Adaptive hit or miss transform

International audienceThe Hit or Miss Transform is a fundamental morphological operator, and can be used for template matching. In this paper, we present a framework for adaptive Hit or Miss Transform, where structuring elements are adaptive with respect to the input image itself. We illustrate the difference between the new adaptive Hit or Miss Transform and the classical Hit or Miss Transform. As an example of its usefulness, we show how the new adaptive Hit or Miss Transform can detect particles in single molecule imaging

Riemannian mathematical morphology

This paper introduces mathematical morphology operators for real-valued images whose support space is a Riemannian manifold. The starting point consists in replacing the Euclidean distance in the canonic quadratic structuring function by the Riemannian distance used for the adjoint dilation/erosion. We then extend the canonic case to a most general framework of Riemannian operators based on the notion of admissible Riemannian structuring function. An alternative paradigm of morphological Riemannian operators involves an external structuring function which is parallel transported to each point on the manifold. Besides the definition of the various Riemannian dilation/erosion and Riemannian opening/closing, their main properties are studied. We show also how recent results on Lasry-Lions regularization can be used for non-smooth image filtering based on morphological Riemannian operators. Theoretical connections with previous works on adaptive morphology and manifold shape morphology are also considered. From a practical viewpoint, various useful image embedding into Riemannian manifolds are formalized, with some illustrative examples of morphological processing real-valued 3D surfaces

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Adding fuel to the flames: how TTIP reinvigorated the politicization of trade

It is a truism to state that the Transatlantic Trade and Investment Partnership (TTIP) is a politicized issue, yet the explanations that account for this politicization are mostly singular in nature. In this paper I add to this understanding theoretically and empirically by presenting a broad analytic framework that puts TTIP at the intersection of two evolutions. There is, firstly, a longer-term trend of increasing political authority of (European) trade policy that is (at least by several organizations and citizens) not considered legitimate. I argue that TTIP is an extension and an intensification of this perceived authority-without-legitimacy trend. Secondly, the particular explosive situation that has occurred since 2013 is furthermore the result of a specific combination of a favoring political opportunity structure, combined with pre-existing mobilization resources that have facilitated a large mobilization by civil society organizations. This explains the spike of politicization that is attached onto this longer term trend. Relying on several exploratory interviews, I try to uncover the determinants in the different categories

Modeling Option and Strategy Choices with Connectionist Networks: Towards an Integrative Model of Automatic and Deliberate Decision Making

We claim that understanding human decisions requires that both automatic and deliberate processes be considered. First, we sketch the qualitative differences between two hypothetical processing systems, an automatic and a deliberate system. Second, we show the potential that connectionism offers for modeling processes of decision making and discuss some empirical evidence. Specifically, we posit that the integration of information and the application of a selection rule are governed by the automatic system. The deliberate system is assumed to be responsible for information search, inferences and the modification of the network that the automatic processes act on. Third, we critically evaluate the multiple-strategy approach to decision making. We introduce the basic assumption of an integrative approach stating that individuals apply an all-purpose rule for decisions but use different strategies for information search. Fourth, we develop a connectionist framework that explains the interaction between automatic and deliberate processes and is able to account for choices both at the option and at the strategy level.System 1, Intuition, Reasoning, Control, Routines, Connectionist Model, Parallel Constraint Satisfaction

Retinal Vessel Segmentation using Tensor Voting

Medical imaging studies generate tremendous amounts of data that are reviewedmanually by physicians every day. Medical image segmentation aims to automate theprocess of extracting (segmenting) “interesting” structures from background structuresin the images, saving physicians time and opening the door to more sophisticatedanalysis such as automatically correlating studies over time. This work focuseson segmenting blood vessels (in particular the retinal vasculature), a task that requiresintegrating both local and global properties of the vasculature to produce goodquality segmentations. We use the Tensor Voting framework as it naturally groupsstructures together based on the consensus of locally voting segments. We investigateseveral ways of encoding the image data as tensors and compare our results quantitativelywith a publically available hand-labeled data set. We demonstrate competitiveperformance versus previously published techniques

