As a software engineer, I can claim that any piece of software has A. A true artificially-intelligent system is one that can learn on its own. True A. That type of A. We're not talking about that. At least not yet. While companies like Apple, Facebook and Tesla rollout ground-breaking updates and revolutionary changes to how we interact with machine-learning technology, many of us are still clueless on just how A. How much of an effect will this technology have on our future lives and what other ways will it seep into day-to-day life?
When A. The truth is that, whether or not true A. Humans have always fixated themselves on improving life across every spectrum, and the use of technology has become the vehicle for doing just that. Quantum computers will not only solve all of life's most complex problems and mysteries regarding the environment, aging, disease, war, poverty, famine, the origins of the universe and deep-space exploration, just to name a few, it'll soon power all of our A.
However, quantum computers hold their own inherent risks. What happens after the first quantum computer goes online, making the rest of the world's computing obsolete? How will existing architecture be protected from the threat that these quantum computers pose? However, when the first quantum computer is built, Smart tells me that:.
Clearly, there's no stopping a quantum computer led by a determined party without a solid QRC. While all of it is still what seems like a far way off, the future of this technology presents a Catch, able to solve the world's problems and likely to power all the A. The center of each normal line is placed on the predicted contour position and indexed as 0. If the object had moved exactly as predicted, the detected contour points on all normal lines would have been at the center, i. We describe the observation model based on color i.
Because of noise and image clutter, there can be multiple edges along each normal line.
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Of the J edges, at most one is the true contour. Hypothesis H0 therefore means the true contour is not detected by the edge detection. A typical edge-based observation along one normal line is shown in Fig. Multiple peaks appear due to clutter. As we will show in Sect. It determines how one state transits to another. In this subsection, we use the standard contour smoothness constraint to derive the transition probability.
We defer a more complete smoothness constraint based on region properties to Sect. The contour smoothness constraint can be encoded in transition probability. To enforce the contour smoothness constraint in the HMM, the constraint needs to be represented in a causal form. This transition probability penalizes sudden changes of the adjacent contour points, resulting in a smoother contour.
Unlike traditional active contour model [8, 80], this method can give us the optimal contour without recursively searching the 2D image plane. It encodes the spatial constraints between the neighboring contour points. Even though simple, it only considers the contour points themselves and ignores all the other pixels on the normal lines, which can be dangerous especially when the clutter also has smooth contour and is close to the tracked objects e.
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To estimate the contour transition robustly, we should consider all detected edges jointly similar to the Joint probability data association JPDAF in . With contexture information, the new transition probabilities are more robust. These two contour points segment the two lines into foreground FG segments and background BG segments. If the object is opaque, the edges on FG cannot be matched to BG. That is, it is not a matching of contour points only, but rather a matching of the whole neighboring normal lines.
The transition probabilities based on this new matching paradigm enforce not only the contour smoothness but also region smoothness constraint and are therefore more accurate and robust to clutter. Let E F i, j and E B i, j be the matching errors of the neighboring foreground segments i. There are two regions where the grey region is the object to track and the darker rectangle is a background object.
The pixel intensities along these two lines are shown in Fig. They are similar to each other except for some distortions. Illustration of the JPM: a Synthesized image with the grey object to track. The comparison between traditional smoothness constraint and JPM based smoothness constraint is shown in Fig.
Without joint matching terms, the contour is distracted by the strong edge of the background clutter in Fig. Hence we obtain the correct contour. Unlike the uniform statistic region model in , our matching term is more relaxed. The object can have multiple regions e. To illustrate this, another test is shown in Fig. The algorithm favors the result in Fig. Foreground object with multiple regions: a The synthesized image. With all the matching cost, the state transition probabilities can be computed as in Eq.
In this section, we will extend it to tracking in video sequences. In the temporal domain, there are also object dynamics constraints, based on which we can combine the contour detection results from successive frames to achieve more robust tracking. But in visual tracking systems, the object parameters e. Then, in Sect. For example, Xt can be the position and orientation of a head, and Yt can be the contour of the head.
Equation 15 is the object observation model which relates the object states to the observations. The terms mt and nt are process noise and observation noise, respectively. As a concrete example, we use head tracking see Sect. As shown in Fig. The detected best contour point on each normal line in Eq. It is nonlinearly related to the object state Xt by the observation model h in Eq. The UKF provides a better alternative [, ]. It therefore not only outperforms the EKF in accuracy second-order approximation vs.
