Statistical Object Recognition

Two formulations of model-based object recognition are described. MAP Model Matching evaluates joint hypotheses of match and pose, while Posterior Marginal Pose Estimation evaluates the pose only. Local search in pose space is carried out with the Expectation--Maximization (EM) algorithm. Recognition experiments are described where the EM algorithm is used to refine and evaluate pose hypotheses in 2D and 3D. Initial hypotheses for the 2D experiments were generated by a simple indexing method: Angle Pair Indexing. The Linear Combination of Views method of Ullman and Basri is employed as the projection model in the 3D experiments.

Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal

Object life cycles & interaction environments for cross media production & distribution

This paper addresses the requirements for a Work/flow Management System that is intended to automate the production and distribution chain for cross-media content which is by nature multi-partner and multi-site. It advocates the requirements for an ontology-based object lifecycle tracking within work/flow integration by identifying various types of interfaces, object life cycles and the work-flow interaction environments within the AXMEDIS Framework.

O capital ganha asas: reestruturação produtiva no setor aeroespacial brasileiro : o caso da EMBRAER

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Sulfur-containing polymers for surface attachment

Different concepts for the synthesis of sulfur-containing polymers as well as their adsorption onto gold surfaces were studied. The present work is divided into three parts. The main part focuses on the synthesis of poly(1,2-alkylene sulfides) (“polysulfides”) with complex architectures on the basis of polyether-based macroinitiators by the anionic ring-opening polymerization of ethylene sulfide and propylene sulfide. This synthetic tool kit allowed the synthesis of star-shaped, brush-like, comb-like and pom-pom-like polysulfides, the latter two with an additional poly(ethylene glycol) chain. Additionally, the number of polysulfide arms as well as the monomer composition could be varied over a wide range to obtain copolymers with multiple thioether functionalities.rnThe second section deals with the synthesis of a novel lipoic acid-based initiator for ring-opening polymerizations for lactones and epoxides. A straightforward approach was selected to accomplish the ability to obtain tailored polymers with a common used disulfide-anchoring group, without the drawbacks of post-polymerization functionalization. rnIn the third part, a new class of block-copolymers consisting of polysulfides and polyesters were investigated. For the first time this approach enabled the use of hydroxyl-terminated poly(propylene sulfide) as macroinitiator for the synthesis of a second block.rnThe adsorption efficiency of those different polymer classes onto gold nanoparticles as well as gold rnsupports was studied via different methods.rn

The developmental and evolutionary origins of psychological essentialism lie in sortal object individuation

Cimpian & Salomon (C&S) present promising steps towards understanding the cognitive underpinnings of adult essentialism. However, their approach is less convincing regarding ontogenetic and evolutionary aspects. In contrast to C&S's claim, the so-called inherence heuristic, though perhaps vital in adult reasoning, seems an implausible candidate for the developmental and evolutionary foundations of psychological essentialism. A more plausible candidate is kind-based object individuation that already embodies essentialist modes of thinking and that is present in infants and nonhuman primates.

Effects of a health advocacy program to promote physical activity and nutrition behaviors among adolescents: An evaluation of the Young Leaders for Healthy Change program

Background. This study was designed to evaluate the effects of the Young Leaders for Healthy Change program, an internet-delivered program in the school setting that emphasized health advocacy skills-development, on nutrition and physical activity behaviors among older adolescents (13–18 years). The program consisted of online curricular modules, training modules, social media, peer and parental support, and a community service project. Module content was developed based on Social Cognitive Theory and known determinants of behavior for older adolescents. ^ Methods. Of the 283 students who participated in the fall 2011 YL program, 38 students participated in at least ten of the 12 weeks and were eligible for this study. This study used a single group-only pretest/posttest evaluation design. Participants were 68% female, 58% white/Caucasian, 74% 10th or 11th graders, and 89% mostly A and/or B students. The primary behavioral outcomes for this analysis were participation in 60-minutes of physical activity per day, 20-minutes of vigorous- or moderate- intensity physical activity (MVPA) participation per day, television and computer time, fruit and vegetable (FV) intake, sugar-sweetened beverage intake, and consumption of breakfast, home-cooked meals, and fast food. Other outcomes included knowledge, beliefs, and attitudes related to healthy eating, physical activity, and advocacy skills. ^ Findings. Among the 38 participants, no significant changes in any variables were observed. However, among those who did not previously meet behavioral goals there was an 89% increase in students who participated in more than 20 minutes of MVPA per day and a 58% increase in students who ate home-cooked meals 5–7 days per week. The majority of participants met program goals related to knowledge, beliefs, and attitudes prior to the start of the program. Participants reported either maintaining or improving to the goal at posttest for all items except FV intake knowledge, taste and affordability of healthy foods, interest in teaching others about being healthy, and ease of finding ways to advocate in the community. ^ Conclusions. The results of this evaluation indicated that promoting healthy behaviors requires different strategies than maintaining healthy behaviors among high school students. In the school setting, programs need to target the promotion and maintenance of health behaviors to engage all students who participate in the program as part of a class or club activity. Tailoring the program using screening and modifying strategies to meet the needs of all students may increase the potential reach of the program. The Transtheoretical Model may provide information on how to develop a tailored program. Additional research on how to utilize the constructs of TTM effectively among high school students needs to be conducted. Further evaluation studies should employ a more expansive evaluation to assess the long-term effectiveness of health advocacy programming.^

