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3rd international conference on computer vision and image processing (cvip)

作者： Saurav, Sumeet Singh, Sanjay Yadav, Madhulika Saini, Ravi Acad Sci & Innovat Res AcSIR Chennai Tamil Nadu India CSIR Cent Elect Engn Res Inst Pilani Rajasthan India Banasthali Vidyapith Dept Elect Vanasthali Rajasthan India

ISBN: (纸本)9789813290884;9789813290877

Automatic facial expression recognition (FER) has gained enormous interest among the computer vision researchers in recent years because of its potential deployment in many industrial, consumer, automobile, and societal applications. There are a number of techniques available in the literature for FER;among them, many appearance-based methods such as local binary pattern (LBP), local directional pattern (LDP), local ternary pattern (LTP), gradient local ternary pattern (GLTP), and improved local ternary pattern (IGLTP) have been shown to be very efficient and accurate. In this paper, we propose a new descriptor called local neighborhood difference binary pattern (LNDBP). This new descriptor is motivated by the recent success of local neighborhood difference pattern (LNDP) which has been proven to be very effective in image retrieval. The basic characteristic of LNDP as compared with the traditional LBP is that it generates binary patterns based on a mutual relationship of all neighboring pixels. Therefore, in order to use the benefit of both LNDP and LBP, we have proposed LNDBP descriptor. Moreover, since the extracted LNDBP features are of higher dimension, therefore a dimensionality reduction technique has been used to reduce the dimension of the LNDBP features. The reduced features are then classified using the kernel extreme learning machine (K-ELM) classifier. In order to, validate the performance of the proposed method, experiments have been conducted on two different FER datasets. The performance has been observed using well-known evaluation measures, such as accuracy, precision, recall, and F1-score. The proposed method has been compared with some of the state-of-the-art works available in the literature and found to be very effective and accurate.

关键词： Facial expression recognition (FER) Local neighborhood difference pattern (LNDP) Principal component analysis (PCA) Kernel extreme learning machine (K-ELM)

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VQAR: Review on Information Retrieval Techniques based on computer vision and Natural Language processing 3

VQAR: Review on Information Retrieval Techniques based on Co...

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3rd international conference on Computing Methodologies and Communication (ICCMC)

作者： Modi, Shivangi Pandya, Dhatri SCET Comp Engn Dept Surat India

ISBN: (纸本)9781538678084

Recently computer vision and Natural language processing paradigm contains enormous research progress in their respective areas. Despite the progress in both areas, still it remains as a challenging task for machines to extract image semantics and then communicate this extracted information with the desired users. These problems will be solved by Visual Question Answering (VQA) system by connecting both computer vision and natural language processing paradigms. In VQA, system is presented with an image and textual question related to that image. The system will generate the answer by processing on both image and textual features. Answer generated by VQA is in one word, phrase or in sentence. Various datasets are available for training and evaluating VQA system which contains real or abstract images and question-answer pairs related to the semantics available in the image. VQA is being used in many areas such as for blind and visually impaired users, robotics, art gallery and many more areas. This paper discusses VQA techniques, VQA datasets and highlights the parametric evaluation of these techniques along with generic issues in VQA system.

关键词： visual question answering computer vision natural language processing attention model joint embedding compositional model external knowledge base mechanism

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Analysis and Comparison of Grayscale Correction 18

Analysis and Comparison of Grayscale Correction

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3rd international conference on Robotics, Control and Automation (ICRCA) / 3rd international conference on Robotics and Machine vision (ICRMV)

作者： Zhang, Longzhi Jin, Huan Wu, Dongmei Harbin Inst Technol State Key Lab Robot & Syst Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781450365307

image enhancement as an effective means to improve image quality, is still a basic topic in image processing, which has attracted largely attentions from researches. Yet among enhancement algorithms, grayscale correction, including gray transformation and histogram processing, is widely used because of its simple principle. Hence, this paper discuss commonly used algorithms in above two categories, and apply them for different scenarios, to obtain their enhance effect. Through analyze and compare their distinctions from theory to experiment, we found that these methods are still to be improved, for instance in real-time, and grayscale. This paper is a fundamental investigate on image processing, which could provide a reference for further research on image enhancement.

