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Automated X-Ray Object recognition Using an Efficient Search Algorithm in Multiple Views

Automated X-Ray Object Recognition Using an Efficient Search...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Domingo Mery Vladimir Riffo Irene Zuccar Christian Pieringer Department of Computer Science Pontificia Universidad Católica de Chile Santiago Chile

In order to reduce the security risk of a commercial aircraft, passengers are not allowed to take certain items in their carry-on baggage. For this reason, human operators are trained to detect prohibited items using a manually controlled baggage screening process. In this paper, we propose the use of an automated method based on multiple X-ray views to recognize certain regular objects with highly defined shapes and sizes. The method consists of two steps: 'monocular analysis', to obtain possible detections in each view of a sequence, and 'multiple view analysis', to recognize the objects of interest using matchings in all views. The search for matching candidates is efficiently performed using a lookup table that is computed off-line. In order to illustrate the effectiveness of the proposed method, experimental results on recognizing regular objects --clips, springs and razor blades-- in pen cases are shown achieving around 93% accuracy for 120 objects. We believe that it would be possible to design an automated aid in a target detection task using the proposed algorithm.

关键词： X-ray imaging Three-dimensional displays Blades Image recognition Search problems Algorithm design and analysis Security

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Online multimodal video registration based on shape matching

Online multimodal video registration based on shape matching

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Pierre-Luc St-Charles Guillaume-Alexandre Bilodeau Robert Bergevin Dept. of Computer & Software Eng. Polytechnique Montréal Montréal QC Canada LVSN - REPARTI Université Laval Québec City QC Canada

The registration of video sequences captured using different types of sensors often relies on dense feature matching methods, which are very costly. In this paper, we study the problem of “almost planar” scene registration (i.e. where the planar ground assumption is almost respected) in multimodal imagery using target shape information. We introduce a new strategy for robustly aligning scene elements based on the random sampling of shape contour correspondences and on the continuous update of our transformation model's parameters. We evaluate our solution on a public dataset and show its superiority by comparing it to a recently published method that targets the same problem. To make comparisons between such methods easier in the future, we provide our evaluation tools along with a full implementation of our solution online.

关键词： Shape Image edge detection Image segmentation Reservoirs Context Streaming media Smoothing methods

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Subset feature learning for fine-grained category classification

Subset feature learning for fine-grained category classifica...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： ZongYuan Ge Christopher McCool Conrad Sanderson Peter Corke Queensland University of Technology (QUT) Brisbane Australia Australian Centre for Robotic Vision Brisbane Australia Queensland University of Technology Brisbane QLD AU University of Queensland Brisbane Australia NICTA Australia

Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images. We propose a learning system which first clusters visually similar classes and then learns deep convolutional neural network features specific to each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset show that the proposed method outperforms recent fine-grained categorisation methods under the most difficult setting: no bounding boxes are presented at test time. It achieves a mean accuracy of 77.5%, compared to the previous best performance of 73.2%. We also show that progressive transfer learning allows us to first learn domain-generic features (for bird classification) which can then be adapted to specific set of bird classes, yielding improvements in accuracy.

关键词： Birds Accuracy Training Feature extraction Australia Learning systems Neural networks

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Self-Supervised Normalizing Flows for Image Anomaly Detection and Localization

Self-Supervised Normalizing Flows for Image Anomaly Detectio...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Li-Ling Chiu Shang-Hong Lai Department of Computer Science National Tsing Hua University Taiwan

Image anomaly detection aims to detect out-of-distribution instances. Most existing methods treat anomaly detection as an unsupervised task because anomalous training data and labels are usually scarce or unavailable. Recently, image synthesis has been used to generate anomalous samples which deviate from normal sample distribution for model training. By using the synthesized anomalous training samples, we present a novel self-supervised normalizing flow-based density estimation model, which is trained by maximizing the likelihood of normal images and minimizing the likelihood of synthetic anomalous images. By adding constraints to abnormal samples in our loss function, our model training is focused on normal samples rather than synthetic samples. Moreover, we improve the transformation subnet of the affine coupling layers in our flow-based model by dynamic stacking convolution and self-attention blocks. We evaluate our method on MVTec-AD, BTAD, and DAGM datasets and achieve state-of-the-art performance compared to flow-based and self-supervised methods on both anomaly detection and localization tasks.

