The effect of relative distance enlargement on visual acuity in the visually impaired

Background: Prescribing magnification is typically based on distance or near visual acuity. this presumes a constant minimum angle of visual resolution with working distance and therefore enlargement of an object moved to a shorter working distance (relative distance enlargement). this study examines this premise in a visually impaired population. methods: distance letter visual acuity was measured prospectively for 380 low vision patients (distance visual acuity between 0.3 and 2.1 logmar) over the age of 57 years, along with near word visual acuity at an appropriate distance for near lens additions from +4 d to +20 D. demographic information, the disease causing low vision, contrast sensitivity, visual field and psychological status were also recorded. results: distance letter acuity was significantly related to (r = 0.84) but on average 0.1 ± 0.2 logmar better (1 ± 2 lines on a logmar chart) than near word acuity at 25 cm with a +4 d lens addition. in 39. 8 per cent of patients, near word acuity was more than 0.1 logmar worse than distance letter acuity. in 11.0 per cent of subjects, near visual acuity was more than 0.1 logmar better than distance letter acuity. the group with near word acuity worse than distance letter acuity also had lower contrast sensitivity. the group with near word acuity better than distance letter acuity was less likely to have age-Related macular degeneration. smaller print size could be read by reducing working distance (achieved by using higher near lens additions) in 86. 1 per cent, although not by as much as predicted by geometric progression in 14. 5 per cent. discussion: although distance letter and near word acuity are highly related, they are on average 1 logmar line different and this varies significantly between individuals. near word acuity did not increase linearly with relative distance enlargement in approximately one in seven visually impaired, suggesting that the measurement of visual resolution over a range of working distances will assist appropriate prescribing of magnification aids.

Inhibition of object identity in inhibition of return:implications for encoding and retrieving inhibitory processes

Inhibition of return (IOR) effects, in which participants detect a target in a cued box more slowly than one in an uncued box, suggest that behavior is aided by inhibition of recently attended irrelevant locations. To investigate the controversial question of whether inhibition can be applied to object identity in these tasks, in the present research we presented faces upright or inverted during cue and/or target sequences. IOR was greater when both cue and target faces were upright than when cue and/or target faces were inverted. Because the only difference between the conditions was the ease of facial recognition, this result indicates that inhibition was applied to object identity. Interestingly, inhibition of object identity affected IOR both whenencoding a cue face andretrieving information about a target face. Accordingly, we propose that episodic retrieval of inhibition associated with object identity may mediate behavior in cuing tasks.

Imagery in multi-modal object learning

Spatial objects may not only be perceived visually but also by touch. We report recent experiments investigating to what extent prior object knowledge acquired in either the haptic or visual sensory modality transfers to a subsequent visual learning task. Results indicate that even mental object representations learnt in one sensory modality may attain a multi-modal quality. These findings seem incompatible with picture-based reasoning schemas but leave open the possibility of modality-specific reasoning mechanisms.

Resolving ambiguous descriptions through visual information

The project “Reference in Discourse” deals with the selection of a specific object from a visual scene in a natural language situation. The goal of this research is to explain this everyday discourse reference task in terms of a concept generation process based on subconceptual visual and verbal information. The system OINC (Object Identification in Natural Communicators) aims at solving this problem in a psychologically adequate way. The system’s difficulties occurring with incomplete and deviant descriptions correspond to the data from experiments with human subjects. The results of these experiments are reported.

Incremental tensor biased discriminant analysis:a new color-based visual tracking method

Most existing color-based tracking algorithms utilize the statistical color information of the object as the tracking clues, without maintaining the spatial structure within a single chromatic image. Recently, the researches on the multilinear algebra provide the possibility to hold the spatial structural relationship in a representation of the image ensembles. In this paper, a third-order color tensor is constructed to represent the object to be tracked. Considering the influence of the environment changing on the tracking, the biased discriminant analysis (BDA) is extended to the tensor biased discriminant analysis (TBDA) for distinguishing the object from the background. At the same time, an incremental scheme for the TBDA is developed for the tensor biased discriminant subspace online learning, which can be used to adapt to the appearance variant of both the object and background. The experimental results show that the proposed method can track objects precisely undergoing large pose, scale and lighting changes, as well as partial occlusion. © 2009 Elsevier B.V.

