Are Muslims the New Catholics? Europe’s Headscarf Laws in Comparative Historical Perspective

In this paper a biologically-inspired model for partly occluded patterns is proposed. The model is based on the hypothesis that in human visual system occluding patterns play a key role in recognition as well as in reconstructing internal representation for a pattern’s occluding parts. The proposed model is realized with a bidirectional hierarchical neural network. In this network top-down cues, generated by direct connections from the lower to higher levels of hierarchy, interact with the bottom-up information, generated from the un-occluded parts, to recognize occluded patterns. Moreover, positional cues of the occluded as well as occluding patterns, that are computed separately but in the same network, modulate the top-down and bottom-up processing to reconstruct the occluded patterns. Simulation results support the presented hypothesis as well as effectiveness of the model in providing a solution to recognition of occluded patterns. The behavior of the model is in accordance to the known human behavior on the occluded patterns

Automatic Face Recognition System Based on Local Fourier-Bessel Features

We present an automatic face verification system inspired by known properties of biological systems. In the proposed algorithm the whole image is converted from the spatial to polar frequency domain by a Fourier-Bessel Transform (FBT). Using the whole image is compared to the case where only face image regions (local analysis) are considered. The resulting representations are embedded in a dissimilarity space, where each image is represented by its distance to all the other images, and a Pseudo-Fisher discriminator is built. Verification test results on the FERET database showed that the local-based algorithm outperforms the global-FBT version. The local-FBT algorithm performed as state-of-the-art methods under different testing conditions, indicating that the proposed system is highly robust for expression, age, and illumination variations. We also evaluated the performance of the proposed system under strong occlusion conditions and found that it is highly robust for up to 50% of face occlusion. Finally, we automated completely the verification system by implementing face and eye detection algorithms. Under this condition, the local approach was only slightly superior to the global approach.Comment: 2005, Brazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI

Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition

Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically cornbine botton-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from incorrect labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs (cns.bu.edulvisionlab and cns.bu.edu/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.Air Force Office of Scientific Research (F40620-01-1-0423); National Geographic-Intelligence Agency (NMA 201-001-1-2016); National Science Foundation (SBE-0354378; BCS-0235298); Office of Naval Research (N00014-01-1-0624); National Geospatial-Intelligence Agency and the National Society of Siegfried Martens (NMA 501-03-1-2030, DGE-0221680); Department of Homeland Security graduate fellowshi

BIOLOGICALLY INSPIRED OBJECT RECOGNITION SYSTEM

Object Recognition has been a field of interest to many researchers. In fact, it has been referred to as the most important problem in machine or computer vision. Researchers have developed many algorithms to solve the problem of object recognition that are machine vision motivated. On the other hand, biology has motivated researchers to study the visual system of humans and animals such as monkeys and map it into a computational model. Some of these models are based on the feed-forward mechanism of information communication in cortex where the information is communicated between the different visual areas from the lower areas to the top areas in a feed-forward manner; however, the performance of these models has been affected much by the increase of clutter in the scene as well as occlusion. Another mechanism of information processing in the cortex is called the feedback mechanism, where the information from the top areas in the visual system is communicated to the lower areas in a feedback manner; this mechanism has also been mapped into computational models. All these models which are based on the feed-forward or feedback mechanisms have shown promising results. However, during the testing of these models, there have been some issues that affect their performance such as occlusion that prevents objects from being visible. In addition, scenes that contain high amounts of clutter in them, where there are so many objects, have also affected the performance of these models. In fact, the performance has been reported to drop to 74% when systems that are based on these models are subjected to one or both of the issues mentioned above. The human visual system, naturally, utilizes both feed-forward and feedback mechanisms in the operation of perceiving the surrounding environment. Both feed-forward and feedback mechanisms are integrated in a way that makes the visual system of the human outperforms any state-of-the-art system. In this research, a proposed model of object recognition based on the integration concept of the feed-forward and feedback mechanisms in the human visual system is presented

Perceptual Annotation: Measuring Human Vision to Improve Computer Vision

For many problems in computer vision, human learners are considerably better than machines. Humans possess highly accurate internal recognition and learning mechanisms that are not yet understood, and they frequently have access to more extensive training data through a lifetime of unbiased experience with the visual world. We propose to use visual psychophysics to directly leverage the abilities of human subjects to build better machine learning systems. First, we use an advanced online psychometric testing platform to make new kinds of annotation data available for learning. Second, we develop a technique for harnessing these new kinds of information – “perceptual annotations” – for support vector machines. A key intuition for this approach is that while it may remain infeasible to dramatically increase the amount of data and high-quality labels available for the training of a given system, measuring the exemplar-by-exemplar difficulty and pattern of errors of human annotators can provide important information for regularizing the solution of the system at hand. A case study for the problem face detection demonstrates that this approach yields state-ofthe- art results on the challenging FDDB data set.Engineering and Applied SciencesMolecular and Cellular Biolog

The perceptual consequences and neural basis of monocular occlusions

Occluded areas are abundant in natural scenes and play an important role in stereopsis. However, due to the treatment of occlusions as noise by early researchers of stereopsis, this field of study has not seen much development until the last two decades. Consequently, many aspects of depth perception from occlusions are not well understood. The goal of this thesis was to study several such aspects in order to advance the current understanding of monocular occlusions and their neural underpinnings. The psychophysical and computational studies described in this thesis have demonstrated that: 1) occlusions play an important role in defining the shape and depth of occluding surfaces, 2) depth signals from monocular occlusions and disparity interact in complex ways, 3) there is a single mechanism underlying depth perception from monocular occlusions and 4) this mechanism is likely to rely on monocular occlusion geometry. A unified theory of depth computation from monocular occlusions and disparity was proposed based on these findings. A biologically-plausible computational model based on this theory produced results close to observer percepts for a variety of monocular occlusion phenomena

Deep Neural Networks - A Brief History

Introduction to deep neural networks and their history.Comment: 14 pages, 14 figure

Visual motion processing and human tracking behavior

The accurate visual tracking of a moving object is a human fundamental skill that allows to reduce the relative slip and instability of the object's image on the retina, thus granting a stable, high-quality vision. In order to optimize tracking performance across time, a quick estimate of the object's global motion properties needs to be fed to the oculomotor system and dynamically updated. Concurrently, performance can be greatly improved in terms of latency and accuracy by taking into account predictive cues, especially under variable conditions of visibility and in presence of ambiguous retinal information. Here, we review several recent studies focusing on the integration of retinal and extra-retinal information for the control of human smooth pursuit.By dynamically probing the tracking performance with well established paradigms in the visual perception and oculomotor literature we provide the basis to test theoretical hypotheses within the framework of dynamic probabilistic inference. We will in particular present the applications of these results in light of state-of-the-art computer vision algorithms