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RIBBONS: Rapid Inpainting Based on Browsing of Neighborhood Statistics 26

RIBBONS: Rapid Inpainting Based on Browsing of Neighborhood ...

引用

26th Iranian conference on Electrical Engineering (ICEE)

作者： Akbari, Mojtaba Mohrekesh, Majid Karimi, Nader Samavi, Shadrokh Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran

ISBN: (纸本)9781538649169

image inpainting refers to filling missing places in images using neighboring pixels. It also has many applications in different tasks of image processing. Most of these applications enhance the image quality by significant unwanted changes or even elimination of some existing pixels. These changes require considerable computational complexities which in turn results in remarkable processing time. In this paper, we propose a fast inpainting algorithm called RIBBONS based on the selection of patches around each missing pixel. Patch selection would accelerate the execution speed and the capability of online frame inpainting in the video. The applied cost-function is a combination of statistical and spatial features in all neighboring pixels. We evaluate some candidate patches using the proposed cost function and minimize it to achieve the final patch. Experimental results show the higher speed of RIBBONS method in comparison with previous methods while being comparable in terms of PSNR and SSIM for the images in MISC dataset.

关键词： image inpainting Patch selection Object removing image enhancement online inpainting

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Automated textural features based skin disease diagnosis system

Automated textural features based skin disease diagnosis sys...

引用

IEEE International conference on Signal processing, Computing and Control (ISPCC)

作者： Khushmeen Kaur Brar Ashima Kalra Piyush Samant Chandigarh engineering college Mohali India

ISBN: (纸本)9781728139890

Skin texture analysis is one of the demanding issues in the field of therapeutic diagnosis. Various categories of skin diseases are distressing human life like skin dryness, fungus, and allergic indications. The purpose of this paper is to examine the skin disease using texture analysis of skin image and by comparing the test image to a definite images or reference images. In the accessible situation, the skin images are investigated in frequency domain. However, it is scrutinized that the skin color in texture images does not vary over a wide range. Hence, the histogram profile of the skin texture remains almost flat. In the proposed work, we have shifted the skin texture analysis towards the gray level profile analysis. In the proposed thesis work, Gray Level Co-occurrence Matrix of the skin image is computed. The GLCM functions characterize the texture of an image by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in an image, creating a GLCM, and then extracting statistical measures from this matrix. Further, the image entropy and energies are also computed in order to correlate the skin symptoms to the skin texture images. The segmentation performance is evaluated maneuvering six performance metrics that are accuracy(ACC), sensitivity(SE), specificity(SP), jaccard index(JA), dice co-efficient(DI) and the divergence value(DV). The application on the PH2 dataset acquired good segmentation results with JA value of 89.88% of Fine Gaussian SVM classifier. Better results are achieved with DV of 10.3% and DI of 94.67% by Fine Gaussian SVM classifier. Promising results with sensitivity of 97.26%, specificity of 84.61% maneuvering RUS Boosted Tree and Fine Gaussian SVM classifier respectively are achieved. The accuracy of 91% is achieved by different classifiers namely Quadratic SVM, Cosine KNN, Boosted Tree, and Fine Gaussian SVM classifiers respectively. Fine Gaussian SVM classifier outperformed all other classif

关键词： diseases entropy feature extraction frequency-domain analysis image classification image colour analysis image texture matrix algebra medical image processing nearest neighbour methods skin statistical analysis support vector machines trees (mathematics) Support Vector Network Frequency-domain analysis skin texture image colour analysis image texture Medical image processing Skin Diseases matrix algebra images image classification Classifiers Feature extraction nearest neighbor algorithm entropy epidermis (animal)

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A Painting Artist Recognition System Based on image processing and Hierarchical SVM

A Painting Artist Recognition System Based on Image Processi...

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International conference on Knowledge-Based Engineering and Innovation (KBEI)

作者： Mohammad M. AlyanNezhadi Hossein Dabbaghan Samira Moghani Majid Forghani Department of computer engineering Shahrood Non-profit and Non-government Higher Edu. Inst Shahrood Iran Institute of natural sciences and mathematics Ural Federal University Yekaterinburg Russia

Over the past years, forgery paintings of famous artists have been sold as original. In order to spot the fake painting, the experts make the decision based on personal experience and with the help of examining some characteristics of the painting and painter. Applying the image processing methods to artwork can reduce the need of the expert and provide quick and reliable results to recognize the originality of the artwork. In this paper, the proposed method is able to identify the painter of artwork using image processing and data mining techniques. The method consists of two typical main stages, feature extraction, and classification. In the feature extraction, 11 statistical features are extracted from each image. These features have been selected in such a way that maximize the distinction of painters. In the second step, the painters are identified by hierarchical classification. In order to evaluate the performance of proposed method, it has applied to a collection of 348 paintings from eight Iranian artists. The method has been able to identify the artwork painter with the accuracy about 84.21%.

关键词： Feature extraction Painting Support vector machines Databases Training image color analysis

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No-Reference Stereoscopic image Quality Algorithm Based on Features on DCT Domain

No-Reference Stereoscopic Image Quality Algorithm Based on F...

