CogNovo.net

CogNovo Network is a multinational and interdisciplinary web-project which synthesises knowledge from a variety of domains (for instance, cognitive neuroscience, psychology, creativity research, and computer science, inter alia). It is a research spin-off that emerged from the European Union funded Marie Curie Actions CogNovo program at the University of Plymouth (United Kingdom). The founder, web-developer, and author of CogNovo Network is Dr. Christopher B. Germann (PhD, MSc, BSc / Marie Curie Alumnus).

The CogNovo Network logo has a deeper semantic/hermeneutical interpretative meaning and symbolises “cognitive liberty” which is a condicio sine qua non for creativity, realisation of psychological potential, and brain development (i.e., neuro/synapto-plasticity, neuronal integration

N., Pam M.S., 'NEURAL INTEGRATION', in Psychology_Dictionary_org, April 7, 2013, (accessed June 13, 2019).

Cf. Sensory summation/binding and the formation of higher-order abstract cognitive concepts., inter-hemispheric connectivity/synchronisation, etc.). The logo consists of a fully connected network which is also known as a non-centralised/non-hirachical full mesh topology in network science. In this specific network topology all nodes are connected to each other (viz., exhaustive interconnectivity). In graph theory this specific arrangement is termed a “complete graph” and the number of connections grows quadratically based on the number of network-nodes. This inclusive relational principle can be mathematically formalised as follows:

This document provides a comprehensive primer on various network typologies and contains numerous code-snippets for their implementation in R (statistical open-source software).

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See also:
Amaldi, E., Capone, A., Cesana, M., Filippini, I., & Malucelli, F. (2008). Optimization models and methods for planning wireless mesh networks. Computer Networks, 52(11), 2159–2171. doi.org/10.1016/j.comnet.2008.02.020

Click to close…

See also: Lewin, K. (1936). Principles of topological psychology. New York: McGraw-Hill.
Full-text: archive.org/details/PrinciplesOfTopologicalPsychology/

R code to plot various network structures (igraph package)

    #https://igraph.org/r/doc/igraph.pdf
    #Ognyanova, K. (2016) Network analysis with R and igraph: NetSci X Tutorial. Retrieved from www.kateto.net/networks-r-igraph.
    install.packages("igraph") 
    library(igraph)
    #full graph
    fg <- make_full_graph(40)
    plot(fg, vertex.size=10, vertex.label=NA)
    #simple star
    st <- make_star(40)
    plot(st, vertex.size=10, vertex.label=NA) 
    #tree
    tr <- make_tree(40, children = 3, mode = "undirected")
    plot(tr, vertex.size=10, vertex.label=NA) 
    #ring
    rn <- make_ring(40)
    plot(rn, vertex.size=10, vertex.label=NA)
    #Erdos-Renyi random graph model
    er <- sample_gnm(n=100, m=40) 
    plot(er, vertex.size=6, vertex.label=NA)  
    #Watts-Strogatz small-world model
    sw <- sample_smallworld(dim=2, size=10, nei=1, p=0.1)
    plot(sw, vertex.size=6, vertex.label=NA, layout=layout_in_circle)
    #Barabasi-Albert preferential attachment model for scale-free graphs
     ba <-  sample_pa(n=100, power=1, m=1,  directed=F)
     plot(ba, vertex.size=6, vertex.label=NA)

#https://igraph.org/r/doc/igraph.pdf

#Ognyanova, K. (2016) Network analysis with R and igraph: NetSci X Tutorial. Retrieved from www.kateto.net/networks-r-igraph.

install.packages("igraph")

library(igraph)

#full graph

fg <- make_full_graph(40)

plot(fg, vertex.size=10, vertex.label=NA)

#simple star

st <- make_star(40)

plot(st, vertex.size=10, vertex.label=NA)

#tree

tr <- make_tree(40, children = 3, mode = "undirected")

plot(tr, vertex.size=10, vertex.label=NA)

