Background: Looking pattern differences are shown to separate individuals with Autism Spectrum Disorder (ASD) and Typically Developing (TD) controls. Recent studies have shown that, in children with ASD, these patterns change with intellectual and social impairments, suggesting that patterns of social attention provide indices of clinically meaningful variation in ASD. Method: We conducted a naturalistic study of children with ASD (n = 55) and typical development (TD, n = 32). A battery of eye-tracking video stimuli was used in the study, including Activity Monitoring (AM), Social Referencing (SR), Theory of Mind (ToM), and Dyadic Bid (DB) tasks. This work reports on the feasibility of spatial and spatiotemporal scanpaths generated from eye-gaze patterns of these paradigms in stratifying ASD and TD groups. Algorithm: This article presents an approach for automatically identifying clinically meaningful information contained within the raw eye-tracking data of children with ASD and TD. The proposed mechanism utilizes combinations of eye-gaze scan-paths (spatial information), fused with temporal information and pupil velocity data and Convolutional Neural Network (CNN) for stratification of diagnosis (ASD or TD). Results: Spatial eye-gaze representations in the form of scanpaths in stratifying ASD and TD (ASD vs. TD: DNN: 74.4%) are feasible. These spatial eye-gaze features, e.g., scan-paths, are shown to be sensitive to factors mediating heterogeneity in ASD: age (ASD: 2–4 y/old vs. 10–17 y/old CNN: 80.5%), gender (Male vs. Female ASD: DNN: 78.0%) and the mixture of age and gender (5–9 y/old Male vs. 5–9 y/old Female ASD: DNN:98.8%). Limiting scan-path representations temporally increased variance in stratification performance, attesting to the importance of the temporal dimension of eye-gaze data. Spatio-Temporal scan-paths that incorporate velocity of eye movement in their images of eye-gaze are shown to outperform other feature representation methods achieving classification accuracy of 80.25%. Conclusion: The results indicate the feasibility of scan-path images to stratify ASD and TD diagnosis in children of varying ages and gender. Infusion of temporal information and velocity data improves the classification performance of our deep learning models. Such novel velocity fused spatio-temporal scan-path features are shown to be able to capture eye gaze patterns that reflect age, gender, and the mixed effect of age and gender, factors that are associated with heterogeneity in ASD and difficulty in identifying robust biomarkers for ASD.

The purpose of this research is to provide a puzzle-based framework to study how global and local information is interacted on human's visual perception during the decision making process. Since the Deep Convolutional Neural Networks (DCNN) has shown the state of the art performance in image classification and object detection, DCNN can output scores to reflect the level of global information, which is highly similar to human's recognition for partial objects in images, i.e. puzzle pieces. Besides, the local information is also taken into consideration in decision making when playing puzzle, therefore, the puzzle-based instrument is proposed in this paper to study how local and global information can be integrated in decision making. The results suggest the proposed method in evaluations of local and global aspects can reflect subjects' behaviors in decision making successfully.

Several studies on autism spectrum disorder (ASD) show that there exists significant heterogeneity in phenotype of the disorder. Additionally, many published findings also suggested that ASD is defined by atypical local/global processing. In this paper, we designed a puzzled-based intervention to examine the sensitiveness to the information of local /global processing on individuals with ASD. Additionally, we surveyed the participants and their parents regarding their perceptions and feelings while completing the puzzles. The results verify that the proposed method can evaluate the degree of perception of local/global information by those on the spectrum. As a result, the design of puzzle-based visual stimuli with local/global information involved may become one of the crucial phenotypes to define ASD and can be further applied to facilitate a sub grouping for people with ASD.

Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). However, visibility is inadequate for continuous and automated tracking. In such applications, a sufficient overlap between FOVs should be secured so that camera handoff can be executed successfully and automatically before the object of interest becomes untraceable or unidentifiable. In addition, most of the existing algorithms employ discrete solution space, which suffers from limited solution accuracy and high computational complexity due to the high dimension of sampled solution space. In this paper, we propose to perform the optimisation of the camera parameters in a continuous solution space. In addition, to incorporate the optimisation of coverage and sufficient overlapped FOVs, a weighted sum approach is utilised to translate a multiple objective optimisation problem into a single objective optimisation process in our previous work. Significantly improved handoff success rates are illustrated via experiments using typical office floor plans in comparison with Erdem and Sclaroff's method. Improved accuracy, enhanced robustness, completeness of the solution set, and reduced computational complexity are accomplished in comparison with our previous algorithms.

In this paper, we describe a novel algorithm that counts and classifies highway vehicles based on regression analysis. This algorithm requires no explicit segmentation or tracking of individual vehicles, which is usually an important part of many existing algorithms. Therefore, this algorithm is particularly useful when there are severe occlusions or vehicle resolution is low, in which extracted features are highly unreliable. There are mainly two contributions in our proposed algorithm. First, a warping method is developed to detect the foreground segments that contain unclassified vehicles. The common used modeling and tracking (e.g., Kalman filtering) of individual vehicles are not required. In order to reduce vehicle distortion caused by the foreshortening effect, a nonuniform mesh grid and a projective transformation are estimated and applied during the warping process. Second, we extract a set of low-level features for each foreground segment and develop a cascaded regression approach to count and classify vehicles directly, which has not been used in the area of intelligent transportation systems. Three different regressors are designed and evaluated. Experiments show that our regression-based algorithm is accurate and robust for poor quality videos, from which many existing algorithms could fail to extract reliable features.

Currently in the United States, the most commonly diagnosed developmental disorder in young children is Autism Spectrum Disorder (ASD). ASD is a neurodevelopmental syndrome marked by qualitative impairments of social communication skills and restricted, repetitive patterns of behavior. However, due to the heterogeneous nature of the disorder, to date existing therapies/interventions for children with ASD have failed to identify why some subjects with ASD experience more positive response to therapies than others. Additionally, there is the lack of consensus on how to identify and evaluate scientifically valid and effective interventions. The contribution of this work is to investigate a computational model driven intervention technology to ease subjects' anxiety when facing a new situation, e.g., dental visit. Via this computational model, we can explain why subjects with ASD do not positively respond to the intervention. Our experimental results show our proposed approach can shed the light on the development of mathematical modeling for visual perception and cognition in individuals with ASD.

An increasing number of three-dimensional (3D) building models are being made available on Web-based model-sharing platforms. Motivated by the concept of data reuse, an encoding approach is proposed for 3D building model retrieval using point clouds acquired by airborne light detection and ranging (LiDAR) systems. To encode LiDAR point clouds with sparse, noisy, and incomplete sampling, we introduce a novel encoding scheme based on a set of low-frequency spherical harmonic basis functions. These functions provide compact representation and ease the encoding difficulty coming from inherent noises of point clouds. Additionally, a data filling and resampling technique is proposed to solve the aliasing problem caused by the sparse and incomplete sampling of point clouds. Qualitative and quantitative analyses of LiDAR data show a clear superiority of the proposed method over related methods. A cyber campus generated by retrieving 3D building models with airborne LiDAR point clouds demonstrates the feasibility of the proposed method.

This study assessed the Balanced Scorecard (BSC) approach to strategy and its usefulness for three buffet-service restaurants in Taiwan. Data were gathered from fourteen members of upper management via face-to-face in-depth interviews. Content analysis showed an application of the BSC in all four category groupings identified in the BSC framework. The BSC approach provides a summary of the results from a large number of performance measures, focuses on the key performance issues, and enables buffet restaurant managers and owners to identify problems early in their operations. Every performance measure selected for the four perspectives of the BSC was a vital element in linking strategy from each business unit to actual operations in the buffet restaurants, so that shareholders and management make appropriate decisions to lead the operations toward continued success in an increasingly competitive environment.