Normal ECG Recognition for Express-Diagnostics Based on Scale-Space Representation and Dynamic Matching

A novel approach of normal ECG recognition based on scale-space signal representation is proposed. The approach utilizes curvature scale-space signal representation used to match visual objects shapes previously and dynamic programming algorithm for matching CSS representations of ECG signals. Extraction and matching processes are fast and experimental results show that the approach is quite robust for preliminary normal ECG recognition.

Self-organising zooms for decentralised redundancy management in visual sensor networks

When visual sensor networks are composed of cameras which can adjust the zoom factor of their own lens, one must determine the optimal zoom levels for the cameras, for a given task. This gives rise to an important trade-off between the overlap of the different cameras’ fields of view, providing redundancy, and image quality. In an object tracking task, having multiple cameras observe the same area allows for quicker recovery, when a camera fails. In contrast having narrow zooms allow for a higher pixel count on regions of interest, leading to increased tracking confidence. In this paper we propose an approach for the self-organisation of redundancy in a distributed visual sensor network, based on decentralised multi-objective online learning using only local information to approximate the global state. We explore the impact of different zoom levels on these trade-offs, when tasking omnidirectional cameras, having perfect 360-degree view, with keeping track of a varying number of moving objects. We further show how employing decentralised reinforcement learning enables zoom configurations to be achieved dynamically at runtime according to an operator’s preference for maximising either the proportion of objects tracked, confidence associated with tracking, or redundancy in expectation of camera failure. We show that explicitly taking account of the level of overlap, even based only on local knowledge, improves resilience when cameras fail. Our results illustrate the trade-off between maintaining high confidence and object coverage, and maintaining redundancy, in anticipation of future failure. Our approach provides a fully tunable decentralised method for the self-organisation of redundancy in a changing environment, according to an operator’s preferences.

Multiple Dipole Source Models for Scalp-Recorded Event-Related Potentials: Example from Complex Visual Processing in the Human Brain

2000 Mathematics Subject Classification: 62P10, 92C20

Neural activity during object perception in schizophrenia patients is associated with illness duration and affective symptoms

Background - Abnormalities in visual processes have been observed in schizophrenia patients and have been associated with alteration of the lateral occipital complex and visual cortex. However, the relationship of these abnormalities with clinical symptomatology is largely unknown. Methods - We investigated the brain activity associated with object perception in schizophrenia. Pictures of common objects were presented to 26 healthy participants (age = 36.9; 11 females) and 20 schizophrenia patients (age = 39.9; 8 females) in an fMRI study. Results - In the healthy sample the presentation of pictures yielded significant activation (pFWE (cluster) < 0.001) of the bilateral fusiform gyrus, bilateral lingual gyrus, and bilateral middle occipital gyrus. In patients, the bilateral fusiform gyrus and bilateral lingual gyrus were significantly activated (pFWE (cluster) < 0.001), but not so the middle occipital gyrus. However, significant bilateral activation of the middle occipital gyrus (pFWE (cluster) < 0.05) was revealed when illness duration was controlled for. Depression was significantly associated with increased activation, and anxiety with decreased activation, of the right middle occipital gyrus and several other brain areas in the patient group. No association with positive or negative symptoms was revealed. Conclusions - Illness duration accounts for the weak activation of the middle occipital gyrus in patients during picture presentation. Affective symptoms, but not positive or negative symptoms, influence the activation of the right middle occipital gyrus and other brain areas.

