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Cardinal color fusion network for single image haze removal

MACHINE vision AND APPLICATIONS 2019年第2期30卷 231-242页

作者： Dudhane, Akshay Murala, Subrahmanyam Indian Inst Technol Ropar Dept Elect Engn CVPR Lab Rupnagar India

Haze during bad weather degrades visibility of the scene drastically. Degradation of the scene visibility varies concerning the transmission map (TrMap) of the scene. Estimation of accurate TrMap is a key step to reconstruct the haze-free scene. Previous approaches, DehazeNet (Cai et al. in IEEE Trans image Process 25(11):5187-5198, 2016), MSCNN (Ren et al., in: European conference on computer vision, Springer, New York, 2016), overcome the drawbacks of handcrafted features. However, they fail to recover the haze-free scene without color distortion. In this paper, cardinal color (red, green and blue) fusion network is proposed to estimate the robust TrMap. Proposed network recovers haze-free scene without color distortion. In the first stage, it generates multi-channel depth maps using a novel parallel convolution filter bank. The second stage comprises of multi-scale convolution filter bank which extracts the scale-invariant features. Further, spatial pooling followed by convolution filter estimates the robust scene transmission map. In the last stage, estimated TrMap is given to the optical model to recover the haze-free scene. Three benchmark datasets namely D-HAZY (Ancuti et al., in: 2016 IEEE international conference on image processing (ICIP), 2016), imageNet (Deng et al., in: IEEE conference on computer vision and pattern recognition, 2009 (CVPR 2009), 2009) and a set of real-world hazy scenes are used to analyze the effectiveness of the proposed approach. Structural similarity index, mean square error and peak signal-to-noise ratio are used to evaluate the proposed network for single image haze removal. Further, the effect of haze on object detection is analyzed and robust cascaded pipeline, i.e., haze removal followed by Faster RCNN is proposed for object detection in the hazy environment. Performance analysis shows that the proposed network estimates robust TrMap and recovers haze-free scene without color distortion.

关键词： Haze removal CNN Color fusion

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Color Beaver: Bounding Illumination Estimations for Higher Accuracy 14

Color Beaver: Bounding Illumination Estimations for Higher A...

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14th international Joint conference on computer vision, Imaging and computer Graphics Theory and Applications (VISAPP)

作者： Koscevic, Karlo Banic, Nikola Loncaric, Sven Univ Zagreb Fac Elect Engn & Comp Image Proc Grp Dept Elect Syst & Informat Proc Zagreb 10000 Croatia

ISBN: (纸本)9789897583544

The image processing pipeline of most contemporary digital cameras performs illumination estimation in order to remove the influence of illumination on image scene colors. In this paper an experiment is described that examines some of the basic properties of illumination estimation methods for several Canon's camera models. Based on the obtained observations, an extension to any illumination estimation method is proposed that under certain conditions alters the results of the underlying method. It is shown that with statistics-based methods as underlying methods the proposed extension can outperform camera's illumination estimation in terms of accuracy. This effectively demonstrates that statistics-based methods can still be successfully used for illumination estimation in digital cameras. The experimental results are presented and discussed. The source code is available at https://***/ipg/resources/color_constancy.

关键词： Chromaticity Color Constancy Genetic Algorithm Illumination Estimation image Enhancement White Balancing

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RAIN STREAKS REMOVAL FOR SINGLE image VIA DIRECTIONAL TOTAL VARIATION REGULARIZATION 26

RAIN STREAKS REMOVAL FOR SINGLE IMAGE VIA DIRECTIONAL TOTAL ...

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26th IEEE international conference on image processing (ICIP)

作者： Wang, Yugang Huang, Ting-Zhu Zhao, Xi-Le Deng, Liang-Jian Jiang, Tai-Xiang Univ Elect Sci & Technol China Res Ctr Image & Vis Comp Sch Math Sci Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538662496

images captured in rainy conditions are often corrupted by unexpected rain streaks, which severely degrade the performance of subsequent processes in outdoor computer vision systems. In this paper, we exploit the directional smoothness of rain streaks for the single-image rain streaks removal and propose a convex model that uses the directional total variation (DTV) to characterize the smoothness of rain streaks in arbitrary orientations. The proposed model consists of four terms: the fidelity term, the l(1) norm for the sparsity of rain streaks, and two DTV regularization terms for the directional smoothness and the piecewise smoothness of rain streaks and rain-free backgrounds, respectively. To solve the proposed model, we develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) framework. Extensive experimental results on both synthetic and real rainy images show that our method outperforms the recent state-of-the-art methods visually and quantitatively.

