When natural scenes are viewed, a multitude of objects that are stable in their environments are brought in and out of view by eye movements. The posterior parietal cortex is crucial for the analysis of space, visual attention and movement 1 . Neurons in one of its subdivisions, the lateral intraparietal area (LIP), have visual responses to stimuli appearing abruptly at particular retinal locations (their receptive fields)2. We have tested the responses of LIP neurons to stimuli that entered their receptive field by saccades. Neurons had little or no response to stimuli brought into their receptive field by saccades, unless the stimuli were behaviourally significant. We established behavioural significance in two ways: either by making a stable stimulus task-relevant, or by taking advantage of the attentional attraction of an abruptly appearing stimulus. Our results show that under ordinary circumstances the entire visual world is only weakly represented in LIP. The visual representation in LIP is sparse, with only the most salient or behaviourally relevant objects being strongly represented.

The representation of visual salience in monkey parietal cortex

In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals-saccades, fixations, and blinks-and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance (mRMR) feature selection. We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and person-independent (leave-one-person-out) training, we obtain an average precision of 76.1 percent and recall of 70.5 percent over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.

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Eye Movement Analysis for Activity Recognition Using Electrooculography

Current computed tomography (CT) technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities. In addition, research trends and challenges are identified and directions for future research are discussed.

Computer analysis of computed tomography scans of the lung: a survey

In one embodiment of the invention, a method for controlling a robotic surgical tool is disclosed. The method for controlling a robotic surgical tool includes moving a monitor displaying an image of a robotic surgical tool; sensing motion of the monitor; and translating the sensed motion of the monitor into motion of the robotic surgical tool.

Controlling a robotic surgical tool with a display monitor

Review of health information technology usability study methodologies

Visual search: psychophysical models and practical applications

The identification of bronchi on Computed Tomography (CT) images of the lungs provides valuable clinical information in patients with suspected airways diseases including bronchiectasis, emphysema, or constrictive obliterative bronchiolitis. The automated recognition of the airways is, therefore, an important part of a diagnosis aid system for resolving potential ambiguities associated with intensity-based feature extractors. On CT images, near-perpendicular cross sections of bronchi normally appear as elliptical rings and this paper presents a novel technique for their recognition. The proposed method, the edge-radius-symmetry (ERS) transform, is based on the analysis of the distribution of edges in local polar coordinates. Pixels are ranked according to local edge (E) strength, radial (R), uniformity and local symmetry (S). A discrete implementation of the technique is provided which reduces the computational cost of the ERS transform by using a geometric approximation of the intensity patterns. The identification of the adjacent pulmonary vessels with template matching then allows for the automated measurement of bronchial dilatation and bronchial wall thickening. Computationally, the method compares favorably with other methods such as the Hough transform. Noise-sensitivity of the technique was evaluated on a set of synthetic images and 9 patients under investigation for suspected airways disease. Agreement for the automated scoring of the presence and severity of bronchial abnormalities was demonstrated to be comparable to that of an experienced radiologist (kappa statistics /spl kappa/>0.5).

ERS transform for the automated detection of bronchial abnormalities on CT of the lungs

This paper presents a new method of knowledge gathering for decision support in image understanding based on information extracted from the dynamics of saccadic eye movements. The framework involves the construction of a generic image feature extraction library, from which the feature extractors that are most relevant to the visual assessment by domain experts are determined automatically through factor analysis. The dynamics of the visual search are analyzed by using the Markov model for providing training information to novices on how and where to look for image features. The validity of the framework has been evaluated in a clinical scenario whereby the pulmonary vascular distribution on Computed Tomography images was assessed by experienced radiologists as a potential indicator of heart failure. The performance of the system has been demonstrated by training four novices to follow the visual assessment behavior of two experienced observers. In all cases, the accuracy of the students improved from near random decision making (33%) to accuracies ranging from 50% to 68%.

The use of visual search for knowledge gathering in image decision support

Adaptive energy-efficient clustering path planning routing protocols for heterogeneous wireless sensor networks

An efficient and secure environment is necessary for data transmission and storage, especially for large-column multimedia data. In this study, a novel compression–encryption scheme is presented using a fractal dictionary and Julia set. For the compression in this scheme, fractal dictionary encoding not only reduces time consumption, but also gives good quality image reconstruction. For the encryption in the scheme, the key has large key space and high sensitivity, even to tiny perturbation. Besides, the stream cipher encryption and the diffusion process adopted in this study help spread perturbation in the plaintext, achieving high plain sensitivity and giving an effective resistance to chosen-plaintext attacks.

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Xiaopeng Hu

Papers

Visual search: psychophysical models and practical applications

ERS transform for the automated detection of bronchial abnormalities on CT of the lungs

The use of visual search for knowledge gathering in image decision support

Adaptive energy-efficient clustering path planning routing protocols for heterogeneous wireless sensor networks

Image compression and encryption scheme using fractal dictionary and Julia set