Its performance has been demonstrated in many applications [, ]. However, if we take a closer look, it is clear that  addresses a very special case. Second, their system dynamics is an integral function and demands more computation for evaluation. Third, they speed up the EKF by making approximations in calculating the Jacobian matrix, which can be dangerous when not used appropriately. In our system, we have highly non-linear system see Eq.
Our observation is consistent with other researchers [, , , ], i. Remember that we use ellipse to model the object and Langevin process to model the dynamics and use 30 normal lines to detect the contour i. An online training is therefore necessary to adapt the observation likelihood models dynamically. However, if error Real Time Object Tracking in Video Sequences 81 occurs on the current frame, this procedure may adapt the model in the wrong way.
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The color distribution model i. The adapted color models follow the changing color distributions during the tracking. It is worth noting that the color close to the contour is more important than that of the middle in helping us locate the contour. So we only use the pixels on the normal lines to update the color models. The updated color models are then plugged back into Eq.
Observations are collected along a set of normal lines of the predicted contour. Update the velocity of the objects e. Go to step 1 when new frame arrives. To begin this tracking procedure, a separate initialization module is needed. This can be done either manually or by change detection . For each ellipse, 30 normal lines are used, i. Each line has 21 observation locations, i. It is worth noting that the proposed HMM-UKF tracking framework is a general framework for parametric contour tracking.
While we apply it in head tracking in this section, it is applicable to many other domains. To demonstrate the strength of the proposed multicue energy-driven tracking paradigm, we conduct three sets of comparisons in this section. First, we compare our multicue tracker with the CamShift tracker in OpenCV package , which tracks objects using color histogram only. While CamShift represents one of the best single cue trackers available, it is still easily distracted when similar color is presented in the background.
On the other hand, by using multiple cues, our tracker achieves much more robust tracking result. Single Cue Sequence A, shown in Fig. Note that the sharp edges of the door and the blinds impose great challenges to contour-only tracking algorithms. The tracking results of the CamShift tracker are shown in the top row and the results of our approach are shown in the bottom row. The CamShift tracker relies on an object color histogram and runs in real time.
It is quite robust when the object color remains the same and there is no similar color presented in the background. Because it only models the foreground color and no information about background color is used, it is easily distracted when patches of similar color appear in the background see frames in the top row in Fig.
Also, there is no principled way to update the color histogram of the object. When the person turns her head and the face is not visible Frame , CamShift tracker loses track. Furthermore, both foreground and background color are modeled and probabilistically updated during tracking. Comparison between CamShift tracker and proposed approach on Sequence A: The top row is the result from CamShift and the bottom row is the result from the proposed approach Real Time Object Tracking in Video Sequences 85 Frame and is robust to similar color in the background e.
When the color-based likelihood cannot reliably discriminate the object from background, edge-based likelihood will be weighted more during the contour detection as shown in Eq. Sequence B, shown in Fig. This can be dangerous, especially in a cluttered environment. The comparison for sequence A is shown in Fig. In the plain HMM, tracking is distracted by the strong edges on the background when the foreground boundary does not have high contrast. The error gradually increases from frame 39 to frame Because JPM models a more comprehensive spatial constraint i.
Note that the observation model in Eqs. An ad hoc method to get around this situation is not to model the nonlinearity explicitly. Although simple, it does not take advantage of all the information available. The UKF, on the other hand, not only exploits system dynamics in Eq. As we can see in Fig. Neither the color nor the edge detection can reliably obtain the true contour points. Initialization is necessary to start the tracking process.
It can be done either manually or by an automatic object detection module e. Some tracking methods require strict and precise initialization. For example, many color based tracking methods e. In , a side view of the human head is used to train a typical color model of both skin color and hair color to track the human head with out-of-plane rotation. Our proposed HMM-UKF framework can be initialized by a rough bounding box indicating the object position and then adapt itself to the changing 88 Yunqiang Chen and Yong Rui appearance or environments, which allows it to be easily integrated with external face detector or manual initialization.
