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Review on image processing Based Adversarial Example Defenses in computer vision 6

Review on Image Processing Based Adversarial Example Defense...

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6th IEEE Int Conf on Big Data Security on Cloud (IEEE BigDataSecurity) / 6th IEEE Int Conf High Performance and Smart Computing (IEEE HPSC) / 5th IEEE Int Conf on Intelligent Data and Security (IEEE IDS)

作者： Qiu, Meikang Qiu, Han Harrisburg Univ Dept Comp Sci Harrisburg PA 17101 USA Inst Polytech Paris Telecom Paris LTCI Palaiseau France

ISBN: (纸本)9781728168739

Recent research works showed that deep neural networks are vulnerable to adversarial examples, which are usually maliciously created by carefully adding deliberate and imperceptible perturbations to examples. Several states of the art defense methods are proposed based on the existing image processing methods like image compression and image denoising. However, such approaches are not the final optimal solution for defense adversarial perturbations in DNN models. In this paper, we reviewed two main approaches to deploying image processing methods as a defense. By analyzing and discussing the remaining issues, we present two open questions for future research direction including the definition of adversarial perturbations and noises, the novel defense-aware threat model. A further research direction is also given by re-thinking the impacts of adversarial perturbations on all frequency bands.

关键词： Deep learning adversarial examples image denoising image compression computer vision

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A Review: Object Detection Models 6

A Review: Object Detection Models

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6th International conference for Convergence in Technology (I2CT)

作者： Shetty, Aakash K. Saha, Ishani Sanghvi, Rutvik M. Save, Siddhesh A. Patel, Yashkumar J. NMIMS Univ Comp Engn Dept Mumbai Maharashtra India

ISBN: (纸本)9781728188768

Object detection and recognition is an integral part of computer vision. It has a multitude of applications ranging from character recognition to video analysis. Object detection now play a crucial role in industries like security, video, medical, sports, and many more. With the latest research, rapid development in deep learning, and computational image understanding object detection will be more prevalent. In the future, we will be able to implement object detection in much more mundane workings of our life. Our review paper will focus on the existing techniques that are present in the community and how each technique is different from the other techniques. We will start with an introduction about object detection as a tool and technique. this will be followed by a brief background discussing the most fundamental steps involved and the basic architecture of object detection. then, we focus on several existing techniques and perform comparative analyses of these techniques to draw meaningful conclusions. these techniques include two-stage models such as R-CNN, Fast R-CNN, and Faster R-CNN and single-stage models YOLOv1, YOLOv2, YOLOv3, YOLOv4, and SSD. Finally, we will conclude by presenting some directions that provide a scope for further analyses along that direction and hope to suggest different techniques that can be implemented in different sectors for optimal results.

关键词： YOLO R-CNN Deep Learning Object Detection image processing Convolutional Neural Networks Single-Stage Object Detection Model Two-Stage Object Detection Model

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Classification of Gender in Celebrity Cartoon images 6th

Classification of Gender in Celebrity Cartoon Images

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6th International conference on computer vision and image processing, CVIP 2021

作者： Prajna, S. Vinay Kumar, N. Guru, D.S. Department of Studies in Computer Science Manasagangotri University of Mysore Mysuru570006 India NTT Data Services Bangalore India

ISBN: (纸本)9783031113451

this paper presents a model by integrating deep architecture-based feature extraction with classical learning algorithms for the effective gender classification of celebrity cartoon images. the proposed model makes use of colored celebrity cartoon images. For face representation, we have extracted features using various conventional approaches such as Scale-Invariant Feature Transform (SIFT), Fast Fourier Transform (FFT), and Discrete Cosine Transform (DCT), and the deep approach using deep features from a pre-trained architecture. After extracting the features to classify the gender of celebrity cartoon images, we have used conventional classifiers. Here in this work, we have recommended the integration of random forest (RF) with deep features extracted from FaceNet pre-trained architecture. We have used an existing dataset, the Cartoon Faces in the wild (IIIT-CFW) database of celebrity cartoon faces consisting of 8928 cartoon images. At the same time, we segregate it into two distinct classes, male and female, comprising 4594 and 2073, respectively. there is an imbalance in the proportion of the dataset;we have attempted to balance the unbalanced data using the SMOTE technique. Lastly, to classify the gender of celebrity cartoon images, we have adopted distinct supervised conventional learning models. the classification results are validated using classification metrics such as accuracy, precision, recall, f-measure. We show that our approach model establishes a state-of-the-art F1-score of 95% on the task of gender recognition of cartoons faces. the model has been compared against other existing contemporary models for its effectiveness. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature extraction

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Design and Implementation of Flow Visualization Software based on MATLAB 5

Design and Implementation of Flow Visualization Software bas...