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Control Technology and Applications (CCTA),

作者： Tian Liu Yuxia Sheng Li Chai Jingxin Zhang Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan University of Science and Technology Wuhan China Faculty of Engineering and Industrial Sciences Swinburne University of Technology VIC Australia

Existing image quality assessment methods can not extract stereoscopic features effectively. In this paper, a no-reference quality assessment method is proposed for stereoscopic images based on DCT domain features. The main steps of the proposed methods areas follows: (1)Synthesize the left and right images to two “cyclopean”maps respectively according to the binocular fusion characteristics and the binocular rivalry characteristics. (2)Extract the statistical features in DCT domain by using normalized DCT coefficient of the distorted image pairs and the obtained two “cyclopean”maps. (3)Use support vector regression(SVR) to predict the objective scores of stereoscopic images by establishing a map between the stereoscopic image feature vector and the difference mean opinion score(DMOS). The proposed method is evaluated by images from the LIVE Phase I and the LIVE Phase II database. Experimental results show that compared with other methods, the proposed method is consistent with subjective perception, and hence outperforms the existing methods.

关键词： Discrete cosine transforms Feature extraction Stereo image processing image quality Visualization Computational modeling Distortion

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On Finding Gray Pixels

On Finding Gray Pixels

引用

IEEE/CVF conference on Computer Vision and Pattern Recognition

作者： Yanlin Qian Joni-Kristian Kamarainen Jarno Nikkanen Jiri Matas Computing Sciences Tampere University Intel Finland

ISBN: (纸本)9781728132945

We propose a novel grayness index for finding gray pixels and demonstrate its effectiveness and efficiency in illumination estimation. The grayness index, GI in short, is derived using the Dichromatic Reflection Model and is learning-free. GI allows to estimate one or multiple illumination sources in color-biased images. On standard single-illumination and multiple-illumination estimation benchmarks, GI outperforms state-of-the-art statistical methods and many recent deep methods. GI is simple and fast, written in a few dozen lines of code, processing a 1080p image in ～ 0.4 seconds with a non-optimized Matlab code.

关键词： statistical methods pixel roentgen equivalent man Sievert Units FINDINGS Codes illumination Lighting lines of code Fasting Dichroism

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Novel Stop Sign Detection Algorithm Based on Vehicle Speed Profile

Novel Stop Sign Detection Algorithm Based on Vehicle Speed P...

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American Control conference

作者： Soodeh Dadras Homayoun Jamshidi Sara Dadras Thomas Edward Pilutti Utah State University Logan UT 84322 USA Ford Motor Company

ISBN: (纸本)9781538679012;9781538679265

Automated driving technology involves various modern in-vehicle systems that are designed to increase road traffic safety by helping drivers gain a better awareness of the road and its potential hazards as well as other drivers around them. Stop sign detection module is an integral part of these systems as it has great utility in automated or assisted driving applications. Although promising results have been achieved in the areas of stop sign detection and classification, these methods are heavily dependent on images and image processing algorithms and detection problem in the real world remains a challenging issue. In this paper, we propose a method to detect the stop sign, based on statistical analysis of data obtained by drive history, a project conducted by Ford Motor Company. Our detection algorithm is based on the speed profile where speed is zero or close to zero. Then, we apply clustering to our data set to extract the points with this common feature. In the end, results demonstrate that our algorithm can improve the stop sign detection efficiency and afford high precision where other algorithms are prone to failure.

关键词： driver information systems image classification intelligent transportation systems object detection pattern clustering road safety road traffic road vehicles statistical analysis parking signs driver information systems Road traffic statistical methods statistical analysis Intelligent transportation systems road vehicles Object detection Road safety image classification Cluster Analysis Detection algorithms Land transportation

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A spatiotemporal multiscale statistical matching (SMSM) model for human actions detection 10

A spatiotemporal multiscale statistical matching (SMSM) mode...

引用

10th International conference on Digital image processing (ICDIP)

作者： Han, Jing Zhu, Junwei Bai, Lianfa Yue, Jiang Nanjing Univ Sci & Technol Jiangsu Key Lab Spectral Imaging & Intelligent Se Nanjing 210094 Jiangsu Peoples R China

ISBN: (数字)9781510622005

ISBN: (纸本)9781510622005

Considering the noise, background interference and massive information, it is a challenging issue to recognize human actions from videos. We, in this paper, present a non-training spatiotemporal multiscale statistical matching (SMSM) model based on the dense computation of so-called spatiotemporal local adaptive regression kernel to identify non-compact human actions. Therefore, our model can avoid the overfitting problem caused by large sample training. First, we encode the local context similarity by exploiting Gaussian difference LARK (GLARK) features. This feature can well describe the shape and trend of the weak edge. Second, we propose multiscale composite template set in SMSM, whose robustness to the detection of variable human actions in different sizes. The proposed SMSM model can balance the relationship between GLARK structure of local small window and neighborhood structure of local large window. Moreover, our statistical process solves the problem of weak edge missed detection brought by background interference and promotes the multiscale matching efficiency. In our experiments, the proposed algorithm significantly outperforms existing matching methods and some supervised methods on the universally acknowledged challenging dataset.