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This results in increased robustness in detecting object contours. For reviews of the literature and commercial stateof-the-art see [21, ] and [, ]. Much AFR research has concentrated on the user authentication paradigm e. A query to the system consists of the user choosing the person of interest in one or more keyframes. Possible applications include: 1. Content-based web search: Many web search engines have very popular image search features e. Face recognition can make the retrieval much more accurate by focusing on the content of images.
We proceed from the face detection stage, assuming localized faces. Face detection technology is fairly mature and a number of reliable face detectors have been built, see [, , , ]. The background is cluttered, pose, expression and illumination very variable a b c Fig. Lighting conditions, and especially light angle, drastically change the appearance of a face . Facial expressions, including closed or partially closed eyes, also complicate the problem, just as head pose does. Partial occlusions, be they artefacts in front of a face or resulting from hair style change, or growing a beard or moustache also cause problems.
Figure 2 depicts the appearance of a face under various illumination conditions, head poses and facial expressions. Films therefore provide an especially challenging, realistic working environment for face recognition algorithms. Method Overview Our approach consists of computing a numerical value, a distance, expressing the degree of belief that two face images belong to the same person. The preprocessing stages of our algorithm are summarized in Fig. Berg et al. Method overview: A summary of the main steps of the proposed algorithm.
The intermediate results of pre-processing are also shown in Fig. Output: distance d I, Sr. However, rather than using facial feature detection, a quasi-3D model of the head is used to correct for varying pose. Temporal information via shot tracking is exploited for enriching the training corpus. In contrast, we do not use any temporal information, and the use of local features Sect.
In our method, these facial points are the locations of the mouth and the eyes. We represent each facial feature, i. An SVM with a set of parameters kernel type, its bandwidth and a regularization constant is then trained on a part of the training data and its performance iteratively optimized on the remainder. Examples are extracted by taking rectangular image patches centred at feature locations see Figs.
This is done by complementing the image appearance vector vA with the greyscale intensity gradient vector vG , as in Eq. In uncontrolled Fig. Notice the low resolution and the importance of the surrounding image context for precise localization see Fig. This makes the approach with manual feature extraction impractical. In our method, a large portion of training data out of training examples was synthetically generated. We use a prior on feature locations to focus on the cluster of interest. Priors corresponding to the three features are assumed to be independent and Gaussian 2D, with full covariance matrices and are learnt from the training corpus of manually localized features described in Sect.
Intermediate results of the method are shown in Fig. Automatic detection of facial features: High accuracy is achieved in spite of wide variation in facial expression, pose, illumination and the presence of facial wear e. The six transformation parameters are uniquely determined from three pairs of point correspondences — between detected facial features the eyes and the mouth and this canonical frame. In contrast to global appearance-based methods e. It is summarized in Table 2 with typical results shown in Fig.
Registration: A summary of the proposed facial feature-based registration of faces and removal of face detector false positives canonical facial feature locations xcan , face image I, facial feature locations xin. Output: registered image Ireg. The eyes and the mouth in all registered images are at the same, canonical locations. To realize a reliable comparison of two faces, segmentation to foreground i. The proposed method requires only grey level information, performing equally well for colour and greyscale input, unlike previous approaches which typically use skin colour for segmentation e.
Also shown is a single measurement of image intensity in the radial direction and the detected high probability points. The plot of image intensity along this direction is shown in b along with the gradient magnitude used to select the high probability locations mesh point we measure the image intensity gradient in the radial direction — if its magnitude is locally maximal and greater than a threshold t, we assign it a constant high-probability and a constant low probability otherwise, see Fig.
Formally: P m1 ,.. Feathering The described method of segmentation of face images to foreground and background produces as a result a binary mask image M. Face outline detection and background removal: Original image, image with detected face outline, and the resulting image with the background masked Fig. We now show how the accuracy of facial feature alignment and the robustness to partial occlusion can be increased further when two signature images are compared.
In our algorithm, the corresponding characteristic regions of two faces, see Fig. Some results are shown in Fig. The residual rotation between b and c is removed. We now describe how this threshold is determined. Multiple Query Images The distance introduced in Eq. Often, however, more than a single image of a person is available as a query: these may be supplied by the user or can be automatically added to the the query corpus as the highest ranking matches of a single image-based retrieval. We assume that the corresponding manifold of expression is linear, making the problem that of point-to-subspace matching .
