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Defect Detection of Wood Grain images with image Gradient Applied to AE-based generation 6

Defect Detection of Wood Grain Images with Image Gradient Ap...

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6th IEEE International conference on Knowledge Innovation and Invention, ICKII 2023

作者： Si, Jongwook Kim, Sungyoung Kumoh National Institute of Technology Department of Computer Ai Convergence Engineering Gumi Korea Republic of Kumoh National Institute of Technology Department of Computer Engineering Gumi Korea Republic of

ISBN: (纸本)9798350323535

Detecting and managing various types of defects that occur in the manufacturing process is important for product quality control. Detecting flaws in product presentation is an ongoing research topic in computer vision. Recently, deep learning technology has been used to improve defect detection performance compared to traditional computer vision methods. In this study, we proposed a new method for detecting defects on the surface of wooden boards using image reconstruction with an autoencoder-based model and image gradient filters in image processing using edge detection. Although the reconstruction performance of the autoencoder is significantly inferior to the newest technologies, this characteristic is used to remove noise from input images. We used the publicly available anomaly detection dataset, MV-Tec AD, for training and evaluation and presented experimental results focusing on the "Wood"category. the proposed method in this study demonstrated better detection performance with image processing. © 2023 IEEE.

关键词： image reconstruction

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Machine learning content adaptive filters for image de-blurring 13

Machine learning content adaptive filters for image de-blurr...

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13th International conference on Interfaces and Human computer Interaction 2019, IHCI 2019, 12th International conference on Game and Entertainment Technologies 2019, GET 2019 and 13th International conference on computer graphics, Visualization, computer vision and image processing 2019, CGVCVIP 2019

作者： Mainali, Pradip OneSpan Belgium

ISBN: (纸本)9789898533913

this paper proposes a novel algorithm to recover blindly a sharp image from its degraded form by pixel pattern classification and filtering by using the deconvolution filters trained for the corresponding pixel pattern. Noise amplification is a well-known phenomenon that arises due to an ill-posed nature of the deconvolution, which is tackled better with the pattern classification, thereby state-of-the-art image de-blurring quality is demonstrated, yet at the very low complexity. For the computational efficiency, the pixel pattern classes are learnt in a multi-layer structure consisting of the coarser and the finer patterns, reducing the pattern search complexity in the dictionary by a factor of 6 without losing any quality. As compared to deep-learning based methods, the proposed approach provides a smaller memory footprint and lightweight implementation. For lightweight and real-time implementation, the paper also validates a simple class matching metric of l1-norm. the proposed method is suitable for embedded applications such as camera, TV systems, etc. © Copyright 2019 IADIS Press. All rights reserved.

关键词： Pixels

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Effect of Particle image Velocimetry processing on CT Dicom images in a deep learning based pipeline for lung nodule segmentationg± 13

Effect of Particle Image Velocimetry processing on CT Dicom ...

引用

13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Suji, R Jenkin Godfrey, W Wilfred Dhar, Joydip ABV-IIITM Gwalior Madhya Pradesh Gwalior India

ISBN: (纸本)9781450398237

Segmentation of lung nodules is critical to computer-aided diagnosis systems for lung cancer diagnosis. In recent times, with the application of deep learning in medical image processing, novel architectures have been proposed for segmentation. However, most of these architectures work on a single modality and work on a single image or whole image volume. In this work, we present Particle image Velocimetry approach as a preprocessing technique by processing sequence of two input images. We used with and without PIV processed images on a deep learning-based pipeline to extract features from these images for candidate lung nodule detection. the results show that the use of particle image velocimetry processing significantly improves the performance of the deep learning pipeline. © 2022 ACM.

关键词： Pipelines

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Reconstruction of Sparse-View Tomography via Banded Matrices 10

Reconstruction of Sparse-View Tomography via Banded Matrices

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10th indian conference on vision, graphics and image processing (ICVGIP)

作者： Prasad, T. Kumar, P. U. Praveen Sastry, C. S. Jampana, P. V. Indian Inst Technol Hyderabad Dept Math Hyderabad Telangana India Indian Inst Technol Hyderabad Dept Chem Engn Hyderabad Telangana India

ISBN: (纸本)9783319681245;9783319681238

Computed Tomography (CT) is one of the significant research areas in medical image analysis. One of the main aspects of CT that researchers remain focused, is on reducing the dosage as Xrays are generally harmful to human bodies. In order to reduce radiation dosage, compressed sensing (CS) based methodologies appear to be promising. the basic premise is that medical images have inherent sparsity in some transformation domain. As a result, CS provides the possibility of recovering a high quality image from fewer projection data. In general, the sensing matrix in CT is generated from Radon projections by appropriately sampling the radial and angular parameters. In our work, by restricting the number of such parameters, we generate an under-determined linear system involving projection (Radon) data and a sparse sensing matrix, bringing thereby the problem into CS framework. Among various recent solvers, the Split-Bregman iterative scheme has of late become popular due to its suitability for solving a wide variety of optimization problems. Intending to exploit the underlying structure of sensing matrix, the present work analyzes its properties and finds a banded structure for an associated intermediate matrix. Using this observation, we simplify the Split-Bregman solver, proposing thereby a CT-specific solver of low complexity. We also provide the efficacy of proposed method empirically.