Optimized HOG for on-road video based vehicle verification

Vision-based object detection from a moving platform becomes particularly challenging in the field of advanced driver assistance systems (ADAS). In this context, onboard vision-based vehicle verification strategies become critical, facing challenges derived from the variability of vehicles appearance, illumination, and vehicle speed. In this paper, an optimized HOG configuration for onboard vehicle verification is proposed which not only considers its spatial and orientation resolution, but descriptor processing strategies and classification. An in-depth analysis of the optimal settings for HOG for onboard vehicle verification is presented, in the context of SVM classification with different kernels. In contrast to many existing approaches, the evaluation is realized in a public and heterogeneous database of vehicle and non-vehicle images in different areas of the road, rendering excellent verification rates that outperform other similar approaches in the literature.

The budget of the United States Government.

Latest issue consulted: Fiscal year 2006.

Toward Never-Ending Object Learning for Robots

Thesis (Ph.D.)--University of Washington, 2016-06

Non-rigid object tracking in complex scenes

A recently proposed colour based tracking algorithm has been established to track objects in real circumstances [Zivkovic, Z., Krose, B. 2004. An EM-like algorithm for color-histogram-based object tracking. In: Proc, IEEE Conf. on Computer Vision and Pattern Recognition, pp. 798-803]. To improve the performance of this technique in complex scenes, in this paper we propose a new algorithm for optimally adapting the ellipse outlining the objects of interest. This paper presents a Lagrangian based method to integrate a regularising component into the covariance matrix to be computed. Technically, we intend to reduce the residuals between the estimated probability distribution and the expected one. We argue that, by doing this, the shape of the ellipse can be properly adapted in the tracking stage. Experimental results show that the proposed method has favourable performance in shape adaption and object localisation.

Face context interferes with local part processing in a prosopagnosic patient

We investigated the role of local and global information on perceptual encoding of faces in patient HJA, who shows prosopagnosia and visual agnosia following occipito-temporal damage. HJA and an age-matched control were tested in a simultaneous matching task which focused on detection of local changes in faces: the inversion of central parts (eyes and mouth) relative to their context (as in the Thatcher illusion). Same-different judgements were made to normal, “thatcherised” and mixed type face pairs. Whole faces (Experiment 1), or face parts (Experiment 2), were presented in upright and inverted orientations. Compared to the control, HJA was severely impaired at matching whole faces, but he improved dramatically when face parts were presented in isolation. This suggests an inhibitory influence of face context on HJAs processing of local parts and a relatively intact ability to process part-based information from a face (when context cannot interfere). Face inversion did not affect HJAs performance. A control experiment (Experiment 3) with non-face stimuli (houses) suggested that the inhibitory influence of context on HJAs performance was restricted to faces. These results indicate that contextual information in a face can have an adverse influence on the processing of local part-based information in prosopagnosia.

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

Hybrid approaches to estimating freeway travel times using point traffic detector data

The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.

Development and evaluation of a geographic information system based method to estimate flooding susceptibility in an area of Broward County, Florida

The objective of this study was to develop a GIS-based multi-class index overlay model to determine areas susceptible to inland flooding during extreme precipitation events in Broward County, Florida. Data layers used in the method include Airborne Laser Terrain Mapper (ALTM) elevation data, excess precipitation depth determined through performing a Soil Conservation Service (SCS) Curve Number (CN) analysis, and the slope of the terrain. The method includes a calibration procedure that uses "weights and scores" criteria obtained from Hurricane Irene (1999) records, a reported 100-year precipitation event, Doppler radar data and documented flooding locations. Results are displayed in maps of Eastern Broward County depicting types of flooding scenarios for a 100-year, 24-hour storm based on the soil saturation conditions. As expected the results of the multi-class index overlay analysis showed that an increase for the potential of inland flooding could be expected when a higher antecedent moisture condition is experienced. The proposed method proves to have some potential as a predictive tool for flooding susceptibility based on a relatively simple approach.