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Adaptive Multi-Gradient Kernels for Handwritting Based Gender Identification

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Adaptive Multi-Gradient Kernels for Handwritting Based Gende...

International Workshop on Frontiers in Handwriting recognition

作者： B. J Navya Palaiahnakote Shivakumara G.C Shwetha Sangheeta Roy D. S. Guru Umapada Pal Tong Lu Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

Handwriting based Gender identification is challenging due to unconstrained handwriting and individual differences in writing. To solve this problem, we propose a new adaptive multi-gradient of Sobel kernels for extracting Adaptive Multi-Gradient Features (AMGF). For extracted text lines, the proposed method finds dominant pixels based on directional symmetry of text pixels given by AMGF. We perform histogram operation for adaptive multi-gradient values extracted corresponding to dominant pixels. The gradient values that give the highest peak in respective histograms is chosen as features. This results in feature vector having four AMGF values. The same vector are generated for successive text lines in each image to study either consistency, which is expected for females or inconsistency, which is expected for males in writing styles. The correlation is estimated based on feature vectors of the first and the successive text lines until converging or diverging criteria is met. If convergence happens, the input document is considered as female else is considered as male. The method is tested on our own dataset, which includes large variations and standard datasets, namely, QUWI, IAM-1+IAM-2 and KHATT, to demonstrate the effectiveness of the proposed method. Experimental results show that the proposed method outperforms the existing methods.

关键词： Feature extraction Kernel Image edge detection Writing Histograms Junctions Laplace equations

Investigate indistinguishable points in semantic segmentation of 3D point cloud

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arXiv 2021年

作者： Xu, Mingye Zhou, Zhipeng Zhang, Junhao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China Shanghai AI Lab Shanghai China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

This paper investigates the indistinguishable points (difficult to predict label) in semantic segmentation for large-scale 3D point clouds. The indistinguishable points consist of those located in complex boundary, points with similar local textures but different categories, and points in isolate small hard areas, which largely harm the performance of 3D semantic segmentation. To address this challenge, we propose a novel Indistinguishable Area Focalization Network (IAF-Net), which selects indistinguishable points adaptively by utilizing the hierarchical semantic features and enhance fine-grained features for points especially those indistinguishable points. We also introduce multi-stage loss to improve the feature representation in a progressive way. Moreover, in order to analyze the segmentation performances of indistinguishable areas, we propose a new evaluation metric called Indistinguishable Points Based Metric (IPBM). Our IAF-Net achieves the comparable results with state-of-the-art performance on several popular 3D point cloud datasets e.g. S3DIS and ScanNet, and clearly outperforms other methods on IPBM. Our code will be available at https://***/MingyeXu/IAF-Net. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

Multi-Gradient Directional Features for Gender Identification

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Multi-Gradient Directional Features for Gender Identificatio...

International Conference on pattern recognition

作者： B.J. Navya G. C. Swetha Palaiahnakote Shivakumara Sangheeta Roy D. S. Guru Umapada Pal Tong Lu Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

Gender identification based on handwriting analysis has received a special attention to researchers in the field of document image analysis as it is useful for several real-time applications like forensic, population counting, etc. In this paper, we explore Multi-Gradient Directional (MGD) features, which provide direction of dominant pixels obtained by Canny edge image, and gradient direction symmetry. The proposed method further performs histogram operation for gradient angle information of dominant pixels of respective multi-gradient directional images to select angles, which contribute to the highest peak. This results in feature vectors. The process of feature vector formation continues for the segmented first, second, and third text lines in each image by male or female. Next, correlation is estimated for the vector of the first line with successive lines until converging or diverging criteria is met. If the convergence happens, a document is considered as by female, else is considered as by male. The method is tested on our own dataset, which includes images of different scripts, writers, papers, pens, and ages, and the standard database QUWI which includes Arabic and English texts, to demonstrate the efficiency of the proposed method. Comparative studies with the state of the art methods show that the proposed method is effective and useful.

关键词： Feature extraction Image edge detection Writing Junctions Histograms Face Support vector machines

Fourier Coefficients for Fraud Handwritten Document Classification through Age Analysis

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Fourier Coefficients for Fraud Handwritten Document Classifi...