关键词： Compressed sensing Split-Bregman Computed tomography Least squares

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Realtime motion detection based on the spatio-temporal median filter using GPU integral histograms 12

Realtime motion detection based on the spatio-temporal media...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Poostchi, Mahdieh Palaniappan, Kannappan Bunyak, Filiz Seetharaman, Guna Dept. of Computer Science University of Missouri Columbia MO United States Air Force Research Laboratory Rome NY United States

ISBN: (纸本)9781450316606

Motion detection using background modeling is a widely used technique in object tracking. To meet the demands of real-time multi-target tracking applications in large and/or high resolution imagery fast parallel algorithms for motion detection are desirable. One common method for background modeling is to use an adaptive 3D median filter that is updated appropriately based on the video sequence. We describe a parallel 3D spatiotemporal median filter algorithm implemented in CUDA for many core graphics processing Unit (GPU) architectures using the integral histogram as a building block to support adaptive window sizes. Both 2D and 3D median filters are also widely used in many other computer vision tasks like denoising, segmentation, and recognition. Although fast sequential median algorithms exist, improving performance using parallelization is attractive to reduce the time needed for motion detection in order to support more complex processing in multi-target tracking systems, large high resolution aerial video imagery and 3D volumetric processing. Results show the frame rate of the GPU implementation was 60 times faster than the CPU version for a 1K x 1K image reaching 49 fr/sec and 21 times faster for 512 x 512 frame sizes reaching 194 fr/sec. We characterize performance of the parallel 3D median filter for different image sizes and varying number of histogram bins and show selected results for motion detection. © 2012 ACM.

关键词： graphics processing unit

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Real-time hands-free immersive image navigation system using Microsoft Kinect 2.0 and Leap Motion Controller 5

Real-time hands-free immersive image navigation system using...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2015

作者： Sreejith, M. Rakesh, Siddharth Gupta, Samik Biswas, Samprit Das, Partha Pratim Department of Computer Science and Engineering Indian Institute of Technology Kharagpur721302 India

ISBN: (纸本)9781467385640

this paper presents a real-time hands-free immersive image navigation system that can respond to various gestures and voice commands. We combine Microsoft Kinect 2.0 and Leap Motion Controller, on a single platform, to define a wide range of hand and finger gestures, and voice commands. Using these we create a robust and natural immersive image navigation experience. At present, the immersive environment is simulated using an 1×3 array of 17 inch Dell monitors. For gesture recognition we use three different algorithms, depending on the nature of gesture and the sensor used to capture it. We use Microsoft Speech API to integrate voice commands to the system. Experiments are conducted with 5 subjects each performing 10 gestures each. the system achieves a user accuracy ranging from 94% to 100% for different hands-free controls. We are currently integrating other sensors including an Eye-Gaze Tracker and extending to 2×4 array of monitors for better immersive experience. © 2015 IEEE.

关键词： Navigation systems

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Proceedings of the 2009 6th International conference on computer graphics, Imaging and Visualization: New Advances and Trends, CGIV2009

Proceedings of the 2009 6th International Conference on Comp...

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2009 6th International conference on computer graphics, Imaging and Visualization: New Advances and Trends, CGIV2009

ISBN: (纸本)9780769537894

the proceedings contain 92 papers. the topics discussed include: GPU supported patch-based tessellation for dual subdivision;ball robot and the graphics generation and its image calculation based on cad geometric model;particle importance based fluid simulation;facial expression representation using a quadratic deformation model;implementation of ant colony algorithm based on GPU;a fast method for real-time computation of approximated global illumination;expansion of communication in media art through the intelligent interaction;a motion retargeting method for topologically different characters;real-time simulation of large area nearshore wave for marine simulator;a preliminary study of human motion based on actor physiques using motion capture;providing novel and useful data for game development using usability expert evaluation and testing;digital video watermarking in the discrete wavelet transform domain;and style-sheets extraction from existing digital contents by image processing for web-based BML contents management system.

