Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision

To appear in CVIUStudies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for explaining biological observations. Even though it seems well recognised that computational models of biological vision can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological vision literature as very few studies in biology are organised at a task level. In this paper we aim to bridge this gap by providing a computer vision task centric presentation of models primarily originating in biological vision studies. Not only do we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modelling biological vision, but also we consider three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we present some recent models in biological vision and highlight a few models that we think are promising for future investigations in computer vision. To this extent, this paper provides new insights and a starting point for investigators interested in the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities leading to the development of synergistic models of artificial and biological vision

A biologically inspired spiking model of visual processing for image feature detection

To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence edge or corner features, commonly called interest points are often used for this purpose. Experimental research has illustrated that biological vision systems use neuronal circuits to extract particular features such as edges or corners from visual scenes. Inspired by this biological behaviour, this paper proposes a biologically inspired spiking neural network for the purpose of image feature extraction. Standard digital images are processed and converted to spikes in a manner similar to the processing that transforms light into spikes in the retina. Using a hierarchical spiking network, various types of biologically inspired receptive fields are used to extract progressively complex image features. The performance of the network is assessed by examining the repeatability of extracted features with visual results presented using both synthetic and real images

Learning Enriched Features for Real Image Restoration and Enhancement

With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography, medical imaging, and remote sensing. Recently, convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. Existing CNN-based methods typically operate either on full-resolution or on progressively low-resolution representations. In the former case, spatially precise but contextually less robust results are achieved, while in the latter case, semantically reliable but spatially less accurate outputs are generated. In this paper, we present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network and receiving strong contextual information from the low-resolution representations. The core of our approach is a multi-scale residual block containing several key elements: (a) parallel multi-resolution convolution streams for extracting multi-scale features, (b) information exchange across the multi-resolution streams, (c) spatial and channel attention mechanisms for capturing contextual information, and (d) attention based multi-scale feature aggregation. In a nutshell, our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Extensive experiments on five real image benchmark datasets demonstrate that our method, named as MIRNet, achieves state-of-the-art results for a variety of image processing tasks, including image denoising, super-resolution, and image enhancement. The source code and pre-trained models are available at https://github.com/swz30/MIRNet.Comment: Accepted for publication at ECCV 202

Deep Learning Approach for Chemistry and Processing History Prediction from Materials Microstructure

Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-field method can predict the spatio-temporal evolution of the materials’ microstructure, they are not efficient techniques for predicting processing and chemistry if a specific morphology is desired. In this study, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition simulation of Fe–Cr–Co alloy created by the phase-field method. The mixed dataset, which includes both images, i.e., the morphology of Fe distribution, and continuous data, i.e., the Fe minimum and maximum concentration in the microstructures, are used as input data, and the spinodal temperature and initial chemical composition are utilized as the output data to train the proposed deep neural network. The proposed convolutional layers were compared with pretrained EfficientNet convolutional layers as transfer learning in microstructure feature extraction. The results show that the trained shallow network is effective for chemistry prediction. However, accurate prediction of processing temperature requires more complex feature extraction from the morphology of the microstructure. We benchmarked the model predictive accuracy for real alloy systems with a Fe–Cr–Co transmission electron microscopy micrograph. The predicted chemistry and heat treatment temperature were in good agreement with the ground truth

Design of Resistive Synaptic Devices and Array Architectures for Neuromorphic Computing

abstract: Over the past few decades, the silicon complementary-metal-oxide-semiconductor (CMOS) technology has been greatly scaled down to achieve higher performance, density and lower power consumption. As the device dimension is approaching its fundamental physical limit, there is an increasing demand for exploration of emerging devices with distinct operating principles from conventional CMOS. In recent years, many efforts have been devoted in the research of next-generation emerging non-volatile memory (eNVM) technologies, such as resistive random access memory (RRAM) and phase change memory (PCM), to replace conventional digital memories (e.g. SRAM) for implementation of synapses in large-scale neuromorphic computing systems. Essentially being compact and “analog”, these eNVM devices in a crossbar array can compute vector-matrix multiplication in parallel, significantly speeding up the machine/deep learning algorithms. However, non-ideal eNVM device and array properties may hamper the learning accuracy. To quantify their impact, the sparse coding algorithm was used as a starting point, where the strategies to remedy the accuracy loss were proposed, and the circuit-level design trade-offs were also analyzed. At architecture level, the parallel “pseudo-crossbar” array to prevent the write disturbance issue was presented. The peripheral circuits to support various parallel array architectures were also designed. One key component is the read circuit that employs the principle of integrate-and-fire neuron model to convert the analog column current to digital output. However, the read circuit is not area-efficient, which was proposed to be replaced with a compact two-terminal oscillation neuron device that exhibits metal-insulator-transition phenomenon. To facilitate the design exploration, a circuit-level macro simulator “NeuroSim” was developed in C++ to estimate the area, latency, energy and leakage power of various neuromorphic architectures. NeuroSim provides a wide variety of design options at the circuit/device level. NeuroSim can be used alone or as a supporting module to provide circuit-level performance estimation in neural network algorithms. A 2-layer multilayer perceptron (MLP) simulator with integration of NeuroSim was demonstrated to evaluate both the learning accuracy and circuit-level performance metrics for the online learning and offline classification, as well as to study the impact of eNVM reliability issues such as data retention and write endurance on the learning performance.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Pushing the Boundaries of Biomolecule Characterization through Deep Learning

The importance of studying biological molecules in living organisms can hardly be overstated as they regulate crucial processes in living matter of all kinds.Their ubiquitous nature makes them relevant for disease diagnosis, drug development, and for our fundamental understanding of the complex systems of biology.However, due to their small size, they scatter too little light on their own to be directly visible and available for study.Thus, it is necessary to develop characterization methods which enable their elucidation even in the regime of very faint signals. Optical systems, utilizing the relatively low intrusiveness of visible light, constitute one such approach of characterization. However, the optical systems currently capable of analyzing single molecules in the nano-sized regime today either require the species of interest to be tagged with visible labels like fluorescence or chemically restrained on a surface to be analyzed.Ergo, there exist effectively no methods of characterizing very small biomolecules under naturally relevant conditions through unobtrusive probing. Nanofluidic Scattering Microscopy is a method introduced in this thesis which bridges this gap by enabling the real-time label-free size-and-weight determination of freely diffusing molecules directly in small nano-sized channels. However, the molecule signals are so faint, and the background noise so complex with high spatial and temporal variation, that standard methods of data analysis are incapable of elucidating the molecules\u27 properties of relevance in any but the least challenging conditions.To remedy the weak signal, and realize the method\u27s full potential, this thesis\u27 focus is the development of a versatile deep-learning based computer-vision platform to overcome the bottleneck of data analysis. We find that said platform has considerably increased speed, accuracy, precision and limit of detection compared to standard methods, constituting even a lower detection limit than any other method of label-free optical characterization currently available. In this regime, hitherto elusive species of biomolecules become accessible for study, potentially opening up entirely new avenues of biological research. These results, along with many others in the context of deep learning for optical microscopy in biological applications, suggest that deep learning is likely to be pivotal in solving the complex image analysis problems of the present and enabling new regimes of study within microscopy-based research in the near future

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas