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检索条件"任意字段=MIPPR 2011: Pattern Recognition and Computer Vision"

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mippr 2023: pattern recognition and computer vision

MIPPR 2023: Pattern Recognition and Computer Vision

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SPIE 12th International Symposium on Multispectral Image Processing and pattern recognition, mippr 2023

ISBN: (纸本)9781510674950

The proceedings contain 25 papers. The topics discussed include: accurate extraction of line-structured optical stripe centerlines under low exposure;research on grid inspection technology based on general knowledge enhanced multi-modal large language models;a comparison between deep-learning models for scene recognition;binary network design for dedicated hardware circuits;evaluation of training datasets for typical target detection networks;optimal exposure selection of high dynamic range-based reflection compensation for printed circuit board reconstruction using structured light 3D measurement system;fall detection model based on Alphapose combined with LSTM and Lightgbm;and an image enhancement algorithm based on multi-scale Retinex theory to improve the images quality of sensors.

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mippr 2019: pattern recognition and computer vision

MIPPR 2019: Pattern Recognition and Computer Vision

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11th International Symposium on Multispectral Image Processing and pattern recognition: pattern recognition and computer vision, mippr 2019

ISBN: (纸本)9781510636378

The proceedings contain 63 papers. The topics discussed include: rural settlements extraction based on deep learning from high spatial resolution remote sensing imagery;G-CNN type recognition of typical aircraft based on target characteristics;semantic segmentation network combined with edge detection for building extraction in remote sensing images;multi-scales feature integration single shot multi-box detector on small object detection;a novel neural network approach for error transfer analysis in electronic instrument transformer;large-scale book page retrieval by deep hashing networks;and object direction estimation by constrained convolutional neural network.

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Residual multi-scale pixel attention fusion network for image deblurring 12

Residual multi-scale pixel attention fusion network for imag...

引用

SPIE 12th International Symposium on Multispectral Image Processing and pattern recognition, mippr 2023

作者： Li, Zezheng Xiang, Guoliang Shu, Ting Yang, Mingjuan Hua, Xia Hubei Key Laboratory of Optical Information and Pattern Recognition School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China Wuhan Raycus Fiber Laser Technologies Co. Ltd Wuhan430075 China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

The applications of multi-scale fusion strategy to feature are very effective and common in image restoration. However, there is a lack of research on the application of the multi-scale fusion strategy to attention mechanism, although attention mechanism has also been verified to be effective in image restoration. To address this problem, we propose a residual multi-scale pixel attention fusion block (RMPAFB) to refine the input feature, which successfully combine the multi-scale fusion strategy with pixel attention. RMPAFB can capture feature correspondences from multi-scale pixel attention map, which can be more effective for feature refinement than single-scale pixel attention map. Based on RMPAFB, we build an efficient and effective network called RMPAFNet for image deblurring. Substantial experiments on several benchmark datasets have showed that multi-scale pixel attention performs better than single-scale pixel attention and our proposed RMPAFNet achieves state-of-The-Art performance while requiring fewer overheads than recent competing deblurring models. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Pixels

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Optimal exposure selection of high dynamic range-based reflection compensation for printed circuit board reconstruction using structured light 3D measurement system 12

Optimal exposure selection of high dynamic range-based refle...

引用

SPIE 12th International Symposium on Multispectral Image Processing and pattern recognition, mippr 2023

作者： Wang, He Zhu, Ying Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

Structured light (SL) scanning in industrial metrology offers the advantages of non-contact measurement, high-speed operation, and high precision. However, fringes may exhibit excessive modulation when measuring highly reflective metal parts, leading to the calculation of invalid phases and the reconstruction of abnormal point clouds. High dynamic range (HDR) scanning is a commonly used approach to address this issue, but the optimal exposure time using this method is usually influenced by the reflective properties of the object. In this paper, we propose an optimal exposure time selection method based on the clustering of images in the fringe modulation. Firstly, we acquire modulation degree images containing only objects. Subsequently, the K-Means method is employed to cluster the modulation degree images. The K best exposure times are calculated based on the average maximum gray value of the fringe images in each cluster. Finally, K sets of fused fringe images are captured and utilized to reconstruct three-dimensional (3D) information with enhanced coverage. Using our method, the three-dimensional information of the printed circuit board (PCB) is reconstructed, achieving an improved coverage rate of 98.1% compared to the 88.3% coverage rate obtained from a single-shot capture. Additionally, our method achieves a measurement accuracy of 0.08mm. Experimental results demonstrate that the proposed method efficiently selects the optimal fusion exposure time, making it suitable for measuring highly reflective PCBs. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

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Accurate extraction of line-structured optical stripe centerlines under low exposure 12

Accurate extraction of line-structured optical stripe center...

