This article deals with automatic object recognition. The goal is that in a certain grey-level image, possibly containing many objects, a certain object can be recognized and localized, based upon its shape. The assumption is that this shape has no special characteristics on which a dedicated recognition algorithm can be based (e.g. if we know that the object is circular, we could use a Hough transform or if we know that it is the only object with grey level 90, we can simply use thresholding). Our starting point is an object with a random shape. The image in which the object is searched is called the Search Image. A well known technique for this is Template Matching, which is described first.
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This paper describes the work that is done by a group of I3 students at Philips CFT in Eindhoven, Netherlands. I3 is an initiative of Fontys University of Professional Education also located in Eindhoven. The work focuses on the use of computer vision in motion control. Experiments are done with several techniques for object recognition and tracking, and with the guidance of a robot movement by means of computer vision. These experiments involve detection of coloured objects, object detection based on specific features, template matching with automatically generated templates, and interaction of a robot with a physical object that is viewed by a camera mounted on the robot.
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Digital surveillance technologies using artificial intelligence (AI) tools such as computer vision and facial recognition are becoming cheaper and easier to integrate into governance practices worldwide. Morocco serves as an example of how such technologies are becoming key tools of governance in authoritarian contexts. Based on qualitative fieldwork including semi-structured interviews, observation, and extensive desk reviews, this chapter focusses on the role played by AI-enhanced technology in urban surveillance and the control of migration between the Moroccan–Spanish borders. Two cross-cutting issues emerge: first, while international donors provide funding for urban and border surveillance projects, their role in enforcing transparency mechanisms in their implementation remains limited; second, Morocco’s existing legal framework hinders any kind of public oversight. Video surveillance is treated as the sole prerogative of the security apparatus, and so far public actors have avoided to engage directly with the topic. The lack of institutional oversight and public debate on the matter raise serious concerns on the extent to which the deployment of such technologies affects citizens’ rights. AI-enhanced surveillance is thus an intrinsically transnational challenge in which private interests of economic gain and public interests of national security collide with citizens’ human rights across the Global North/Global South divide.
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This article focuses on the recent judgment of the Court of Justice, Aranyosi and Caldararu. After conducting a legal analysis on this case, three issues are identified and they are separately discussed in three sections. The aim of this paper is to show the impact of this judgment on public order and public security in Europe on the one hand and on the individual’s fundamental rights, on the other hand. It is going to be argued that even though there are limits to the principle of mutual recognition, this new exception based on fundamental rights establishes a new procedure for non-surrender. Therefore, the Court of Justice creates a non-execution ground which the EU legislator did not intend to include in the Framework Decision on the European arrest warrant. This is explained by looking at the three interconnected notions of Freedom, Security and Justice.
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Over the past few years a growing number of artists have critiqued the ubiquity of identity recognition technologies. Specifically, the use of these technologies by state security programs, tech-giants and multinational corporations has met with opposition and controversy. A popular form of resistance to recognition technology is sought in strategies of masking and camouflage. Zach Blas, Leo Selvaggio, Sterling Crispin and Adam Harvey are among a group of internationally acclaimed artists who have developed subversive anti-facial recognition masks that disrupt identification technologies. This paper examines the ontological underpinnings of these popular and widely exhibited mask projects. Over and against a binary understanding and criticism of identity recognition technology, I propose to take a relational turn to reimagine these technologies not as an object for our eyes, but as a relationship between living organisms and things. A relational perspective cuts through dualist and anthropocentric conceptions of recognition technology opening pathways to intersectional forms of resistance and critique. Moreover, if human-machine relationships are to be understood as coming into being in mutual dependency, if the boundaries between online and offline are always already blurred, if the human and the machine live intertwined lives and it is no longer clear where the one stops and the other starts, we need to revise our understanding of the self. A relational understanding of recognition technology moves away from a notion of the self as an isolated and demarcated entity in favour of an understanding of the self as relationally connected, embedded and interdependent. This could alter the way we relate to machines and multiplies the lines of flight we can take out of a culture of calculated settings.