Visual driver support system for traffic signs

It has been well documented that traffic accidents that can be avoided occur when the motorists miss or ignore traffic signs. With the attention of drivers getting diverted due to distractions like cell phone conversations, missing traffic signs has become more prevalent. Also, poor weather and other unfriendly driving conditions sometimes makes the motorists not to be alert all the time and see every traffic sign on the road. Besides, most cars do not have any form of traffic assistance. Because of heavy traffic and proliferation of traffic signs on the roads, there is a need for a system that assists the driver not to miss a traffic sign to reduce the probability of an accident. Since visual information is critical for driving, processed video signals from cameras have been chosen to assist drivers. These inexpensive cameras can be easily mounted on the automobile. The objective of the present investigation and the traffic system development is to recognize the traffic signs electronically and alert drivers. For the case study and the system development, five important and critical traffic signs have been selected. They are: STOP, NO ENTER, NO RIGHT TURN, NO LEFT TURN, and YIELD. The system was evaluated processing still pictures taken from the public roads, and the recognition results were presented in an analysis table to indicate the correct identifications and the false ones. The system reached the acceptable recognition rate of 80% for all five traffic signs. The processing rate was about three seconds. The capabilities of MATLAB, VLSI design platforms and coding have been used to generate a visual warning to complement the visual driver support system with a Field Programmable Gate Array (FPGA) on a XUP Virtex-II Pro Development System.

Development of face recognition: Infancy to early childhood

Perception and recognition of faces are fundamental cognitive abilities that form a basis for our social interactions. Research has investigated face perception using a variety of methodologies across the lifespan. Habituation, novelty preference, and visual paired comparison paradigms are typically used to investigate face perception in young infants. Storybook recognition tasks and eyewitness lineup paradigms are generally used to investigate face perception in young children. These methodologies have introduced systematic differences including the use of linguistic information for children but not infants, greater memory load for children than infants, and longer exposure times to faces for infants than for older children, making comparisons across age difficult. Thus, research investigating infant and child perception of faces using common methods, measures, and stimuli is needed to better understand how face perception develops. According to predictions of the Intersensory Redundancy Hypothesis (IRH; Bahrick & Lickliter, 2000, 2002), in early development, perception of faces is enhanced in unimodal visual (i.e., silent dynamic face) rather than bimodal audiovisual (i.e., dynamic face with synchronous speech) stimulation. The current study investigated the development of face recognition across children of three ages: 5 – 6 months, 18 – 24 months, and 3.5 – 4 years, using the novelty preference paradigm and the same stimuli for all age groups. It also assessed the role of modality (unimodal visual versus bimodal audiovisual) and memory load (low versus high) on face recognition. It was hypothesized that face recognition would improve across age and would be enhanced in unimodal visual stimulation with a low memory load. Results demonstrated a developmental trend (F(2, 90) = 5.00, p = 0.009) with older children showing significantly better recognition of faces than younger children. In contrast to predictions, no differences were found as a function of modality of presentation (bimodal audiovisual versus unimodal visual) or memory load (low versus high). This study was the first to demonstrate a developmental improvement in face recognition from infancy through childhood using common methods, measures and stimuli consistent across age.

An incremental multilinear system for human face learning and recognition

This dissertation establishes a novel system for human face learning and recognition based on incremental multilinear Principal Component Analysis (PCA). Most of the existing face recognition systems need training data during the learning process. The system as proposed in this dissertation utilizes an unsupervised or weakly supervised learning approach, in which the learning phase requires a minimal amount of training data. It also overcomes the inability of traditional systems to adapt to the testing phase as the decision process for the newly acquired images continues to rely on that same old training data set. Consequently when a new training set is to be used, the traditional approach will require that the entire eigensystem will have to be generated again. However, as a means to speed up this computational process, the proposed method uses the eigensystem generated from the old training set together with the new images to generate more effectively the new eigensystem in a so-called incremental learning process. In the empirical evaluation phase, there are two key factors that are essential in evaluating the performance of the proposed method: (1) recognition accuracy and (2) computational complexity. In order to establish the most suitable algorithm for this research, a comparative analysis of the best performing methods has been carried out first. The results of the comparative analysis advocated for the initial utilization of the multilinear PCA in our research. As for the consideration of the issue of computational complexity for the subspace update procedure, a novel incremental algorithm, which combines the traditional sequential Karhunen-Loeve (SKL) algorithm with the newly developed incremental modified fast PCA algorithm, was established. In order to utilize the multilinear PCA in the incremental process, a new unfolding method was developed to affix the newly added data at the end of the previous data. The results of the incremental process based on these two methods were obtained to bear out these new theoretical improvements. Some object tracking results using video images are also provided as another challenging task to prove the soundness of this incremental multilinear learning method.

