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Performance Lossless Optimization of image Interpolation in H.264/AVC

Performance Lossless Optimization of Image Interpolation in ...

引用

2006 8th International conference on signal processing

作者： Fang Wei Xueming Li School of Information Engineering Beijing Univ.of Posts & Telecom

the motion estimation in the newest international video coding standard H.264/AVC employs many advanced techniques including multiple reference frame selection,flexible macroblock partitioning,and 1/4-pel accuracy motion vectors,*** these techniques improve the coding efficiency dramatically, they also issue heavy computational burden to H.264/AVC *** which the high resolution image interpolation for multiple reference frame is one of the most computational intensive module,therefore requiring more emphasis to be improved In this paper an optimization scheme of image interpolation is *** results show that this scheme is quite effective and could result in at most 14.4 times calculation speed *** it is worthy to note that no performance lost occurs during the optimization *** results also show that when combined with efforts in other modules,it can bring our H.264 codec to be easily used in real time video applications with CIF requirement.

关键词： image Performance Lossless Optimization of image Interpolation in H.264/AVC

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An Unsupervised HIV Lateral Flow Immunochromatography Detection Algorithm Based on K-Means++ 22

An Unsupervised HIV Lateral Flow Immunochromatography Detect...

引用

Proceedings of the 2022 4th International conference on Video, signal and image processing

作者： Guijun Miao Yifan Wu Xianbo Qiu Fang Li College of Information Science and Technology Beijing University of Chemical Technology China

ISBN: (纸本)9781450397810

As a convenient biochemical detection technology, colloidal gold strip lateral flow immunochromatography has been widely used in disease detection and diagnosis, food safety and other fields. In order to achieve high accuracy and efficient test strip detection, the artificial intelligence-based image classification method was applied to HIV colloidal gold test strip detection in this paper. An unsupervised HIV colloidal gold strip detection algorithm based on K-means++ and machine learning is proposed, and key technologies such as strip image preprocessing, K-means++ image segmentation method, data enhancement and K value selection are studied. three image classifiers KNN(K nearest neighbor classification algorithm), SVC(support vector machine) and GaussianNB(Gaussian Bayes classifier) were used to compare the classification effect. Experiments show that the classification effect of the proposed algorithm is better than that of the deep learning yolox network. the classification accuracy of the unsupervised detection algorithm based on the combination of K-means++ and KNN can reach 94%, the sensitivity is 98%, and the specificity is 80%, which can well solve the misjudgment problem caused by the insignificant T-line of weak positive test strips.

关键词： K-means++ segmentation Biological strip image Classification and evaluation Machine learning

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wganBCS: Block-wise image Compressive Sensing: Model using Wasserstein GAN 22

wganBCS: Block-wise Image Compressive Sensing: Model using W...

引用

Proceedings of the 2022 4th International conference on Video, signal and image processing

作者： Boyan Chen Kaoru Uchida cis Hosei University Japan

ISBN: (纸本)9781450397810

Block-wise compressive sensing (BCS) uses a fix size sensing matrix to raster-scan the entire image to acquire the signal block by block. Compared with traditional image compressive sensing methods, BCS can greatly reduce the memory consumption of sensing matrix. However, BCS paradigm still suffers from two issues. One is that block-wise sensing causes heavy block effect on the reconstructed image, which leads to degradation in the image quality metrics. Another is that the state of art block wise image compressive sensing methods use mean square error loss function to optimize their models, which causes the reconstructed images over smoothed. In this paper, we incorporate generative adversarial training into BCS paradigm and propose a new block wise image compressive sensing and reconstruction model called wganBCS, which a combination of traditional L2 loss and the Wasserstein loss are used to optimize the model. We use a modified Wasserstein GAN (WGAN) network to deal with the block effect caused by the block wise compressive sensing. Specifically speaking, at the first stage training, the generator network will focus on minimize the mean square error loss to keep the overall pixel accuracy between reconstructed images and their ground truths. then the output images from the generator are sent to a critic network to calculate the Wasserstein distance between the ground truth images, the generator network will minimize this Wasserstein distance to keep the reconstructed images visually authentic to ground truth images. Experimental result shows that our model is superior both in visual authenticity and the image quality metrics compared to the most state of art image compressive sensing methods.

关键词： Generative adversarial networks image compressed sensing image reconstruction

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A Low Complexity Gabor Based CNN Model for Defect Detection in Fabrics

A Low Complexity Gabor Based CNN Model for Defect Detection ...

