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international conference on image, vision and Computing (ICIVC)

作者： Xu Zhang Wei Pan Perry Xiao Auckland Tongji Medical & Rehabilitation Equipment Research Centre Tongji Zhejiang College Jiaxing China School of Computer Science and Engineering Changshu Institute of Technology Changshu China School of Engineering London South Bank University London UK

ISBN: (纸本)9781538649923

Skin capacitive imaging is a novel technique which has been developed for skin hydration and skin solvent penetration measurements. This research is to assess the performance of deep learning in in-vivo skin capacitive image analysis using AlexNet model. The image classifier has been trained by using pretrained model to implement the specific feature extraction, prediction and classification specifically for the skin characteristics such as hydration level, skin damage level etc. There are over 1000 skin capacitive images used in this study. The objectives of the research are: feature extraction implementation using the pretrained model AlexNet; accuracy assessment of the model; and further improve the system for multiple features classification. The image classification programme shows a good result which has accuracy over 0.98, and the test images were classified correctly comparing with the experimental results of skin hydration, skin damaged level and the gender of the volunteers.

关键词： Skin Training Instruments Imaging Permittivity Fingerprint recognition Machine learning

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VIANO-The Virtual Piano 3

VIANO-The Virtual Piano

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3rd IEEE international conference on Computational Intelligence and Communication Technology (CICT)

作者： Saxena, Ankit Meerut Inst Engn & Technol Meerut Uttar Pradesh India

ISBN: (纸本)9781509062188

image processing is a comprehensive term for delineating and scrutinizing data in its very visual form. Virtual reality right since its embryonic stage holds big potential to become the next big computing platform. images can be wielded to create visual experiences and employ it to solve existing problems. This paper flaunts the fusion of image processing and computer vision by proposing a method to create a prototype of virtual piano that would allow user to play actual piano by just touching its image or projection, showcasing virtual reality. This in turn would help to eradicate the problem of portability of bulky pianos by replacing them with just an 'image', thus making it cost efficient. This whole process is subsumed in stages of extracting piano from a real world image, finding the particular key user touch and then generating corresponding key's sound. This paper also explains the future possibilities of making touch or pressure sensitive piano by using depth sensing techniques. Thus, this paper would open the gates for exploring other possibilities for blending these two techniques to solve other prevailing problems and making life of people easier.

关键词： computer vision image processing image Segmentation Virtual Reality

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Mango Leaf Deficiency Detection Using Digital image processing and Machine Learning

Mango Leaf Deficiency Detection Using Digital Image Processi...

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international conference for Convergence in Technology

作者： Mustafa Merchant Vishwajeet Paradkar Meghna Khanna Soham Gokhale Department of Computer Engineering SavitribaiPhule Pune University Pune India

Mango known to a national fruit of India, its leaves are exorbitantly affected by various nutrient deficiencies like nitrogen, iron, potassium and copper. These nutrients can change the natural color of mango leaves. Such leaves are considered to be deficient. The main purpose of this work is to detect various nutrient deficiencies of mango leaves. Initially a data set is created by extracting the different features of mango leaves using digital image processing. The extracted features include the RGB values and the texture of the leaves. This dataset is then used in the unsupervised machine learning model like clustering to cluster the various deficiencies which will help in further detection

关键词： Machine Learning Agriculture K-Means Clustering image processing Deficiency

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Adaptive Enhancement for Low-Contrast Color images via Histogram Modification and Saturation Adjustment

Adaptive Enhancement for Low-Contrast Color Images via Histo...