Detection was performed on every 10th frame, producing respectively , , and detected faces including incorrect detections. As expected, more query images produced better retrieval accuracy, also illustrated in Fig. The corresponding rank ordering scores across 35 retrievals are shown in e and f , sorted for the ease of visualization is the ranking better on average but also more robust, as demonstrated by a decreased standard deviation of rank order scores. This is very important in practice, as it implies that less care needs to be taken by the user in the choice of query images.
For the case of multiple query images, we compared the proposed subspace-based matching with the k-nearest neighbours approach, which was found to consistently produce worse results. The improvement of recognition with each stage of the proposed algorithm is shown in Fig. Registered Filtered Segmented Occlusion 2 training 4 training Processing det. Performance evaluation: The average rank ordering score of the baseline algorithm and its improvement as each of the proposed processing stages is added.
Notice the robustness of our method to pose, expression, illumination and background clutter. Another possible improvement to the method that we are considering is the extension of the current image-to-image and set-to-image matching to set-to-set comparisons. Typical retrieval result is shown in b — query images are outlined by a solid line, the incorrectly retrieved face by a dashed line. Typical retrieval result is shown in b — query images are outlined. Typical retrieval results are shown in b and c — query images are outlined.
There are no incorrectly retrieved faces in the top 50 Acknowledgements The authors would like to express their gratitude to to Mark Everingham for a number of helpful discussions and suggestions, and Krystian Mikolajczyk and Cordelia Schmid of INRIA Grenoble who supplied face detection code.
Our thanks also go to Toshiba Corporation and Trinity College, Cambridge for their kind support of our research. The reference-sensor for audio, the human ear, is of amazing capabilities and high quality. In contrast editing and synthesizing audio is an indirect and non-intuitive task, which needs great expertise.
This situation is depicted in Fig. The input 2 and the output 3 are evaluated acoustically and sometimes but rarely also with a spectrogram 4,5. More direct audio editing is desirable, but not yet possible. The goal of Visual Audio is to lower these limitations by providing a means to directly and visually edit audio spectrograms, out of which high quality audio can be reproduced. Figure 2 shows the new approach: A user can edit the spectrogram of a sound directly.
The result can be evaluated either visually or acoustically, resulting in a shorter closed loop for editing and evaluating. This has several advantages: 1. A spectrogram is a very good representation of an audio-signal. Often speech-experts are able to read text out of speech-spectrograms. In our approach, the spectrogram is used as a representation of both, the original and the recreated audio-signal, which both can be represented visually and acoustically.
It therefore narrows the gap between hearing and editing audio. Audio is transient. It is emitted by a source through a dynamic process, travels through the air and is received by the human ear. It cannot be C. Lienhart visual visual 4 5 aural aural 6 manual 2 audioeffect 3 1 Fig. The input 2 and the output 3 are evaluated acoustically and sometimes visually 4,5.
Visual Audio: The spectrogram of a sound is edited directly. The result can be evaluated either visually or acoustically frozen for investigation at a given point in time and a given frequency band. This limitation is overcome by representing the audio signal as a spectrogram. The spectrogram can be studied in detail and edited appropriately before transforming back into the transient audio domain. Figure 3 gives an overview over the several stages of Visual Audio Editing. A time signal 1 is transformed 2 into one of a manifold of timefrequency representations 3.
One representation is chosen 4 and edited 5. By inverse transformation 6 an edited time signal 7 is reproduced. By the appropriate choice of one of the manifold time-frequency representations, which refer to higher time or higher frequency resolution, it is possible to edit with high accuracy in time and frequency. If necessary, the process is repeated 8. Figure 4 shows a screenshot of our implementation of Visual Audio Editing.
Related Work: Time-frequency transformations are a well-known and intensively used tool in automatic processing of audio signals. The Gabor transformation  is closely related to the MLT Modulated lapped transform , which has many applications in audio processing. The time-frequency transformations are mainly used either Visual Audio Fig. Overview of Visual Audio Editing: a time signal 1 is transformed 2 into one of a manifold of time-frequency representations 3.
By inverse transformation 6 an edited time signal 7 is recreated. If necessary, the process is repeated 8 Fig. Screen shot of our implementation of Visual Audio Editing C. There is no way back from the features to the audio signal. An inverse transformation is not performed, but the features are used to derive higher semantics from the audio signal. In the second case, e. However the editing is not based on any visualization and thus no visual manipulation concepts can be applied.
Nevertheless the approach of editing audio in its spectrogram has already been presented earlier. The user has to choose several parameters for transformation and reconstruction, e. Another approach is reported by Horn . Is is based on auditory spectrograms, which model the spectral-transformation of the ear and is dedicated to speech, i. This transformation is optimal in terms of time-frequency resolution according to the Heisenberg uncertainty principle as well as in reconstruction quality.
The uncertainty principle allows choosing the ratio of time to frequency-resolution. Therefore the user is allowed to choose this himself continuously and as only transformation parameter. In the following we refer to it as resolution zooming operation. As sound images are more or less complex structures, we secondly introduce techniques for smart user assisted editing by the usage of template sounds, so called audio objects, which help to structure and handle the sound image.
This chapter is organized as follows: In Sect. A single parameter, the resolution zooming factor, is used in order to adapt the time-frequency resolution to the task at hand. This is enabled by the use of audio objects, i. In order to edit it visually, a 2D representation is necessary, which gives the user descriptive information about the signal and out of which the original or edited signal can be reconstructed. In this section we therefore discuss how to convert an audio signal into the image domain and how to recreate audio from that image domain.
The spectrogram of an audio signal is called imaged sound. It shows the magnitude of a time-frequency transformation of the audio signal. Time-frequency transformations reveal local properties of a signal and allow to recreate the signal under certain conditions. The kind of revealed properties, however, depend strongly on the window and the window length. Fundamentals of the Gabor transformation: The Gabor transformation was introduced by Dennis Gabor  and has gained much attention in the near past see e.
In conjunction with the Gaussian window, which is not the only possible choice, the Gabor transformation has perfect time-frequency localization properties. The Gabor system covers the whole time-frequency plane. The appropriate choices are discussed in the following two paragraphs. The uncertainty principle of Heisenberg says that the product of temporal and frequency extent of a window function has a total lower limit. Also this is best achieved by the Gaussian as dual window-function.
To ensure perfect reconstruction some restrictions are imposed on the choice of lattice constants a and b as discussed in the following paragraph. One still has the freedom to choose either acrit or bcrit , i. Therefore they have to be discretized and the sums and integrals have to be truncated in order to be implemented. This corresponds to bandlimiting and sampling x t and to truncating g t. We still have to determine tcut and N respectively, which correspond to the truncation of the Gaussian window as already mentioned.
It furthermore adapts its current time-frequency resolution to the current content of the signal according to the Heisenberg uncertainty principle see . It is therefore advantageous also to adapt the resolution of the transformation to the current editing task. The resolution zooming feature is discussed in this section. Heisenberg uncertainty principle: We already applied the Heisenberg uncertainty principle to the functions of the window and the dual window see Sect.
It also holds for the function of the signal, which of course in general has a worse resolution than the theoretical limit. As time and frequency resolution are interconnected, one has to give up time resolution in order to improve the frequency resolution and vice versa. Which window length to choose: So one has to choose the frequency 1 or vice versa. This can be understood as zooming, which allows increasing the resolution of the representation of a signal either in time or in frequency, while the resolution of the other domain decreases. From this discussion it also follows, that in contrast to images, the two axis time and frequency are not equivalent.
A rotation of an image or of a region will lead to rather undesirable results and has to be omitted. Example: Figure 6 a shows a typical spectrogram of a speech signal. One property of the signal is hidden in this spectrogram: The recording was accidentally interfered by power line hum at 50Hz common in Europe. The hum can be heard, if the sound is played at higher volume levels, but it cannot be seen in this spectrogram.
In this spectrogram it is easy to distinguish the hum at 50Hz and his higher harmonics from the rest of the signal. The energy of the hum is distributed widely in the spectrogram and the higher harmonics are totally blurred and cannot be recognized at this frequency-resolution. Visual Audio f in Hz f in Hz a 14 0. Typical spectrograms for speech signal with an interfering power line hum. For convenience b and c are cut above Hz. The interfering power line hum and higher harmonics can be recognized easily in the middle spectrogram.
The energy of the hum is distributed widely in the right spectrogram and the higher harmonics are totally blurred 2. At every multiple of the time shift aover a DFT has to be computed. As the DFT as then restricted to dedicated fs. As for Visual Audio Editing bover shall be chosen continuously the only possible solution up to now was to calculate a much more time consuming DFT.
