Combining depth and intensity images to produce enhanced object detection for use in a robotic colony

Robotic colonies that can communicate with each other and interact with their ambient environments can be utilized for a wide range of research and industrial applications. However amongst the problems that these colonies face is that of the isolating objects within an environment. Robotic colonies that can isolate objects within the environment can not only map that environment in de-tail, but interact with that ambient space. Many object recognition techniques ex-ist, however these are often complex and computationally expensive, leading to overly complex implementations. In this paper a simple model is proposed to isolate objects, these can then be recognize and tagged. The model will be using 2D and 3D perspectives of the perceptual data to produce a probability map of the outline of an object, therefore addressing the defects that exist with 2D and 3D image techniques. Some of the defects that will be addressed are; low level illumination and objects at similar depths. These issues may not be completely solved, however, the model provided will provide results confident enough for use in a robotic colony

Real-Time Object Recognition Based on Cortical Multi-scale Keypoints

In recent years, a large number of impressive object categorisation algorithms have surfaced, both computational and biologically motivated. While results on standardised benchmarks are impressive, very few of the best-performing algorithms took run-time performance into account, rendering most of them useless for real-time active vision scenarios such as cognitive robots. In this paper, we combine cortical keypoints based on primate area V1 with a state-of-the-art nearest neighbour classifier, and show that such a system can approach state-of-the-art categorisation performance while meeting the real-time constraint

A game-theoretic approach to the enforcement of global consistency in multi-view feature matching

In this paper we introduce a robust matching technique that allows to operate a very accurate selection of corresponding feature points from multiple views. Robustness is achieved by enforcing global geometric consistency at an early stage of the matching process, without the need of ex-post verification through reprojection. Two forms of global consistency are proposed, but in both cases they are reduced to pairwise compatibilities making use of the size and orientation information provided by common feature descriptors. Then a game-theoretic approach is used to select a maximally consistent set of candidate matches, where highly compatible matches are enforced while incompatible correspondences are driven to extinction. The effectiveness of the approach in estimating camera parameters for bundle adjustment is assessed and compared with state-of-the-art techniques. © 2010 Springer-Verlag Berlin Heidelberg

Transactional Support for Visual Instance Search

International audienceThis article addresses the issue of dynamicity and durability for scalable indexing of very large and rapidly growing collections of local features for visual instance retrieval. By extending the NV-tree, a scalable disk-based high-dimensional index, we show how to implement the ACID properties of transactions which ensure both dynamicity and durability. We present a detailed performance evaluation of the transactional NV-tree, showing that the insertion throughput is excellent despite the effort to enforce the ACID properties

Patch-Based Experiments with Object Classification in Video Surveillance

We present a patch-based algorithm for the purpose of object classification in video surveillance. Within detected regions-of-interest (ROIs) of moving objects in the scene, a feature vector is calculated based on template matching of a large set of image patches. Instead of matching direct image pixels, we use Gabor-filtered versions of the input image at several scales. This approach has been adopted from recent experiments in generic object-recognition tasks. We present results for a new typical video surveillance dataset containing over 9,000 object images. Furthermore, we compare our system performance with another existing smaller surveillance dataset. We have found that with 50 training samples or higher, our detection rate is on the average above 95%. Because of the inherent scalability of the algorithm, an embedded system implementation is well within reach

Generic 3D Representation via Pose Estimation and Matching

Though a large body of computer vision research has investigated developing generic semantic representations, efforts towards developing a similar representation for 3D has been limited. In this paper, we learn a generic 3D representation through solving a set of foundational proxy 3D tasks: object-centric camera pose estimation and wide baseline feature matching. Our method is based upon the premise that by providing supervision over a set of carefully selected foundational tasks, generalization to novel tasks and abstraction capabilities can be achieved. We empirically show that the internal representation of a multi-task ConvNet trained to solve the above core problems generalizes to novel 3D tasks (e.g., scene layout estimation, object pose estimation, surface normal estimation) without the need for fine-tuning and shows traits of abstraction abilities (e.g., cross-modality pose estimation). In the context of the core supervised tasks, we demonstrate our representation achieves state-of-the-art wide baseline feature matching results without requiring apriori rectification (unlike SIFT and the majority of learned features). We also show 6DOF camera pose estimation given a pair local image patches. The accuracy of both supervised tasks come comparable to humans. Finally, we contribute a large-scale dataset composed of object-centric street view scenes along with point correspondences and camera pose information, and conclude with a discussion on the learned representation and open research questions.Comment: Published in ECCV16. See the project website http://3drepresentation.stanford.edu/ and dataset website https://github.com/amir32002/3D_Street_Vie

