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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In case of a major cyber incident, organizations usually rely on external providers of Cyber Incident Response (CIR) services. CIR consultants operate in a dynamic and constantly changing environment in which they must actively engage in information management and problem solving while adapting to complex circumstances. In this challenging environment CIR consultants need to make critical decisions about what to advise clients that are impacted by a major cyber incident. Despite its relevance, CIR decision making is an understudied topic. The objective of this preliminary investigation is therefore to understand what decision-making strategies experienced CIR consultants use during challenging incidents and to offer suggestions for training and decision-aiding. A general understanding of operational decision making under pressure, uncertainty, and high stakes was established by reviewing the body of knowledge known as Naturalistic Decision Making (NDM). The general conclusion of NDM research is that experts usually make adequate decisions based on (fast) recognition of the situation and applying the most obvious (default) response pattern that has worked in similar situations in the past. In exceptional situations, however, this way of recognition-primed decision-making results in suboptimal decisions as experts are likely to miss conflicting cues once the situation is quickly recognized under pressure. Understanding the default response pattern and the rare occasions in which this response pattern could be ineffective is therefore key for improving and aiding cyber incident response decision making. Therefore, we interviewed six experienced CIR consultants and used the critical decision method (CDM) to learn how they made decisions under challenging conditions. The main conclusion is that the default response pattern for CIR consultants during cyber breaches is to reduce uncertainty as much as possible by gathering and investigating data and thus delay decision making about eradication until the investigation is completed. According to the respondents, this strategy usually works well and provides the most assurance that the threat actor can be completely removed from the network. However, the majority of respondents could recall at least one case in which this strategy (in hindsight) resulted in unnecessary theft of data or damage. Interestingly, this finding is strikingly different from other operational decision-making domains such as the military, police and fire service in which there is a general tendency to act rapidly instead of searching for more information. The main advice is that training and decision aiding of (novice) cyber incident responders should be aimed at the following: (a) make cyber incident responders aware of how recognition-primed decision making works; (b) discuss the default response strategy that typically works well in several scenarios; (c) explain the exception and how the exception can be recognized; (d) provide alternative response strategies that work better in exceptional situations.
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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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Growing research in sign language recognition, generation, and translation AI has been accompanied by calls for ethical development of such technologies. While these works are crucial to helping individual researchers do better, there is a notable lack of discussion of systemic biases or analysis of rhetoric that shape the research questions and methods in the field, especially as it remains dominated by hearing non-signing researchers. Therefore, we conduct a systematic review of 101 recent papers in sign language AI. Our analysis identifies significant biases in the current state of sign language AI research, including an overfocus on addressing perceived communication barriers, a lack of use of representative datasets, use of annotations lacking linguistic foundations, and development of methods that build on flawed models. We take the position that the field lacks meaningful input from Deaf stakeholders, and is instead driven by what decisions are the most convenient or perceived as important to hearing researchers. We end with a call to action: the field must make space for Deaf researchers to lead the conversation in sign language AI.
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Ubiquitous computing, new sensor technologies, and increasingly available and accessible algorithms for pattern recognition and machine learning enable automatic analysis and modeling of human behavior in many novel ways. In this introductory paper of the 6th International Workshop on Human Behavior Understanding (HBU’15), we seek to critically assess how HBU technology can be used for elderly. We describe and exemplify some of the challenges that come with the involvement of aging subjects, but we also point out to the great potential for expanding the use of ICT to create many applications to provide a better life for elderly.
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This survey is about recognizing patterns in the way Small and Medium Enterprises (SMEs) organize their procurement activities. The scope of the survey is limited to the key commodities of the SME.A key commodity is defined as the purchased product or service group which is essential for realizing the value proposition for the customers of the SME.Prior outcome of our research indicated the existence of four procurement oriented patterns in SMEs. 4 Procurement Oriented Patterns where part of the study: Pattern 1 Focal company: ICT turn-key designerValue proposition of the focal company: ICT Design and assembly of offices on a high quality level at a reasonable price. Operational excellence: standardization in commodities, low transaction costs internally and externallyPurchased key commodity: Standard ICT software and hardwarePattern 2 Focal company: Horse shoes manufacturerValue proposition of the focal company: Standard horse shoes assortment at reasonable prices in a competitive environmentPurchased key commodity: Standard quality iron, reliable deliveryPattern 3 Focal company: IT innovation driven companyValue proposition of the focal company: Developing innovative software made applicable for practical usage in devices at a reasonable pricePurchased key commodity: Delivering applicable solutions on the bases of regular soft- and hardware, to enable the companies’ innovative software function in practicePattern 4 Focal company: designer and manufacturer of trailersValue proposition of the focal company: Designing and manufacturing trailers tailor made for specific requirements of customersPurchased key commodity: Designing and manufacturing axles which align to the specific trailer wishes of the customer of the focal companyFINDINGS Pattern recognitionAbout 50 % of the respondents recognized the four presented patterns from own experience and/or read literature. Respondents also suggested pattern variants. It is concluded that this Delphi study strengthens the view that these patterns exist in SMEs. Further research may include further empirical testing of these patterns and their variants. Perceived strengths or weaknesses. Respondents mentioned a wide variety of strengths and weaknesses of the patterns. No clear conclusions can be drawn from this data. Adequacy of the pattern descriptions. One of the outcomes of this Delphi study is an improved conceptual framework for describing procurement activity patterns. This framework can be used for collecting SME data in future research, for example by modifying the existing survey questions which are used in the WIM research program to describe SME procurement activities. The improved model includes more variables and values than the initial model. Thus future research may lead to more detailed patterns descriptions. Missing patterns and pattern variantsApart from the suggested pattern variants, respondents do not miss patterns which are quite different from the four patterns suggested by the research team. Methodological remarksThe Delphi study method did not allow for fast feedback on panel member contributions and fast group think processes. For the future it is advised to consider other methods in similar cases, for example the World Cafe method.
