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Hepatitis diagnosis using facial color image

Hepatitis diagnosis using facial color image

引用

1st International conference on Medical Biometrics

作者： Liu, Mingjia Guo, Zhenhua Harbin Inst Technol Shenzhen Grad Sch Biocomp Res Ctr Harbin Peoples R China

ISBN: (纸本)9783540774105

Facial color diagnosis is an important diagnostic method in traditional Chinese medicine (TCM). However, due to its qualitative, subjective and experience-based nature, traditional facial color diagnosis has a very limited application in clinical medicine. To circumvent the subjective and qualitative problems of facial color diagnosis of Traditional Chinese Medicine, in this paper, we present a novel computer aided facial color diagnosis method (CAFCDM). The method has three parts: face image Database, image Preprocessing.Module and Diagnosis Engine. Face image Database is carried out on a group of 116 patients affected by 2 kinds of liver diseases and 29 healthy volunteers. The quantitative color feature is extracted from facial images by using popular digital image processing.techniques. Then, KNN classifier is employed to model the relationship between the quantitative color feature and diseases. The results show that the method can properly identify three groups: healthy, severe hepatitis with jaundice and severe hepatitis without jaundice with accuracy higher than 73%.

关键词： Face recognition

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What value do explicit high level concepts have in vision to language problems?

What value do explicit high level concepts have in vision to...

引用

2016 ieee conference on computer Vision and pattern recognition, CVPR 2016

作者： Wu, Qi Shen, Chunhua Liu, Lingqiao Dick, Anthony Van Den Hengel, Anton School of Computer Science University of Adelaide Australia

ISBN: (纸本)9781467388511

Much recent progress in Vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image features to text. In this paper we investigate whether this direct approach succeeds due to, or despite, the fact that it avoids the explicit representation of high-level information. We propose a method of incorporating high-level concepts into the successful CNN-RNN approach, and show that it achieves a significant improvement on the state-of-the-art in both image captioning and visual question answering. We also show that the same mechanism can be used to introduce external semantic information and that doing so further improves performance. We achieve the best reported results on both image captioning and VQA on several benchmark datasets, and provide an analysis of the value of explicit high-level concepts in V2L problems.

关键词： High level languages

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Optical flow with semantic segmentation and localized layers

Optical flow with semantic segmentation and localized layers

引用

2016 ieee conference on computer Vision and pattern recognition, CVPR 2016

作者： Sevilla-Lara, Laura Sun, Deqing Jampani, Varun Black, Michael J. MPI for Intelligent Systems Germany NVIDIA Korea Republic of Harvard University United States

ISBN: (纸本)9781467388511

Existing optical flow methods make generic, spatially homogeneous, assumptions about the spatial structure of the flow. In reality, optical flow varies across an image depending on object class. Simply put, different objects move differently. Here we exploit recent advances in static semantic scene segmentation to segment the image into objects of different types. We define different models of image motion in these regions depending on the type of object. For example, we model the motion on roads with homographies, vegetation with spatially smooth flow, and independently moving objects like cars and planes with affine motion plus deviations. We then pose the flow estimation problem using a novel formulation of localized layers, which addresses limitations of traditional layered models for dealing with complex scene motion. Our semantic flow method achieves the lowest error of any published monocular method in the KITTI-2015 flow benchmark and produces qualitatively better flow and segmentation than recent top methods on a wide range of natural videos.

关键词： image segmentation

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A Remote Sensing image Registration Method Based on Multi-features 4

A Remote Sensing Image Registration Method Based on Multi-fe...

引用

4th ieee International conference on image, Vision and Computing, ICIVC 2019

作者： Xie, Kun Chen, Jinlong Yang, Minghao University of Electronic Technology Guangxi Key Laboratory of Trusted Software of Guilin Guilin541004 China University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security of Guilin Guilin541004 China Chinese Academy of Sciences Key Laboratory of Pattern Recognition Beijing100190 China

ISBN: (纸本)9781728123257

Remote sensing image registration technology has important significance in the field of image processing. The registration technology of SAR images has been a hot research in this field, and it is also a challenge. Aiming at this difficulty, this paper proposes a fusion feature algorithm, which combines gradient, grayscale, texture, geometry and other features in heterogeneous remote sensing images for comprehensive matching. Compared with the traditional single feature, the improved detector has good performance and registration accuracy in SAR image and visible light image registration. © 2019 ieee.

