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PointCU: Multiscale Sparse Convolutional Learning for Point Cloud Color Upsampling

PointCU: Multiscale Sparse Convolutional Learning for Point ...

引用

IEEE visual communications and image processing (VCIP)

作者： Birendra Kathariya Anique Akthar Zhu Li Geert Van Der Auwera University of Missouri-Kansas City Kansas City USA Qualcomm San Diego USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Upsampling a sparse point cloud is a common operation in various applications, such as high-quality reconstruction, rendering. Point-cloud geometry upsampling has been extensively studied for this purpose. However, when a sparse point cloud includes attributes, such as color, their upsampling must also be carried out in addition to geometry. Despite the apparent need of such work, not a lot of effective solutions are developed that can work with real world large scale point cloud. In this work, we propose a novel solution called PointCU, which is a sparse convolution learning-based Point cloud Color Upsampling method that enables high-fidelity dense point color reconstruction from sparse point color. The proposed method first prepares multiple representations of a dense point cloud through voxelization at different scales, then transfers the color from sparse to the newly created point clouds including the dense point cloud itself through devoxelization. Then by learning feature on these multiple point cloud representation through sparse convolution neural network (SparseCNN) while also expanding and fusing the feature to higher scale, PointCU achieves an excellent color super-resolving capability. Our experimental results on four times (4x) and eight times (8x) upsampling tasks demonstrate that the color upsampling performance of the proposed method is superior to the previous known color upsampling schemes by a large margin.

关键词： Point cloud compression Geometry Representation learning image resolution image color analysis Convolution visual communication Neural networks Color Rendering (computer graphics)

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Frame Level Content Adaptive λ for Neural Video Compression

Frame Level Content Adaptive λ for Neural Video Compression

引用

IEEE visual communications and image processing (VCIP)

作者： Zhirui Zuo Junqi Liao Xiaomin Song Zheng Liu Huiming Zheng Dong Liu University of Science and Technology of China Sichuan Xinshi Chuangwei Ultra HD Technology Co. Ltd

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Neural video compression (NVC) methods have made significant advances in recent years. In most NVC methods, all frames share the same Rate-Distortion trade-off parameter λ, which might be sub-optimal. Recently, inspired by traditional video codecs' hierarchical quality structure, DCVC-DC proposed allocating periodic weights to λ to equip NVC with the hierarchical quality structure. However, the inspiration from traditional video codecs is designed to complement their complex reference structure. Compared to traditional video codecs, NVC methods' reference structure is much simpler and may not require large fluctuations in their hierarchical quality structure. Moreover, DCVC-DC's fixed hierarchical quality structure ignored the influence of video content. We conduct an elaborate study on the hierarchical quality structure in DCVC-DC, shedding light on the potential for improving compression performance by proposing a content adaptive λ to achieve a more reasonable hierarchical quality structure based on the fixed hierarchical weights. Experimental results demonstrate that the proposed method achieves a better rate-distortion performance than allocating the fixed weights to the fixed λ. On DCVC-DC and DCVC-SDD, we achieved 4.9% and 8.8% bdrate reduction with our method.

关键词： Training image coding Fluctuations visual communication Rate-distortion Video compression Decoding Video codecs Computational complexity Periodic structures

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Optimizing image Classification Using Bag of Features and Support Vector Machines

Optimizing Image Classification Using Bag of Features and Su...

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Mobile Networks and Wireless communications (ICMNWC), IEEE International conference on

作者： K Mahantesh K S Navyashree Devika S Nairy R Asha B Anshitha Department of CSE (DS) RNS Institute of Technology Visvesvaraya Technological University Bengaluru India Department of ECE SJB Institute of Technology Visvesvaraya Technological University Bengaluru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

image categorization is a fundamental task in computer vision, with applications in domains such as object recognition, medical imaging, and autonomous systems. Traditional approaches frequently fail to balance accuracy, computing efficiency, and scalability, particularly when dealing with big and complex datasets. This work presents a novel picture classification strategy that combines the Bag of Features (BoF) model with Support Vector Machines (SVM). The BoF model describes images by extracting local visual characteristics (such as SIFT, SURF, or ORB) from image patches and quantizing them into visual words to create a histogram representation. SVM, a powerful machine learning classifier, is used to classify these histograms, utilizing its capacity to handle high-dimensional, sparse data. Experiments using common image classification datasets show that the BoF-SVM system greatly outperforms previous methods, resulting in higher classification accuracy and lower processing costs. Furthermore, it has superior generalization to previously unseen data and is more resistant to noise and picture changes. The suggested BoF-SVM system produces promising results for boosting both accuracy and efficiency in image classification tasks, with room for further optimization in more complicated and diversified applications

关键词： Support vector machines visualization Histograms Computer vision Accuracy Computational modeling Scalability Feature extraction Vectors image classification

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ST-CLIP: Learning SpatioTemporal CLIP with GraphTransformer for Action Recognition

ST-CLIP: Learning SpatioTemporal CLIP with GraphTransformer ...

