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检索条件"任意字段=2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition, CVIPPR 2023"

共 327 条记录，以下是61-70 订阅

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Simulation of computer image processing Model Based on Random Forest Algorithm 2

Simulation of Computer Image Processing Model Based on Rando...

引用

2nd International conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Liu, Haotian University of Edinburgh EdinburghEH8 9JU United Kingdom

ISBN: (纸本)9798350323818

In essence, image recognition, as a pattern classification problem, can also be processed by classical pattern classification algorithms. Under the background of network information technology, it is need to use computer vision algorithm to process the image and realize the three-dimensional reconstruction of the image for the distortion of the true three-dimensional display image of intelligent interactive system. Random Forest (RF) is a new ensemble learning algorithm developed in recent years, which has good classification accuracy. In this article, a computer image processing model based on RF algorithm is proposed to enhance the ability of depth feature learning. The experimental results show that this image processing technology is more accurate than BP neural network and can be widely used in image processing. High-level target feature representation can often improve the recognition accuracy. However, the extraction of high-level target features generally has certain computational complexity. Compared with traditional methods, the accuracy of the proposed method is greatly improved, and the generalization performance is superior, which can identify images that have been beautified and repaired. © 2023 IEEE.

关键词： image recognition

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VisiTherS: Visible-thermal infrared stereo disparity estimation of human silhouette

VisiTherS: Visible-thermal infrared stereo disparity estimat...

引用

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

作者： Anwar, Noreen Duplessis-Guindon, Philippe Bilodeau, Guillaume-Alexandre Bouachir, Wassim Polytechn Montreal LITIV Montreal PQ Canada Univ Quebec TELUQ Data Sci Lab Quebec City PQ Canada

ISBN: (纸本)9798350302493

This paper presents a novel approach for visible-thermal infrared stereoscopy, focusing on the estimation of disparities of human silhouettes. Visible-thermal infrared stereo poses several challenges, including occlusions and differently textured matching regions in both spectra. Finding matches between two spectra with varying colors, textures, and shapes adds further complexity to the task. To address the aforementioned challenges, this paper proposes a novel approach where a high-resolution convolutional neural network is used to better capture relationships between the two spectra. To do so, a modified HRNet backbone is used for feature extraction. This HRNet backbone is capable of capturing fine details and textures as it extracts features at multiple scales, thereby enabling the utilization of both local and global information. For matching visible and thermal infrared regions, our method extracts features on each patch using two modified HRNet streams. Features from the two streams are then combined for predicting the disparities by concatenation and correlation. Results on public datasets demonstrate the effectiveness of the proposed approach by improving the results by approximately 18 percentage points on the <= 1 pixel error, highlighting its potential for improving accuracy in this task. The code of VisiTherS is available on GitHub at the following link: https://***/philippeDG/VisiTherS.

关键词： Stereo image processing

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Weakly Supervised Posture Mining for Fine-grained Classification

Weakly Supervised Posture Mining for Fine-grained Classifica...

引用

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

作者： Tang, Zhenchao Yang, Hualin Chen, Calvin Yu-Chian Sun Yat Sen Univ Guangzhou Peoples R China China Med Univ Hosp Taichung Taiwan Asia Univ Taichung Taiwan

ISBN: (纸本)9798350301298

Because the subtle differences between the different sub-categories of common visual categories such as bird species, fine-grained classification has been seen as a challenging task for many years. Most previous works focus towards the features in the single discriminative region isolatedly, while neglect the connection between the different discriminative regions in the whole image. However, the relationship between different discriminative regions contains rich posture information and by adding the posture information, model can learn the behavior of the object which attribute to improve the classification performance. In this paper, we propose a novel fine-grained framework named PMRC (posture mining and reverse cross-entropy), which is able to combine with different backbones to good effect. In PMRC, we use the Deep Navigator to generate the discriminative regions from the images, and then use them to construct the graph. We aggregate the graph by message passing and get the classification results. Specifically, in order to force PMRC to learn how to mine the posture information, we design a novel training paradigm, which makes the Deep Navigator and message passing communicate and train together. In addition, we propose the reverse cross-entropy (RCE) and demomenstate that compared to the cross-entropy (CE), RCE can not only promote the accurracy of our model but also generalize to promote the accuracy of other kinds of fine-grained classification models. Experimental results on benchmark datasets confirm that PMRC can achieve state-of-the-art.

关键词： Message passing

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Expanding the Horizons: Exploring Further Steps in Open-Vocabulary Segmentation 6th

Expanding the Horizons: Exploring Further Steps in Open-Voca...

