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

Hypothesis-based image segmentation for object learning and recognition

Denecke A. Hypothesis-based image segmentation for object learning and recognition. Bielefeld: Universität Bielefeld; 2010.This thesis addresses the figure-ground segmentation problem in the context of complex systems for automatic object recognition as well as for the online and interactive acquisition of visual representations. First the problem of image segmentation in general terms and next its importance for object learning in current state-of-the-art systems is introduced. Secondly a method using artificial neural networks is presented. This approach on the basis of Generalized Learning Vector Quantization is investigated in challenging scenarios such as the real-time figure-ground segmentation of complex shaped objects under continuously changing environment conditions. The ability to fulfill these requirements characterizes the novelty of the approach compared to state-of-the-art methods. Finally our technique is extended towards online adaption of model complexity and the integration of several segmentation cues. This yields a framework for object segmentation that is applicable to improve current systems for visual object learning and recognition

Online Multi-Stage Deep Architectures for Feature Extraction and Object Recognition

Multi-stage visual architectures have recently found success in achieving high classification accuracies over image datasets with large variations in pose, lighting, and scale. Inspired by techniques currently at the forefront of deep learning, such architectures are typically composed of one or more layers of preprocessing, feature encoding, and pooling to extract features from raw images. Training these components traditionally relies on large sets of patches that are extracted from a potentially large image dataset. In this context, high-dimensional feature space representations are often helpful for obtaining the best classification performances and providing a higher degree of invariance to object transformations. Large datasets with high-dimensional features complicate the implementation of visual architectures in memory constrained environments. This dissertation constructs online learning replacements for the components within a multi-stage architecture and demonstrates that the proposed replacements (namely fuzzy competitive clustering, an incremental covariance estimator, and multi-layer neural network) can offer performance competitive with their offline batch counterparts while providing a reduced memory footprint. The online nature of this solution allows for the development of a method for adjusting parameters within the architecture via stochastic gradient descent. Testing over multiple datasets shows the potential benefits of this methodology when appropriate priors on the initial parameters are unknown. Alternatives to batch based decompositions for a whitening preprocessing stage which take advantage of natural image statistics and allow simple dictionary learners to work well in the problem domain are also explored. Expansions of the architecture using additional pooling statistics and multiple layers are presented and indicate that larger codebook sizes are not the only step forward to higher classification accuracies. Experimental results from these expansions further indicate the important role of sparsity and appropriate encodings within multi-stage visual feature extraction architectures

Interactive and life-long learning for identification and categorization tasks

Abstract (engl.) This thesis focuses on life-long and interactive learning for recognition tasks. To achieve these targets the separation into a short-term memory (STM) and a long-term memory (LTM) is proposed. For the incremental build up of the STM a similarity-based one-shot learning method was developed. Furthermore two consolidation algorithms were proposed enabling the incremental learning of LTM representations. Based on the Learning Vector Quantization (LVQ) network architecture an error-based node insertion rule and a node dependent learning rate are proposed to enable life-long learning. For learning of categories additionally a forward-feature selection method was introduced to separate co-occurring categories. In experiments the performance of these learning methods could be shown for difficult visual recognition problems