International Workshop on Frontiers in Handwriting recognition

作者： K.S. Raghunandan Palaiahnakote Shivakumara B.J. Navya G. Pooja Navya Prakash G. Hemantha Kumar Umapada Pal Tong Lu Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9781509009824

As new digital technologies emerge to improve living style, at the same time, it also lead to increase crimes. Unlike existing approaches that use content of handwriting for fraud/forged document identification, in this paper we propose a novel approach that explores the quality of handwritten documents by considering both foreground and background information to identify whether it is old or new. The proposed approach works based on the fact that if a fraud document is created with some gaps after the original one, the fraud document happened to be a new one and the original happened to be an old one in this work. To identify whether a given handwritten document is old or new with gaps, we propose to divide Fourier coefficients of the input image into positive and negative coefficient images, and then reconstruct respective images to conquer two reconstructed ones. The contrast of the reconstructed images obtained before and after divide-conquer is studied to analyze the ages of the document based on image quality. The proposed approach finds a unique relationship between reconstructed images, obtained before and after divide-conquer, to identify the input image as old or new. To evaluate the proposed approach, we conduct experiments on our own handwritten dataset and a standard database, namely, Google-LIFE magazine. Comparative studies with the existing approaches show that the proposed approach outperforms the existing approaches in terms of classification rate.

关键词： Image reconstruction Image edge detection Ink Printers Forgery Distortion Feature extraction

Suppressing model overfitting for image super-resolution networks

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arXiv 2019年

作者： Feng, Ruicheng Gu, Jinjin 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 School of Science and Engineering Chinese University of Hong Kong Shenzhen Hong Kong Chinese University of Hong Kong Hong Kong

Large deep networks have demonstrated competitive performance in single image super-resolution (SISR), with a huge volume of data involved. However, in real-world scenarios, due to the limited accessible training pairs, large models exhibit undesirable behaviors such as overfitting and memorization. To suppress model overfitting and further enjoy the merits of large model capacity, we thoroughly investigate generic approaches for supplying additional training data pairs. In particular, we introduce a simple learning principle MixUp [43] to train networks on interpolations of sample pairs, which encourages networks to support linear behavior in-between training samples. In addition, we propose a data synthesis method with learned degradation, enabling models to use extra high-quality images with higher content diversity. This strategy proves to be successful in reducing biases of data. By combining these components - MixUp and synthetic training data, large models can be trained without overfitting under very limited data samples and achieve satisfactory generalization performance. Our method won the second place in NTIRE2019 Real SR Challenge. Copyright © 2019, The Authors. All rights reserved.

关键词： Optical resolving power

Activating More Pixels in Image Super-Resolution Transformer

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arXiv 2022年

作者： Chen, Xiangyu Wang, Xintao Zhou, Jiantao Qiao, Yu Dong, Chao State Key Laboratory of Internet of Things for Smart City University of Macau China Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Shanghai Artificial Intelligence Laboratory China ARC Lab Tencent PCG China

Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis. This implies that the potential of Transformer is still not fully exploited in existing networks. In order to activate more input pixels for better reconstruction, we propose a novel Hybrid Attention Transformer (HAT). It combines both channel attention and window-based self-attention schemes, thus making use of their complementary advantages of being able to utilize global statistics and strong local fitting capability. Moreover, to better aggregate the cross-window information, we introduce an overlapping cross-attention module to enhance the interaction between neighboring window features. In the training stage, we additionally adopt a same-task pre-training strategy to exploit the potential of the model for further improvement. Extensive experiments show the effectiveness of the proposed modules, and we further scale up the model to demonstrate that the performance of this task can be greatly improved. Our overall method significantly outperforms the state-of-the-art methods by more than 1dB. Copyright © 2022, The Authors. All rights reserved.

关键词： Pixels

A New RGB Based Fusion for Forged IMEI Number Detection in Mobile Images

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A New RGB Based Fusion for Forged IMEI Number Detection in M...