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COMA-Boost: Co-Operative Multi Agent AdaBoost 16

COMA-Boost: Co-Operative Multi Agent AdaBoost

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Lahiri, Avisek Paria, Biswajit Biswas, Prabir Kumar IIT Kharagpur Dept E&ECE Kharagpur W Bengal India IIT Kharagpur Dept CSE Kharagpur W Bengal India

ISBN: (纸本)9781450347532

A Abstract : Multi feature space representation is a common practise in computer vision applications. Traditional features such as HOG, SIFT, SURF etc., individually encapsulates certain discriminative cues for visual classification. On the other hand, each layer of a deep neural network generates multi ordered representations. In this paper we present a novel approach for such multi feature representation learning using Adaptive Boosting (AdaBoost). General practise in AdaBoost [8] is to concatenate components of feature spaces and train base learners to classify examples as correctly/incorrectly classified. We posit that multi feature space learning should be viewed as a derivative of cooperative multi agent learning. To this end, we propose a mathematical framework to leverage performance of base learners over each feature space, gauge a measure of "difficulty" of training space and finally make soft weight updates rather than strict binary weight updates prevalent in regular AdaBoost. this is made possible by periodically sharing of response states by our learner agents in the boosting framework. theoretically, such soft weight update policy allows in finite combinations of weight updates on training space compared to only two possibilities in AdaBoost. this opens up the opportunity to identify 'more difficult' examples compared to 'less difficult' examples. We test our model on traditional multi feature representation of MNIST handwritten character dataset and 100-Leaves classification challenge. We consistently outperform traditional and variants of multi view boosting in terms of accuracy while margin analysis reveals that proposed method fosters formation of more con fident ensemble of learner agents. As an application of using our model in conjecture with deep neural network, we test our model on the challenging task of retinal blood vessel segmentation from fundus images of DRIVE dataset by using kernel dictionaries from layers of unsupervised trained stac

关键词： AdaBoost multi view learning deep learning

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Performance Comparison of Object Detection algorithms with different Feature Extractors 6

Performance Comparison of Object Detection algorithms with d...

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6th International conference on Signal processing and Integrated Networks (SPIN)

作者： Gupta, Archit Puri, Raghav Verma, Mrinal Gunjyal, Siddharth Kumar, Ashish BVCOE ECE Dept New Delhi India

ISBN: (纸本)9781728113807

In this work, speed vs accuracy of different Neural Network architectures using alternate feature extractors in the field of Object Detection is being computed, thereby finding the fastest and most accurate architecture out of the lot in order to carry out Object Detection. We made use of three architectures and three extractors to build different combinations of models in order to compute mAP, which is the metric used or commenting upon accuracy. COCO data-set has been used to extract sample images and the work is implemented on TensorFlow library.

关键词： computer vision Object Detection image processing TensorFlow Faster R-CNN App Development

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Fast and Generalisable License Plate Re-identification using Neural Embedding of Fisher Vectors 11

Fast and Generalisable License Plate Re-identification using...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Kumar, Abhinav Gupta, Shantanu Kozitsky, Vladimir Madhvanath, Sriganesh Univ Utah Salt Lake City UT 84112 USA Univ Wisconsin Madison WI USA MKS Instruments Andover MA USA eBay New York NY USA Conduent Labs Res Triangle Pk NC USA

ISBN: (纸本)9781450366151

We consider the license plate re-identification task, treated here as a one-shot image retrieval problem. Our objective is to learn a feature representation for license plate images, such that a single training image of a given license plate (referred to as a template image) is sufficient to perform nearest-neighbour retrieval with high accuracy at test time. Also, the feature representation should ideally be generalisable across datasets and should be extractable in real-time on resource-constrained embedded hardware or a moderately powerful cellphone. We evaluate representations from person re-identification (re-id) literature, learned from a trained deep convolutional network as well with those derived from a trained Fisher vector. While the convolutional network features perform better than the Fisher vector, we obtain comparable results from a hybrid model projecting the Fisher vector into a lower-dimensional space via two fully connected layers called f2nn using the triplet loss. the proposed hybrid model f2nn generates features which outperform and generalise better than convolutional features on datasets dissimilar to the training corpus. the model can be trained in stages and takes significantly less time to extract features. Further, it uses much smaller feature dimensions for license plate images resulting in faster re-identification, and is therefore well-suited for resource-constrained platforms such as mobile devices.

关键词： image Retrieval Feature Generation Fisher Vectors Neural Networks Triplet Loss Signature Matching Generalisation Dimensionality Reduction License Plate Re-identification Optical Character Recognition

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