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5th Asian Conference on pattern recognition, ACPR 2019

作者： Grouver, Anaica Shivakumara, Palaiahnakote Kaljahi, Maryam Asadzadeh Chetty, Bhaarat Pal, Umapada Lu, Tong Hemantha Kumar, G. Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Google Developers Group NASDAQ Bangalore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China University of Mysore MysoreKarnataka India

ISBN: (纸本)9783030412982

Due to change in mindset and living style of humans, the numbers of diversified marriages are increasing all around the world irrespective of race, color, religion and culture. As a result, it is challenging for research community to identify multi type family photos, namely, normal family (family of the same race, religion or culture), multi-culture family (family of different culture, religion or race) from the family and non-family photos (images with friends, colleagues, etc.). In this work, we present a new method that combines spatial density information with phase angle for multi-type family photo classification. The proposed method uses three facial key points, namely, left-eye, right-eye and nose, for the features which are based on color, roughness and wrinkleless of faces, these are prominent for extracting unique cues for classification. The correlations between features of Left & Right Eyes, Left Eye & Nose and Right Eye & Nose are computed for all the faces in an image. This results in feature vectors for respective spatial density and phase angle information. Furthermore, the proposed method fuses the feature vectors and feeds them to the Convolutional Neural Network (CNN) for the classification of the above-three class problem. Experiments conducted on our database which contains three classes, namely, multi-cultural, normal and non-family images and the benchmark databases (due to Maryam et al. and Wang et al.) which contain two class-family and non-family images, show that the proposed method outperforms the existing methods in terms of classification rate for all the three databases. © 2020, Springer Nature Switzerland AG.

关键词： Database systems

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3D reconstruction based on light field information

3D reconstruction based on light field information

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International Conference on Information and Automation (ICIA)

作者： Yan Zhou Huiwen Guo Ruiqing Fu Guoyuan Liang Can Wang Xinyu Wu Shenzhen Key Lab for Computer Vision and Pattern Recognition Chinese Academy of Sciences Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Dept. Mechanical and Automation Engineering The Chinese University of Hong Kong

ISBN: (纸本)9781467391054

As an important branch of computational photography, light field photography combines the hardware design of optical system with key algorithm of signal processing quite well. Unlike traditional photography which can only record light ray's two-dimensional position, light field photography system can record four-dimensional position and direction. Therefore, much more image information can be obtained from light field photography. With the development of 3D display technology, light field based autofocus and 3D display technology is becoming more and more popular. In this paper, a light field based new 3D reconstruction algorithm for buildings and office environment is proposed by applying Wavelet Transform and SVM (Support Vector Machine) model to obtain the image focusing quality assessment, along with the Mean Shift Algorithm and Random Field Model to get the depth map of the scene. Firstly, light field image is captured by using a light field camera. Secondly, we use frequency domain digital refocus algorithm to manipulate light field image and obtain several serialized refocused images with different focus. Thirdly, wavelet features are extracted from each refocused image, and then an image focusing quality assessment is conducted by using RBF (Radial Basis Function) kernel based SVM model. Finally, we use Mean Shift algorithm to realize color clustering of the original light field image, and then build MRF (Markov Random Field) Model with color nodes. By iterating the likelihood depth result obtained from real scenario depth calibrations according to image focusing quality assessment, finally the depth map of the scene is reconstructed. Experiments are conducted to prove the feasibility of the proposed 3D reconstructed algorithm based on light field. And the experimental results on real datasets demonstrate good performance of this algorithm.

关键词： Three-dimensional displays Cameras Photography Lenses Wavelet transforms Focusing

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LTD: Local Ternary Descriptor for Image Matching

LTD: Local Ternary Descriptor for Image Matching

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IEEE International Conference on Information and Automation

作者： Yongqiang Gao Yu Qiao Zhifeng Li Chunjing Xu Shenzhen Key Lab. of Comput. Vision &amp Pattern Recognition Shenzhen Inst. of Adv. Technol. Chinese Univ. of Hong Kong Shenzhen China|c|

ISBN: (纸本)9781479913329

Binary descriptors are receiving extensive research interests due to their storage and computation efficiency. A good binary descriptor should deliver sufficient information as well as be robust to image deformation and distortion. Recently, Calonder et al proposed Binary Robust Independent Elementary Features (BRIEF), which showed good performance in image matching. In this paper, we extend BRIEF to a Local Ternary Descriptor (LTD). Compared with BRIEF, LTD introduces a threshold to describe the difference of two pixels into three values. Our ternary descriptor can deliver more discriminative information than BRIEF while being robust to image deformation. We examine the key-point matching performance of LTD on several public datasets. The experimental results exhibit that LTD outperforms BRIEF.