关键词： Single-image rain streaks removal directional total variation (DTV) alternating direction method of multipliers

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Cardinal color fusion network for single image haze removal

Cardinal color fusion network for single image haze removal

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18th IEEE Winter conference on Applications of computer vision (WACV)

作者： Dudhane, Akshay Murala, Subrahmanyam Indian Inst Technol Ropar Dept Elect Engn CVPR Lab Rupnagar India

关键词： Haze removal CNN Color fusion

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Application of Fuzzy Logic in the Edge Detection of Real Pieces in Controlled Scenarios 18th

Application of Fuzzy Logic in the Edge Detection of Real Pie...

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18th Mexican international conference on Artificial Intelligence (MICAI)

作者： Vargas-Proa, Jose Daniel Garcia-Martinez, Carlos Fabian Cano-Lara, Miroslava Rostro-Gonzalez, Horacio Inst Tecnol Super Irapuato Dept Mecatron Irapuato Gto Mexico Univ Guanajuato Dept Elect DICIS Salamanca Gto Mexico

ISBN: (纸本)9783030337490;9783030337483

Industrial processes such as manufacturing and machining parts, fault detection and quality control are some of the areas of study that encompass computational vision techniques, image processing and currently fuzzy logic. Particularly, the edge detection of objects in captured images is a technique widely used in industrial automated systems. In this work, we propose a technique for edge detection in digital images obtained from real pieces based on fuzzy logic. The fuzzy inference model works with 18 Mamdani type rules and was built with 8 input variables and one output variable. It is, the processing of the image was performed under the conditions of the lighting scenario, background and the color of the piece. The performance of the algorithm was evaluated on several images captured from different work environments and it was compared with traditional computer vision methods using gradient operators. The use of fuzzy logic in image processing expands the possibilities to solve a problem and provides more answers over the restrictions of classical methods.

关键词： Fuzzy inference computer vision Edge detection

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A benchmark dataset of online handwritten gurmukhi script words and numerals 4th

A benchmark dataset of online handwritten gurmukhi script wo...

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4th international conference on computer vision and image processing, cvip 2019

作者： Singh, Harjeet Sharma, R.K. Kumar, Rajesh Verma, Karun Kumar, Ravinder Kumar, Munish Chitkara University Institute of Engineering and Technology Punjab India Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala147004 India Department of Computational Sciences Maharaja Ranjit Singh Punjab Technical University Bathinda151001 India

ISBN: (纸本)9789811540172

This paper presents an online handwritten benchmark dataset (OHWR-Gurmukhi) for Gurmukhi script. TIET, Patiala released the unconstrained online handwriting databases, OHWR-GNumerals and OHWR-GScript, which contain isolated strokes samples produced by 190 writers. The OHWR-GNumerals covers 10 stroke classes and OHWR-GScript covers 95 stroke classes to represent the Gurmukhi character set. For data collection, two data sets of Gurmukhi words have been finalized after having a consultation with language experts in order to collect the balanced stroke samples. The preprocessing methods used to prepare these datasets include: size normalization, removing duplicate points, interpolating missing points and re-sampling. The purpose of this benchmark is to create a common platform and make the benchmark dataset publically available for research endeavors in the area of online handwriting recognition. The dataset is available as supplement at https://***/view/ohwr-gurmukhi-script/. © Springer Nature Singapore Pte Ltd 2020.

关键词： Character sets

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Coin Classification in a Complex Environment 10

Coin Classification in a Complex Environment

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10th international conference on Graphics and image processing (ICGIP)

作者： Chang, Huanhuan Wei, Zhiqiang Huang, Lei Nie, Jie Zhang, Wenfeng Wang, Lu Ocean Univ China Qingdao 266000 Shandong Peoples R China

ISBN: (数字)9781510628298

ISBN: (纸本)9781510628298

Coin classification automatically plays important roles in many applications, e.g., vending systems. Glossy reflection is one of the key factor that affect the performance of vision-based coin classification, especially in a complex environment. In this paper, we propose a novel method for robust coin classification. Contrary to the previous method, we get the glossy area first. Edge features and texture features are used in glossy area detection. Then the deep learning features are extracted based on non-glossy area instead of the whole coin image. Finally, the coin classification results are got from the VGG nets scheme. Comprehensive experiments show that our method is robust under various complex environments. The comparison experiments demonstrate that our method can outperform the state-of-the-art method. Our method achieves 95.80% accuracy.