关键词： image enhancement Grayscale correction Gray transformation Histogram processing Algorithm

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Research on Embedded Heart Rate Detector Based on vision 3

Research on Embedded Heart Rate Detector Based on Vision

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IEEE 3rd international conference on Signal and image processing (ICSIP)

作者： Lu, Lin-Peng Pang, Han-Bing Pang, Li-Li Nanjing Inst Technol Sch Comp Engn Nanjing Jiangsu Peoples R China Nanjing Inst Technol Instrument Sci Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538663967

At present, the study of non-contact heart rate measurement based on vision is mostly a theoretical method. A few of the relevant algorithms are only described by the MATLAB script language. The specific research and design are still basically blank. Contact-type heart rate measurement devices, while accurate, are expensive, inconvenient to carry, and have a limited range of applications. Existing non-contact heart rate devices require a dedicated light source for measurement. Therefore, aiming at the above issues, this paper studies the design and implementation of a non-contact embedded device based on vision, proposes a design scheme, and perfects the currently known non-contact heart rate detection theory based on actual test results. Combining the high performance, low cost, and low power consumption of the ARM processor, a new method for determining the heartbeat crest is proposed, and a low-cost vision-based embedded heart rate device is designed and implemented for the first time.

关键词： Heart rate measurement Embedded device computer vision

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Adjunctions in pyramids, curve evolution and scale-spaces

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international JOURNAL OF computer vision 2003年第2-3期52卷 139-151页

作者： Keshet, R Heijmans, HJAM Hewlett Packard Labs IL-32000 Haifa Israel Ctr Math & Comp Sci CWI NL-1098 SJ Amsterdam Netherlands

We have been witnessing lately a convergence among mathematical morphology and other nonlinear fields, such as curve evolution, PDE-based geometrical image processing, and scale-spaces. An obvious benefit of such a convergence is a cross-fertilization of concepts and techniques among these fields. The concept of adjunction however, so fundamental in mathematical morphology, is not yet shared by other disciplines. The aim of this paper is to show that other areas in image processing can possibly benefit from the use of adjunctions. In particular, a strong relationship between pyramids and adjunctions is presented. We show how this relationship may help in analyzing existing pyramids, and construct new pyramids. Moreover, it will be explained that adjunctions based on a curve evolution scheme can provide idempotent shape filters. This idea is illustrated in this paper by means of a simple affine-invariant polygonal flow. Finally, the use of adjunctions in scale-space theory is also addressed.

关键词： adjunctions mathematical morphology curve evolution scale-spaces pyramids partially ordered sets image processing

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Rough Spatial Ensemble Kernelized Fuzzy C Means Clustering for Robust Brain MR image Tissue Segmentation 8th

Rough Spatial Ensemble Kernelized Fuzzy C Means Clustering f...

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8th international conference on computer vision and image processing (cvip)

作者： Halder, Amiya Choudhuri, Rudrajit Bhowmick, Arinjay St Thomas Coll Engn & Technol 4 DH Rd Kolkata India

ISBN: (纸本)9783031585340;9783031585357

image segmentation is a crucial step in image processing having various applications in biomedical image analysis. Segmentation of the magnetic resonance images of the brain is one such key area in biomedical image analysis that segments various tissues in the brain and detects tumor regions. In this paper, an unsupervised rough spatial ensemble kernelized fuzzy clustering segmentation algorithm is presented for automated segmentation of magnetic resonance images of the brain. The proposed algorithm is an integration of Rough Fuzzy C Means clustering and the kernel method with a novel ensemble kernel being a combination of spherical kernel, Gaussian, and Cauchy kernels, which improves the performance of the segmentation algorithm. The proposed algorithm performs better than the existing clustering algorithms across a wide range of magnetic resonance images of the brain along with visual indications obtained from the results.