T cells are dominant population in human abdominal aortic aneurysms and their infiltration in the perivascular tissue correlates with disease severity

Abdominal Aortic Aneurysm (AAA) is a major cause of cardiovascular mortality. Adverse changes in vascular phenotype act in concert with chronic inflammation to promote AAA progression. Perivascular adipose tissue (PVAT) helps maintain vascular homeostasis but when inflamed and dysfunctional, can also promote vascular pathology. Previous studies suggested that PVAT may be an important site of vascular inflammation in AAA; however, a detailed assessment of leukocyte populations in human AAA, their anatomic location in the vessel wall and correlation to AAA size remain undefined. Accordingly, we performed in depth immunophenotyping of cells infiltrating the pathologically altered perivascular tissue (PVT) and vessel wall in AAA samples at the site of maximal dilatation (n=51 patients). Flow cytometry revealed that T cells, rather than macrophages, are the major leukocyte subset in AAA and that their greatest accumulations occur in PVT. Both CD4+ and CD8+ T cell populations are highly activated in both compartments, with CD4+ T cells displaying the highest activation status within the AAA wall. Finally, we observed a positive relationship between T cell infiltration in PVT and AAA wall. Interestingly, only PVT T cell infiltration was strongly related to tertiles of AAA size. In summary, this study highlights an important role for PVT as a reservoir of T lymphocytes and potentially as a key site in modulating the underlying inflammation in AAA

Women's attitudes towards mechanisms of action of family planning methods: survey in primary health centres in Pamplona, Spain

Irala J de, Lopez del Burgo C, Lopez de Fez CM, Arredondo J, Mikolajczyk RT, Stanford JB. Women's attitudes towards mechanisms of action of family planning methods: survey in primary health centres in Pamplona, Spain. BMC Women's Health. 2007;7(1): 10.Background: Informed consent in family planning includes knowledge of mechanism of action. Some methods of family planning occasionally work after fertilization. Knowing about postfertilization effects may be important to some women before choosing a certain family planning method. The objective of this survey is to explore women's attitudes towards postfertilization effects of family planning methods, and beliefs and characteristics possibly associated with those attitudes. Methods: Cross-sectional survey in a sample of 755 potentially fertile women, aged 18–49, from Primary Care Health Centres in Pamplona, Spain. Participants were given a 30-item, self-administered, anonymous questionnaire about family planning methods and medical and surgical abortion. Logistic regression was used to identify variables associated with women's attitudes towards postfertilization effects. Results: The response rate was 80%. The majority of women were married, held an academic degree and had no children. Forty percent of women would not consider using a method that may work after fertilization but before implantation and 57% would not consider using one that may work after implantation. While 35.3% of the sample would stop using a method if they learned that it sometimes works after fertilization, this percentage increased to 56.3% when referring to a method that sometimes works after implantation. Women who believe that human life begins at fertilization and those who consider it is important to distinguish between natural and induced embryo loss were less likely to consider the use of a method with postfertilization effects. Conclusion: Information about potential postfertilization effects of family planning methods may influence women's acceptance and choice of a particular family planning method. Additional studies in other populations are necessary to evaluate whether these beliefs are important to those populations

Measured Dynamic Social Contact Patterns Explain the Spread of H1N1v Influenza

Patterns of social mixing are key determinants of epidemic spread. Here we present the results of an internet-based social contact survey completed by a cohort of participants over 9,000 times between July 2009 and March 2010, during the 2009 H1N1v influenza epidemic. We quantify the changes in social contact patterns over time, finding that school children make 40% fewer contacts during holiday periods than during term time. We use these dynamically varying contact patterns to parameterise an age-structured model of influenza spread, capturing well the observed patterns of incidence; the changing contact patterns resulted in a fall of approximately 35% in the reproduction number of influenza during the holidays. This work illustrates the importance of including changing mixing patterns in epidemic models. We conclude that changes in contact patterns explain changes in disease incidence, and that the timing of school terms drove the 2009 H1N1v epidemic in the UK. Changes in social mixing patterns can be usefully measured through simple internet-based surveys