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On Moving Object Segmentation from Monocular Video with Transformers

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arXiv 2024年

作者： Homeyer, Christian Schnörr, Christoph Robert Bosch GmbH Corporate Research Computer Vision Lab Hildesheim Germany Image and Pattern Analysis Group Heidelberg University Germany

Moving object detection and segmentation from a single moving camera is a challenging task, requiring an understanding of recognition, motion and 3D geometry. Combining both recognition and reconstruction boils down to a fusion problem, where appearance and motion features need to be combined for classification and segmentation. In this paper, we present a novel fusion architecture for monocular motion segmentation - M3Former, which leverages the strong performance of transformers for segmentation and multi-modal fusion. As reconstructing motion from monocular video is ill-posed, we systematically analyze different 2D and 3D motion representations for this problem and their importance for segmentation performance. Finally, we analyze the effect of training data and show that diverse datasets are required to achieve SotA performance on Kitti and Davis. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

Adaptive Enhancement for Scanned Historical Document images

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Adaptive Enhancement for Scanned Historical Document Images

IEEE International Conference on Electronics and Communication Engineering (ICECE)

作者： Farouk Suleiman Chris J. Hughes E.B. Obio Pattern Recognition and Image Analysis (PRImA) Research Lab University of Salford Greater Manchester United Kingdom School of Science Engineering & Environment University of Salford Greater Manchester United Kingdom School of Science & Engineering University of Manchester Greater Manchester United Kingdom

ISBN: (纸本)9781728188577

In this paper we propose, a novel adaptative histogram matching method to remove low contrast, smeared ink, bleed-through and uneven illumination artefacts from scanned images of historical documents. The goal is to provide a better representation of document images and therefore improve readability and the source images for Optical Character recognition (OCR). Unlike other methods that are designed for single artefacts, our proposed method enhances multiple (low-contrast, smeared-ink, bleed-through and uneven illumination). The method starts by taking the bimodal peaks of the original grayscale image and multiplying them to generated gaussian windows to create an ideal histogram with weights of importance to distribution. This histogram becomes the reference histogram to be matched to the original image for a more optimized image. Median filtering is also incorporated in the method to remove salt and pepper noise. We demonstrate the technique on the European Newspapers project (ENP) dataset chosen from the pattern recognition and image analysis research lab (PRImA) and establish from the results that, the proposed method significantly reduces background noise and improves image quality on multiple artefacts as compared to other enhancement methods tested. To evaluate the efficiency of the proposed method, we make use of several performance criteria. These include Signal to Noise Ratio (SNR), Mean opinion score (MOS), and visual document image quality assessment (VDIQA) metric. The proposed method performs best in all the evaluation metrics having a 42.6 % increment on the average score of the other methods for MOS, 44.3% increment on average score of other methods for SNR and 61.11% better in VDIQA compared to other methods.

关键词： Measurement image quality Histograms Visualization Filtering Lighting Ink

RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

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RecycleNet: Latent Feature Recycling Leads to Iterative Deci...

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Gregor Koehler Tassilo Wald Constantin Ulrich David Zimmerer Paul F. Jaeger Jörg K. H. Franke Simon Kohl Fabian Isensee Klaus H. Maier-Hein Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Information and Data Science School for Health Karlsruhe/Heidelberg Germany Helmholtz Imaging DKFZ National Center for Tumor Diseases (NCT) NCT Heidelberg a Partnership Between DKFZ University Medical Center Heidelberg Interactive Machine Learning Group DKFZ Machine Learning Lab University of Freiburg Freiburg Germany Latent Labs (***) London UK Applied Computer Vision Lab DKFZ Pattern Analysis and Learning Group Heidelberg University Hospital Heidelberg Germany

Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we can not only form a decision on the spot, but also ponder, revisiting an initial guess from different angles, distilling relevant information, arriving at a better decision. Here, we propose RecycleNet, a latent feature recycling method, instilling the pondering capability for neural networks to refine initial decisions over a number of recycling steps, where outputs are fed back into earlier network layers in an iterative fashion. This approach makes minimal assumptions about the neural network architecture and thus can be implemented in a wide variety of contexts. Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision. We evaluate this across a variety of segmentation benchmarks and show consistent improvements even compared with top-performing segmentation methods. This allows trading increased computation time for improved performance, which can be beneficial, especially for safety-critical applications.