#ring

rn <- make_ring(40)

plot(rn, vertex.size=10, vertex.label=NA)

#Erdos-Renyi random graph model

er <- sample_gnm(n=100, m=40)

plot(er, vertex.size=6, vertex.label=NA)

#Watts-Strogatz small-world model

sw <- sample_smallworld(dim=2, size=10, nei=1, p=0.1)

plot(sw, vertex.size=6, vertex.label=NA, layout=layout_in_circle)

#Barabasi-Albert preferential attachment model for scale-free graphs

ba <- sample_pa(n=100, power=1, m=1, directed=F)

plot(ba, vertex.size=6, vertex.label=NA)

A fully connected network thus possess a non-hierarchical structure without any “centralised authority” and it possess a high degree of reliability/robustness due to the large number of redundant pathways. Besides its generic biological pertinence, the idea of equal distribution without any centralisation has obvious far-reaching political and philosophical implications as it is a truly liberal and democratic topology which allows for an “open dialogue” independent of any “top-down regulation”.
The idea of interconnectivity is also pertinent for the conceptualisation of interdiscipinary research, c.f.: holism.ga
Furthermore, it is an important idea in the context of creativity research. In neuroscience the concept of “spreading neuronal activation” is crucial for information processing in the brain (e.g., associative processes related to semantics, concept formation, and cognitive schemata). In a fully connected mesh topology information can spread freely (without inhibition/depression) and therefore ‘co-activate’ other nodes in the network. This ‘free flow’ of information (ideas/memes) is crucial for creativity and cognitive innovation – specifically in social systems (cf. cybernetics & quasi-evolutionary algorithms).

Join the forum to discuss – freely: forum.cognovo.net

also visit a related project of mine: cognitive-liberty.online

Patterson, K., Nestor, P. J., & Rogers, T. T.. (2007). Where do you know what you know? The representation of semantic knowledge in the human brain. Nature Reviews Neuroscience

, 8(12), 976–987.
Plain numerical DOI: 10.1038/nrn2277
DOI URL
directSciHub download

Show/hide publication abstract

“Mr m, a patient with semantic dementia–a neurodegenerative disease that is characterized by the gradual deterioration of semantic memory–was being driven through the countryside to visit a friend and was able to remind his wife where to turn along the not-recently-travelled route. then, pointing at the sheep in the field, he asked her ‘what are those things?’ prior to the onset of symptoms in his late 40s, this man had normal semantic memory. what has gone wrong in his brain to produce this dramatic and selective erosion of conceptual knowledge?”

Display further relevant references

Collins, A. M., & Loftus, E. F.. (1975). A spreading-activation theory of semantic processing. Psychological Review

Plain numerical DOI: 10.1037/0033-295X.82.6.407
DOI URL
directSciHub download

Show/hide publication abstract

“This paper presents a spreading-activation theory of human semantic processing, which can be applied to a wide range of recent experimental results. the theory is based on quillian’s theory of semantic memory search and semantic preparation, or priming. in conjunction with this, several of the misconceptions concerning qullian’s theory are discussed a number of additional assumptions are proposed for his theory in order to apply it to recent experiments. the present paper shows how the extended theory can account for results of several production experiments by loftus, juola and atkinson’s multiple-category experiment, conrad’s sentence-verification experiments, and several categorization experiments on the effect of semantic relatedness and typicality by holyoak and glass, rips, shoben, and smith, and rosch. the paper also provides a critique of the smith, shoben, and rips model for categorization judgments.”

Anderson, J. R.. (2013). A Spreading Activation Theory of Memory. In Readings in Cognitive Science: A Perspective from Psychology and Artificial Intelligence

Plain numerical DOI: 10.1016/B978-1-4832-1446-7.50016-9
DOI URL
directSciHub download

Show/hide publication abstract

“The act theory of factual memory is presented. according to this theory, information is encoded in an all-or-none manner into cognitive units and the strength of these units increases with practice and decays with delay. the essential process to memory performance is the retrieval operation. it is proposed that the cognitive units form an interconnected network and that retrieval is performed by spreading activation throughout the network. level of activation in the network determines rate and probability of recall. with these assumptions in place, the act theory is shown to predict interference results in memory, judgements of associative relatedness, impact of extensive practice on memory, the differences between recognition and recall, effects of elaborative processing, and effects of reconstructive recall.”