引用

6th Chinese conference on pattern recognition and computer vision (PRCV)

作者： Wang, Xihua Ji, Lei Yan, Kun Sun, Yuchong Song, Ruihua Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Beihang Univ SKLSDE Lab Beijing Peoples R China

ISBN: (纸本)9789819985487;9789819985494

The open vocabulary segmentation (OVS) task has gained significant attention due to the challenges posed by both segmentation and open vocabulary classification, which involves recognizing arbitrary categories. Recent studies have leveraged pretrained vision-Language models (VLMs) as a new paradigm for addressing this problem, leading to notable achievements. However, our analysis reveals that these methods are not yet fully satisfactory. In this paper, we empirically analyze the key challenges in four main categories: segmentation, dataset, reasoning and recognition. Surprisingly, we observe that the current research focus in OVS primarily revolves around recognition issues, while others remain relatively unexplored. Motivated by these findings, we propose preliminary approaches to address the top three identified issues by integrating advanced models and making adjustments to existing segmentation models. Experimental results demonstrate the promising performance gains achieved by our proposed methods on the OVS benchmark.

关键词： Open-vocabulary Segmentation vision-language models

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Micro-expression recognition based on Spatiotemporal Local Gabor Binary patterns on Three Orthogonal Planes

Micro-expression Recognition based on Spatiotemporal Local G...

引用

2023 International conference on image, Signal processing, and pattern recognition, ISPP 2023

作者： Fan, Ling Lin, Chenhan Computer Science and Information Engineering School Xiamen Institute of Technology Xiamen361021 China Center for Digital Media Computation of Xiamen University Xiamen361005 China

ISBN: (纸本)9781510666351

Analyzing facial micro-expressions based on computer vision has become an active research area in recent years. Subtle changes and short duration of micro-expressions bring great challenges to this issue, and extracting effective spatiotemporal features is the key to improving recognition performance. This paper proposes an approach for micro-expression recognition, Spatiotemporal Local Gabor Binary patterns on Three Orthogonal Planes (STLGBP-TOP), by combining spatiotemporal Gabor filtering and LBP-TOP. In this approach, LBP-TOP is employed to encode local dynamic features of micro-expression videos on top of spatiotemporal Gabor filtering. Experimental results on CASME and CASME2 datasets demonstrate the effectiveness of our method. © 2023 SPIE.

关键词： Local binary pattern

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Learning to Detect Mirrors from Videos via Dual Correspondences

Learning to Detect Mirrors from Videos via Dual Corresponden...

引用

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

作者： Lin, Jiaying Tan, Xin Lau, Rynson W. H. City Univ Hong Kong Hong Kong Peoples R China East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9798350301298

Detecting mirrors from static images has received significant research interest recently. However, detecting mirrors over dynamic scenes is still under-explored due to the lack of a high-quality dataset and an effective method for video mirror detection (VMD). To the best of our knowledge, this is the first work to address the VMD problem from a deep-learning-based perspective. Our observation is that there are often correspondences between the contents inside (reflected) and outside (real) of a mirror, but such correspondences may not always appear in every frame, e.g., due to the change of camera pose. This inspires us to propose a video mirror detection method, named VMD-Net, that can tolerate spatially missing correspondences by considering the mirror correspondences at both the intra-frame level as well as inter-frame level via a dual correspondence module that looks over multiple frames spatially and temporally for correlating correspondences. We further propose a first large-scale dataset for VMD (named VMD-D), which contains 14,987 image frames from 269 videos with corresponding manually annotated masks. Experimental results show that the proposed method outperforms SOTA methods from relevant fields. To enable real-time VMD, our method efficiently utilizes the backbone features by removing the redundant multi-level module design and gets rid of post-processing of the output maps commonly used in existing methods, making it very efficient and practical for real-time video-based applications. Code, dataset, and models are available at https://***/cvpr2023-vmd/

关键词： Low-level vision

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VectorFloorSeg: Two-Stream Graph Attention Network for Vectorized Roughcast Floorplan Segmentation

VectorFloorSeg: Two-Stream Graph Attention Network for Vecto...

引用

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

作者： Yang, Bingchen Jiang, Haiyong Pan, Hao Xiao, Jun Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

Vector graphics (VG) are ubiquitous in industrial designs. In this paper, we address semantic segmentation of a typical VG, i.e., roughcast floorplans with bare wall structures, whose output can be directly used for further applications like interior furnishing and room space modeling. Previous semantic segmentation works mostly process well-decorated floorplans in raster images and usually yield aliased boundaries and outlier fragments in segmented rooms, due to pixel-level segmentation that ignores the regular elements (e.g. line segments) in vector floorplans. To overcome these issues, we propose to fully utilize the regular elements in vector floorplans for more integral segmentation. Our pipeline predicts room segmentation from vector floorplans by dually classifying line segments as room boundaries, and regions partitioned by line segments as room segments. To fully exploit the structural relationships between lines and regions, we use two-stream graph neural networks to process the line segments and partitioned regions respectively, and devise a novel modulated graph attention layer to fuse the heterogeneous information from one stream to the other. Extensive experiments show that by directly operating on vector floorplans, we outperform image-based methods in both mIoU and mAcc. In addition, we propose a new metric that captures room integrity and boundary regularity, which confirms that our method produces much more regular segmentations. Source code is available at https://***/DrZiji/VecFloorSeg.

