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 Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model

A graph theoretic approach is proposed for object shape representation in a hierarchical compositional architecture called Compositional Hierarchy of Parts (CHOP). In the proposed approach, vocabulary learning is performed using a hybrid generative-descriptive model. First, statistical relationships between parts are learned using a Minimum Conditional Entropy Clustering algorithm. Then, selection of descriptive parts is defined as a frequent subgraph discovery problem, and solved using a Minimum Description Length (MDL) principle. Finally, part compositions are constructed by compressing the internal data representation with discovered substructures. Shape representation and computational complexity properties of the proposed approach and algorithms are examined using six benchmark two-dimensional shape image datasets. Experiments show that CHOP can employ part shareability and indexing mechanisms for fast inference of part compositions using learned shape vocabularies. Additionally, CHOP provides better shape retrieval performance than the state-of-the-art shape retrieval methods.Comment: Paper : 17 pages. 13th European Conference on Computer Vision (ECCV 2014), Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III, pp 566-581. Supplementary material can be downloaded from http://link.springer.com/content/esm/chp:10.1007/978-3-319-10578-9_37/file/MediaObjects/978-3-319-10578-9_37_MOESM1_ESM.pd

'Thinking like a fish': adaptive strategies for coping with vulnerability and variability emerging from a relational engagement with kob

Based on ethnographic fieldwork amongst a group of commercial handline fishers in the town of Stilbaai in South Africa's southern Cape region, this paper presents a range of flexible, adaptive and evolving strategies through which fishers negotiate constantly shifting variability in weather patterns, fish stocks, fisheries policies, and economic conditions. These variabilities constitute a diverse set of vulnerabilities to which fishers must respond in order to sustain their livelihoods. In this context, the act of 'thinking like a fish' on the part of the fishers provides them with an effective means of adapting to variability and uncertainty. Findings of ethnographic research in 2010-11 suggest that a number of the fishers who participated in the research actively work towards achieving a balance between profit and sustainability. 'Thinking like a fish' is an embodied, interactive way of knowing that emerges from interactions between fishers and fish, offering an ethical and ecological outlook which is a valuable resource for fisheries and conservation management in the region. We suggest that the deeply embodied interactional component of 'thinking like a fish' results from a desire to understand the life world of fish and to think from their perspective in order to more effectively target them while sustaining the species and ecosystem

Shallow waters: social science research in South Africa's marine environment

This paper provides an overview of social science research in the marine environment of South Africa for the period 1994–2012. A bibliography based on a review of relevant literature and social science projects funded under the SEAChange programme of the South African Network for Coastal and Oceanic Research (SANCOR) was used to identify nine main themes that capture the knowledge generated in the marine social science field. Within these themes, a wide diversity of topics has been explored, covering a wide geographic area. The review suggests that there has been a steady increase in social science research activities and outputs over the past 18 years, with a marked increase in postgraduate dissertations in this field. The SEAChange programme has contributed to enhancing understanding of certain issues and social interactions in the marine environment but this work is limited. Furthermore, there has been limited dissemination of these research results amongst the broader marine science community and incorporation of this information into policy and management decisions has also been limited. However, marine scientists are increasingly recognising the importance of taking a more holistic and integrated approach to management, and are encouraging further social science research, as well as interdisciplinary research across the natural and social sciences. Possible reasons for the lack of communication and coordination amongst natural and social scientists, as well as the limited uptake of research results in policy and management decisions, are discussed and recommendations are proposed.Web of Scienc

Toward Self-Referential Autonomous Learning of Object and Situation Models

Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural learning of hierarchical models for situations and behaviors that is triggered by a mismatch between expected and actual action outcome. Besides proposing architectural concepts, we also describe a first implementation of our system within a simulated traffic scenario to demonstrate the feasibility of our approach

A Global Vaccine Injury Compensation System

Vaccines are extremely safe and harm is rare. Worldwide, more than 30000 vaccine doses are delivered per second through routine immunization programs, which, in turn, prevent an estimated 2 million to 3 million deaths annually. Yet the specter of vaccine injury plays a central role in vaccine access and will continue to do so as vaccine technologies evolve

Voting by Grouping Dependent Parts

Abstract. Hough voting methods efficiently handle the high complexity of multiscale, category-level object detection in cluttered scenes. The primary weakness of this approach is however that mutually dependent local observations are independently voting for intrinsically global object properties such as object scale. All the votes are added up to obtain object hypotheses. The assumption is thus that object hypotheses are a sum of independent part votes. Popular representation schemes are, however, based on an overlapping sampling of semi-local image features with large spatial support (e.g. SIFT or geometric blur). Features are thus mutually dependent and we incorporate these dependences into probabilistic Hough voting by presenting an objective function that combines three intimately related problems: i) grouping of mutually dependent parts, ii) solving the correspondence problem conjointly for dependent parts, and iii) finding concerted object hypotheses using extended groups rather than based on local observations alone. Experiments successfully demonstrate that state-of-the-art Hough voting and even sliding windows are significantly improved by utilizing part dependences and jointly optimizing groups, correspondences, and votes.