Dell, G. S.. (1986). A Spreading-Activation Theory of Retrieval in Sentence Production. Psychological Review

Plain numerical DOI: 10.1037/0033-295X.93.3.283
DOI URL
directSciHub download

Show/hide publication abstract

“This article presents a theory of sentence production that accounts for facts about speech errors—the kinds of errors that occur, the constraints on their form, and the conditions that precipitate them. the theory combines a spreading-activation retrieval mechanism with assumptions regarding lin-guistic units and rules. two simulation models are presented to illustrate how the theory applies to phonological encoding processes. one was designed to produce the basic kinds of phonological errors and their relative frequencies of occurrence. the second was used to fit data from an experimental technique designed to create these errors under controlled conditions.”

Crestani, F.. (1997). Application of Spreading Activation Techniques in Information Retrieval. Artificial Intelligence Review

Plain numerical DOI: 10.1023/A:1006569829653
DOI URL
directSciHub download

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Ziegler, C. N., & Lausen, G.. (2004). Spreading activation models for trust propagation. In Proceedings – 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service, EEE 2004

Plain numerical DOI: 10.1109/EEE.2004.1287293
DOI URL
directSciHub download

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“ Semantic web endeavors have mainly focused on issues pertaining to knowledge representation and ontology design. however, besides understanding information metadata stated by subjects, knowing about their credibility becomes equally crucial. hence, trust and trust metrics, conceived as computational means to evaluate trust relationships between individuals, come into play. our major contributions to semantic web trust management are twofold. first, we introduce our classification scheme for trust metrics along various axes and discuss advantages and drawbacks of existing approaches for semantic web scenarios. hereby, we devise our advocacy for local group trust metrics, guiding us to the second part which presents appleseed, our novel proposal for local group trust computation. compelling in its simplicity, appleseed borrows many ideas from spreading activation models in psychology and relates their concepts to trust evaluation in an intuitive fashion.”

Anderson, J. R., & Pirolli, P. L.. (1984). Spread of activation. Journal of Experimental Psychology: Learning, Memory, and Cognition

Plain numerical DOI: 10.1037/0278-7393.10.4.791
DOI URL
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“Spreading activation serves the function of quickly spreading an associative relevancy measure over declarative memory. the assumptions and evolution of the act* theory of spreading activation are discussed with a detailed mathematical formulation. it is assumed that activation is very rapidly spread over a semantic network and that various levels of activation are established throughout the network. level of activation maps onto processing time by cohtrolling the amount of time that it takes for the condition of a production rule to match. the act* spreading-activation theory predicts an exponential decay of activation with distance in network structure and ;an interaction of activation levels with the complexity of productions that are matched and executed.”

Ratcliff, R., & McKoon, G.. (1988). A Retrieval Theory of Priming in Memory. Psychological Review

Plain numerical DOI: 10.1037/0033-295X.95.3.385
DOI URL
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“We present a theory of priming that is designed to account for phenomena usually attributed to the action of a spreading activation process. the theory assumes that a prime and target are combined at retrieval into a compound cue that is used to access memory. if the representations of the prime and target are associated in memory, the match is greater than if they are not associated, and this greater match facilitates the response to the target. the compound cue mechanism can be implemented within the framework of several memory models; descriptions of these implementations are presented. we summarize empirical results that have been taken as evidence for a spreading activation process and show that the retrieval theory can also account for these phenomena and that, in some cases, the retrieval theory provides predictions that are more constrained than those provided by spreading activation theories. also, two experiments are reported that address predictions about the range of priming (in terms of number of connected concepts) and the decay rate of priming (in terms of intervening items). in both cases, the retrieval theory provides a better account of the data than spreading activation. finally, contrasts between the compound cue theory and long-term priming phenomena are presented.”