关键词： Synthetic aperture radar

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Hybridization of Levy Flight and Chaotic Gravitational Search Algorithm for image Segmentation 8

Hybridization of Levy Flight and Chaotic Gravitational Searc...

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8th ieee International Symposium on Smart Electronic Systems, iSES 2022

作者： Rather, Sajad Ahmad Das, Sujit NIT Department of Computer Science and Engineering Telangana Warangal India

ISBN: (纸本)9798350399226

image segmentation is an important step in image processing.due to its enormous application potential in medical image analysis, data mining, and pattern recognition. In fact, image segmentation is a normalization procedure in which image is divided into individual pixels based on threshold values. In this work, Levy flight and Chaos theory based Gravitational Search Algorithm (LCGSA) has been employed for the image segmentation task. In LCGSA, the exploration is carried out by levy flight while exploitation is guaranteed by chaotic maps. Besides, Kapur's entropy method has been utilized to segment the sample image into various regions based on the pixel intensity values. To investigate the segmentation performance of LCGSA, we have used Couple and Pentagon images from the USC-SIPI database. The experimental and simulation analysis has been carried out thoroughly using convergence curves, segmented graphs, box plots, and histogram maps. The overall analysis of the experimental results indicated the efficient performance of LCGSA in terms of providing optimal values for the image thresholds and taking less computational overhead. © 2022 ieee.

关键词： image segmentation

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Development and testing of a LBP-SVM based teeth visibility recognizer

Development and testing of a LBP-SVM based teeth visibility ...

引用

2012 ieee International conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2012

作者： Tian, Qing Tian, Guangjun Center for Intelligent Machines McGill University Montreal QC Canada School of Electrical Engineering Yanshan University Qinhuangdao China

ISBN: (纸本)9781457717772

Human face recognition receives more and more attention for its important role in a wide range of areas such as image searching, video surveillance, and human-computer interaction. This paper focuses on developing and testing a working attribute recognizer for one specific facial characteristic - teeth visibility. Three major steps - image preprocessing. feature extraction and classifier training are involved in the development process. For comparison, both the Local Binary patterns (LBP) features and features derived from normalized cross-correlation (NCC) template matching are extracted and used to train a SVM classifier. After development, the attribute recognizer is tested with various parameter settings and under several different conditions in this report. Experimental results show that the LBP-SVM-based teeth visibility recognizer has a high accuracy and performs differently under different parameter settings such as the block size, sampling radius, and sampling density and is robust to pose, illumination, and small expression changes. Besides, the LBP-based teeth visibility recognizer is generally superior to that based on normalized cross-correlation template matching. The reasons are also explored in the experiment part. © 2012 ieee.

关键词： Local binary pattern

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Semantic image segmentation with task-specific edge detection using CNNs and a discriminatively trained domain transform

Semantic image segmentation with task-specific edge detectio...

引用

2016 ieee conference on computer Vision and pattern recognition, CVPR 2016

作者： Chen, Liang-Chieh Barron, Jonathan T. Papandreou, George Murphy, Kevin Yuille, Alan L. Google Inc. United States

ISBN: (纸本)9781467388511

Deep convolutional neural networks (CNNs) are the backbone of state-of-art semantic image segmentation systems. Recent work has shown that complementing CNNs with fully-connected conditional random fields (CRFs) can significantly enhance their object localization accuracy, yet dense CRF inference is computationally expensive. We propose replacing the fully-connected CRF with domain transform (DT), a modern edge-preserving filtering method in which the amount of smoothing is controlled by a reference edge map. Domain transform filtering is several times faster than dense CRF inference and we show that it yields comparable semantic segmentation results, accurately capturing object boundaries. Importantly, our formulation allows learning the reference edge map from intermediate CNN features instead of using the image gradient magnitude as in standard DT filtering. This produces task-specific edges in an end-to-end trainable system optimizing the target semantic segmentation quality.