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5th IEEE Information Technology, Networking, Electronic and Automation Control conference, ITNEC 2021

作者： Teng, Xianshu Zhang, Mingnan Li, Yilin Qiao, Wensheng Shanghai University The Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai200072 China China Aerospace Science and Industry Corporation The 601st Institute The 6th Academy Hohhot010076 China

ISBN: (纸本)9781665415989

With the continuous development of scientific visualization, visualization has been applied to various fields. Flow field visualization is an important part of the visual field, by using computer graphics, computer vision, and image processing technology, it can use intuitive graphical images to describe the flow field structure of the complex, disorder, which makes the flow field visualization in the field of meteorology, aviation and geography have great application prospect. this paper uses MATLAB App Designer to design a flow field visualization software, which mainly displays the scalar or vector velocity field in two dimensions and three dimensions. the software has a powerful interactive function, can choose different flow field display modes, can choose a specific two-dimensional section for separate display. With this software, flow field data can be clearly, succinctly, and intuitively displayed, and key information such as flow field distribution, movement trend, and velocity change can be understood. In addition, the software uses MATLAB App Designer to encapsulate the form of desktop independent APP, which is simple and convenient to use. © 2021 IEEE.

关键词： Application programs

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Enhancing label transfer in non-parametric scene parsing by superpixel-based dense alignment 21

Enhancing label transfer in non-parametric scene parsing by ...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Bhowmick, Alexy Saharia, Sarat Hazarika, Shyamanta M. Dept. of Computer Science and Engineering Assam Don Bosco University Assam Guwahati781017 India Dept. of Computer Science and Engineering Tezpur University Assam Tezpur784028 India Biomimetic Robotics and Artificial Intelligence Lab Dept. of Mechanical Engineering IIT Guwahati Assam Guwahati781039 India

ISBN: (纸本)9781450391276

Contemporary (parametric) scene parsing methods are learning-based and mostly operate in a closed-universe scenario. We introduce a non-parametric scene parsing framework that is model-free, data-driven, and scales naturally to growing data. the scene parsing performance in the non-parametric approach depends on reliable dense correspondence or alignment across scenes for label transfer. Incorrect correspondence is known to affect the scene parsing results adversely. We propose a label transfer approach that relies on the dense correspondence of super-pixel pairs (in a query and candidate image) matched by a homogeneous kernel map to guide semantic label transfer. the aggregation (fusing) of multiple labels is done through a simple heuristic aggregation scheme (simple majority voting). the Markov Random Field (MRF) provides a principled probabilistic framework for combining the disparate information in the smoothing stage and ensures plausible labeling results. Evaluation results show that our non-parametric system obtains competitive scene parsing performance on the standard SIFT Flow and MSRC-21 datasets. © 2021 ACM.

关键词： Superpixels

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Cauliflower Leaf Disease Detection Using computerized Techniques 6

Cauliflower Leaf Disease Detection Using Computerized Techni...

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6th IEEE International conference on Computing, Communication and Automation, ICCCA 2021

作者： Orin, Taslima Yesmin Mojumdar, Mayen Uddin Siddiquee, Shah Md. Tanvir Chakraborty, Narayan Ranjan Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665414739

Crop cultivation is an important role in the agriculture industry. Presently, food loss is primarily caused by sick crops, which affects growth rate and increase. High yield depends a lot on its growth. However, now crop leaf disease has become a significant problem in agriculture as a result of which the quality and growth rate of yield in agriculture is declining day by day. through this paper, we have tried to diagnose the leaf disease of a particular crop (cauliflower). In this study, we collected data from each cauliflower leaf and created a dataset, and we divided our study was split into two sections: leaf disease detection and machine learning techniques. To detect leaf disease, we have used Matlab and various algorithms of classic machine learning such as Nav Bayes (NB), Decision Tree (TT), Random Forest (RF), Support Vector Machine (SVM), Secular Minimal Optimization (SMO). For each, we determined the accuracy, retraction, F-measurement, accuracy, and ROC values classification. this paper describes a simple but effective technique for reviewing leaf disease detection evidence and applying it to image processing and computer vision. © 2021 IEEE.

关键词： Support vector machines

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Multi Fish Detection and Tracking Using RGB-D Vector

Multi Fish Detection and Tracking Using RGB-D Vector

引用

International conference on Computing Communication Control and Automation (ICCUBEA)

作者： Aditi Bhateja Brejesh Lall Prem K Kalra Muskan Karnani Indian Institute of Technology Delhi India Banasthali Vidyapith Delhi India

ISBN: (纸本)9781665484527

Multi fish detection and tracking has got cogent significance in the area of image processing. the tracking methodologies has improved tremendously during the last decades, however, it is still a complex problem to solve. the importance of object tracking can be seen in various applications like video communication, machine inspection, robot navigation and augmented reality. this paper presents Microsoft Kinect camera sensor-based fish detection and tracking. the Kinect sensor provides depth and RGB images along with reduced noises and holes. the admiring traits of the depth and color information given by the Kinect sensor brings up various opportunities to resolve elementary issues in the field of computer vision. Fish detection and tracking in a constrained environment (aquarium) aids in the research of marine biologists to understand and study the behaviour of fishes. the use of commodity hardware devices like Microsoft Kinect for detection and tracking is economical for the researchers and also helps to automate the study of fishes round the clock without any requirement of manual intervention. In this paper, we have calibrated depth and RGB images to detect and track multiple fishes present in each frame of a recorded video. Due to the bleak body deformation of fish that results in their complex motion and multiple occlusions, robust fish tracking from video image sequences is a highly challenging task. In this paper a methodology is proposed to overcome these problems and multiple fishes can be tracked using centroid of the fish as central component and also the detection accuracy for RGB and RGBD frames are compared. the results show that the performance of RGBD method has 12.51% more accuracy on average over RGB based input.