McNamara, T. P.. (1992). Theories of Priming: I. Associative Distance and Lag. Journal of Experimental Psychology: Learning, Memory, and Cognition

Plain numerical DOI: 10.1037/0278-7393.18.6.1173
DOI URL
directSciHub download

Show/hide publication abstract

“Automatic associative priming occurs in a large number of memory retrieval tasks, including semantic categorization, lexical decisions, item recognition, naming, and judgments of spatial location. priming has been commonly attributed to one or the other of 2 alternative mechanisms: spreading activation or construction of compound retrieval cues. this article reports the results of 3 experiments that were designed to test spreading-activation and non-spreading-activation models of priming. the findings were consistent with the spreading-activation models, but inconsistent with the non-spreading-activation models. these results suggest that a rejection of spreading-activation mechanisms is premature. (psycinfo database record (c) 2005 apa, all rights reserved).”

Neely, J. H.. (1977). Semantic priming and retrieval from lexical memory: Roles of inhibitionless spreading activation and limited-capacity attention. Journal of Experimental Psychology: General

Plain numerical DOI: 10.1037/0096-3445.106.3.226
DOI URL
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“Prior to each target letter string presented visually to 120 university students in a speeded word-nonword classification task, either {bird, body, building,} or {xxx} appeared as a priming event. five types of word-prime/word-target trials were used: bird-robin, bird-arm, body-door, body-sparrow, and body-heart. the stimulus onset asynchrony (soa) between prime and target letter string varied between 250 and 2,000 msec. at 2,000-msec soa, reaction times (rts) on bird-robin type trials were faster than on xxx-prime trials (facilitation), whereas rts on bird-arm type trials were slower than on xxx-prime (inhibition). as soa decreased, the facilitation effect on bird-robin trials remained constant, but the inhibition effect on bird-arm decreased until, at 250-msec soa, there was no inhibition. for shift conditions at 2,000-msec soa, facilitation was obtained on body-door type trials and inhibition was obtained on body-sparrow type. these effects decreased as soa decreased until there was no facilitation or inhibition. on body-heart type trials, there was an inhibition effect at 2,000 msec soa, which decreased as soa decreased until, at 250-msec soa, it became a facilitation effect. results support the theory of m. i. posner and s. r. snyder (1975) that postulated 2 distinct components of attention: a fast automatic inhibitionless spreading-activation process and a slow limited-capacity conscious-attention mechanism. (27 ref) (psycinfo database record (c) 2006 apa, all rights reserved). © 1977 american psychological association.”

McKoon, G., & Ratcliff, R.. (1992). Spreading Activation Versus Compound Cue Accounts of Priming: Mediated Priming Revisited. Journal of Experimental Psychology: Learning, Memory, and Cognition

Plain numerical DOI: 10.1037/0278-7393.18.6.1155
DOI URL
directSciHub download

Show/hide publication abstract

“Spreading activation theories and compound cue theories have both been proposed as accounts of priming phenomena. according to spreading activation theories, the amount of activation that spreads between a prime and a target should be a function of the number of mediating links between the prime and target in a semantic network and the strengths of those links. the amount of activation should determine the amount of facilitation given by a prime to a target in lexical decision. to predict the amount of facilitation, it is necessary to measure the associative links between prime and target in memory. free-association production probability has been the variable chosen in previous research for this measurement. however, in 3 experiments, the authors show priming effects that free-association production probabilities cannot easily predict. instead, they argue that amount of priming depends on the familiarity of the prime and target as a compound, where the compound is formed by the simultaneous presence of the prime and target in short-term memory as a test item.”

cognovo network logo large

CogNovo Network

computational-creativity-pandemonium

cognovo-network-european-union-creativity-neuroscience-psychology-3d-model-virtual-reality-augmented-reality

cognovo network

cognovo eu network mindmap

Use the mouse to interact with the virtual object (i.e., to change the viewpoint/perspective on the mesh geometry interactively).