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Groupwise Morphometric Analysis based on Morphological Appearance Manifold

Groupwise Morphometric Analysis based on Morphological Appea...

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ieee computer society conference on computer vision and pattern recognition workshops

作者： Nai-Xiang Lian Christos Davatzikos Department of Radiology University of Pennsylvania

ISBN: (纸本)9781424439942

The field of computational anatomy has developed rigorous frameworks for analyzing anatomical shape, based on diffeomorphic transformations of a template. However, differences in algorithms used for template warping, in regularization parameters, and in the template itself, lead to different representations of the same anatomy. Variations of these parameters are considered as confounding factors as they give rise to non-unique representation. Recently, extensions of the conventional computational anatomy framework to account for such confounding variations have shown that learning the equivalence class derived from the multitude of representations can lead to improved and more stable morphological descriptors. Herein, we follow that approach, estimating the morphological appearance manifold obtained by varying parameters of the template warping procedure. Our approach parallels work in the computer vision field, in which variations lighting, pose and other parameters leads to image appearance manifolds representing the exact same figure in different ways. The proposed framework is then used for groupwise registration and statistical analysis of biomedical images, by employing a minimum variance criterion on selected complete morphological descriptor to perform manifold-constrained optimization, i.e. to traverse each individual's morphological appearance manifold until group variance is minimal. Effectively, this process removes the aforementioned confounding effects and potentially leads to morphological representations reflecting purely biological variations, instead of variations introduced by modeling assumptions and parameter settings. The nonlinearity of a morphological appearance manifold is treated via local approximations of the manifold via PCA.

关键词： Anatomy Shape Biological system modeling Principal component analysis Biology computing Biomedical measurements Smoothing methods Radiology computer vision Statistical analysis

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Protein Interaction Inference as a MAX-SAT Problem

Protein Interaction Inference as a MAX-SAT Problem

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ya Zhang Hongyuan Zha Chao-Hisen Chu Xiang Ji School of Information Sciences and Technology USA Department of Computer Science and Engineering Pennsylvania State University USA NEC Laboratories of America Inc. USA

Discovering interacting proteins is essential for understanding protein functions. However, high throughput interaction data are inherently noisy and only cover a small portion of the whole interactome. Domains, the building block of proteins, are believed to be responsible for the interactions among proteins. An abstract representation of interactome is achieved at domain level and this representation also facilitates the discovery of unobserved proteinprotein interactions. Many domain-based approaches have been proposed to predict protein-protein interactions and promising results have been obtained. Existing methods generally assume that domain interactions are independent of each other for the convenience of computational modeling. In this paper, a new framework of learning is proposed. The framework makes no assumption about domain interactions and consider protein interactions resulting from multiple domain interactions which may be dependent of each other. With a conjunctive normal form representation of the relationship between protein interactions and domain interactions, the problem of interaction inference is modeled as a constraint satis?ability problem and solved via linear programming. Experimental results on a combined yeast data set have demonstrated the robustness of and the accuracy of the proposed algorithm.

关键词： Protein engineering Throughput Computational modeling Fungi Proteomics Genomics Bioinformatics Chaos computer science National electric code

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Semantic Visual Understanding of Indoor Environments: From Structures to Opportunities for Action

Semantic Visual Understanding of Indoor Environments: From S...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Grace Tsai Collin Johnson Benjamin Kuipers Dept. of Electrical Engineering and Computer Science University of Michigan

We present a two-layer representation of the locally sensed 3D indoor environment. Our representation moves one step forward from capturing the geometric structure of the environment to reason about the navigation opportunities for an agent in the environment. The first layer is the Planar Semantic Model (PSM), a geometric representation in terms of meaningful planes (ground and walls). PSM captures more semantics of the indoor environment than a pure planar model because it represents a richer set of relationships among planar segments. In the second layer, we present the Action Opportunity Star (AOS), which describes the set of qualitatively distinct opportunities for robot action available in the neighborhood of the robot. Our two-layer representation is a concise representation of indoor environments, semantically meaningful to both robot and to human. It is capable of capturing incomplete knowledge of the local environment so that unknown areas can be incrementally learned as observations become available. Experimental results on a variety of indoor environments demonstrate the expressive power of our representation.