关键词： Iterative Optimization Magnetic Resonance Imaging image Segmentation Rough Set Kernel Method

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Research on the Dimensional Accuracy Measurement Method of Cylindrical Spun Parts Based on Machine vision 3

Research on the Dimensional Accuracy Measurement Method of C...

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3rd international conference on Mechanical, Manufacturing, Modeling and Mechatronics (IC4M) / 3rd international conference on Design, Engineering and Science (ICDES)

作者： Xiao Gangfeng Zhong Xingwei Shen Weixian Xia Qinxiang Chen Weiping South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Peoples R China

A new method for measuring the dimensional accuracy of the cylindrical spun parts based on machine vision was proposed to overcome the artificial deviation and low efficiency of manual measurement. The image acquisition system of machine vision was built up. The methods of image processing and edge extraction of cylindrical spun parts were studied. The straightness and ovality of the cylindrical spun parts were obtained by the proposed new method. The results showed that the edge contour of the cylindrical spun parts extracted by Canny edge detector is better than Sobel and Prewitt edge detector. The dimensional accuracy of the cylindrical spun parts can be obtained accurately by the proposed measurement method based on machine vision. The relative errors of the straightness and ovality between the machine vision and the manual measurement are less than 10%.

关键词： computer vision

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Crater wear measurement using computer vision and automatic focusing

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JOURNAL OF MATERIALS processing TECHNOLOGY 1996年第4期58卷 362-367页

作者： Yang, MY Kwon, OD SAMSUNG ELECT SEOULSOUTH KOREA

In this paper we present a technique to measure the crater wear using image processing and automatic focusing. The new contour detection algorithm, which can adapt in a noisy image, is suggested. It is suitable for eliminating high frequency noises without blurring and with lower processing time. An automatic focusing technique is applied to measure a crater wear depth with a one-dimensional search algorithm for finding the best focus. This method is implemented in the tool microscope driven by a servo motor. The results show that the contour and the depth of crater wear can be measured reliably.

关键词： crater wear contour detection autofocusing

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image Augmentation for Classifying Facial Expression images by Using Deep Neural Network Pre-trained with Object image Database 18

Image Augmentation for Classifying Facial Expression Images ...

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3rd international conference on Robotics, Control and Automation (ICRCA) / 3rd international conference on Robotics and Machine vision (ICRMV)

作者： Shima, Yoshihiro Omori, Yuki Meisei Univ Sch Sci & Engn 2-1-1 Hodokubo Hino Tokyo 1918506 Japan

ISBN: (纸本)9781450365307

image augmentation of automatic facial expression classification is proposed on the basis of a combination of a deep neural network and a support vector machine. A neural network pre-trained with a large-scale object image database is used as a feature extractor for facial images. The accuracy of system performance is evaluated using the database "ATR Facial Expression image Database (DB99)." By using image augmentation, an average recognition rate of 97.92% was obtained, which was a 9.84 percentage point improvement compared with that without augmentation. The experimental results showed the effectiveness of our scheme.

关键词： computer vision facial expression feature extraction neural networks support vector machine deep learning

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A Method for Distance Measurement of Moving Objects in a Monocular image 3

A Method for Distance Measurement of Moving Objects in a Mon...

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IEEE 3rd international conference on Signal and image processing (ICSIP)

作者： Xu, Zhengguang Wang, Luyao Wang, Jie Univ Sci & Technol Beijing Lab Pattern Based Mot Control Beijing Peoples R China

ISBN: (纸本)9781538663967

In this paper, a method for distance measurement only using a single target image without any internal camera parameters is developed. The mapping relation between image row pixel values and the actual distances is established by detecting and locating the corners on the reference target image. The distance information is extracted in real time by combining the moving target detection method based on Gaussian mixture model (GMM) and the shadow elimination in Hue-Saturation-Intensity (HSI) color space. The method has a simple calibration procedure with a single image, which is suitable for practical application. The experimental result shows that the algorithm is effective and satisfies the real-time and accurate requirements of distance detection.

关键词： monocular vision distance measurement machine vision corner detection Gaussian mixture model shadow elimination

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