关键词： local ternary descriptor descriptors binary pattern Hamming distance

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WaveDM: Wavelet-Based Diffusion Models for Image Restoration

arXiv

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arXiv 2023年

作者： Huang, Yi Huang, Jiancheng Liu, Jianzhuang Yan, Mingfu Dong, Yu Lyu, Jiaxi Chen, Chaoqi Chen, Shifeng Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100039 China The University of Hong Kong Hong Kong

Latest diffusion-based methods for many image restoration tasks outperform traditional models, but they encounter the long-time inference problem. To tackle it, this paper proposes a Wavelet-Based Diffusion Model (WaveDM). WaveDM learns the distribution of clean images in the wavelet domain conditioned on the wavelet spectrum of degraded images after wavelet transform, which is more time-saving in each step of sampling than modeling in the spatial domain. To ensure restoration performance, a unique training strategy is proposed where the low-frequency and high-frequency spectrums are learned using distinct modules. In addition, an Efficient Conditional Sampling (ECS) strategy is developed from experiments, which reduces the number of total sampling steps to around 5. Evaluations on twelve benchmark datasets including image raindrop removal, rain steaks removal, dehazing, defocus deblurring, demoiréing, and denoising demonstrate that WaveDM achieves state-of-the-art performance with the efficiency that is comparable to traditional one-pass methods and over 100× faster than existing image restoration methods using vanilla diffusion models. The code is available at https://***/stayalive16/WaveDM. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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Visual compositional learning for Human-Object interaction detection

arXiv

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arXiv 2020年

作者： Hou, Zhi Peng, Xiaojiang Qiao, Yu Tao, Dacheng UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering University of Sydney DarlingtonNSW2008 Australia Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Human-Object interaction (HOI) detection aims to localize and infer relationships between human and objects in an image. It is challenging because an enormous number of possible combinations of objects and verbs types forms a long-tail distribution. We devise a deep Visual Compositional Learning (VCL) framework, which is a simple yet efficient framework to effectively address this problem. VCL first decomposes an HOI representation into object and verb specific features, and then composes new interaction samples in the feature space via stitching the decomposed features. The integration of decomposition and composition enables VCL to share object and verb features among different HOI samples and images, and to generate new interaction samples and new types of HOI, and thus largely alleviates the long-tail distribution problem and benefits low-shot or zero-shot HOI detection. Extensive experiments demonstrate that the proposed VCL can effectively improve the generalization of HOI detection on HICO-DET and V-COCO and outperforms the recent state-of-the-art methods on HICO-DET. Code is available at https://***/zhihou7/VCL. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Blind Super-Resolution With Iterative Kernel Correction

Blind Super-Resolution With Iterative Kernel Correction

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IEEE/CVF Conference on computer vision and pattern recognition

作者： Jinjin Gu Hannan Lu Wangmeng Zuo Chao Dong The School of Science and Engineering The Chinese University of Hong Kong School of Computer Science and Technology Harbin Institute of Technology ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

ISBN: (纸本)9781728132945

Deep learning based methods have dominated super-resolution (SR) field due to their remarkable performance in terms of effectiveness and efficiency. Most of these methods assume that the blur kernel during downs ampling is predefined/known (e.g., bicubic). However, the blur kernels involved in real applications are complicated and unknown, resulting in severe performance drop for the advanced SR methods. In this paper, we propose an Iterative Kernel Correction (IKC) method for blur kernel estimation in blind SR problem, where the blur kernels are unknown. We draw the observation that kernel mismatch could bring regular artifacts (either over-sharpening or over-smoothing), which can be applied to correct inaccurate blur kernels. Thus we introduce an iterative correction scheme - IKC that achieves better results than direct kernel estimation. We further propose an effective SR network architecture using spatial feature transform (SFT) layers to handle multiple blur-kernels, named SFTMD. Extensive experiments on synthetic and real-world images show that the proposed IKC method with SFTMD can provide visually favorable SR results and the state-of-the-art performance in blind SR problem.

关键词： Seeds Kernel SAVANNAH RIVER OPERATIONS OFFICE Strontium super-resolution Blind Iterative

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Context-transformer: Tackling object confusion for few-shot detection

arXiv

引用

arXiv 2020年

作者： Yang, Ze Wang, Yali Chen, Xianyu Liu, Jianzhuang Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Huawei Noah’s Ark Lab. SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors. A popular approach to handle this problem is transfer learning, i.e., fine-tuning a detector pretrained on a source-domain benchmark. However, such transferred detector often fails to recognize new objects in the target domain, due to low data diversity of training samples. To tackle this problem, we propose a novel Context-Transformer within a concise deep transfer framework. Specifically, Context-Transformer can effectively leverage source-domain object knowledge as guidance, and automatically exploit contexts from only a few training images in the target domain. Subsequently, it can adaptively integrate these relational clues to enhance the discriminative power of detector, in order to reduce object confusion in few-shot scenarios. Moreover, Context-Transformer is flexibly embedded in the popular SSD-style detectors, which makes it a plug-and-play module for end-to-end few-shot learning. Finally, we evaluate Context-Transformer on the challenging settings of few-shot detection and incremental few-shot detection. The experimental results show that, our framework outperforms the recent state-of-the-art approaches. The codes are available at https://***/Ze-Yang/ContextTransformer. Copyright © 2020, The Authors. All rights reserved.