We propose a solution to soften the hard restriction by the FFT window length, which allows to take advantage of the higher performance of the FFT. This introduces some computational overhead, which is generally acceptable if the overall execution time is still lower than that of a DFT, which is often the case. For fast calculation of a DFT it is common practice to zero pad a given signal to a power of two boundary. We follow a very similar idea. Because of the frequency-shape of the window, which is not altered by altering the FFT C. Lienhart Fig. This illustrates the downsampling.
The vertical arrows mark in each case bover. The blue circles mark a frequency vector generated by downsampling with factor 3. This alters the lattice constants. An obvious approach is to edit them like bitmaps — a well understood paradigm as documented by standard software packages such as Adobe PhotoshopTM. Bitmap editing operations serve as a basis for developing and understanding of content based audio manipulation techniques.
Our tool also allows to listen to selected regions of an imaged sound. This can be used to provide an instant feedback for the performed sound manipulations and thus serves as a perfect evaluation tool for the achieved sound quality. The right resolution allows to select a given sound very accurately. This will be illustrated with three prototypical sounds.
Figure 8 shows the imaged sound of music with three instruments: guitar, keyboard and drums. The sound of a cymbal is marked with a rectangle. Because of the chosen time-frequency resolution the cymbal-sound can be found very compact in the spectrogram. Music with three instruments: guitar, keyboard and drums. The sound of a cymbal is marked with a rectangle C. Clicks of a ball-pen. This sound has mainly transient components and is very localized in time as can be seen clearly in the spectrogram.
A third example illustrates, that changing the time-frequency resolution not only changes the visualization but also more clearly reveals or hides important information. See Fig. It is even possible to verify, that a major scale and not a minor scale was played. The temporal decay of each harmonic can now be perceived separately.
Zooming to a higher resolution on one axis reduces the resolution on the opposite axis. If the right time-frequency resolution is chosen, the sound qualities of interest are separated and can be selected separately. The original sound can still be reconstructed from an imaged sound at any given timefrequency resolution. The combined time-frequency resolution is always at the total optimum. Time-frequency resolution zooming is therefore a strong feature of Visual Audio. Sound of a piano playing a C-major scale, each note separately.
Timefrequency resolutions: 1. The better the mask matches a sound, the easier it is to select. We review some sensible selecting masks with corresponding sound examples. The rectangle mask furthermore exists in two extreme shapes. One is useful, in order to select sounds with very temporal characteristics, the other is useful for sounds with strong tonal character. Figure 11 shows left an example of a click of a ball-pen and right an example of a whistling sound.
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Rectangle mask in the two extreme shapes. Rectangle mask with temporal characteristics. Left: C-major scale. Right: Short polyphonic piece Comb masks: In contrast to the whistling sound, most tonal sounds not only incorporate the fundamental frequency, but also higher harmonics.
This leads us to the comb masks. The mask useful in this case is called comb mask. As in this case a sound with Visual Audio a prominent pitch always generates a regular structure of higher harmonics which in its regularity is similar to a comb. Many industry leaders are increasingly reverting to media convergence as a way of making sense in an era of disorientating change.
In that respect, media convergence in theory is essentially an old concept taking on a new meaning. Media convergence, in reality, is more than just a shift in technology. It alters relationships between industries, technologies, audiences, genres and markets. Media convergence changes the rationality media industries operate in, and the way that media consumers process news and entertainment.
Media convergence is essentially a process and not an outcome, so no single black box controls the flow of media. With proliferation of different media channels and increasing portability of new telecommunications and computing technologies, we have entered into an era where media constantly surrounds us. Media convergence requires that media companies rethink existing assumptions about media from the consumer's point of view, as these affect marketing and programming decisions.
Media producers must respond to newly empowered consumers. Conversely, it would seem that hardware is instead diverging whilst media content is converging. Media has developed into brands that can offer content in a number of forms. Two examples of this are Star Wars and The Matrix. Both are films, but are also books, video games, cartoons, and action figures. Branding encourages expansion of one concept, rather than the creation of new ideas. Hardware must be specific to each function. While most scholars argue that the flow of cross-media is accelerating,  O'Donnell suggests, especially between films and video game, the semblance of media convergence is misunderstood by people outside of the media production industry.