关键词： Logic gates Robots Three-dimensional displays Semantics Indoor environments Navigation Trajectory

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Robust Automatic Motorcycle Helmet Violation Detection for an Intelligent Transportation System

Robust Automatic Motorcycle Helmet Violation Detection for a...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Duong Nguyen-Ngoc Tran Long Hoang Pham Hyung-Joon Jeon Huy-Hung Nguyen Hyung-Min Jeon Tai Huu-Phuong Tran Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

Video surveillance-based automatic detection of motorcycle helmet usage can enhance the effectiveness of educational and enforcement initiatives aimed at boosting road safety. Current detection methods, however, have room for enhancement, such as the inability to pinpoint individual motorcycles or differentiate between drivers and passengers in terms of helmet usage. This paper introduces a framework designed to detect and identify individual motorcycles while recording specific helmet usage for riders. The proposed classification approach for helmet usage demonstrates increased efficiency in comparison to previous research. Our findings highlight the exceptional accuracy of deep learning, with our method achieving a score of 0.7754 on the AI City 2023 Challenge Track 5 public leaderboard.

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Learning-based deformation estimation for fast non-rigid registration

Learning-based deformation estimation for fast non-rigid reg...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Min-Jeong Kim Myoung-Hee Kim Dinggang Shen Department of Computer Science and Engineering Ewha Womans University Seoul South Korea Department of Radiology and Biomedical Research Imaging Center North Carolina State University Chapel Hill NC USA

This paper presents a learning-based deformation estimation method for fast non-rigid registration. First, a PCA-based statistical deformation model is constructed using the deformation fields obtained by conventional registration algorithms between a template image and training subject images. Then, the constructed statistical model is used to generate a large number of sample deformation fields by resampling in the PCA space. In the meanwhile, by warping the template using these sample deformation fields, the respective sample images in the PCA space can be also generated. Finally, after learning the correlation between the features of the sample images and their deformation coefficients, given a new test image, we can immediately estimate its relative deformations to the template based on its image information. Using this estimated deformation, we can warp the template to generate an intermediate template close to the test image. Since the intermediate template is more similar to the test image compared to the original template, the deformable registration via the intermediate template becomes much easier and faster. Experimental results show that the proposed learning-based registration method can fast register MR brain image with robust performance.

关键词： Shape Deformable models Testing Principal component analysis Brain Image registration Biomedical imaging computer science Biomedical engineering Radiology

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Revisiting overlap invariance in medical image alignment

Revisiting overlap invariance in medical image alignment

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nathan D. Cahill Julia A. Schnabel J. Alison Noble David J. Hawkes Wolfson Medical Vision Laboratory University of Oxford Oxford UK Research & Innovation Carestream Health Inc. Rochester NY USA Centre for Medical Image Computing University College London London UK

In Studholme et al. introduced normalized mutual information (NMI) as an overlap invariant generalization of mutual information (MI). Even though Studholme showed how NMI could be used effectively in multimodal medical image alignment, the overlap invariance was only established empirically on a few simple examples. In this paper, we illustrate a simple example in which NMI fails to be invariant to changes in overlap size, as do other standard similarity measures including MI, cross correlation (CCorr), correlation coefficient (CCoeff), correlation ratio (CR), and entropy correlation coefficient (ECC). We then derive modified forms of all of these similarity measures that are proven to be invariant to changes in overlap size. This is done by making certain assumptions about background statistics. Experiments on multimodal rigid registration of brain images show that 1) most of the modified similarity measures outperform their standard forms, and 2) the modified version of MI exhibits superior performance over any of the other similarity measures for both CT/MR and PET/MR registration.

关键词： Biomedical imaging Mutual information Measurement standards Size measurement Chromium Entropy Statistics Brain Computed tomography Positron emission tomography

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