关键词： Actions detection spatiotemporal multiscale regression kernel analysis statistical matching

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Machine Learning Models to Estimate the Sugarcane Brix Values from Multitemporal Vegetation Indices

Machine Learning Models to Estimate the Sugarcane Brix Value...

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International conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： Sandeep Kumar Singla Rahul Dev Garg Om Prakash Dubey Indian Institute of Technology Roorkee India

ISBN: (数字)9781728106663

ISBN: (纸本)9781728106670

Remotely sensed data in combination with image processing and geoscience is essential to support decision making related to the crop productivity and assessment of the crop quality in terms of its growth. Continuous improvements in accuracy by eliminating all the radiometric and geometric errors, as well as the use of sophisticated statistical techniques goad to effective and optimized decision making. Although previous studies found mathematical relationships between final crop yield and different forms of spectral indices all around the world, few studies conducted in small areas. The relation of crop growth stages and sugarcane yield after applying the atmospheric and geometric corrections is also not considered by the researchers in the past. This study aims to estimate the sugar content of the sugarcane crop grown in a small area with machine learning models. The process based on correlation analysis to identify of the best periods for the reliable and efficient estimation of the crops is presented in this study. The study has been carried out in the Khedli Minor area near Bahadrabad District Haridwar Uttarakhand, which belongs to the Himalayan foothill region. The performance evaluation of the models has been analyzed using Mean Absolute Error and Root Mean Square Error (RMSE). The minimum value of RMSE =1.52 has been found with the cost parameter C=1 and Sigma = 0.1 for the Support Vector Regression (SVR) model. Compared with the other models, the SVR model exhibit better performances on Brix estimation based on the multitemporal vegetation indices. Although, this study exhibits some promising results in the yield estimation, a more generalized model can be developed with additional inputs from physical characteristics such as temperature, precipitation and soil moisture.

关键词： agricultural products crops decision making geophysical techniques learning (artificial intelligence) mean square error methods moisture regression analysis remote sensing soil support vector machines vegetation vegetation mapping water resources Mean square error geophysical techniques vegetation mapping agricultural products agricultural byproducts crops Support Vector Network vegetation Water Resources remote sensing regression analysis Brix decision making Moisture geometric correction

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BFAGC: A Bias-Free Adaptive Gamma Correction Method for image Enhancement

BFAGC: A Bias-Free Adaptive Gamma Correction Method for Imag...

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International conference on Advances in Science, Engineering and Robotics Technology (ICASERT)

作者： Nazifa Akter Sharmin Sultana Rahat Hossain Faisal Md. Mostafijur Rahman Dept. of Computer Science and Engineering University of Barishal Barishal Bangladesh

One of the most interesting and visually appealing areas of image processing is image enhancement. In order to enhance the images, various methods have already been introduced. But, these existing enhancement methods are hardly able to enhance all types of images. To address this issue, Adaptive Gamma Correction (AGC) method has been introduced which classifies images into several classes based on the statistical information of the image and then applies an adaptive gamma correction method. However, AGC method can be affected by outlier pixels and fails to appropriately enhance the images. To solve this problem, we propose an improved version of Adaptive Gamma Correction (AGC) namely Bias-Free Adaptive Gamma Correction (BFAGC). The proposed BFAGC is rarely affected by the outlier pixels. The extensive experimental analysis has been performed to evaluate the efficacy of the proposed BFAGC method. Both the qualitative and quantitative evaluation metrics have shown that BFAGC produces comparatively better enhanced images than AGC and other existing state-of-the-art methods.

关键词： Harmonic analysis Brightness Histograms image enhancement image color analysis Adaptive equalizers

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Submodular Field Grammars: Representation, Inference, and Application to image Parsing 32

Submodular Field Grammars: Representation, Inference, and Ap...

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32nd conference on Neural Information processing Systems (NIPS)

作者： Friesen, Abram L. Domingos, Pedro Univ Washington Paul G Allen Sch Comp Sci & Engn Seattle WA 98195 USA

Natural scenes contain many layers of part-subpart structure, and distributions over them are thus naturally represented by stochastic image grammars, with one production per decomposition of a part. Unfortunately, in contrast to language grammars, where the number of possible split points for a production A -> BC is linear in the length of A, in an image there are an exponential number of ways to split a region into subregions. This makes parsing intractable and requires image grammars to be severely restricted in practice, for example by allowing only rectangular regions. In this paper, we address this problem by associating with each production a submodular Markov random field whose labels are the subparts and whose labeling segments the current object into these subparts. We call the resulting model a submodular field grammar (SFG). Finding the MAP split of a region into subregions is now tractable, and by exploiting this we develop an efficient approximate algorithm for MAP parsing of images with SFGs. Empirically, we show promising improvements in accuracy when using SFGs for scene understanding, and demonstrate exponential improvements in inference time compared to traditional methods, while returning comparable minima.

关键词： Markov processes

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