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international conference on image, vision and Computing (ICIVC)

作者： Yanfang Wang Qian Huang Jing Hu College of Computer and Information Hohai University Nanjing China Department of Computer Science Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9781538649923

images taken under dim or bright illumination are prone to suffer from degenerated details because of contrast compression. In order to improve the visual quality of low-contrast color images, both global and local contrast should be manipulated accordingly. For global contrast enhancement, a traditional and effective method is histogram equalization (HE). However, HE-based algorithms are prone to be badly affected by the spikes in histogram. Besides, applying HE algorithms to color images is complicated to gain vivid colors, and color distortion often occurs when color channels are processed independently. In this paper, a histogram modification method assisted by saturation and local contrast adjustment is proposed to enhance low-contrast color images. First, in order to improve global contrast, RGB histograms are redistributed according to the relative strength of RGB channels. Then, color saturation and local contrast are promoted based on the relative strength between neighboring pixels and the color-opponent mechanism. Compared with several recently published algorithms, experimental results confirm that the proposed approach produces vivid details and has the merits of protecting image naturalness and consistent regions.

关键词： image color analysis Histograms Color Dynamic range Distortion Heuristic algorithms Indexes

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Multimodal Biometric Verification Using Sparse Representation Based Classification

Multimodal Biometric Verification Using Sparse Representatio...

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international conference on image, vision and Computing (ICIVC)

作者： Ke Meng Zengxi Huang Xiaoming Wang Kun Wang School of Computer and Software Engineering Xihua University Chengdu China University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781538649923

In this paper, we propose a multimodal verification system integrating face and ear based on sparse representation-based classification (SRC). The face and ear query samples are first encoded respectively to derive sparsity-based match scores, and which are then combined with the Sum-rule for verification. Our contribution is two-fold: (1) use two sparsity-based metrics and two biometric traits to demonstrate the superior performance of SRC-based verification to the well-known multimodal fusion methods using cosine similarity like likelihood ratio (LLR), support vector machine (SVM), and the Sum-rule; (2) empirically analyze the weakness and feasibility of SRC-based verification and validate the correlation between its performance and the inter-class separability of samples in dictionary. The SRC-based one-to-many comparison is not preferable to the biometrics or scenarios where inferior inter-class separability is available. This could be a selection guideline of SRC for verification applications.

关键词： Face Ear Databases Encoding Dictionaries Measurement Training

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Bi-Directional Vectors from Apex in CNN for Micro-Expression Recognition

Bi-Directional Vectors from Apex in CNN for Micro-Expression...

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international conference on image, vision and Computing (ICIVC)

作者： Y. S. Gan Sze-Teng Liong Department of Mathematics Xiamen University Malaysia Sepang Selangor Malaysia Department of Electronic Engineering Feng Chia University Taichung Taiwan R.O.C.

ISBN: (纸本)9781538649923

The impressive performance of utilizing deep learning or neural network has attracted much attention in both the industry and research communities, especially towards computer vision aspect related applications. Despite its superior capability of learning, generalization and interpretation on various form of input, micro-expression analysis field is yet remains new in applying this kind of computing system in automated expression recognition system. A new feature extractor, BiVACNN is presented in this paper, where it first estimates the optical flow fields from the apex frame, then encode the flow fields features using CNN. Concretely, the proposed method consists of three stages: apex frame acquisition, multivariate features formation and feature learning using CNN. In the multivariate features formation stage, we attempt to derive six distinct features from the apex details, which include: the apex itself, difference between the apex and onset frames, horizontal optical flow, vertical optical flow, magnitude and orientation. It is demonstrated that utilizing the horizontal and vertical optical flow capable to achieve 80% recognition accuracy in CASME II and SMIC-HS databases.

关键词： Feature extraction Optical flow Databases Video sequences Brightness image recognition computer vision

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3rd Annual international conference on Information System and Artificial Intelligence (ISAI2018)

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Journal of Physics: conference Series 2018年第1期1069卷

作者： Tseng, Juinling Minghsin University of Science and Technology Taiwan

The international conference on Information Systems and Artificial Intelligence is an academic conference on the advancement of artificial intelligence and information systems. The conference includes the fields of machine vision, natural language processing, speech recognition and the industry application of AI, and is of great significance to the development of intelligent machines, intelligent human-computer interaction, and effective use of information, human health and social security. The primary goal of the conference is to promote research and developmental activities in information system and artificial intelligence and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in information system and artificial intelligence and related areas. In order to further promote the development of artificial intelligence and information systems disciplines and strengthen domestic and international academic exchanges and cooperation.