关键词： image segmentation

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Design of image recognition Monitoring System of Hydrological Monitoring Station Based on Edge Intelligence

Design of Image Recognition Monitoring System of Hydrologica...

引用

2022 International conference on Automation, Robotics and computer Engineering, ICARCE 2022

作者： Tan, Liwu Yao, Xianzhe Long, Yuan Zhang, Zhizheng Li, Zhiwei Li, Yan Nanchang Institute of Technology School of Electrical Engineering Nanchang China Nanchang Institute of Technology School of Information Engineering Nanchang China

ISBN: (纸本)9781665475488

In the safety monitoring of hydrological monitoring stations, the target detection algorithm can be used to avoid unnecessary trouble caused by artificial supervision through intelligent monitoring. However, the accuracy of target recognition and time - delay is always a contradiction in security monitoring. SSD target detection algorithm is the latest target recognition algorithm after Faster RCNN and YOLOv1 algorithm, combining the advantages of both. The algorithm is faster than the fast RCNN algorithm and has higher accuracy than the YOLOv1 algorithm. In this paper, a PSO intelligent algorithm based on hyperlight fast generic face detection and 1mb network convergence is proposed, called PSO-1MB. The device is deployed in or near the hydrologic monitoring station on the edge node server for calculation and processing. In this paper, the test whether the staff wear a hard hat as an example, using the Pytorch environmental framework, experiment simulation. Experimental results show that this model and algorithm can detect helmet features more accurately and quickly, and can better meet the engineering requirements. © 2022 ieee.

关键词： Face recognition

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Reinforcement learning for visual object detection

Reinforcement learning for visual object detection

引用

2016 ieee conference on computer Vision and pattern recognition, CVPR 2016

作者： Mathe, Stefan Pirinen, Aleksis Sminchisescu, Cristian Department of Mathematics Faculty of Engineering Lund University Sweden Institute of Mathematics Romanian Academy Romania Department of Computer Science University of Toronto Canada

ISBN: (纸本)9781467388511

One of the most widely used strategies for visual object detection is based on exhaustive spatial hypothesis search. While methods like sliding windows have been successful and effective for many years, they are still brute-force, independent of the image content and the visual category being searched. In this paper we present principled sequential models that accumulate evidence collected at a small set of image locations in order to detect visual objects effectively. By formulating sequential search as reinforcement learning of the search policy (including the stopping condition), our fully trainable model can explicitly balance for each class, specifically, the conflicting goals of exploration - sampling more image regions for better accuracy-, and exploitation - stopping the search efficiently when sufficiently confident about the target's location. The methodology is general and applicable to any detector response function. We report encouraging results in the PASCAL VOC 2012 object detection test set showing that the proposed methodology achieves almost two orders of magnitude speed-up over sliding window methods.

关键词： Object detection

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Research on point-based object detection algorithm for small objects in point clouds

Research on point-based object detection algorithm for small...

引用

2024 International conference on image, Signal processing. and pattern recognition, ISPP 2024

作者： Dong, Wei Li, Yueyang School of Artificial Intelligence and Computer Jiangnan University Wuxi214122 China

ISBN: (纸本)9781510680425

Due to the characteristics of unordered and sparse point cloud data, as well as large scale variations and diverse shapes, existing detectors have low accuracy in detecting small objects. To address the problem of difficult small object detection in point clouds, this paper proposes a Point-based Small Object Detection (PB-SOD) algorithm model. By introducing a new category-aware sampling layer, the perception ability of the model is improved, thereby enhancing the detection accuracy of small objects. At the same time, a new data augmentation strategy is proposed to improve the generalization ability of the model. Experimental results on the KITTI dataset demonstrate that the PB-SOD model achieves better detection performance for the pedestrian class, a small object category, compared to the baseline model IA-SSD and other mainstream models, effectively completing small object detection tasks on point cloud datasets. © 2024 SPIE.

关键词： Object detection Point clouds Education and training Detection and tracking algorithms Feature extraction Ablation

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