On Some Applications of Nonlinear Differential Equations in Image Processing: Concepts and Electronic Implementation

International audienc

A Novel Cloning Template Designing Method by Using an Artificial Bee Colony Algorithm for Edge Detection of CNN Based Imaging Sensors

Cellular Neural Networks (CNNs) have been widely used recently in applications such as edge detection, noise reduction and object detection, which are among the main computer imaging processes. They can also be realized as hardware based imaging sensors. The fact that hardware CNN models produce robust and effective results has attracted the attention of researchers using these structures within image sensors. Realization of desired CNN behavior such as edge detection can be achieved by correctly setting a cloning template without changing the structure of the CNN. To achieve different behaviors effectively, designing a cloning template is one of the most important research topics in this field. In this study, the edge detecting process that is used as a preliminary process for segmentation, identification and coding applications is conducted by using CNN structures. In order to design the cloning template of goal-oriented CNN architecture, an Artificial Bee Colony (ABC) algorithm which is inspired from the foraging behavior of honeybees is used and the performance analysis of ABC for this application is examined with multiple runs. The CNN template generated by the ABC algorithm is tested by using artificial and real test images. The results are subjectively and quantitatively compared with well-known classical edge detection methods, and other CNN based edge detector cloning templates available in the imaging literature. The results show that the proposed method is more successful than other methods

Dense implementations of binary cellular nonlinear networks : from CMOS to nanotechnology

This thesis deals with the design and hardware realization of the cellular neural/nonlinear network (CNN)-type processors operating on data in the form of black and white (B/W) images. The ultimate goal is to achieve a very compact yet versatile cell structure that would allow for building a network with a very large spatial resolution. It is very important to be able to implement an array with a great number of cells on a single die. Not only it improves the computational power of the processor, but it might be the enabling factor for new applications as well. Larger resolution can be achieved in two ways. First, the cell functionality and operating principles can be tailored to improve the layout compactness. The other option is to use more advanced fabrication technology – either a newer, further downscaled CMOS process or one of the emerging nanotechnologies. It can be beneficial to realize an array processor as two separate parts – one dedicated for gray-scale and the other for B/W image processing, as their designs can be optimized. For instance, an implementation of a CNN dedicated for B/W image processing can be significantly simplified. When working with binary images only, all coefficients in the template matrix can also be reduced to binary values. In this thesis, such a binary programming scheme is presented as a means to reduce the cell size as well as to provide the circuits composed of emerging nanodevices with an efficient programmability. Digital programming can be very fast and robust, and leads to very compact coefficient circuits. A test structure of a binary-programmable CNN has been designed and implemented with standard 0.18 µm CMOS technology. A single cell occupies only 155 µm2, which corresponds to a cell density of 6451 cells per square millimeter. A variety of templates have been tested and the measured chip performance is discussed. Since the minimum feature size of modern CMOS devices has already entered the nanometer scale, and the limitations of further scaling are projected to be reached within the next decade or so, more and more interest and research activity is attracted by nanotechnology. Investigation of the quantum physics phenomena and development of new devices and circuit concepts, which would allow to overcome the CMOS limitations, is becoming an increasingly important science. A single-electron tunneling (SET) transistor is one of the most attractive nanodevices. While relying on the Coulomb interactions, these devices can be connected directly with a wire or through a coupling capacitance. To develop suitable structures for implementing the binary programming scheme with capacitive couplings, the CNN cell based on the floating gate MOSFET (FG-MOSFET) has been designed. This approach can be considered as a step towards a programmable cell implementation with nanodevices. Capacitively coupled CNN has been simulated and the presented results confirm the proper operation. Therefore, the same circuit strategies have also been applied to the CNN cell designed for SET technology. The cell has been simulated to work well with the binary programming scheme applied. This versatile structure can be implemented either as a pure SET design or as a SET-FET hybrid. In addition to the designs mentioned above, a number of promising nanodevices and emerging circuit architectures are introduced.reviewe