关键词： coin classification deep learning mirror reflection computer vision glossy area

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Shrimps Classification Based on Multi-layer Feature Fusion 10

Shrimps Classification Based on Multi-layer Feature Fusion

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10th international conference on Graphics and image processing (ICGIP)

作者： Zhang, Xiaoxue Wei, Zhiqiang Huang, Lei Ji, Xiaopeng Ocean Univ China Qingdao 266000 Shandong Peoples R China

ISBN: (数字)9781510628298

ISBN: (纸本)9781510628298

This paper aims to highlight vision related tasks centered on "shrimps". With the further study of computer vision of marine life, we show that "shrimps" has been largely neglected in comparison to other objects. In image classification, the degree of visual separation between different shrimp categories is highly uneven, the appearance of some categories in same genus is very similar, and it is more difficult to distinguish than others. Based on the classification model of traditional convolutional neural network, this paper presents a method of merging shallow and deep features extracting feature maps from different levels according to the characteristics of shrimp. In order to facilitate future shrimps-related research, we present our on-going effort in collecting a dataset in this paper, "ShrimpX", that covers not only shrimps and lobsters living in the sea, but also some freshwater shrimps. The "ShrimpX" dataset contains a variety of shrimp images crawled from image search engines. Experimental results on the "ShrimpX" dataset demonstrate that the proposed method can effectively improve the accuracy.

关键词： Convolutional Neural Network (CNN) image classification deep feature shallow feature multi-layer feature fusion

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VEDI: vision Exploitation for Data Interpretation 20th

VEDI: Vision Exploitation for Data Interpretation

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20th international conference on image Analysis and processing (ICIAP)

作者： Farinella, G. M. Signorello, G. Battiato, S. Furnari, A. Ragusa, F. Leonardi, R. Ragusa, E. Scuderi, E. Lopes, A. Santo, L. Samarotto, M. Univ Catania IPLAB Dept Math & Comp Sci Catania Italy Univ Catania CUTGANA Catania Italy Xenia Gest Documentale Srl Xenia Progetti Srl Aci Castello Italy

ISBN: (纸本)9783030306458;9783030306441

We present VEDI (vision Exploitation for Data Interpretation), an integrated system to jointly assist the visitors of cultural sites and provide meaningful statistics about the visits to the managers of the sites. To address both goals, VEDI includes a wearable assistant (implemented through a wearable device such as HoloLens) which leverages computer vision algorithms to understand where the user is and what they are paying attention to. At the visitor's end, such information is leveraged to augment the visit by displaying additional information on the observed points of interest, helping the visitors to navigate the site and suggesting what to see next. Concurrently, a back-end extracts high-level behavioral information from the captured video content which is used to provide the site manager with meaningful statistics and performance indexes on the cultural site. Experiments show that VEDI achieves good results on both the indoor and outdoor cultural sites considered for the experimentation.

关键词： First person vision Egocentric vision AI for cultural sites

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Polyp Detection in Gastrointestinal images using Faster Regional Convolutional Neural Network 14

Polyp Detection in Gastrointestinal Images using Faster Regi...

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14th international Joint conference on computer vision, Imaging and computer Graphics Theory and Applications (VISAPP)

作者： Duran-Lopez, Lourdes Luna-Perejon, Francisco Amaya-Rodriguez, Isabel Civit-Masot, Javier Civit-Balcells, Anton Vicente-Diaz, Saturnino Linares-Barranco, Alejandro Univ Seville Robot & Comp Technol Lab Av Reina Mercedes S-N Seville Spain

ISBN: (纸本)9789897583544

Colorectal cancer is the third most frequently diagnosed malignancy in the world. To prevent this disease, polyps, the principal precursor, are removed during a colonoscopy. Automatic detection of polyps in this technique could play an important role to assist doctors for achieving an accurate diagnosis. In this work, we apply a state-of-the-art Deep Learning algorithm called Faster Regional Convolutional Neural Network to each colonoscopy frame in order to detect the presence of polyps. The proposed detection system contains two main stages: (1) processing of the colonoscopy frames for training and testing datasets generation, where artifacts are extracted and the number of images in the dataset is augmented;and (2) the Neural Network model, which performs feature extraction over the frames in order to detect polyps within the frames. After training the algorithm under different conditions, our result shows that the proposed system detection has a precision of 80.31%, a recall of 75.37%, an accuracy of 71.99% and a specificity of 65.70%.

关键词： Polyp Colonoscopy Deep Learning image Analysis Faster Regional Convolutional Neural Network

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