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International conference on Signal processing Proceedings (ICSP)

作者： Zhenxing Zheng Jing Lyu Junlong Gong Gaoyun An School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China Institute of Information Science Beijing Jiaotong University Beijing China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Large pretrained models applied to image data have showcased significant progress in visual representation learning. However, the direct application of pretrained image models to video data is insufficient for capturing spatiotemporal context. The challenge lies in effectively transferring image-modal knowledge from web-scale data to the video domain. To enable spatiotemporal modeling, we propose SpatioTemporal CLIP (ST-CLIP) that incorporates a lightweight graph reasoning unit into each encoder of the vision Transformer to efficiently reason about temporal relations between frames. Extensive experiments and ablation studies highlight the competitiveness of ST-CLIP compared to state-of-the-art methods. The proposed model achieves impressive performance on fully-supervised experiments on Kinetics-400.

关键词： Representation learning visualization image recognition Neural networks Signal processing Transformers Data models Cognition Spatiotemporal phenomena Context modeling

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image-Based Indoor Localization Using Smartphone Cameras in Textureless Environment 6

Image-Based Indoor Localization Using Smartphone Cameras in ...

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6th International conference on Information and Computer Technologies, ICICT 2023

作者： Zheng, Ping Qin, Danyang Bai, Jianan Ma, Lin Heilongjiang University Department of Electronics and Communication Engineering Harbin China Heilongjiang University Harbin China Southeast University National Mobile Communications Research Laboratory Nanjing China Heilongjiang University Department of Information and Communication Engineering Harbin China

ISBN: (纸本)9798350300956

In recent years, Location Based Services (LBS) have developed rapidly. There is an urgent need to establish a low-cost and convenient method to get precise positions indoors. Therefore, we choose an easier way of using monocular camera mounted on a mobile terminal to achieve indoor visual positioning. In this paper, two main problems are studied under this condition. Firstly, the feature extraction method is designed to minimize the loss of positioning information while processing quickly in textureless environment. Secondly, the relative direction vector obtained from the epipolar geometry cannot converge at the point to be located due to errors, so a position determination method is proposed to transform the problem of positioning into distance calculation. A WeChat mini program was built for users to obtain location information. Experimental results show that 90% positioning error was less than 0. 5m, and the average error was less than 0.32m. © 2023 IEEE.

关键词： Location based services

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Attention-Based Deep Autoencoder for Hyperspectral image Denoising 1

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6th International conference on Computer Vision and image processing, CVIP 2021

作者： Kumar, Shashi Aetesam, Hazique Saha, Anirban Maji, Suman Kumar Department of Computer Science and Engineering Indian Institute of Technology Patna Patna801103 India

ISBN: (数字)9783031113499

ISBN: (纸本)9783031113482

Hyperspectral image (HSI) denoising is a crucial pre-processing task due to its widespread applications in areas that include geology, medicine, agriculture, surveillance, and the food industry. Denoising HSI data improves high-level vision tasks like classification, object tracking, and video surveillance. In this work, we propose a novel attention-based deep auto-encoder for HSI denoising which learns a mapping from noisy observation to clean the data. To exploit the spatial-spectral information present in HSI data, 3D symmetric convolution and deconvolution are used. The 3D features extracted from 3D convolutional layers (CL) helps the restoration across the depth dimension. However, feature maps obtained from the convolutional layers are localized and are not able to capture long-range dependencies in the data. Furthermore, CL does not differentiate between low and high degradation levels;producing smoothed results in low-textured areas of the image and artifacts in the high-textured regions. To avoid these adverse effects, attention blocks are plugged into the network to focus on more relevant features during the restoration process. Features obtained from attention blocks are fed as symmetric skip connections into the corresponding deconvolution layers. The proposed model is fully convolutional;it can process 3D images of arbitrary dimensions during training and inference. The experimental results show that our proposed model: Attention-based Deep Auto-encoder (AbDAE) outperforms the state-of-the-art methods in terms of visual as well as quantitative results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Relation-aware Semantic Alignment Network for Text-to-image Person Retrieval