Antipredator behavior and cue recognition by multiple Everglades prey to a novel cichlid predator

Novel predator introductions are thought to have a high impact on native prey, especially in freshwater systems. Prey may fail to recognize predators as a threat, or show inappropriate or ineffective responses. The ability of prey to recognize and respond appropriately to novel predators may depend on the prey’s use of general or specific cues to detect predation threats.We used laboratory experiments to examine the ability of three native Everglades prey species (Eastern mosquitofish, flagfish and riverine grass shrimp) to respond to the presence, as well as to the chemical and visual cues of a native predator (warmouth) and a recentlyintroduced non-native predator (African jewelfish). We used prey from populations that had not previously encountered jewelfish. Despite this novelty, the native warmouth and nonnative jewelfish had overall similar predatory effects, except on mosquitofish, which suffered higher warmouth predation. When predators were present, the three prey taxa showed consistent and strong responses to the non-native jewelfish, which were similar in magnitude to the responses exhibited to the native warmouth. When cues were presented, fish prey responded largely to chemical cues, while shrimp showed no response to either chemical or visual cues. Overall, responses by mosquitofish and flagfish to chemical cues indicated low differentiation among cue types, with similar responses to general and specific cues. The fact that antipredator behaviours were similar toward native and non-native predators suggests that the susceptibility to a novel fish predator may be similar to that of native fishes, and prey may overcome predator novelty, at least when predators are confamilial to other common and longer-established non-native threats.

Dynamic image precompensation for improving visual performance of computer users with ocular aberrations

With the progress of computer technology, computers are expected to be more intelligent in the interaction with humans, presenting information according to the user's psychological and physiological characteristics. However, computer users with visual problems may encounter difficulties on the perception of icons, menus, and other graphical information displayed on the screen, limiting the efficiency of their interaction with computers. In this dissertation, a personalized and dynamic image precompensation method was developed to improve the visual performance of the computer users with ocular aberrations. The precompensation was applied on the graphical targets before presenting them on the screen, aiming to counteract the visual blurring caused by the ocular aberration of the user's eye. A complete and systematic modeling approach to describe the retinal image formation of the computer user was presented, taking advantage of modeling tools, such as Zernike polynomials, wavefront aberration, Point Spread Function and Modulation Transfer Function. The ocular aberration of the computer user was originally measured by a wavefront aberrometer, as a reference for the precompensation model. The dynamic precompensation was generated based on the resized aberration, with the real-time pupil diameter monitored. The potential visual benefit of the dynamic precompensation method was explored through software simulation, with the aberration data from a real human subject. An "artificial eye'' experiment was conducted by simulating the human eye with a high-definition camera, providing objective evaluation to the image quality after precompensation. In addition, an empirical evaluation with 20 human participants was also designed and implemented, involving image recognition tests performed under a more realistic viewing environment of computer use. The statistical analysis results of the empirical experiment confirmed the effectiveness of the dynamic precompensation method, by showing significant improvement on the recognition accuracy. The merit and necessity of the dynamic precompensation were also substantiated by comparing it with the static precompensation. The visual benefit of the dynamic precompensation was further confirmed by the subjective assessments collected from the evaluation participants.

A ROS-based Workspace Control and Trajectory Planner for a seven degrees of freedom Robotic arm

In this Bachelor Thesis I want to provide readers with tools and scripts for the control of a 7DOF manipulator, backed up by some theory of Robotics and Computer Science, in order to better contextualize the work done. In practice, we will see most common software, and developing environments, used to cope with our task: these include ROS, along with visual simulation by VREP and RVIZ, and an almost "stand-alone" ROS extension called MoveIt!, a very complete programming interface for trajectory planning and obstacle avoidance. As we will better appreciate and understand in the introduction chapter, the capability of detecting collision objects through a camera sensor, and re-plan to the desired end-effector pose, are not enough. In fact, this work is implemented in a more complex system, where recognition of particular objects is needed. Through a package of ROS and customized scripts, a detailed procedure will be provided on how to distinguish a particular object, retrieve its reference frame with respect to a known one, and then allow navigation to that target. Together with technical details, the aim is also to report working scripts and a specific appendix (A) you can refer to, if desiring to put things together.