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SPIE 12th International Symposium on Multispectral Image Processing and pattern recognition, mippr 2023

作者： Zhu, Cheng Tian, Keyun Gao, Yao Ding, Zhiqiang Hubei Key Laboratory of Optical Information and Pattern Recognition China Hubei Engineering Research Center of Video Image and HD Projection China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

The line-structure light bar images acquired in industrial metal parts inspection may suffer from inaccurate extraction of the centerline of the line structure light bar due to the interference of ambient lighting and strong diffuse reflection and weak reflection of the part surface material. For this reason, this study proposes a method based on G-HL (Gauss-Hyper-Laplace Distribution) curve fitting to improve the center extraction accuracy of the line structure light bar in complex environments. First, the method uses Gaussian filtering to denoise the line-structured light image. Then, the laser line target pixel region is localized by finding the upper and lower boundaries of the line-structured light bar using the neighborhood gradient for each column of the line-structured light image, and the width of the line-structured light bar is calculated by the difference of the boundary coordinates, and the fitting interval is determined. Finally, within the fitting interval, the G-HL curve is used to fit the gray scale of the line-structured light cross-section to obtain the G-HL parameter, and the point of the maximum value of the G-HL curve is taken to be the coordinates of the center of the line-structured light. Experiments prove that the proposed method can accurately extract the center of the line structure light in low exposure. Compared with the methods such as the grayscale center of gravity method and the extreme value method, the proposed method has high accuracy and robustness. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Curve fitting

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Adversarial Normalization: I Can visualize Everything (ICE)

Adversarial Normalization: I Can visualize Everything (ICE)

引用

IEEE/CVF Conference on computer vision and pattern recognition (CVPR)

作者： Choi, Hoyoung Jin, Seungwan Han, Kyungsik Hanyang Univ Seoul South Korea

ISBN: (纸本)9798350301298

vision transformers use [CLS] tokens to predict image classes. Their explainability visualization has been studied using relevant information from [CLS] tokens or focusing on attention scores during self-attention. Such visualization, however, is challenging because of the dependence of the structure of a vision transformer on skip connections and attention operators, the instability of non-linearities in the learning process, and the limited reflection of self-attention scores on relevance. We argue that the output vectors for each input patch token in a vision transformer retain the image information of each patch location, which can facilitate the prediction of an image class. In this paper, we propose ICE (Adversarial Normalization: I Can visualize Everything), a novel method that enables a model to directly predict a class for each patch in an image;thus, advancing the effective visualization of the explainability of a vision transformer. Our method distinguishes background from foreground regions by predicting background classes for patches that do not determine image classes. We used the DeiT-S model, the most representative model employed in studies, on the explainability visualization of vision transformers. On the ImageNet-Segmentation dataset, ICE outperformed all explainability visualization methods for four cases depending on the model size. We also conducted quantitative and qualitative analyses on the tasks of weakly-supervised object localization and unsupervised object discovery. On the CUB200-2011 and PASCALVOC07/12 datasets, ICE achieved comparable performance to the state-of-the-art methods. We incorporated ICE into the encoder of DeiT-S and improved efficiency by 44.01% on the ImageNet dataset over that achieved by the original DeiT-S model. We showed performance on the accuracy and efficiency comparable to EViT, the state-of-the-art pruning model, demonstrating the effectiveness of ICE. The code is available at https://***/Hanyang-HCC-La

关键词： Explainable computer vision

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Bridging the Gap between Classification and Localization for Weakly Supervised Object Localization

Bridging the Gap between Classification and Localization for...

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IEEE/CVF Conference on computer vision and pattern recognition (CVPR)

作者： Kim, Eunji Kim, Siwon Lee, Jungbeom Kim, Hyunwoo Yoon, Sungroh Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea Seoul Natl Univ LG AI Res Seoul South Korea Seoul Natl Univ Interdisciplinary Program AI AILS ASRI INMC & ISR Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Weakly supervised object localization aims to find a target object region in a given image with only weak supervision, such as image-level labels. Most existing methods use a class activation map (CAM) to generate a localization map;however, a CAM identifies only the most discriminative parts of a target object rather than the entire object region. In this work, we find the gap between classification and localization in terms of the misalignment of the directions between an input feature and a class-specific weight. We demonstrate that the misalignment suppresses the activation of CAM in areas that are less discriminative but belong to the target object. To bridge the gap, we propose a method to align feature directions with a class-specific weight. The proposed method achieves a state-of-the-art localization performance on the CUB-200-2011 and ImageNet-1K benchmarks.