Source Code

<script src="../build/three.js"></script>

		<script src='js/libs/dat.gui.min.js'></script>
		<script src="js/libs/stats.min.js"></script>
		<script src="js/WebGL.js"></script>

		<video id="video" style="display: none;" crossorigin="anonymous" loop="loop" muted="" width="300" height="150">
			<source src="textures/kinect.webm" />
			<source src="textures/kinect.mp4" />
		</video> <script id="vs" type="x-shader/x-vertex">

			uniform sampler2D map;

			uniform float width;
			uniform float height;
			uniform float nearClipping, farClipping;

			uniform float pointSize;
			uniform float zOffset;

			varying vec2 vUv;

			const float XtoZ = 1.11146; // tan( 1.0144686 / 2.0 ) * 2.0;
			const float YtoZ = 0.83359; // tan( 0.7898090 / 2.0 ) * 2.0;

			void main() {

				vUv = vec2( position.x / width, position.y / height );

				vec4 color = texture2D( map, vUv );
				float depth = ( color.r + color.g + color.b ) / 3.0;

				// Projection code by @kcmic

				float z = ( 1.0 - depth ) * (farClipping - nearClipping) + nearClipping;

				vec4 pos = vec4(
					( position.x / width - 0.5 ) * z * XtoZ,
					( position.y / height - 0.5 ) * z * YtoZ,
					- z + zOffset,
					1.0);

				gl_PointSize = pointSize;
				gl_Position = projectionMatrix * modelViewMatrix * pos;

			}

		</script> <script id="fs" type="x-shader/x-fragment">

			uniform sampler2D map;

			varying vec2 vUv;

			void main() {

				vec4 color = texture2D( map, vUv );
				gl_FragColor = vec4( color.r, color.g, color.b, 0.2 );

			}

		</script> <script>

			var container;

			var scene, camera, renderer;
			var geometry, mesh, material;
			var mouse, center;
			var stats;

			if ( WEBGL.isWebGLAvailable() ) {

				init();
				animate();

			} else {

				document.body.appendChild( WEBGL.getWebGLErrorMessage() );

			}

			function init() {

				container = document.createElement( 'div' );
				document.body.appendChild( container );

				var info = document.createElement( 'div' );
				info.id = 'info';
				info.innerHTML = '<a href="http://threejs.org" target="_blank" rel="noopener noreferrer">three.js</a> - kinect';
				document.body.appendChild( info );

				stats = new Stats();
				// container.appendChild( stats.dom );

				camera = new THREE.PerspectiveCamera( 50, window.innerWidth / window.innerHeight, 1, 10000 );
				camera.position.set( 0, 0, 500 );

				scene = new THREE.Scene();
				center = new THREE.Vector3();
				center.z = - 1000;

				var video = document.getElementById( 'video' );
				video.addEventListener( 'loadedmetadata', function () {

					var texture = new THREE.VideoTexture( video );
					texture.minFilter = THREE.NearestFilter;

					var width = 640, height = 480;
					var nearClipping = 850, farClipping = 4000;

					geometry = new THREE.BufferGeometry();

					var vertices = new Float32Array( width * height * 3 );

					for ( var i = 0, j = 0, l = vertices.length; i < l; i += 3, j ++ ) {

						vertices[ i ] = j % width;
						vertices[ i + 1 ] = Math.floor( j / width );

					}

					geometry.addAttribute( 'position', new THREE.BufferAttribute( vertices, 3 ) );

					material = new THREE.ShaderMaterial( {

						uniforms: {

							"map": { value: texture },
							"width": { value: width },
							"height": { value: height },
							"nearClipping": { value: nearClipping },
							"farClipping": { value: farClipping },