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Prevent the language model from being overconfident in neural machine translation

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arXiv 2021年

作者： Miao, Mengqi Meng, Fandong Liu, Yijin Zhou, Xiao-Hua Zhou, Jie Peking University China Pattern Recognition Center WeChat AI Tencent Inc China Beijing International Center for Mathematical Research National Engineering Lab for Big Data Analysis and Applications Department of Biostatistics Peking University Beijing China

The Neural Machine Translation (NMT) model is essentially a joint language model conditioned on both the source sentence and partial translation. Therefore, the NMT model naturally involves the mechanism of the Language Model (LM) that predicts the next token only based on partial translation. Despite its success, NMT still suffers from the hallucination problem, generating fluent but inadequate translations. The main reason is that NMT pays excessive attention to the partial translation while neglecting the source sentence to some extent, namely overconfidence of the LM. Accordingly, we define the Margin between the NMT and the LM, calculated by subtracting the predicted probability of the LM from that of the NMT model for each token. The Margin is negatively correlated to the overconfidence degree of the LM. Based on the property, we propose a Margin-based Token-level Objective (MTO) and a Margin-based Sentencelevel Objective (MSO) to maximize the Margin for preventing the LM from being overconfident. Experiments on WMT14 English-to-German, WMT19 Chinese-to-English, and WMT14 English-to-French translation tasks demonstrate the effectiveness of our approach, with 1.36, 1.50, and 0.63 BLEU improvements, respectively, compared to the Transformer baseline. The human evaluation further verifies that our approaches improve translation adequacy as well as fluency. Copyright © 2021, The Authors. All rights reserved.

关键词： Neural machine translation

Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks

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Trainable Spectrally Initializable Matrix Transformations in...

International Conference on pattern recognition

作者： Michele Alberti Angela Botros Narayan Schutz Rolf Ingold Marcus Liwicki Mathias Seuret Document Image and Voice Analysis Group (DIVA) University of Fribourg Switzerland V7 Ltd London United Kingdom ARTORG Center for Biomedical Engineering Research University of Bern Switzerland EISLAB Machine Learning Luleå University of Technology Sweden Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

In this work, we introduce a new architectural component to Neural Network (NN), i.e., trainable and spectrally initializable matrix transformations on feature maps. While previous literature has already demonstrated the possibility of adding static spectral transformations as feature processors, our focus is on more general trainable transforms. We study the transforms in various architectural configurations on four datasets of different nature: from medical (ColorectalHist, HAM10000) and natural (Flowers) images to historical documents (CB55). With rigorous experiments that control for the number of parameters and randomness, we show that networks utilizing the introduced matrix transformations outperform vanilla neural networks. The observed accuracy increases appreciably across all datasets. In addition, we show that the benefit of spectral initialization leads to significantly faster convergence, as opposed to randomly initialized matrix transformations. The transformations are implemented as auto-differentiable PyTorch modules that can be incorporated into any neural network architecture. The entire code base is open-source.

关键词： Program processors Discrete Fourier transforms Transforms Documentation pattern recognition Discrete cosine transforms Convolutional neural networks

LEDNet: Deep Network for Single image Haze Removal 2018

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LEDNet: Deep Network for Single Image Haze Removal

Proceedings of the 11th Indian Conference on Computer Vision, Graphics and image Processing

作者： Akshay Dudhane Subrahmanyam Murala Abhinav Dhall Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar India Learning Affect and Semantic Image Analysis Group Indian Institute of Technology Ropar India

ISBN: (纸本)9781450366151

Haze during the bad weather, degrades the visibility of the scene drastically. Degradation of scene visibility varies with respect to the transmission coefficient/map (Tc) of the scene. Estimation of accurate Tc is key step to reconstruct the haze free scene. Previously, local as well as global priors were proposed to estimate the Tc. We, on the other hand, propose integration of local and global approaches to learn both point level and object level Tc. The proposed local encoder decoder network (LEDNet) estimates the scene transmission map in two stages. During first stage, network estimates the point level Tc using parallel convolutional filters and spatial invariance filtering. The second stage comprises of a two level encoder-decoder architecture which anticipates the object level Tc. We also propose, local air-light estimation (LAE) algorithm, which is able to obtain the air-light component of the outdoor scene. Combination of LEDNet and LAE improves the accuracy of haze model to recover the scene radiance. Structural similarity index, mean square error and peak signal to noise ratio are used to evaluate the performance of the proposed approach for single image haze removal. Experiments on benchmark datasets show that LEDNet outperforms the existing state-of-the-art methods for single image haze removal.

