Vision systems with the human in the loop

The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed

Intermedia Remediated & the Question of Designing Discourse

New “engines of discourse” (neural networks, algorithms and other forms of artificial intelligence, combined with the devices that record and interpret viewer actions) bring to the fore rhetorical concerns that challenge discipline-based notions of process and form. We shall focus here on the tradition of intermedial art practices to better understand the ever more complex question of how to inter-relate three aspects of digital communication: authorial “intent”, the digital sign and its interactive exploration by a “spect-actor”. We shall argue that the digital sign is an extension of intermedial thinking rooted in a pre-digital, photographic practice and esthetic. The writings of several French theorists on the subject of interactive digital design will provide a context for understanding examples of “virtual art-realities”, whose specificity is staging relationships between objects and people. Keywords: Rhetoric; Discourse; Intermedia; Interactivity; Digital Sign; Esthetics; Artificial Intelligence; Behavior-based Art.</p

Hand Gesture Recognition Using Different Algorithms Based on Artificial Neural Network

Gesture is one of the most natural and expressive ways of communications between human and computer in a real system. We naturally use various gestures to express our own intentions in everyday life. Hand gesture is one of the important methods of non-verbal communication for human beings. Hand gesture recognition based man-machine interface is being developed vigorously in recent years. This paper gives an overview of different methods for recognizing the hand gestures using MATLAB. It also gives the working details of recognition process using Edge detection and Skin detection algorithms

Of epistemic tools: musical instruments as cognitive extensions

This paper explores the differences in the design and performance of acoustic and new digital musical instruments, arguing that with the latter there is an increased encapsulation of musical theory. The point of departure is the phenomenology of musical instruments, which leads to the exploration of designed artefacts as extensions of human cognition – as scaffolding onto which we delegate parts of our cognitive processes. The paper succinctly emphasises the pronounced epistemic dimension of digital instruments when compared to acoustic instruments. Through the analysis of material epistemologies it is possible to describe the digital instrument as an epistemic tool: a designed tool with such a high degree of symbolic pertinence that it becomes a system of knowledge and thinking in its own terms. In conclusion, the paper rounds up the phenomenological and epistemological arguments, and points at issues in the design of digital musical instruments that are germane due to their strong aesthetic implications for musical culture

Learning to recognize touch gestures: recurrent vs. convolutional features and dynamic sampling

We propose a fully automatic method for learning gestures on big touch devices in a potentially multi-user context. The goal is to learn general models capable of adapting to different gestures, user styles and hardware variations (e.g. device sizes, sampling frequencies and regularities). Based on deep neural networks, our method features a novel dynamic sampling and temporal normalization component, transforming variable length gestures into fixed length representations while preserving finger/surface contact transitions, that is, the topology of the signal. This sequential representation is then processed with a convolutional model capable, unlike recurrent networks, of learning hierarchical representations with different levels of abstraction. To demonstrate the interest of the proposed method, we introduce a new touch gestures dataset with 6591 gestures performed by 27 people, which is, up to our knowledge, the first of its kind: a publicly available multi-touch gesture dataset for interaction. We also tested our method on a standard dataset of symbolic touch gesture recognition, the MMG dataset, outperforming the state of the art and reporting close to perfect performance.Comment: 9 pages, 4 figures, accepted at the 13th IEEE Conference on Automatic Face and Gesture Recognition (FG2018). Dataset available at http://itekube7.itekube.co