							"pointSize": { value: 2 },
							"zOffset": { value: 1000 }

						},
						vertexShader: document.getElementById( 'vs' ).textContent,
						fragmentShader: document.getElementById( 'fs' ).textContent,
						blending: THREE.AdditiveBlending,
						depthTest: false, depthWrite: false,
						transparent: true

					} );

					mesh = new THREE.Points( geometry, material );
					scene.add( mesh );

					var gui = new dat.GUI();
					gui.add( material.uniforms.nearClipping, 'value', 1, 10000, 1.0 ).name( 'nearClipping' );
					gui.add( material.uniforms.farClipping, 'value', 1, 10000, 1.0 ).name( 'farClipping' );
					gui.add( material.uniforms.pointSize, 'value', 1, 10, 1.0 ).name( 'pointSize' );
					gui.add( material.uniforms.zOffset, 'value', 0, 4000, 1.0 ).name( 'zOffset' );
					gui.close();


				}, false );

				video.play();

				renderer = new THREE.WebGLRenderer();
				renderer.setPixelRatio( window.devicePixelRatio );
				renderer.setSize( window.innerWidth, window.innerHeight );
				container.appendChild( renderer.domElement );

				mouse = new THREE.Vector3( 0, 0, 1 );

				document.addEventListener( 'mousemove', onDocumentMouseMove, false );

				//

				window.addEventListener( 'resize', onWindowResize, false );

			}

			function onWindowResize() {

				camera.aspect = window.innerWidth / window.innerHeight;
				camera.updateProjectionMatrix();

				renderer.setSize( window.innerWidth, window.innerHeight );

			}

			function onDocumentMouseMove( event ) {

				mouse.x = ( event.clientX - window.innerWidth / 2 ) * 8;
				mouse.y = ( event.clientY - window.innerHeight / 2 ) * 8;

			}

			function animate() {

				requestAnimationFrame( animate );

				render();
				stats.update();

			}

			function render() {

				camera.position.x += ( mouse.x - camera.position.x ) * 0.05;
				camera.position.y += ( - mouse.y - camera.position.y ) * 0.05;
				camera.lookAt( center );

				renderer.render( scene, camera );

			}

		</script>Source URL: https://github.com/mrdoob/three.js/blob/master/examples/webgl_kinect.html

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</video> <script id="vs" type="x-shader/x-vertex">

uniform sampler2D map;

uniform float width;

uniform float height;

uniform float nearClipping, farClipping;

uniform float pointSize;

uniform float zOffset;

varying vec2 vUv;

const float XtoZ = 1.11146; // tan( 1.0144686 / 2.0 ) * 2.0;

const float YtoZ = 0.83359; // tan( 0.7898090 / 2.0 ) * 2.0;

void main() {

vUv = vec2( position.x / width, position.y / height );

vec4 color = texture2D( map, vUv );

float depth = ( color.r + color.g + color.b ) / 3.0;

// Projection code by @kcmic

float z = ( 1.0 - depth ) * (farClipping - nearClipping) + nearClipping;

vec4 pos = vec4(

( position.x / width - 0.5 ) * z * XtoZ,

( position.y / height - 0.5 ) * z * YtoZ,

- z + zOffset,

1.0);

gl_PointSize = pointSize;

gl_Position = projectionMatrix * modelViewMatrix * pos;

}

</script> <script id="fs" type="x-shader/x-fragment">

uniform sampler2D map;

varying vec2 vUv;

void main() {

vec4 color = texture2D( map, vUv );

gl_FragColor = vec4( color.r, color.g, color.b, 0.2 );

}

</script> <script>

var container;

var scene, camera, renderer;

var geometry, mesh, material;

var mouse, center;

var stats;

if ( WEBGL.isWebGLAvailable() ) {

init();

animate();

} else {

document.body.appendChild( WEBGL.getWebGLErrorMessage() );