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In daily interaction with horses, humans primarily rely on facial expression as a non-verbal equine cue for emotional information. Difficulties in correctly recognizing these signals might arise due to the species-specificity of facial cues, possibly leading to diminished equine welfare and health. This study aimed to explore human visual search patterns when assessing equine facial expressions indicative of various pain levels, utilizing eye-tracking technology. Hundred and eight individuals (N = 108), classified into three groups (affinity with horses (N = 60), pet owners with no affinity with horses (N = 32), and individuals with no affinity with animals (N = 16)) participated in the study; with their eye movements recorded using eye tracking glasses they evaluated four photos of horses with different levels of pain. Error score, calculated by comparing participant scores to Gold Standard Visual Analogue Score levels and fixation metrics (number of fixations and duration of fixations) were analysed across the four photos, participant group and Areas of Interest (AOIs): eyes, ears, nostrils, and mouth. Statistical analysis utilized linear mixed models. Highlighting the critical role of the eyes as key indicators of pain, findings showed that the eyes played a significant role in assessing equine emotional states, as all groups focused on them for a longer time and more frequently compared to other facial features. Also, participants showed a consistent pattern in how they looked at a horse's face, first focusing on the eyes, then the ears, and finally the nose/mouth region, indicating a horse-specific pattern. Moderate pain was assessed with similar accuracy across all groups, indicating that these signals are broadly recognizable. Nevertheless, non-equestrians faced challenges with recognizing the absence of pain, possibly highlighting the role of experience in interpreting subtle equine expressions. The study's limitations, such as variability in assessment conditions may have impacted findings. Future work could further investigate why humans follow this visual search pattern and whether they recognize the significance of a horse's ears. Additionally, emphasis should be placed on developing targeted training interventions to improve equine pain recognition, possibly benefiting equine welfare and health.
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Many studies have shown that experts possess better perceptual-cognitive skills than novices (e.g., in anticipation, decision making, pattern recall), but it remains unclear whether a relationship exists between performance on those tests of perceptual-cognitive skill and actual on-field performance. In this study, we assessed the in situ performance of skilled soccer players and related the outcomes to measures of anticipation, decision making, and pattern recall. In addition, we examined gaze behaviour when performing the perceptual-cognitive tests to better understand whether the underlying processes were related when those perceptual-cognitive tasks were performed. The results revealed that on-field performance could not be predicted on the basis of performance on the perceptual-cognitive tests. Moreover, there were no strong correlations between the level of performance on the different tests. The analysis of gaze behaviour revealed differences in search rate, fixation duration, fixation order, gaze entropy, and percentage viewing time when performing the test of pattern recall, suggesting that it is driven by different processes to those used for anticipation and decision making. Altogether, the results suggest that the perceptual-cognitive tests may not be as strong determinants of actual performance as may have previously been assumed.
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Existing research on the recognition of Activities of Daily Living (ADL) from simple sensor networks assumes that only a single person is present in the home. In real life there will be situations where the inhabitant receives visits from family members or professional health care givers. In such cases activity recognition is unreliable. In this paper, we investigate the problem of detecting multiple persons in an environment equipped with a sensor network consisting of binary sensors. We conduct a real-life experiment for detection of visits in the oce of the supervisor where the oce is equipped with a video camera to record the ground truth. We collected data during two months and used two models, a Naive Bayes Classier and a Hidden Markov Model for a visitor detection. An evaluation of these two models shows that we achieve an accuracy of 83% with the NBC and an accuracy of 92% with a HMM, respectively.
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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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