Non-Uniform Cellular Neural Network and its Applications

セルラーニューラルネットワーク(CNN)には連続時間的な ものと，離散時間的なものがあり，本研究は主に後者について議論 する. CNNは1988年にカリフォルニア大学バークレ校のL.O.Chua 教授らによって提案され，現在，アメリカ，ヨーロッパを中心に盛 んに研究が進められている. CNNは従来のニューラルネットワー クと異なり，近傍のセルとのみ結合しているため集積回路としての 実現が容易であり，画像処理用CNNとして注目されている. 第一章では，ニューラルネットワークに関する研究の動向，お よび，人間の目と同様な処理機能を持つ連続時間CNNに関する研 究の動向と，この論文で議論している離散時間CNNの背景につい て簡単に述べている.　 第二章では，離散時間的な非均一CNNとして，二相同期信号 の回路モデルを提案し，その安定性等について議論してある. この モデルは各セルについて二相同期信号1個で実現できるため，VLS1 の実現が容易であると云う特徴がある. まず，モデルの動作原理か ら状態電圧，出力電圧の動作領域を明かにした. このことは物理的 に実現可能なCNNを設計するために重要である.つぎに，安定性 を議論するためにエネルギ一関数からリアフノフ関数を定義し，そ の関数の時間単調減少の条件を利用して，大域的な安定性を持つ離 散時間CNNの設計方法を明らかにした. 第三章では，非線形システムにおける平衡点の求解法について 議論している.連想記憶に用いられるCNNは多くの平衡点をもち， 入力信号によってどの平衡点に到達するかが決定せられる. ロバス トな連想記憶用CNNを設計するためには，このような平衡点を調 べることが必要である. ここでは，解曲線追跡法に基づいた複数解 の求解アルゴリズムを提案している. このアルゴリズムは急激な解 曲線の変化を効率よく追跡できるように，エルミー卜予測子とBDF 積分公式に基づいている. また，大規模系に適用できるようにニュ ートン・ラフソン法の代わりにブラウンの反復法を採用している. このようなアルゴリズを採用することによりロバストなCNNの設 計が可能となる. 第四章では， 離散時間CNNによる連想記憶について述べてい る. 連想記憶は人間の脳の基本的な機能であり，ニューラルネット ワーク応用研究の一つとして古くから盛んに研究されている.本章 では， 離散的なCNNを用いた外積学習アルゴリズムと中点写像ア ルゴリズムの2種類の記憶方式を提案し，その性質を解明している. まず，前者は，入力パターンに対して，エネルギー関数の値が最少 になるようにニューロン間の接続を表す重み行列を設定しようと云 うものであり，これはHebbの理論に基づいている. また，上のよう な手法で学習されたパターンを連想記憶できる条件について議論し た.中点写像アルゴリズムは重み行列の設定方法に対して， いま考 えている中心セルからの近傍を定義し，近傍に存在するセルの状態、 をベクトル表示する.これを全てパターンについて実行し，このよ うにして決定された行列によって写像されるセルのパターンが，元 の中心セルと同一のパターンを持つように重み行列を設定しようと いうもので，数学的には一般化逆行列の理論に基づいている.この ような学習方法の特徴は入力された画像が全て連想されると云うこ とである. 本章では，さらに，このことを応用例によって実証した. 第五章では，画像処理への応用として，輪郭抽出，雑音除去， 視覚パターンの認識に対する離散的なCNNについて述べている. 多くの結果から処理時間は従来のものと比較して極端に短縮される ことが分かった. また，不均一離散時間CNNによって，一つ画面 中に多数の異なる視覚パターンを同時に認識できることも分かった。 第六章では，不均一離散的なCNNの特徴と今後の問題点につ いて述べている

Contributions to unsupervised and supervised learning with applications in digital image processing

311 p. : il.[EN]This Thesis covers a broad period of research activities with a commonthread: learning processes and its application to image processing. The twomain categories of learning algorithms, supervised and unsupervised, have beentouched across these years. The main body of initial works was devoted tounsupervised learning neural architectures, specially the Self Organizing Map.Our aim was to study its convergence properties from empirical and analyticalviewpoints.From the digital image processing point of view, we have focused on twobasic problems: Color Quantization and filter design. Both problems have beenaddressed from the context of Vector Quantization performed by CompetitiveNeural Networks. Processing of non-stationary data is an interesting paradigmthat has not been explored with Competitive Neural Networks. We have statesthe problem of Non-stationary Clustering and related Adaptive Vector Quantizationin the context of image sequence processing, where we naturally havea Frame Based Adaptive Vector Quantization. This approach deals with theproblem as a sequence of stationary almost-independent Clustering problems.We have also developed some new computational algorithms for Vector Quantizationdesign.The works on supervised learning have been sparsely distributed in time anddirection. First we worked on the use of Self Organizing Map for the independentmodeling of skin and no-skin color distributions for color based face localization. Second, we have collaborated in the realization of a supervised learning systemfor tissue segmentation in Magnetic Resonance Imaging data. Third, we haveworked on the development, implementation and experimentation with HighOrder Boltzmann Machines, which are a very different learning architecture.Finally, we have been working on the application of Sparse Bayesian Learningto a new kind of classification systems based on Dendritic Computing. This lastresearch line is an open research track at the time of writing this Thesis

Efficient Human Activity Recognition in Large Image and Video Databases

Vision-based human action recognition has attracted considerable interest in recent research for its applications to video surveillance, content-based search, healthcare, and interactive games. Most existing research deals with building informative feature descriptors, designing efficient and robust algorithms, proposing versatile and challenging datasets, and fusing multiple modalities. Often, these approaches build on certain conventions such as the use of motion cues to determine video descriptors, application of off-the-shelf classifiers, and single-factor classification of videos. In this thesis, we deal with important but overlooked issues such as efficiency, simplicity, and scalability of human activity recognition in different application scenarios: controlled video environment (e.g.~indoor surveillance), unconstrained videos (e.g.~YouTube), depth or skeletal data (e.g.~captured by Kinect), and person images (e.g.~Flicker). In particular, we are interested in answering questions like (a) is it possible to efficiently recognize human actions in controlled videos without temporal cues? (b) given that the large-scale unconstrained video data are often of high dimension low sample size (HDLSS) nature, how to efficiently recognize human actions in such data? (c) considering the rich 3D motion information available from depth or motion capture sensors, is it possible to recognize both the actions and the actors using only the motion dynamics of underlying activities? and (d) can motion information from monocular videos be used for automatically determining saliency regions for recognizing actions in still images

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book

New Directions for Contact Integrators

Contact integrators are a family of geometric numerical schemes which guarantee the conservation of the contact structure. In this work we review the construction of both the variational and Hamiltonian versions of these methods. We illustrate some of the advantages of geometric integration in the dissipative setting by focusing on models inspired by recent studies in celestial mechanics and cosmology.Comment: To appear as Chapter 24 in GSI 2021, Springer LNCS 1282

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal