The Role of Early Recurrence in Improving Visual Representations

This dissertation proposes a computational model of early vision with recurrence, termed as early recurrence. The idea is motivated from the research of the primate vision. Specifically, the proposed model relies on the following four observations. 1) The primate visual system includes two main visual pathways: the dorsal pathway and the ventral pathway; 2) The two pathways respond to different visual features; 3) The neurons of the dorsal pathway conduct visual information faster than that of the neurons of the ventral pathway; 4) There are lower-level feedback connections from the dorsal pathway to the ventral pathway. As such, the primate visual system may implement a recurrent mechanism to improve visual representations of the ventral pathway. Our work starts from a comprehensive review of the literature, based on which a conceptualization of early recurrence is proposed. Early recurrence manifests itself as a form of surround suppression. We propose that early recurrence is capable of refining the ventral processing using results of the dorsal processing. Our work further defines a set of computational components to formalize early recurrence. Although we do not intend to model the true nature of biology, to verify that the proposed computation is biologically consistent, we have applied the model to simulate a neurophysiological experiment of a bar-and-checkerboard and a psychological experiment involving a moving contour illusion. Simulation results indicated that the proposed computation behaviourally reproduces the original observations. The ultimate goal of this work is to investigate whether the proposal is capable of improving computer vision applications. To do this, we have applied the model to a variety of applications, including visual saliency and contour detection. Based on comparisons against the state-of-the-art, we conclude that the proposed model of early recurrence sheds light on a generally applicable yet lightweight approach to boost real-life application performance

A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)

Object contour completion by combining object recognition and local edge cues

We developed a top-down and bottom-up segmentation of objects using shape contours through a two-stage procedure.First, the object was identified using an edge-based contour feature and then the object contour was obtained using a constraint optimization procedure based on the results from the earlier identified contours.The initial object detection provides object category specific information for the contour completion to be effected.We argue that top-down bottom-up interaction architecture has plausible neurological correlates.This method has an advantage in that it does not require learning boundaries with large datasets

Graph Signal Processing: Overview, Challenges and Applications

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE

Object detection and recognition in complex scenes

Dissertação de Mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014Contour-based object detection and recognition in complex scenes is one of the most dificult problems in computer vision. Object contours in complex scenes can be fragmented, occluded and deformed. Instances of the same class can have a wide range of variations. Clutter and background edges can provide more than 90% of all image edges. Nevertheless, our biological vision system is able to perform this task effortlessly. On the other hand, the performance of state-of-the-art computer vision algorithms is still limited in terms of both speed and accuracy. The work in this thesis presents a simple, efficient and biologically motivated method for contour-based object detection and recognition in complex scenes. Edge segments are extracted from training and testing images using a simple contour-following algorithm at each pixel. Then a descriptor is calculated for each segment using Shape Context, including an offset distance relative to the centre of the object. A Bayesian criterion is used to determine the discriminative power of each segment in a query image by means of a nearest-neighbour lookup, and the most discriminative segments vote for potential bounding boxes. The generated hypotheses are validated using the k nearest-neighbour method in order to eliminate false object detections. Furthermore, meaningful model segments are extracted by finding edge fragments that appear frequently in training images of the same class. Only 2% of the training segments are employed in the models. These models are used as a second approach to validate the hypotheses, using a distancebased measure based on nearest-neighbour lookups of each segment of the hypotheses. A review of shape coding in the visual cortex of primates is provided. The shape-related roles of each region in the ventral pathway of the visual cortex are described. A further step towards a fully biological model for contourbased object detection and recognition is performed by implementing a model for meaningful segment extraction and binding on the basis of two biological principles: proximity and alignment. Evaluation on a challenging benchmark is performed for both k nearestneighbour and model-segment validation methods. Recall rates of the proposed method are compared to the results of recent state-of-the-art algorithms at 0.3 and 0.4 false positive detections per image.Erasmus Mundus action 2, Lot IIY 2011 Scholarship Program

The research for shape-based visual recognition of object categories

摘要 视觉目标类识别旨在识别图像中特定的某类目标，基于形状的目标类识别是目前计算机视觉研究的热点之一。真实图像中物体姿态的多样性以及环境的复杂性，给目标的形状提取和识别带来巨大挑战。本文借鉴生物视觉机制的研究成果，对基于形状的目标类识别算法进行研究。主要研究内容如下： 1. 研究与形状认知相关的视觉机制，分析形状知觉整体性的生理基础及其生理模型。以形状知觉整体性为基础，建立基于形状的目标类识别系统框架。框架既重视整体性在自下而上的特征加工中的作用，也重视整体约束在自上而下的识别中的作用。 2. 受生物视觉上的整合野模型启发，本文提出了一个三阶段轮廓检测算法。阶段1利用结构自适应滤波器平滑...Categorical object detection addresses determining the number of instances of a particular object category in an image, and localizing those instances in space and scale. The shape-based visual recognition of object categories is one of hot topics in computer vision. The diversity of poses of targets and complexity of the environment in real images bring huge challenges to shape extraction and obj...学位：工学博士院系专业：信息科学与技术学院自动化系_控制理论与控制工程学号：2322006015337

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