Informational Complexity and the Flow of Knowledge across social boundaries

Scholars from a variety of backgrounds – economists, sociologists, strategists, and students of technology management – have sought a better understanding of why some knowledge disperses widely while other knowledge does not. In this quest, some researchers have focused on the characteristics of the knowledge itself (e.g., Polanyi, 1966; Reed and DeFillippi, 1990; Zander and Kogut, 1995) while others have emphasized the social networks that constrain and enable the flow of knowledge (e.g., Coleman et al., 1957; Davis and Greve, 1997). This chapter examines the interplay between these two factors. Specifically, we consider how the complexity of knowledge and the density of social relations jointly influence the movement of knowledge. Imagine a social network composed of patches of dense connections with sparse interstices between them. The dense patches might reflect firms, for instance, or geographic regions or technical communities. When does knowledge diffuse within these dense patches circumscribed by social boundaries but not beyond them? Synthesizing social network theory with a view of knowledge transfer as a search process, we argue that knowledge inequality across social boundaries should reach its peak when the underlying knowledge is of moderate complexity. To test this hypothesis, we analyze patent data and compare citation rates across three types of social boundaries: within versus outside the firm, geographically near to versus far from the inventor, and internal versus external to the technological class. In all three cases, the disparity in knowledge diffusion across these borders is greatest for knowledge of an intermediate level of complexity.evolutionary economics, informational complexity, knowledge flow, social boundaries

Doctor of Philosophy

dissertationThe present study explored data presentation and human cognition with the objective of improving electronic Decision Support Systems (DSS). Computers have been used as tools for decision support for over 60 years, with the intent to supplement or replace human cognition. However, electronic computing has failed to reliably replace human cognition in complex domains. The suboptimal properties of the data and complexities of the domain often require human interpretation and intervention. Human interpretation relies on experience, values, intuition, insight and learning; which can lead to shortcuts or heuristics. Heuristics in the correct context can be economical and effective in solving many problems. When heuristics fail the results are labeled as cognitive biases or errors. Biases all share the elements of structuring incorrect or inappropriate models or hypotheses and/or insufficient consideration of the data. Most biases can be linked to confirmation bias - which is manifested by searches for and consideration of only confirming data. De-biasing techniques share the concept of shifting cognitive processing from an automatic associative mode to a more deliberate, conscious rule-based mode. This study used a modified Wason 2-4-6 task that combined methods of, 1) increased salience through data visualization with 2) appealing to the rule-based system through task instructions. The results indicate that neither increased salience nor instructions ensure increased search sufficiency, efficiency or decision accuracy. However, this study provides insight into the perceived value of evidence and iv four potential limitations related to self-directed searches: 1) The selection of necessary disconfirming evidence cannot be assumed, regardless of the perceived value of disconfirming evidence. 2) The selection of sufficient evidence does not ensure accuracy; however, 3) insufficient selection of disconfirming evidence results in lower accuracy. 4) Ambiguous evidence is considered more valuable than potentially disconfirming evidence. Implications for the design of decision support systems are presented along with limitations and directions for future research

The Impact of User Interface Design on Idea Integration in Electronic Brainstorming: An Attention-Based View

This paper introduces an attention-based view of idea integration that underscores the importance of IS user interface design. The assumption is that presenting ideas via user interface plays a key role in enabling and motivating idea integration in electronic brainstorming (EBS), and thus advances productivity. Building upon Cognitive Network Model of Creativity and ability-motivation framework, our attention-based theory focuses on two major attributes of user interface: visibility and prioritization. While visibility enables idea integration via directing attention to a limited set of ideas, prioritization enhances the motivation for idea integration by providing individuals with a relevant and legitimate proxy for value of the shared ideas. The theory developed in this paper is distinct from previous research on EBS in at least two ways: (1) this theory exclusively focuses on idea integration as the desired outcome and studies it in the context of IS user interface; and (2) rather than debating whether or not EBS universally outperforms verbal brainstorming, the proposed theory revisits the links between user interface and idea integration as an attention-intensive process that contributes to EBS productivity. Idea integration by individuals within a group is an essential process for organizational creativity and thus for establishing knowledge-based capabilities. Lack of such integration significantly reduces the value of idea sharing, which has been a predominant focus of the EBS literature in the past. The current theory posits that the ability of electronic brain-storming to outperform nominal or verbal brainstorming depends on its ability to leverage information system (IS) artifact capabilities for enhancing idea integration to create a key pattern of productivity. The developed theory provides a foundation for new approaches to EBS research and design, which use visibility and prioritization, and also identify new user interface features for fostering idea integration. By emphasizing idea integration, designers and managers are provided with practical, cognition-based criteria for choosing interface features, which can improve EBS productivity. This theory also has implications for both the practice and research of knowledge management, especially for the attention-based view of the organization.