关键词： Performance evaluation computer vision three-dimensional displays image color analysis Fish Cameras Trajectory

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Feature generation for long-tail classification 21

Feature generation for long-tail classification

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Vigneswaran, Rahul Law, Marc T. Balasubramanian, Vineeth N. Tapaswi, Makarand Indian Institute of Technology Hyderabad India NVIDIA IIIT Hyderabad India

ISBN: (纸本)9781450391276

the visual world naturally exhibits an imbalance in the number of object or scene instances resulting in a long-tailed distribution. this imbalance poses significant challenges for classification models based on deep learning. Oversampling instances of the tail classes attempts to solve this imbalance. However, the limited visual diversity results in a network with poor representation ability. A simple counter to this is decoupling the representation and classifier networks and using oversampling only to train the classifier. In this paper, instead of repeatedly re-sampling the same image (and thereby features), we explore a direction that attempts to generate meaningful features by estimating the tail category's distribution. Inspired by ideas from recent work on few-shot learning [53], we create calibrated distributions to sample additional features that are subsequently used to train the classifier. through several experiments on the CIFAR-100-LT (long-tail) dataset with varying imbalance factors and on mini-imageNet-LT (long-tail), we show the efficacy of our approach and establish a new state-of-the-art. We also present a qualitative analysis of generated features using t-SNE visualizations and analyze the nearest neighbors used to calibrate the tail class distributions. Our code is available at https://***/rahulvigneswaran/TailCalibX. © 2021 ACM.

关键词： Deep learning

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An Autoencoder Convolutional Neural Network Framework for Sarcopenia Detection Based on Multi-frame Ultrasound image Slices 17th

An Autoencoder Convolutional Neural Network Framework for Sa...

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17th IFIP WG 12.5 International conference on Artificial Intelligence Applications and Innovations (AIAI) / 6th 5G-PINE Workshop / AI-BIO Workshop / DAAI Workshop / DARE Workshop / EEAI Workshop / 10th MHDW Workshop

作者： Pintelas, Emmanuel Livieris, Ioannis E. Barotsis, Nikolaos Panayiotakis, George Pintelas, Panagiotis Univ Patras Dept Math Patras Greece Univ Patras Dept Med Patras Greece

ISBN: (纸本)9783030791520;9783030791506;9783030791490

Multi-Frame classification applications are constituted by instances composed by a package of image frames, such as videos, which frequently require very high computational re-sources. Furthermore, when the input instances contain a large proportion of noise, then the incorporation of noise filtering pre-processing techniques are considered essential. In this work, we propose an AutoEncoder Convolutional Neural Network model for Multi-Frame input applications. the AutoEncoder model aims to reduce the huge dimensional size of the initial instances, compress useful information while simultaneously remove the noise from each frame. Finally, a Convolutional Neural Network classification model is applied on the new transformed and compressed data instances. As a case study scenario for the proposed framework, we utilize Ultrasound images (image slices/frames extracted from every patient via a portable ultrasound device) for Sarcopenia detection. Based on our experimental re-sults the proposed framework outperforms traditional approaches.

关键词： AutoEncoders Deep learning Transfer learning computer vision image classification Sarcopenia detection Ultrasound

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Detection of Diabetic Retinopathy in Retinal Fundus image Using YOLO-RF Model 6

Detection of Diabetic Retinopathy in Retinal Fundus Image Us...

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6th International conference on image Information processing, ICIIP 2021

作者： Lalitha, R. Padyana, Ajith Science Acharya Institute of Technology Department of Computer Bangalore India

ISBN: (纸本)9781665433617

Diabetic Retinopathy (DR) is one of the complications of diabetes that impacts blood vessels of a retina because of increased blood sugar. So, it's better to detect and treat at the initial stage. the biggest challenges are inadequate technology assistance for ophthalmologists and difficulty in the manual identification process. these issues can be addressed by technological advancement in the field of Artificial Intelligence for automizing the identification and detection process. An automatic detection helps to identify different stages of DR and helps ophthalmologists to provide treatment according to the stages in order to avoid vision loss. In this paper, proposed system aims to detect the various stages of DR that allows ophthalmologists to identify the DR at its different stage. the proposed system classifies the image data into defined classes using YOLO-RF. the proposed system compared with various traditional machine learning classifiers such as SVM, Decision Tree (DT), Random Forest (RF) and DL model such as YOLO. We have used data from the retinal fundus images of KAGGLE and IDRID. the result showed that proposed system YOLO-RF model performed with good accuracy of 99.3%, precision score of 97.2 and Recall of 99.1. © 2021 IEEE.

关键词： Eye protection

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