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Artificial Intelligence and signal processing (AISP), INDIA, International conference on

作者： Gnanaprakash V Jeen Retna Kumar R Preethi D Shoukath Ali K Saranya N Joel Devadass Daniel D J Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Electronics and Communication Engineering Bannari Amman Institute of Technology Erode India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

In the textile industry, automatic fabric defect detection is the most important process to ensure the quality of the fabric. the current technology uses a learning-based methodology to identify defects in fabrics with simple patterns. In the current system, Convolutional Neural Network (CNN) models are used to identify the defects present in the fabric image with maximum accuracy. However, the complexity of the CNN becomes more and it takes much time to process the data. To cater to this problem, a novel Gabor based CNN (GCNN) architectures namely Gabor based VGG-16 (GVGG-16) and Gabor based MobileNet (GMN) models are developed for detecting defects in simple and complex patterned fabrics. In this case, the initial convolution layer uses the Gabor filter bank rather than traditional filters. Gabor filters are more beneficial for examining textures with varying sizes and orientations. the analysis of the texture and the information extracted from it are influenced differently by each of the Gabor filter's parameters. thus, this paper examines how Gabor filter parameters are applied in GCNN architecture. the TILDA textile image database shows that the suggested model for fabric defect detection can detect defects with an accuracy of 99.17% using the fewest trainable parameters possible.

关键词： Accuracy image databases Filter banks Computer architecture Fabrics Complexity theory Convolutional neural networks Gabor filters Data mining Defect detection

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Research on Smart Safety Electronic Fence Technology Based on image processing

Research on Smart Safety Electronic Fence Technology Based o...

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2019 4th Asia conference on Power and Electrical Engineering (ACPEE 2019)

作者： Liu Qin Wang Minkun Xu Kehang Zhang Jiawei Xie Junhu Chen Jiong Southwest China Branch of State Grid Corporation School of Electrical Engineering Shanghai University of Electric Power

With the development of scale of the power grid,the phenomenon that the construction personnel across the safety fence at will occurs frequently,which brings great hidden trouble to the safety *** allusion to the existing problem of the safety fence without monitoring and alarm function,in which the principle of inter-frame difference method is used to screen out the illegal workers,making accurate diagnosis of the illegal behavior,and sending warning information to remind the illegal workers by using the method of trajectory tracking of moving object to filter out the random disturbance signal,so as to ensure the safety of the construction.

关键词： Smart Safety Electronic Fence Inter-frame Difference Method Target Tracking Technology

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Automatic Classification System for Diagnosis of Cognitive Impairment Based on the Clock-Drawing Test 9th

Automatic Classification System for Diagnosis of Cognitive I...

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9th International Work-conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： Jimenez-Mesa, C. Arco, Juan E. Valenti-Soler, M. Frades-Payo, B. Zea-Sevilla, M. A. Ortiz, A. Avila-Villanueva, M. Castillo-Barnes, Diego Ramirez, J. Del Ser-Quijano, T. Carnero-Pardo, C. Gorriz, J. M. Data Sci & Computat Intelligence DASCI Inst Granada Spain Univ Granada Signal Theory Telemat & Commun Dept Granada Spain Univ Malaga Commun Engn Dept Malaga Spain Fdn CIEN Madrid Spain FIDYAN Neuroctr Granada Spain

ISBN: (纸本)9783031062421;9783031062414

the prevalence of dementia is currently increasing worldwide. this syndrome produces a deterioration in cognitive function that can not be reverted. However, an early diagnosis can be crucial for slowing its progress. the Clock Drawing Test (CDT) is a widely used paperand-pencil test for cognitive assessment in which an individual has to manually draw a clock on a paper during a certain time. Nevertheless, there are a lot of scoring systems for this test and most of them depend on the subjective assessment of the expert. this study proposes a computeraided diagnosis (CAD) system based on deep learning in order to automate the diagnosis of cognitive impairment (CI) from the result of the CDT. this is addressed by employing a preprocessing pipeline in which the clock is detected and centered, as well as binarized for decreasing the computational burden. then, the resulting image is fed into a Convolutional Neural Network (CNN), which is used to identify the informative patterns within the CDT drawings that are relevant for the assessment of the patient's cognitive status. Performance is evaluated in a real context where differentiating between CI patients and controls. the proposed method provides an accuracy of 68.62% in this classification task, with an AUC of 74.53%. A validation method using resubstitution with upper bound correction is also discussed.

关键词： Alzheimer's disease Clock Drawing Test Cognitive impairment Deep learning image processing Machine learning

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image Feature Extraction with Fourier Transform and Multi-task Swin-Transformer for Alzheimer’s Disease Prediction and Detection

Image Feature Extraction with Fourier Transform and Multi-ta...

引用

signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Chengli Hu Shenyang Aerospace University Shenyang China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Alzheimer’s disease (AD) patients often miss the optimal treatment time due to their late onset and unclear early clinical manifestations. there is little difference between the CT images of the brain of AD patients and normal people, and the efficiency of manual diagnosis is low. However, classic models (such as Resnet, Swin Transformer, CNN, etc.) do not have strong targeting for brain disease diagnosis, and they are more inclined towards general classification tasks, so there is great room for improving the accuracy of the model. this article (FTM Swin) is based on a learnable mask mechanism with algorithm combined with SVM and swin-transformer, to filter and extract features in the frequency domain graph of 3D images. Fast Fourier transform (FFT) is used to achieve the interaction and combination of frequency domain and spatial domain feature matrices, ultimately completing the classification task of data points in the time domain, which simplifies the feature extraction process and makes the classification boundary of the classifier more accurate. Subsequently, this article utilized the ADNI brain imaging dataset and trained the previous classifier model using a dataset annotated with the hippocampus region, which plays a crucial role in Alzheimer’s disease lesions. this further enhanced the model’s ability to detect lesion sites and provided information on the stage of the condition. Compared with the other eight advanced models for image classification tasks in the past three years, FTM-Swin has an accuracy of 97.18% on the validation set, with the best performance.