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NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge

NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semant...

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12th international Workshop on Semantic Evaluation, SemEval 2018, co-located with the 16th Annual conference of the North American Chapter of the

作者： Shiue, Yow-Ting Huang, Hen-Hsen Chen, Hsin-Hsi Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan MOST Joint Research Center for AI Technology All Vista Healthcare Taiwan

ISBN: (纸本)9781948087209

This paper presents the NTU NLP Lab system for the SemEval-2018 Capturing Discriminative Attributes task. Word embeddings, pointwise mutual information (PMI), ConceptNet edges and shortest path lengths are utilized as input features to build binary classifiers to tell whether an attribute is discriminative for a pair of concepts. Our neural network model reaches about 73% F1 score on the test set and ranks the 3rd in the task. Though the attributes to deal with in this task are all visual, our models are not provided with any image data. The results indicate that visual information can be derived from textual data. © 2018 Association for Computational Linguistics

关键词： Natural language processing systems

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Methods for Long-Distance Crack Location and Detection of Concrete Bridge Structures

Methods for Long-Distance Crack Location and Detection of Co...

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international conference on image, vision and Computing (ICIVC)

作者： Youfa Cai Xing Fu Yanna Shang Jingxin Shi State Key Laboratory of Precision Measurement Technology and Instruments Tianjin University Tianjin City China Beijing Instrument Industry Research Institute Beijing City China

ISBN: (纸本)9781538649923

In order to improve the efficiency of crack detection of concrete bridge structures, a new method based on computer vision technology and coordinate mapping is proposed. In this research, this crack measurement system is integrated mainly with a high magnification image acquisition system, a two-dimensional electric cradle head device and a laser ranging system. It has a set of observing coordinate system. Firstly, the marking points' image coordinates are mapped to the observation coordinates. Secondly, according to the marking points' observation coordinates, the measured crack's coordinates are mapped to a same world coordinates so as to realize the spatial location of the measured cracks regardless of different test cycles or instrument's setup positions, which is a great convenience for the review detection of surface cracks of concrete bridge structures. The experiments show that this method is efficient and convenient. It can automatically locate the measured cracks within 16 s, and the deviation is not more than ± 0.07 °. At a distance of 100 m, the measurement accuracy of crack width is better than ± 0.12 mm.

关键词： Bridges image edge detection Head Coordinate measuring machines Surface cracks Electric variables measurement Position measurement

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Color Feature Unified-Based Approach for Visual Fixation

Color Feature Unified-Based Approach for Visual Fixation

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international conference on image, vision and Computing (ICIVC)

作者： Zhaoxia Xie School of mechanical and electrical engineering Beijing Institute of Graphic Communication BIGC Beijing China

ISBN: (纸本)9781538649923

Human visual attention can be deal with complex scenes in real time effortlessly and efficiently and detect the most interesting regions quickly. Based on the characteristic of human visual attention, one more comprehensive computation framework is proposed which fully takes the advantage of color contrast to obtain visual fixations of natural color images. Firstly, the color space conversion strategy is employed. The RGB color images are converted into the HSV color space and Lab color space respectively. Then, the superpixels generation algorithm is utilized to segment natural images in the HSV color space and in the Lab color space. Next, color feature-contrast in the two color space is respectively implemented and the corresponding single visual fixation is obtained. Finally, the color feature-fused strategy is adopted in order to get the final visual fixation. Experimental results show that our proposed framework can effectively improve the effect of visual fixations compared with a single color space for the natural color images. Moreover, the full resolution visual fixations can be obtained by employing the proposed framework in this paper compared to the context-aware approach. Meanwhile, these experimental results also clearly demonstrate that the proposed model for saliency estimation is effective.

关键词： image color analysis Visualization image segmentation Color Task analysis computer vision image resolution

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