关键词： Haze removal transmission map CNN

nnDetection: A Self-configuring Method for Medical Object Detection

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arXiv 2021年

作者： Baumgartner, Michael Jäger, Paul F. Isensee, Fabian Maier-Hein, Klaus H. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Interactive Machine Learning Group German Cancer Research Center Germany HIP Applied Computer Vision Lab. German Cancer Research Center Germany Pattern Analysis and Learning Group Heidelberg University Hospital Germany

Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions often depend on rating of objects rather than e.g. pixels. For this task, the cumbersome and iterative process of method configuration constitutes a major research bottleneck. Recently, nnU-Net has tackled this challenge for the task of image segmentation with great success. Following nnU-Net’s agenda, in this work we systematize and automate the configuration process for medical object detection. The resulting self-configuring method, nnDetection, adapts itself without any manual intervention to arbitrary medical detection problems while achieving results en par with or superior to the state-of-the-art. We demonstrate the effectiveness of nnDetection on two public benchmarks, ADAM and LUNA16, and propose 11 further medical object detection tasks on public data sets for comprehensive method evaluation. Code is at https://***/MIC-DKFZ/nnDetection. Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition

Partial Differential Equations is All You Need for Generating Neural Architectures - A Theory for Physical Artificial Intelligence Systems

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arXiv 2021年

作者： Guo, Ping Huang, Kaizhu Xu, Zenglin Image Processing & Pattern Recognition Lab. Beijing Normal University Beijing100875 China Data Science Research Center Duke Kunshan University Jiangsu Kunshan215316 China School of Computer Science and Technology Harbin Institute of Technology at ShenZhen Peng Cheng National Lab Guangdong Shenzhen510855 China

In this work, we generalize the reaction-diffusion equation in statistical physics, Schrödinger equation in quantum mechanics, and Helmholtz equation in paraxial optics into the neural partial differential equations (NPDE), which can be considered as the fundamental equations in the field of artificial intelligence research. We take the finite difference method to discretize NPDEs for finding the numerical solution. Moreover, the basic building blocks of deep neural network architectures, including multi-layer perceptron, convolutional neural networks, and recurrent neural networks, are generated. The learning strategies, such as Adaptive moment estimation, L-BFGS, pseudoinverse learning algorithms, and partial differential equation constrained optimization, are also developed. This work is of significance in that it presents a clear view of interpretable deep neural networks physically, enables high possibility to be applied to analog computing device design, and paves the road to physical artificial intelligence. © 2021, CC BY-NC-SA.

关键词： Schrodinger equation

Automatic plane adjustment of orthopedic intra-operative flat panel detector CT-volumes

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arXiv 2021年

作者： Vicario, Celia Martín Kordon, Florian Denzinger, Felix El Barbari, Jan Siad Privalov, Maxim Franke, Jochen Thomas, Sarina Kausch, Lisa Maier, Andreas Kunze, Holger Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Erlangen Germany Siemens Healthcare GmbH Forchheim Germany Department for Trauma and Orthopaedic Surgery BG Trauma Center Ludwigshafen Ludwigshafen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany

Purpose 3D acquisitions are often acquired to assess the result in orthopedic trauma surgery. With a mobile C-Arm system, these acquisitions can be performed intra-operatively. That reduces the number of required revision surgeries. However, due to the operation room setup, the acquisitions typically cannot be performed such that the acquired volumes are aligned to the anatomical regions. Thus, the multiplanar reconstructed (MPR) planes need to be adjusted manually during the review of the volume. In this paper, we present a detailed study of multi-task learning (MTL) regression networks to estimate the parameters of the MPR planes. Approach First, various mathematical descriptions for rotation, including Euler angle, quaternion, and matrix representation, are revised. Then, three different MTL network architectures based on the PoseNet are compared with a single task learning network. Results Using a matrix description rather than the Euler angle description, the accuracy of the regressed normals improves from 7.7◦ to 7.3◦ in the mean value for single anatomies. The multi-head approach improves the regression of the plane position from 7.4mm to 6.1mm, while the orientation does not benefit from this approach. Conclusions The results show that a multi-head approach can lead to slightly better results than the individual tasks networks. The most important benefit of the MTL approach is that it is a single network for standard plane regression for all body regions with a reduced number of stored parameters. Copyright © 2021, The Authors. All rights reserved.

关键词： Learning systems