Inter-query Learning in Content-based Image Retrieval

Computer Scienc

Robust Machine Learning In Computer Vision

Deep neural networks have been shown to be successful in various computer vision tasks such as image classification and object detection. Although deep neural networks have exceeded human performance in many tasks, robustness and reliability are always the concerns of using deep learning models. On the one hand, degraded images and videos aggravate the performance of computer vision tasks. On the other hand, if the deep neural networks are under adversarial attacks, the networks can be broken completely. Motivated by the vulnerability of deep neural networks, I analyze and develop image restoration and adversarial defense algorithms towards a vision of robust machine learning in computer vision. In this dissertation, I study two types of degradation making deep neural networks vulnerable. The first part of the dissertation focuses on face recognition at long range, whose performance is severely degraded by atmospheric turbulence. The theme is on improving the performance and robustness of various tasks in face recognition systems such as facial keypoints localization, feature extraction, and image restoration. The second part focuses on defending adversarial attacks in the images classification task. The theme is on exploring adversarial defense methods that can achieve good performance in standard accuracy, robustness to adversarial attacks with known threat models, and good generalization to other unseen attacks

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.Framework of the IQONIC Project; European Union’s Horizon 2020 Research and Innovation Program

Proceedings of the Third International Workshop on Mathematical Foundations of Computational Anatomy - Geometrical and Statistical Methods for Modelling Biological Shape Variability

International audienceComputational anatomy is an emerging discipline at the interface of geometry, statistics and image analysis which aims at modeling and analyzing the biological shape of tissues and organs. The goal is to estimate representative organ anatomies across diseases, populations, species or ages, to model the organ development across time (growth or aging), to establish their variability, and to correlate this variability information with other functional, genetic or structural information. The Mathematical Foundations of Computational Anatomy (MFCA) workshop aims at fostering the interactions between the mathematical community around shapes and the MICCAI community in view of computational anatomy applications. It targets more particularly researchers investigating the combination of statistical and geometrical aspects in the modeling of the variability of biological shapes. The workshop is a forum for the exchange of the theoretical ideas and aims at being a source of inspiration for new methodological developments in computational anatomy. A special emphasis is put on theoretical developments, applications and results being welcomed as illustrations. Following the successful rst edition of this workshop in 20061 and second edition in New-York in 20082, the third edition was held in Toronto on September 22 20113. Contributions were solicited in Riemannian and group theoretical methods, geometric measurements of the anatomy, advanced statistics on deformations and shapes, metrics for computational anatomy, statistics of surfaces, modeling of growth and longitudinal shape changes. 22 submissions were reviewed by three members of the program committee. To guaranty a high level program, 11 papers only were selected for oral presentation in 4 sessions. Two of these sessions regroups classical themes of the workshop: statistics on manifolds and diff eomorphisms for surface or longitudinal registration. One session gathers papers exploring new mathematical structures beyond Riemannian geometry while the last oral session deals with the emerging theme of statistics on graphs and trees. Finally, a poster session of 5 papers addresses more application oriented works on computational anatomy

Classification task-driven efficient feature extraction from tensor data

Automatic classification of complex data is an area of great interest as it allows to make efficient use of the increasingly data intensive environment that characterizes our modern world. This thesis presents to two contributions to this research area. Firstly, the problem of discriminative feature extraction for data organized in multidimensional arrays. In machine learning, Linear Discriminant Analysis (LDA) is a popular discriminative feature extraction method based on optimizing a Fisher type criterion to find the most discriminative data projection. Various extension of LDA to high-order tensor data have been developed. The method proposed is called the Efficient Greedy Feature Extraction method (EGFE). This method avoids solving optimization problems of very high dimension. Also, it can be stopped when the extracted features are deemed to be sufficient for a proper discrimination of the classes. Secondly, an application of EGFE methods to early detection of dementia disease. For the early detection task, four cognitive scores are used as the original data while we employ our greedy feature extraction method to derive discriminative privileged information feature from fMRI data. The results from the experiments presented in this thesis demonstrate the advantage of using privileged information for the early detection task

DEEP LEARNING-BASED APPROACHES FOR IMAGE RESTORATION

Image restoration is the operation of taking a corrupted or degraded low-quality image and estimating a high-quality clean image that is free of degradations. The most common degradations that affect the quality of the image are blur, atmospheric turbulence, adverse weather conditions (like rain, haze, and snow), and noise. Images captured under the influence of these corruptions or degradations can significantly affect the performance of subsequent computer vision algorithms such as segmentation, recognition, object detection, and tracking. With such algorithms becoming vital components in several applications such as autonomous navigation and video surveillance, it is increasingly important to develop sophisticated algorithms to remove these degradations and high-quality clean images. These reasons have motivated a plethora of research on single image restoration methods to remove such effects. Recently, following the success of deep learning-based convolutional neural networks, many approaches have been proposed to remove the degradations from the corrupted image. We study the following single image restoration problems: (i) atmospheric turbulence removal, (ii) deblurring, (iii) removing distortions introduced by adverse weather conditions such as rain, haze, and snow, and (iv) removing noise. However, existing single image restoration techniques suffer from the following major limitations: (i) They construct global priors without taking into account that these degradations can have a different effect on different local regions of the image. (ii) They use synthetic datasets for training which often results in sub-optimal performance on the real-world images, typically because of the distributional-shift between synthetic and real-world degraded images. (iii) Existing semi-supervised approaches don't account for the effect of unlabeled or real-world degraded image on semi-supervised performance. To address these limitations, we propose supervised image restoration techniques where we use uncertainty to improve the restoration performance. To overcome the second limitation, we propose a Gaussian process-based pseudo-labeling approach to leverage the real-world rain information and train the deraininng network in a semi-supervised fashion. Furthermore, to address the third limitation we theoretically study the effect of unlabeled images on semi-supervised performance and propose an adaptive rejection technique to boost semi-supervised performance. Finally, we recognize that existing supervised and semi-supervised methods need some kind of paired labeled data to train the network, and training on any kind of synthetic paired clean-degraded images may not completely solve the domain gap between synthetic and real-world degraded image distributions. Thus we propose a self-supervised transformer-based approach for image denoising. Here, given a noisy image, we generate multiple down-sampled images and learn the joint relation between these down-sampled using the Gaussian process to denoise the image

Bifurcation analysis of the Topp model

In this paper, we study the 3-dimensional Topp model for the dynamicsof diabetes. We show that for suitable parameter values an equilibrium of this modelbifurcates through a Hopf-saddle-node bifurcation. Numerical analysis suggests thatnear this point Shilnikov homoclinic orbits exist. In addition, chaotic attractors arisethrough period doubling cascades of limit cycles.Keywords Dynamics of diabetes · Topp model · Reduced planar quartic Toppsystem · Singular point · Limit cycle · Hopf-saddle-node bifurcation · Perioddoubling bifurcation · Shilnikov homoclinic orbit · Chao