International Workshop on Frontiers in Handwriting recognition

作者： Palaiahnakote Shivakumara V. Basavaraja Harsha S. Gowda D. S. Guru Umapada Pal Tong Lu Faculty of Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Studies in Computer Science University of Mysore Mysore Karnataka India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

As technology advances to make living comfortable for people, at the same time, different crimes also increase. One such sensitive crime is creating fake International Mobile Equipment Identity (IMEI) for smart mobile devices. In this paper, we present a new fusion based method using R, G and B color components for detecting forged IMEI numbers. To the best of our knowledge, this is the first work for forged IMEI number detection in mobile images. The proposed method first finds variances for R, G and B images of a forged input image to study local changes. The variances are used to derive weights for respective color components. The same weights are convolved with respective pixel values of R, G and B components, which results in the fused image. For the fused image, the proposed method extracts features based on sparsity, the number of connected components, and the average intensity values for edge components in respective R, G and B components, which gives six features. The proposed method finds absolute difference between fused and input images, which gives feature vector containing six difference values. The proposed method constructs templates based on samples chosen randomly. Feature vectors are compared with the templates for detecting forged IMEI numbers. Experiments are conducted on our own dataset and standard datasets to evaluate the proposed method. Furthermore, comparative studies with the related existing methods show that the proposed method outperforms the existing methods.

关键词： Feature extraction Image color analysis Image edge detection Forgery Software Printers Image segmentation

Conditional sequential modulation for efficient global image retouching

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arXiv 2020年

作者： He, Jingwen Liu, Yihao 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 SIAT Branch Shenzhen Institute of Artificial Intelligence Robotics for Society Korea Republic of University of Chinese Academy of Sciences China

Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation. Practically, photo retouching can be accomplished by a series of image processing operations. In this paper, we investigate some commonly-used retouching operations and mathematically find that these pixel-independent operations can be approximated or formulated by multi-layer perceptrons (MLPs). Based on this analysis, we propose an extremely light-weight framework - Conditional Sequential Retouching Network (CSRNet) - for efficient global image retouching. CSRNet consists of a base network and a condition network. The base network acts like an MLP that processes each pixel independently and the condition network extracts the global features of the input image to generate a condition vector. To realize retouching operations, we modulate the intermediate features using Global Feature Modulation (GFM), of which the parameters are transformed by condition vector. Benefiting from the utilization of 1×1 convolution, CSRNet only contains less than 37k trainable parameters, which is orders of magnitude smaller than existing learning-based methods. Extensive experiments show that our method achieves state-of-the-art performance on the benchmark MIT-Adobe FiveK dataset quantitively and qualitatively. Code is available at https://***/hejingwenhejingwen/CSRNet. Copyright © 2020, The Authors. All rights reserved.

关键词： Pixels

UDC-UNet: Under-Display Camera Image Restoration via U-shape Dynamic Network

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arXiv 2022年

作者： Liu, Xina Hu, Jinfan Chen, Xiangyu Dong, Chao Shenzhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Shanghai China University of Macau Shanghai China Shanghai AI Laboratory Shanghai China

Under-Display Camera (UDC) has been widely exploited to help smartphones realize full-screen displays. However, as the screen could inevitably affect the light propagation process, the images captured by the UDC system usually contain flare, haze, blur, and noise. Particularly, flare and blur in UDC images could severely deteriorate the user experience in high dynamic range (HDR) scenes. In this paper, we propose a new deep model, namely UDC-UNet, to address the UDC image restoration problem with an estimated PSF in HDR scenes. Our network consists of three parts, including a U-shape base network to utilize multi-scale information, a condition branch to perform spatially variant modulation, and a kernel branch to leverage the prior knowledge of the PSF. According to the characteristics of HDR data, we additionally design a tone mapping loss to stabilize network optimization and achieve better visual quality. Experimental results show that the proposed UDC-UNet outperforms the state-of-the-art methods in quantitative and qualitative comparisons. Our approach won second place in the UDC image restoration track of the MIPI challenge. Codes and models are available at https://***/J-FHu/UDCUNet. © 2022, CC BY.

关键词： Image reconstruction