Biologically Inspired Computer Vision/ Applications of Computational Models of Primate Visual Systems in Computer Vision and Image Processing

Biologically Inspired Computer VisionApplications of Computational Models of Primate Visual Systems in Computer Vision and Image Processing Reza Hojjaty Saeedy Abstract Biological vision systems are remarkable at extracting and analyzing the information that is essential for vital functional needs. They perform all these tasks with both high sensitivity and strong reliability. They can efficiently and quickly solve most of the difficult computa- tional problems that are still challenging for artificial systems, such as scene segmentation, 3D/depth perception, motion recognition, etc. So it is no surprise that biological vision systems have been a source of inspiration for computer vision problems. In this research, we aim to provide a computer vision task centric framework out of models primarily originating in biological vision studies. We try to address two specific tasks here: saliency detection and object classification. In both of these tasks we use features extracted from computational models of biological vision systems as a starting point for further processing. Saliency maps are 2D topographic maps that catch the most conspicuous regions of a scene, i.e. the pixels in an image that stand out against their neighboring pixels. So these maps can be thought of as representations of the human attention process and thus have a lot of applications in computer vision. We propose a cascade that combines two well- known computational models for perception of color and orientation in order to simulate the responses of the primary areas of the primate visual cortex. We use these responses as inputs to a spiking neural network(SNN) and finally the output of this SNN will serve as the input to our post-processing algorithm for saliency detection. Object classification/detection is the most studied task in computer vision and machine learning and it is interesting that while it looks trivial for humans it is a difficult problem for artificial systems. For this part of the thesis we also design a pipeline including feature extraction using biologically inspired systems, manifold learning for dimensionality reduction and self-organizing(vector quantization) neural network as a supervised method for prototype learning

Biologically Inspired Computer Vision/ Applications of Computational Models of Primate Visual Systems in Computer Vision and Image Processing

Biologically Inspired Computer VisionApplications of Computational Models of Primate Visual Systems in Computer Vision and Image Processing Reza Hojjaty Saeedy Abstract Biological vision systems are remarkable at extracting and analyzing the information that is essential for vital functional needs. They perform all these tasks with both high sensitivity and strong reliability. They can efficiently and quickly solve most of the difficult computa- tional problems that are still challenging for artificial systems, such as scene segmentation, 3D/depth perception, motion recognition, etc. So it is no surprise that biological vision systems have been a source of inspiration for computer vision problems. In this research, we aim to provide a computer vision task centric framework out of models primarily originating in biological vision studies. We try to address two specific tasks here: saliency detection and object classification. In both of these tasks we use features extracted from computational models of biological vision systems as a starting point for further processing. Saliency maps are 2D topographic maps that catch the most conspicuous regions of a scene, i.e. the pixels in an image that stand out against their neighboring pixels. So these maps can be thought of as representations of the human attention process and thus have a lot of applications in computer vision. We propose a cascade that combines two well- known computational models for perception of color and orientation in order to simulate the responses of the primary areas of the primate visual cortex. We use these responses as inputs to a spiking neural network(SNN) and finally the output of this SNN will serve as the input to our post-processing algorithm for saliency detection. Object classification/detection is the most studied task in computer vision and machine learning and it is interesting that while it looks trivial for humans it is a difficult problem for artificial systems. For this part of the thesis we also design a pipeline including feature extraction using biologically inspired systems, manifold learning for dimensionality reduction and self-organizing(vector quantization) neural network as a supervised method for prototype learning

Proceedings of the 2019 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

In 2019 fand wieder der jährliche Workshop des Fraunhofer IOSB und des Lehrstuhls für Interaktive Echtzeitsysteme des Karlsruher Insitut für Technologie statt. Die Doktoranden beider Institutionen präsentierten den Fortschritt ihrer Forschung in den Themen Maschinelles Lernen, Machine Vision, Messtechnik, Netzwerksicherheit und Usage Control. Die Ideen dieses Workshops sind in diesem Buch gesammelt in der Form technischer Berichte

TimeSOAP: Tracking high-dimensional fluctuations in complex molecular systems via time variations of SOAP spectra

Many molecular systems and physical phenomena are controlled by local fluctuations and microscopic dynamical rearrangements of the constitutive interacting units that are often difficult to detect. This is the case, for example, of phase transitions, phase equilibria, nucleation events, and defect propagation, to mention a few. A detailed comprehension of local atomic environments and of their dynamic rearrangements is essential to understand such phenomena and also to draw structure-property relationships useful to unveil how to control complex molecular systems. Considerable progress in the development of advanced structural descriptors [e.g., Smooth Overlap of Atomic Position (SOAP), etc.] has certainly enhanced the representation of atomic-scale simulations data. However, despite such efforts, local dynamic environment rearrangements still remain difficult to elucidate. Here, exploiting the structurally rich description of atomic environments of SOAP and building on the concept of time-dependent local variations, we developed a SOAP-based descriptor, TimeSOAP (τSOAP), which essentially tracks time variations in local SOAP environments surrounding each molecule (i.e., each SOAP center) along ensemble trajectories. We demonstrate how analysis of the time-series τSOAP data and of their time derivatives allows us to detect dynamic domains and track instantaneous changes of local atomic arrangements (i.e., local fluctuations) in a variety of molecular systems. The approach is simple and general, and we expect that it will help shed light on a variety of complex dynamical phenomena

Image Anomaly Detection and Localization with Position and Neighborhood Information

Anomaly detection and localization are essential in many areas, where collecting enough anomalous samples for training is almost impossible. To overcome this difficulty, many existing methods use a pre-trained network to encode input images and non-parametric modeling to estimate the encoded feature distribution. In the modeling process, however, they overlook that position and neighborhood information affect the distribution of normal features. To use the information, in this paper, the normal distribution is estimated with conditional probability given neighborhood features, which is modeled with a multi-layer perceptron network. At the same time, positional information can be used by building a histogram of representative features at each position. While existing methods simply resize the anomaly map into the resolution of an input image, the proposed method uses an additional refine network that is trained from synthetic anomaly images to perform better interpolation considering the shape and edge of the input image. For the popular industrial dataset, MVTec AD benchmark, the experimental results show \textbf{99.52\%} and \textbf{98.91\%} AUROC scores in anomaly detection and localization, which is state-of-the-art performance

Recent Advances and Applications of Machine Learning in Metal Forming Processes

Machine learning (ML) technologies are emerging in Mechanical Engineering, driven by the increasing availability of datasets, coupled with the exponential growth in computer performance. In fact, there has been a growing interest in evaluating the capabilities of ML algorithms to approach topics related to metal forming processes, such as: Classification, detection and prediction of forming defects; Material parameters identification; Material modelling; Process classification and selection; Process design and optimization. The purpose of this Special Issue is to disseminate state-of-the-art ML applications in metal forming processes, covering 10 papers about the abovementioned and related topics

Recent advances and applications of machine learning in metal forming processes

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