关键词： vision + graphics

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MIST : Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question Answering

MIST : Multi-modal Iterative Spatial-Temporal Transformer fo...

引用

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

作者： Gao, Difei Zhou, Luowei Ji, Lei Zhu, Linchao Yang, Yi Shou, Mike Zheng Natl Univ Singapore Show Lab Singapore Singapore Microsoft Albuquerque NM USA Microsoft Res Asia Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China Google Brain Mountain View CA USA

ISBN: (纸本)9798350301298

To build Video Question Answering (VideoQA) systems capable of assisting humans in daily activities, seeking answers from long-form videos with diverse and complex events is a must. Existing multi-modal VQA models achieve promising performance on images or short video clips, especially with the recent success of large-scale multimodal pre-training. However, when extending these methods to long-form videos, new challenges arise. On the one hand, using a dense video sampling strategy is computationally prohibitive. On the other hand, methods relying on sparse sampling struggle in scenarios where multievent and multi-granularity visual reasoning are required. In this work, we introduce a new model named Multimodal Iterative Spatial-temporal Transformer (MIST) to better adapt pre-trained models for long-form VideoQA. Specifically, MIST decomposes traditional dense spatial-temporal self-attention into cascaded segment and region selection modules that adaptively select frames and image regions that are closely relevant to the question itself. Visual concepts at different granularities are then processed efficiently through an attention module. In addition, MIST iteratively conducts selection and attention over multiple layers to support reasoning over multiple events. The experimental results on four VideoQA datasets, including AGQA, NExT-QA, STAR, and Env-QA, show that MIST achieves state-of-the-art performance and is superior at efficiency. The code is available at ***/showlab/mist.

关键词： language reasoning vision

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Pyramid Ensemble Structure for High Resolution image Shadow Removal

Pyramid Ensemble Structure for High Resolution Image Shadow ...

引用

2023 IEEE/CVF conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Meituan Group China

ISBN: (纸本)9798350302493

Existing methods for shadow removal in high-resolution images may not be effective due to challenges such as the time-consuming nature of training and the loss of visual data during image cropping or resizing, highlighting the necessity for the development of more efficient methods. In this paper, we propose a novel Pyramid Ensemble Structure (PES) for High Resolution image Shadow Removal. Our approach takes advantage of multiple scales by constructing pyramid inputs that allow for the capturing of a wide range of shadow sizes and shapes. We then train the network in pyramid stages to enhance global information processing. Furthermore, an ensemble of different shadow removal models is employed, and the maximum value is chosen to indicate the least amount of remaining shadow in the output. Experiments on both validation and testing data sets confirm the effectiveness of our method. In the image Shadow Removal Challenge competition, our method obtained 22.36 PSNR score (1st place) and 0.70 SSIM score (2nd place) on the test sets. © 2023 IEEE.

关键词： computer vision

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Bi-directional Feature Fusion Generative Adversarial Network for Ultra-high Resolution Pathological image Virtual Re-staining

Bi-directional Feature Fusion Generative Adversarial Network...

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

作者： Sun, Kexin Chen, Zhineng Wang, Gongwei Iu, Jun Ye, Xiongjun Gang, Yu -Gang Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Shanghai Collaborat Innovat Ctr Intelligent Visua Shanghai Peoples R China Shanghai Qi Zhi Inst Shanghai Peoples R China Peking Univ Peoples Hosp Beijing Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Dept Urol Natl Canc Ctr Canc Hosp Beijing Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Canc Hosp Natl Clin Res Ctr Canc Beijing Peoples R China

ISBN: (纸本)9798350301298

The cost of pathological examination makes virtual re-staining of pathological images meaningful. However, due to the ultra-high resolution of pathological images, traditional virtual re-staining methods have to divide a WSI image into patches for model training and inference. Such a limitation leads to the lack of global information, resulting in observable differences in color, brightness and contrast when the re-stained patches are merged to generate an image of larger size. We summarize this issue as the square effect. Some existing methods try to solve this issue through overlapping between patches or simple post-processing. But the former one is not that effective, while the latter one requires carefully tuning. In order to eliminate the square effect, we design a bi-directional feature fusion generative adversarial network (BFF-GAN) with a global branch and a local branch. It learns the interpatch connections through the fusion of global and local features plus patch-wise attention. We perform experiments on both the private dataset RCC and the public dataset ANHIR. The results show that our model achieves competitive performance and is able to generate extremely real images that are deceptive even for experienced pathologists, which means it is of great clinical significance.

关键词： cell microscopy Medical and biological vision

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