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From the introduction: "There are two variants of fronto-temporal dementia: a behavioral variant (behavioral FTD, bvFTD, Neary et al. (1998)), which causes changes in behavior and personality but leaves syntax, phonology and semantics relatively intact, and a variant that causes impairments in the language processing system (Primary Progessive Aphasia, PPA (Gorno-Tempini et al., 2004). PPA can be subdivided into subtypes fluent (fluent but empty speech, comprehension of word meaning is affected / `semantic dementia') and non-fluent (agrammatism, hesitant or labored speech, word finding problems). Some identify logopenic aphasia as a FTD-variant: fluent aphasia with anomia but intact object recognition and underlying word meaning."
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Objective: To automatically recognize self-acknowledged limitations in clinical research publications to support efforts in improving research transparency.Methods: To develop our recognition methods, we used a set of 8431 sentences from 1197 PubMed Central articles. A subset of these sentences was manually annotated for training/testing, and inter-annotator agreement was calculated. We cast the recognition problem as a binary classification task, in which we determine whether a given sentence from a publication discusses self-acknowledged limitations or not. We experimented with three methods: a rule-based approach based on document structure, supervised machine learning, and a semi-supervised method that uses self-training to expand the training set in order to improve classification performance. The machine learning algorithms used were logistic regression (LR) and support vector machines (SVM).Results: Annotators had good agreement in labeling limitation sentences (Krippendorff's α = 0.781). Of the three methods used, the rule-based method yielded the best performance with 91.5% accuracy (95% CI [90.1-92.9]), while self-training with SVM led to a small improvement over fully supervised learning (89.9%, 95% CI [88.4-91.4] vs 89.6%, 95% CI [88.1-91.1]).Conclusions: The approach presented can be incorporated into the workflows of stakeholders focusing on research transparency to improve reporting of limitations in clinical studies.
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An array of studies has shown that games can be suitable for marketing communication - but that not all game genres can be expected to be equally successful in generating advertising effects. Since the game task in shooter games requests players to strictly focus on objects that can pose a threat, that game genre seems especially problematic for in-game advertising. Subliminal communication can theoretically be expected to overcome (part) of that problem. Therefore, our experimental study (N = 143) focuses on marketing communication effects of subliminally presented brand logos. We aim to find out (i) whether subliminal marketing communication causes recognition effects and (ii) whether pictorial logos differ from textual logos in the size of the effect they generate. This way we can shine a light on the message processing mechanisms that are foundational to subliminal message effects. The results of our study show that subliminally presented brand logos do have a communicative potential: the recognition effects are significant. Besides, our study indicates that pictorial logos have a greater propensity to subliminally communicate than textual logos.
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Article written by Sue Lawrence and Nol Reverda, Directors of the Macess programme. The validation of awards and courses within higher education has traditionally and, to a great extent, continues to be a national issue, with each country using its own protocol for determining standards and academic levels, and validating courses according to its nationally recognised and agreed system. Institutions in some countries, however, are able to validate courses which are delivered in an institution in another country. This practice has led to some useful collaborative arrangements in developing European postgraduate programmes for the social professions, particularly in countries where education for social professionals takes place outside of the university system, for example, in The Netherlands. Largely as a result of such collaboration, facilitated by the Erasmus programme, there is now a proliferation of courses for social professionals, which have ‘European’ in their title or as a major component of the course content. What, then, makes a programme ‘European’?
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The performance of human-robot collaboration tasks can be improved by incorporating predictions of the human collaborator's movement intentions. These predictions allow a collaborative robot to both provide appropriate assistance and plan its own motion so it does not interfere with the human. In the specific case of human reach intent prediction, prior work has divided the task into two pieces: recognition of human activities and prediction of reach intent. In this work, we propose a joint model for simultaneous recognition of human activities and prediction of reach intent based on skeletal pose. Since future reach intent is tightly linked to the action a person is performing at present, we hypothesize that this joint model will produce better performance on the recognition and prediction tasks than past approaches. In addition, our approach incorporates a simple human kinematic model which allows us to generate features that compactly capture the reachability of objects in the environment and the motion cost to reach those objects, which we anticipate will improve performance. Experiments using the CAD-120 benchmark dataset show that both the joint modeling approach and the human kinematic features give improved F1 scores versus the previous state of the art.
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