关键词： Location awareness Bridges computer vision Benchmark testing pattern recognition Object recognition

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Attributable Visual Similarity Learning

Attributable Visual Similarity Learning

引用

IEEE/CVF Conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Borui Zheng, Wenzhao Zhou, Jie Lu, Jiwen Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper proposes an attributable visual similarity learning (AVSL) framework for a more accurate and explainable similarity measure between images. Most existing similarity learning methods exacerbate the unexplainability by mapping each sample to a single point in the embedding space with a distance metric (e.g., Mahalanobis distance, Euclidean distance). Motivated by the human semantic similarity cognition, we propose a generalized similarity learning paradigm to represent the similarity between two images with a graph and then infer the overall similarity accordingly. Furthermore, we establish a bottom-up similarity construction and top-down similarity inference framework to infer the similarity based on semantic hierarchy consistency. We first identify unreliable higher-level similarity nodes and then correct them using the most coherent adjacent lower-level similarity nodes, which simultaneously preserve traces for similarity attribution. Extensive experiments on the CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate significant improvements over existing deep similarity learning methods and verify the interpretability of our framework.(1)

关键词： Learning systems Representation learning Measurement Visualization computer vision Text recognition Semantics

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Proto2Proto: Can you recognize the car, the way I do?

Proto2Proto: Can you recognize the car, the way I do?

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IEEE/CVF Conference on computer vision and pattern recognition (CVPR)

作者： Keswani, Monish Ramakrishnan, Sriranjani Reddy, Nishant Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad India

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Prototypical methods have recently gained a lot of attention due to their intrinsic interpretable nature, which is obtained through the prototypes. With growing use cases of model reuse and distillation, there is a need to also study transfer of interpretability from one model to another. We present Proto2Proto, a novel method to transfer interpretability of one prototypical part network to another via knowledge distillation. Our approach aims to add interpretability to the "dark" knowledge transferred from the teacher to the shallower student model. We propose two novel losses: "Global Explanation" loss and "Patch-Prototype Correspondence" loss to facilitate such a transfer. Global Explanation loss forces the student prototypes to be close to teacher prototypes, and Patch-Prototype Correspondence loss enforces the local representations of the student to be similar to that of the teacher. Further, we propose three novel metrics to evaluate the student's proximity to the teacher as measures of interpretability transfer in our settings. We qualitatively and quantitatively demonstrate the effectiveness of our method on CUB-200-2011 and Stanford Cars datasets. Our experiments show that the proposed method indeed achieves interpretability transfer from teacher to student while simultaneously exhibiting competitive performance. The code is available at https:/***/archmaester/proto2proto

关键词： Measurement Knowledge engineering computer vision Codes Prototypes pattern recognition Automobiles

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Learning to Ask Informative Sub-Questions for Visual Question Answering

Learning to Ask Informative Sub-Questions for Visual Questio...

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IEEE/CVF Conference on computer vision and pattern recognition (CVPR)

作者： Uehara, Kohei Duan, Nan Harada, Tatsuya Univ Tokyo Tokyo Japan Microsoft Res Asia Beijing Peoples R China Univ Tokyo RIKEN Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

VQA (Visual Question Answering) model tends to make incorrect inferences for questions that require reasoning over world knowledge. Recent study has shown that training VQA models with questions that provide lower-level perceptual information along with reasoning questions improves performance. Inspired by this, we propose a novel VQA model that generates questions to actively obtain auxiliary perceptual information useful for correct reasoning. Our model consists of a VQA model for answering questions, a Visual Question Generation (VQG) model for generating questions, and an Info-score model for estimating the amount of information the generated questions contain, which is useful in answering the original question. We train the VQG model to maximize the "informativeness" provided by the Info-score model to generate questions that contain as much information as possible, about the answer to the original question. Our experiments show that by in-putting the generated questions and their answers as additional information to the VQA model, it can indeed predict the answer more correctly than the baseline model.

关键词： Training Visualization computer vision Computational modeling Conferences Reinforcement learning Predictive models

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