The purpose of this study is to construct a framework for the evaluation of tourism destination's competitiveness of cities in Taiwan, in order for the further use of practical evaluation in the future. The framework was conducted based on the destination competitiveness theory, and some indices were revised according to the cities' characteristics. By interviewing 100 experts, this study constructed a framework including five factors with 27 indices for the evalutation and comparison of city tourism competitiveness.

In this paper, we propose a novel outdoor scene image segmentation algorithm based on background recognition and perceptual organization. We recognize the background objects such as the sky, the ground, and vegetation based on the color and texture information. For the structurally challenging objects, which usually consist of multiple constituent parts, we developed a perceptual organization model that can capture the nonaccidental structural relationships among the constituent parts of the structured objects and, hence, group them together accordingly without depending on a priori knowledge of the specific objects. Our experimental results show that our proposed method outperformed two state-of-the-art image segmentation approaches on two challenging outdoor databases (Gould data set and Berkeley segmentation data set) and achieved accurate segmentation quality on various outdoor natural scene environments.

Most existing performance evaluation methods concentrate on defining separate metrics over a wide range of conditions and generating standard benchmarking video sequences for examining the effectiveness of video tracking systems. In other words, these methods attempt to design a robustness margin or factor for the system. These methods are deterministic in which a robustness factor, for example, 2 or 3 times the expected number of subjects to track or the strength of illumination would be required in the design. This often results in over design, thus increasing costs, or under design causing failure by unanticipated factors. In order to overcome these limitations, we propose in this paper an alternative framework to analyze the physics of the failure process and, through the concept of reliability, determine the time to failure in automated video tracking systems. The benefit of our proposed framework is that we can provide a unified and statistical index to evaluate the performance of automated video tracking system for a task to be performed. At the same time, the uncertainty problem about a failure process, which may be caused by the system's complexity, imprecise measurements of the relevant physical constants and variables, or the indeterminate nature of future events, can be addressed accordingly based on our proposed framework.

Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). However, visibility, which is a fundamental requirement of object tracking, is insufficient for automated persistent surveillance. In such applications, a continuous consistently labeled trajectory of the same object should be maintained across different camera views. Therefore, a sufficient uniform overlap between the cameras' FOVs should be secured so that camera handoff can successfully and automatically be executed before the object of interest becomes untraceable or unidentifiable. In this paper, we propose sensor-planning methods that improve existing algorithms by adding handoff rate analysis. Observation measures are designed for various types of cameras so that the proposed sensor-planning algorithm is general and applicable to scenarios with different types of cameras. The proposed sensor-planning algorithm preserves necessary uniform overlapped FOVs between adjacent cameras for an optimal balance between coverage and handoff success rate. In addition, special considerations such as resolution and frontal-view requirements are addressed using two approaches: 1) direct constraint and 2) adaptive weights. The resulting camera placement is compared with a reference algorithm published by Erdem and Sclaroff. Significantly improved handoff success rates and frontal-view percentages are illustrated via experiments using indoor and outdoor floor plans of various scales.

Camera handoff is a crucial step to generate a continuously tracked and consistently labeled trajectory of the object of interest in multi-camera surveillance systems. Most existing camera handoff algorithms concentrate on data association, namely consistent labeling, where images of the same object are matched across different cameras. However, most real-time object tracking systems see a decrease in the system's frame rate as the number of tracked objects increases. To address this issue, we propose to incorporate an adaptive resource management mechanism into camera handoff. In so doing, cameras¿ resources can be dynamically allocated to multiple objects according to their priorities and hence the required minimum frame rate can be maintained. Experimental results illustrate that the proposed camera handoff algorithm is capable of maintaining a constant frame rate and of achieving a substantially improved handoff success rate by approximately 20% in comparison with the algorithm presented by Khan and Shah.

Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera—s field of view (FOV). However, visibility, a fundamental requirement of object tracking, is insufficient for persistent and automated tracking. In such applications, a continuous and consistently labeled trajectory of the same object should be maintained across different cameras’ views. Therefore, a sufficient overlap between the cameras’ FOVs should be secured so that camera handoff can be executed successfully and automatically before the object of interest becomes untraceable or unidentifiable. The proposed sensor planning method improves existing algorithms by adding handoff rate analysis, which preserves necessary overlapped FOVs for an optimal handoff success rate. In addition, special considerations such as resolution and frontal view requirements are addressed using two approaches: direct constraint and adaptive weight. The resulting camera placement is compared with a reference algorithm by Erdem and Sclaroff. Significantly improved handoff success rate and frontal view percentage are illustrated via experiments using typical office floor plans.

Most existing sensor planning algorithms find it difficult to tackle the discrepancy between a PTZ camera¿s limited instant field of view (FOV) and panoramic achievable FOV. In this paper, we introduce the probability of camera overload to resolve this discrepancy and present a sensor planning algorithm for PTZ cameras under the same framework as static cameras. The resulting camera placement achieves the optimal balance between coverage and handoff success rate. Furthermore, our algorithm is able to incorporate the target¿s dynamics into sensor planning. As a result, the system¿s handoff success rate can be maintained in environments with various target densities. Experimental results and comparisons with a reference algorithm proposed by Erdem and Sclaroff verify the effectiveness of our algorithm via a significantly improved handoff success rate.

Linear motion and out-of-focus blur often coexist in a surveillance system, which degrade the quality of acquired images and thus complicate the task of object recognition and event detection. In this work, we present a point spread function-based (PSF-based) approach considering fundamental characteristics of linear motion and out-of-focus blur based on geometric optics to restore coexisting motion and out-of-focus blurred images without application-dependent parameters selection, where a sharpness measure is employed as a cost function to automatically select optimal parameter values for PSF. To verify the effectiveness of our proposed approach, we compare our approach with existing de-blur approaches. Experimental results shows our proposed method can automatically select optimal parameter values for PSF and outperform existing de-blur approaches.

Since the globe is connected tightly by internet, to react the disruption of internet services efficiently and fairly is very important, especially for those real time transactions. However, there is a lack of literature about it. To our best knowledge, this paper is the first try to explore issues under such conditions. A framework, which consists of the traffic rerouting programming, the capacity and cost inventory, and the policy of traffic priority, is proposed for the preparation. Based on the proposed framework, internet traffic under disruption may be rerouted through available capacities efficiently and fairly if the criteria are well established. It is proposed to serve the needs of post-disaster traffic management for both planning and operation sides.

Dual-camera systems have been widely used in surveillance because of the ability to explore the wide field of view (FOV) of the omnidirectional camera and the wide zoom range of the PTZ camera. Most existing algorithms require a priori knowledge of the omnidirectional camera's projection model to solve the nonlinear spatial correspondences between the two cameras. To overcome this limitation, two methods are proposed: 1) geometry and 2) homography calibration, where polynomials with automated model selection are used to approximate the camera's projection model and spatial mapping, respectively. The proposed methods not only improve the mapping accuracy by reducing its dependence on the knowledge of the projection model but also feature reduced computations and improved flexibility in adjusting to varying system configurations. Although the fusion of multiple cameras has attracted increasing attention, most existing algorithms assume comparable FOV and resolution levels among multiple cameras. Different FOV and resolution levels of the omnidirectional and PTZ cameras result in another critical issue in practical tracking applications. The omnidirectional camera is capable of multiple object tracking while the PTZ camera is able to track one individual target at one time to maintain the required resolution. It becomes necessary for the PTZ camera to distribute its observation time among multiple objects and visit them in sequence. Therefore, this paper addresses a novel scheme where an optimal visiting sequence of the PTZ camera is obtained so that in a given period of time the PTZ camera automatically visits multiple detected motions in a target-hopping manner. The effectiveness of the proposed algorithms is illustrated via extensive experiments using both synthetic and real tracking data and comparisons with two reference systems.