}

function init() {

container = document.createElement( 'div' );

document.body.appendChild( container );

var info = document.createElement( 'div' );

info.id = 'info';

info.innerHTML = '<a href="http://threejs.org" target="_blank" rel="noopener noreferrer">three.js</a> - kinect';

document.body.appendChild( info );

stats = new Stats();

// container.appendChild( stats.dom );

camera = new THREE.PerspectiveCamera( 50, window.innerWidth / window.innerHeight, 1, 10000 );

camera.position.set( 0, 0, 500 );

scene = new THREE.Scene();

center = new THREE.Vector3();

center.z = - 1000;

var video = document.getElementById( 'video' );

video.addEventListener( 'loadedmetadata', function () {

var texture = new THREE.VideoTexture( video );

texture.minFilter = THREE.NearestFilter;

var width = 640, height = 480;

var nearClipping = 850, farClipping = 4000;

geometry = new THREE.BufferGeometry();

var vertices = new Float32Array( width * height * 3 );

for ( var i = 0, j = 0, l = vertices.length; i < l; i += 3, j ++ ) {

vertices[ i ] = j % width;

vertices[ i + 1 ] = Math.floor( j / width );

}

geometry.addAttribute( 'position', new THREE.BufferAttribute( vertices, 3 ) );

material = new THREE.ShaderMaterial( {

uniforms: {

"map": { value: texture },

"width": { value: width },

"height": { value: height },

"nearClipping": { value: nearClipping },

"farClipping": { value: farClipping },

"pointSize": { value: 2 },

"zOffset": { value: 1000 }

vertexShader: document.getElementById( 'vs' ).textContent,

fragmentShader: document.getElementById( 'fs' ).textContent,

blending: THREE.AdditiveBlending,

depthTest: false, depthWrite: false,

transparent: true

} );

mesh = new THREE.Points( geometry, material );

scene.add( mesh );

var gui = new dat.GUI();

gui.add( material.uniforms.nearClipping, 'value', 1, 10000, 1.0 ).name( 'nearClipping' );

gui.add( material.uniforms.farClipping, 'value', 1, 10000, 1.0 ).name( 'farClipping' );

gui.add( material.uniforms.pointSize, 'value', 1, 10, 1.0 ).name( 'pointSize' );

gui.add( material.uniforms.zOffset, 'value', 0, 4000, 1.0 ).name( 'zOffset' );

gui.close();

}, false );

video.play();

renderer = new THREE.WebGLRenderer();

renderer.setPixelRatio( window.devicePixelRatio );

renderer.setSize( window.innerWidth, window.innerHeight );

container.appendChild( renderer.domElement );

mouse = new THREE.Vector3( 0, 0, 1 );

document.addEventListener( 'mousemove', onDocumentMouseMove, false );

window.addEventListener( 'resize', onWindowResize, false );

}

function onWindowResize() {

camera.aspect = window.innerWidth / window.innerHeight;

camera.updateProjectionMatrix();

renderer.setSize( window.innerWidth, window.innerHeight );

}

function onDocumentMouseMove( event ) {

mouse.x = ( event.clientX - window.innerWidth / 2 ) * 8;

mouse.y = ( event.clientY - window.innerHeight / 2 ) * 8;

}

function animate() {

requestAnimationFrame( animate );

render();

stats.update();

}

function render() {

camera.position.x += ( mouse.x - camera.position.x ) * 0.05;

camera.position.y += ( - mouse.y - camera.position.y ) * 0.05;

camera.lookAt( center );

renderer.render( scene, camera );

}

</script>Source URL: https://github.com/mrdoob/three.js/blob/master/examples/webgl_kinect.html

CogNovo Web-Forum
cognovo-webforum-synopsis

Slide 1

CogNovo Network

Real-Time Autocompletion Function “ON” (On-the-Fly Probabilistic Semantic Integration)

Intelligent AJAX Search Function

Maschine learning powered AI translation

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