The conglomeration of media industry continues to sell the same story line in different media. For example, Batman is in comics, films, anime, and games. However, the data to create the image of batman in each media is created individually by different teams of creators. The same character and the same visual effect repetitively appear in different media is because of the synergy of media industry to make them similar as possible. In addition, convergence does not happen when the game of two different consoles is produced.
No flows between two consoles because it is faster to create game from scratch for the industry. One of the more interesting new media journalism forms is virtual reality. The Reuters Island in Second Life is a virtual version of the Reuters real-world news service but covering the domain of Second Life for the citizens of Second Life numbering 11,, residents as of January 5, Media convergence in the digital era means the changes that are taking place with older forms of media and media companies. Media convergence has two roles, the first is the technological merging of different media channels — for example, magazines, radio programs, TV shows, and movies, now are available on the Internet through laptops, iPads, and smartphones.
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As discussed in Media Culture by Campbell , convergence of technology is not new. It has been going on since the late s. Next came the TV, and radio lost some of its appeal as people started watching television, which has both talking and music as well as visuals. As technology advances, convergence of media change to keep up. The second definition of media convergence Campbell discusses is cross-platform by media companies. This usually involves consolidating various media holdings, such as cable, phone, television over the air, satellite, cable and Internet access under one corporate umbrella.
This is not for the consumer to have more media choices, this is for the benefit of the company to cut down on costs and maximize its profits. Henry Jenkins determines convergence culture to be the flow of content across multiple media platforms, the cooperation between multiple media industries, and the migratory behavior of media audiences who will go almost anywhere in search of the kinds of entertainment experiences they want.
The convergence culture is an important factor in transmedia storytelling. Convergence culture introduces new stories and arguments from one form of media into many. Transmedia storytelling is defined by Jenkins as a process "where integral elements of a fiction get dispersed systematically across multiple delivery channels for the purpose of creating a unified and coordinated entertainment experience. Ideally, each medium makes its own unique contribution to the unfolding of the story".
It becomes a story not only told in the movies but in animated shorts , video games and comic books, three different media platforms. Online, a wiki is created to keep track of the story's expanding canon. Fan films, discussion forums, and social media pages also form, expanding The Matrix to different online platforms. Convergence culture took what started as a film and expanded it across almost every type of media. Convergence culture is a part of participatory culture.
Because average people can now access their interests on many types of media they can also have more of a say. Fans and consumers are able to participate in the creation and circulation of new content. Some companies take advantage of this and search for feedback from their customers through social media and sharing sites such as YouTube. Besides marketing and entertainment, convergence culture has also affected the way we interact with news and information. We can access news on multiple levels of media from the radio, TV, newspapers, and the internet.
The internet allows more people to be able to report the news through independent broadcasts and therefore allows a multitude of perspectives to be put forward and accessed by people in many different areas. Convergence allows news to be gathered on a much larger scale. For instance, photographs were taken of torture at Abu Ghraib. These photos were shared and eventually posted on the internet. This led to the breaking of a news story in newspapers, on TV, and the internet. Media scholar Henry Jenkins has described the media convergence with participatory culture as:.
Star Wars fan films represent the intersection of two significant cultural trends—the corporate movement towards media convergence and the unleashing of significant new tools, which enable the grassroots archiving, annotation, appropriation, and recirculation of media content. These fan films build on long-standing practices of the fan community but they also reflect the influence of this changed technological environment that has dramatically lowered the costs of film production and distribution.
The social function of the cell phone changes as the technology converges. Because of technological advancement, cell phones function more than just as a phone. They contain an internet connection, video players, MP3 players, gaming, and a camera. The integration of social movements in cyberspace is one of the potential strategies that social movements can use in the age of media convergence.
Because of the neutrality of the internet and the end-to-end design , the power structure of the internet was designed to avoid discrimination between applications. Mexico's Zapatistas campaign for land rights was one of the most influential case in the information age; Manuel Castells defines the Zapatistas as "the first informational guerrilla movement".
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The Zapatistas were able to construct a grassroots, decentralized social movement by using the internet. The Zapatistas Effect, observed by Cleaver,  continues to organize social movements on a global scale. A sophisticated webmetric analysis, which maps the links between different websites and seeks to identify important nodal points in a network, demonstrates that the Zapatistas cause binds together hundreds of global NGOs.
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