Relation-aware Semantic Alignment Network for Text-to-Image ...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yong Wu Rongxi Zhou Hongchao Li Ze Zhou Feifei Wei Min Li Guodui He School of Computer and Information Anhui Normal University Wuhu China Guangxi Academy of Agricultural Sciences Nanning China China Communications Service Guangxi Technical Service Branch Nanning China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Text-to-image Person Retrieval (TIPR) aims to utilize natural language descriptions as queries to retrieve pedestrian images. However, existing methods only concentrated on aligning individual text-image pairs and ignored the specific self-representations within both visible images and textual descriptions of the same identity. This neglects the impact of intra-modal information distribution on TIPR. In this paper, a novel Relation-aware Semantic Alignment Network (RSAN) is proposed to learn reliable and comprehensive semantic visual-textual associations across different modalities. Specifically, A Global Semantic Alignment Matching (GSAM) loss is introduced to enhance the coherence of inter-modality features while preserving intra-modal representations for cross-modal matching. Additionally, an Adapter-assisted Information Aggregation (AIA) module is designed to further complement contextual information fusion between the image features and text embeddings. Extensive experiments conducted on two public benchmark datasets demonstrate the superiority of the proposed RSAN.

关键词： visualization Pedestrians Semantics Natural languages Text to image Coherence Benchmark testing Signal processing Reliability Speech processing

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Dynamic Target Tracking Method of Surveillance Video Based on image processing

Dynamic Target Tracking Method of Surveillance Video Based o...

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communications Technology and Computer Science (ACCTCS), Asia-Pacific conference on

作者： Yiping Chen Fengshan Yuan Department of Computer Science and Engineering Guangzhou Institute of Science and Technology Guangzhou China

In surveillance video, target tracking is an important part. Based on image processing technology, this paper studies a real-time and effective method to collect and recognize camera motion information. Firstly, the influence of visual dead angle and illumination on recognition is analyzed. Secondly, according to the characteristic of background light intensity, the corresponding algorithm is designed to realize the positioning and tracking control strategy of the target and surrounding environment scenery. Finally, the correctness of the method is verified by MATLAB simulation software, so as to obtain a better and scalable scheme, which is more economical and feasible after the occlusion rate is minimized.

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Real-time Learned image Codec on FPGA

Real-time Learned Image Codec on FPGA

引用

IEEE visual communications and image processing (VCIP)

作者： Heming Sun Qingyang Yi Fangzheng Lin Lu Yu Jiro Katto Masahiro Fujita Waseda University Zhejiang University JST PRESTO The University of Tokyo AIST Japan

This demo paper gives a real-time learned image codec on FPGA. By using Xilinx VCU128, the proposed system reaches 720P@30fps codec, which is 7.76x faster than prior work.

ISBN: (纸本)9781665475938

This demo paper gives a real-time learned image codec on FPGA. By using Xilinx VCU128, the proposed system reaches 720P@30fps codec, which is 7.76x faster than prior work.

关键词： Codecs visual communication image processing Real-time systems Field programmable gate arrays

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Self-Supervised image Restoration with Blurry and Noisy Pairs 36

Self-Supervised Image Restoration with Blurry and Noisy Pair...

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36th conference on Neural Information processing Systems (NeurIPS)

作者： Zhang, Zhilu Xu, Rongjian Liu, Ming Yan, Zifei Zuo, Wangmeng Harbin Inst Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781713871088

When taking photos under an environment with insufficient light, the exposure time and the sensor gain usually require to be carefully chosen to obtain images with satisfying visual quality. For example, the images with high ISO usually have inescapable noise, while the long-exposure ones may be blurry due to camera shake or object motion. Existing solutions generally suggest to seek a balance between noise and blur, and learn denoising or deblurring models under either fullor self-supervision. However, the real-world training pairs are difficult to collect, and the self-supervised methods merely rely on blurry or noisy images are limited in performance. In this work, we tackle this problem by jointly leveraging the short-exposure noisy image and the long-exposure blurry image for better image restoration. Such setting is practically feasible due to that short-exposure and long-exposure images can be either acquired by two individual cameras or synthesized by a long burst of images. Moreover, the short-exposure images are hardly blurry, and the long-exposure ones have negligible noise. Their complementarity makes it feasible to learn restoration model in a self-supervised manner. Specifically, the noisy images can be used as the supervision information for deblurring, while the sharp areas in the blurry images can be utilized as the auxiliary supervision information for self-supervised denoising. By learning in a collaborative manner, the deblurring and denoising tasks in our method can benefit each other. Experiments on synthetic and real-world images show the effectiveness and practicality of the proposed method. Codes are available at https://***/cszhilu1998/SelfIR.

关键词： image reconstruction

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