关键词： Accuracy three-dimensional displays Frequency-domain analysis Predictive models Brain modeling Feature extraction Transformers Multitasking Alzheimer's disease Time-domain analysis

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Continual Learning Based Ship Detection in Multiple Weathers 22

Continual Learning Based Ship Detection in Multiple Weathers

引用

Proceedings of the 2022 4th International conference on Video, signal and image processing

作者： Baosheng Li Jishui Han Zhenliang Qi Liqiang Gao Ruijie Duan Tongtong Wang Peng Yan Ran Song Xiaolei Li Laiwu Power Supply Company State Grid Shandong Electric Power Company China Shandong TCCA Information Technology Research Institute China School of Control Science and Engineering Shandong University China

ISBN: (纸本)9781450397810

Ship detection in complex weathers is an important application of object detection approaches in real life. In recent years, ship detection approaches for single weather have gradually matured. However, there are few ship detection approaches for multiple weathers, and traditional ship detection approaches are only applicable to a specific weather and have poor generalization to other weathers. In this paper, we propose a continual learning based approach for ship detection to solve the task of ship detection in various weather conditions, while enabling the model to be capable of expanding to new weather conditions. We also present a ship detection dataset under three types of weather, including rain, fog, and snow, using weather effect simulation methods.

关键词： image Enhancement Continual Learning Ship Detection

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Heating Energy Performance Assessment using KNN Classification method: Investigating Test Size, Neighbor Parameter, and Class Count Effects

Heating Energy Performance Assessment using KNN Classificati...

引用

International conference on image and signal processing and their applications (ISPA)

作者： Aissa Boudjella Manal Yasmine Boudjella Chuang Liu Dept. of Applied Mathematics Samarkand International University of Ttechnology Samarkand Uzbekistan Dept. Electrical Engineering Bircham International University Miami USA Dept. of Physics University of Sciences and Technology USTO Oran Algerie Dept. of Thermal Engineering Samarkand International University of Technology Samarkand Uzbekistan

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

We analyze the energy requirements for assessing the heating load of building systems using the k-Nearest Neighbor (KNN) method for classification. Unsupervised transformations of the dataset are employed in learning algorithms. Two distinct data representation classes are generated separately from the original dataset based on captured heating load magnitudes. the unsupervised transformations of the dataset yield 4 and 8 classes, *** evaluate performance, various test and training sizes are employed, comparing measurement outcomes and predicting class label descriptions for two dataset representations as the parameter for the nearest neighbor (k) ranges from 1 to 20. Results obtained from the simulation indicate that accuracy is influenced by factors such as test size, the k parameter, and dataset class count. the training and test accuracy rates reach maximum values of 95% and 85%, respectively. Keeping k constant, the test size has a negligible impact on accuracy. the classes exhibit three critical regions: 1) Region I, where accuracy fluctuates with increasing k in the interval [1]-[5], and 2) Region II, where accuracy declines with increasing k in the interval [5]-[10]. In Region III k [10]-[20], accuracy remains relatively constant. While reducing the number of classes may result in a slight improvement in accuracy by simplifying the classification task, the trade-offs and complexities of increasing the number of classes for data representation should be carefully considered. Although this approach can lead to a more flexible design, it may slightly reduce accuracy.

关键词： Heating systems Training Shape Buildings Machine learning signal processing Size measurement

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Compression of Bayer Colour Filter Array images

Compression of Bayer Colour Filter Array Images

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IEEE International conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)

作者： Shridhar Patil P Deepika R. V. College of Engineering Bengaluru India

ISBN: (数字)9781728106304

ISBN: (纸本)9781728106311

Bayer Colour Filter Array is a matrix of photosensors covered with red, green, and blue colour filters. this setup is advantageous in smartphones as only a third of the required data is captured by the sensor in the camera. the rest of the components based on the colour format can be interpolated using a suitable algorithm to arrive at a full-colour image. Increasing the resolution of the camera sensor will translate to increased bandwidth in the image signal processing pipeline, and consequently power consumption. In addition to that, the bit depth is also on the rise to enhance the colours. these two factors will create a huge impact on the data to be handled in the pertinent processor. Hence, compression of the Bayer data is of immense significance. the existing standard compression schemes can be adapted to suit the Bayer format. Also, several compression schemes, specific to Bayer format have been proposed. Two compression methods, viz. JPEG-LS and Hierarchical Prediction based compression have been tested and the corresponding results are presented in this paper. the former is a standard while the latter has been proposed keeping the Bayer format in mind. Modelling of the algorithms shows that JPEG-LS is best suited in the use cases where lossless compression is desirable, and Hierarchical Prediction based compression is the better option where some amount of loss is acceptable.

关键词： image coding image color analysis Interpolation Standards Context modeling Prediction algorithms Transforms

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