关键词： Object detection

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A New DCT-FFT Fusion Based Method for Caption and Scene Text Classification in Action Video Images 2nd

A New DCT-FFT Fusion Based Method for Caption and Scene Text...

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2nd International Conference on pattern recognition and Artificial Intelligence, ICPRAI 2020

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Manna, Suvojit Pal, Umapada Lu, Tong Blumenstein, Michael Faculty of Computer Science and Information Technology University of Malayasia Kuala Lumpur Malaysia Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China University of Technology Sydney Ultimo Australia

ISBN: (纸本)9783030598297

Achieving better recognition rate for text in video action images is challenging due to multi-type texts with unpredictable backgrounds. We propose a new method for the classification of captions (which is edited text) and scene texts (which is part of an image in video images of Yoga, Concert, Teleshopping, Craft, and Recipe classes). The proposed method introduces a new fusion criterion-based on DCT and Fourier coefficients to extract features that represent good clarity and visibility of captions to separate them from scene texts. The variances for coefficients of corresponding pixels of DCT and Fourier images are computed to derive the respective weights. The weights and coefficients are further used to generate a fused image. Furthermore, the proposed method estimates sparsity in Canny edge image of each fused image to derive rules for classifying caption and scene texts. Lastly, the proposed method is evaluated on images of five above-mentioned action image classes to validate the derived rules. Comparative studies with the state-of-the-art methods on the standard databases show that the proposed method outperforms the existing methods in terms of classification. The recognition experiments before and after classification show that the recognition performance rate improves significantly after classification. © 2020, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Efficient Image Super-Resolution using Vast-Receptive-Field Attention

arXiv

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arXiv 2022年

作者： Zhou, Lin Cai, Haoming Gu, Jinjin Li, Zheyuan Liu, Yingqi Chen, Xiangyu Qiao, Yu Dong, Chao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Shanghai AI Laboratory Shanghai China The University of Sydney Australia University of Macau China

The attention mechanism plays a pivotal role in designing advanced super-resolution (SR) networks. In this work, we design an efficient SR network by improving the attention mechanism. We start from a simple pixel attention module and gradually modify it to achieve better super-resolution performance with reduced parameters. The specific approaches include: (1) increasing the receptive field of the attention branch, (2) replacing large dense convolution kernels with depth-wise separable convolutions, and (3) introducing pixel normalization. These approaches paint a clear evolutionary roadmap for the design of attention mechanisms. Based on these observations, we propose VapSR, the VAst-receptive-field Pixel attention network. Experiments demonstrate the superior performance of VapSR. VapSR outperforms the present lightweight networks with even fewer parameters. And the light version of VapSR can use only 21.68% and 28.18% parameters of IMDB and RFDN to achieve similar performances to those networks. The code and models are available at https://***/zhoumumu/VapSR. Copyright © 2022, The Authors. All rights reserved.

关键词： Pixels

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Neighbourhood-guided feature reconstruction for occluded person re-identification

arXiv

引用

arXiv 2021年

作者： Yu, Shijie Chen, Dapeng Zhao, Rui Chen, Haobin Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China SenseTime Group Limited Shanghai AI Lab Shanghai China

Person images captured by surveillance cameras are often occluded by various obstacles, which lead to defective feature representation and harm person re-identification (Re-ID) performance. To tackle this challenge, we propose to reconstruct the feature representation of occluded parts by fully exploiting the information of its neighborhood in a gallery image set. Specifically, we first introduce a visible part-based feature by body mask for each person image. Then we identify its neighboring samples using the visible features and reconstruct the representation of the full body by an outlierremovable graph neural network with all the neighboring samples as input. Extensive experiments show that the proposed approach obtains significant improvements. In the large-scale Occluded- DukeMTMC benchmark, our approach achieves 64.2% mAP and 67.6% rank-1 accuracy which outperforms the state-of-the-art approaches by large margins, i.e.,20.4% and 12.5%, respectively, indicating the effectiveness of our method on occluded Re-ID problem. Copyright © 2021, The Authors. All rights reserved.

关键词： Security systems

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