Engineering South-Center University of Science and Technology Wuhan China Institute of Pattern Recognition & Artification Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)078038511X

This paper proposes a novel fast architecture for a 2D discrete wavelet transform by using a lifting scheme, Parallel and embedded decimation techniques are employed to optimize the architecture, which is mainly composed of two horizontal filter modules and one vertical filter module, working in parallel and pipeline fashion with 100% hardware utilization. The architecture is designed to generate two outputs in one working clock cycle, with every two subbands coefficients alternately. The total time for computing J levels of decomposition for an N/spl times/N image is approximately 2N/sup 2/(1-4/sup -J/)/3 clock cycles. In comparison with the other devices reported in previous literature, the design has many advantages including lower hardware complexity and area and power efficiency. The design is also fast, regular and simple, as well as well suited for VLSI implementation.

关键词： Discrete wavelet transforms Very large scale integration Filters Computer architecture Wavelet transforms Hardware Clocks image coding Matrix converters Pipelines

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Modeling multisource remote sensing image classifier based on the MDL principle: Theoretical aspects

Modeling multisource remote sensing image classifier based o...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qian Yin Ping Guo Zhi-Yong Yuan Zu-Kuan Wei Wen-Yi Zeng Image Processing & Pattern Recognition Laboratory Beijing Normal University Beijing China School of Computer Science Wuhan University of China Wuhan China School of Comp. Science & Engr. University of Electronic Science and Technology Chengdu China

A theoretical study for modeling technique of the remote sensing image classification based on the minimum description length (MDL) principle is presented in the paper. According to the MDL principle, modeling problem is an optimization procedure to find the shortest expected code length. Kullback-Leibler (KL) divergence is adopted as the system cost function to measure expected codelength, and the codelength will be the model we desired. The advantage of using the MDL principle to build appropriate model is analyzed theoretically, model optimization technique also is described.

关键词： Data models Remote sensing Mathematical model Classification algorithms Computational modeling Equations Artificial neural networks

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Face Forgery Detection Algorithm Based on Improved MobileViT Network

Face Forgery Detection Algorithm Based on Improved MobileViT...

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6th International Conference on Intelligent Computing and Signal processing (ICSP)

作者： Tiantian Wang Xiaoqi Lu School of Information Engineering Inner Mongolia University of Science and Technology Baotou China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Baotou China Institute of Information Engineering Inner Mongolia University of Technology Hohhot China

DeepFakes blur the boundaries between reality and forgery, resulting in the collapse of exiting credit system, causing immeasurable consequences for national security and social order. Through analysis of existing face forgery techniques, it is found that most generation techniques rely on random noise distribution, and global information will be lost after up sampling. Therefore, we propose a deepfake detection algorithm based on improved MobileViT, which uses CNN local space biasing and the global space representation of the Transformer network to learn the local features and global representation of forged faces, respectively. Coordinate attention is introduced to obtain directional perception and position sensitive information, making the model locate synthetic traces of fake faces better and fusion local and global representation more effectively. For the improved generalization of the model, with the GELU activation function to solve the problem of neuron death. Our model achieved 96.2% on FF++(C23) datasets, and 93.7%,94.1%,96.3%,87.9% on DF, F2F, FS, and NT datasets, respectively. Comparing with previous methods, our model has shown detection robustness and better generalization.

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