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90-434: Event camera pixels independently respond to changes in brightness as they occur. Each pixel stores a reference brightness level, and continuously compares it to the current brightness level. If the difference in brightness exceeds a threshold, that pixel resets its reference level and generates an event: a discrete packet that contains the pixel address and timestamp. Events may also contain the polarity (increase or decrease) of

180-622: A correlation of their attributes. Examples for such clustering algorithms are CLIQUE and SUBCLU . Ideas from density-based clustering methods (in particular the DBSCAN / OPTICS family of algorithms) have been adapted to subspace clustering (HiSC, hierarchical subspace clustering and DiSH ) and correlation clustering (HiCO, hierarchical correlation clustering, 4C using "correlation connectivity" and ERiC exploring hierarchical density-based correlation clusters). Several different clustering systems based on mutual information have been proposed. One

270-469: A multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including parameters such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It

360-488: A surface , denoted H e ("e" for "energetic", to avoid confusion with photometric quantities) and measured in J/m , is given by where Luminous exposure of a surface , denoted H v ("v" for "visual", to avoid confusion with radiometric quantities) and measured in lx⋅s , is given by where If the measurement is adjusted to account only for light that reacts with the photo-sensitive surface, that is, weighted by

450-448: A "cluster" cannot be precisely defined, which is one of the reasons why there are so many clustering algorithms. There is a common denominator: a group of data objects. However, different researchers employ different cluster models, and for each of these cluster models again different algorithms can be given. The notion of a cluster, as found by different algorithms, varies significantly in its properties. Understanding these "cluster models"

540-581: A better ability to record a range of brightness than slide/transparency film or digital. Digital should be considered to be the reverse of print film, with a good latitude in the shadow range, and a narrow one in the highlight area; in contrast to film's large highlight latitude, and narrow shadow latitude. Slide/Transparency film has a narrow latitude in both highlight and shadow areas, requiring greater exposure accuracy. Negative film's latitude increases somewhat with high ISO material, in contrast digital tends to narrow on latitude with high ISO settings. Areas of

630-552: A brightness change, or an instantaneous measurement of the illumination level, depending on the specific sensor model. Thus, event cameras output an asynchronous stream of events triggered by changes in scene illumination. Event cameras typically report timestamps with a microsecond temporal resolution, 120 dB dynamic range, and less under/overexposure and motion blur than frame cameras. This allows them to track object and camera movement ( optical flow ) more accurately. They yield grey-scale information. Initially (2014), resolution

720-458: A built-in light meter , or multiple point meters interpreted by a built-in computer, see metering mode . Negative and print film tends to bias for exposing for the shadow areas (film dislikes being starved of light), with digital favouring exposure for highlights. See latitude below. Latitude is the degree by which one can over, or under expose an image, and still recover an acceptable level of quality from an exposure. Typically negative film has

810-483: A clustering result is evaluated based on the data that was clustered itself, this is called internal evaluation. These methods usually assign the best score to the algorithm that produces clusters with high similarity within a cluster and low similarity between clusters. One drawback of using internal criteria in cluster evaluation is that high scores on an internal measure do not necessarily result in effective information retrieval applications. Additionally, this evaluation

900-410: A common use case in artificial data – the cluster borders produced by these algorithms will often look arbitrary, because the cluster density decreases continuously. On a data set consisting of mixtures of Gaussians, these algorithms are nearly always outperformed by methods such as EM clustering that are able to precisely model this kind of data. Mean-shift is a clustering approach where each object

990-426: A density criterion, in the original variant defined as a minimum number of other objects within this radius. A cluster consists of all density-connected objects (which can form a cluster of an arbitrary shape, in contrast to many other methods) plus all objects that are within these objects' range. Another interesting property of DBSCAN is that its complexity is fairly low – it requires a linear number of range queries on

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1080-441: A family of algorithms and tasks rather than one specific algorithm . It can be achieved by various algorithms that differ significantly in their understanding of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances between cluster members, dense areas of the data space, intervals or particular statistical distributions . Clustering can therefore be formulated as

1170-464: A fixed (to avoid overfitting) number of Gaussian distributions that are initialized randomly and whose parameters are iteratively optimized to better fit the data set. This will converge to a local optimum , so multiple runs may produce different results. In order to obtain a hard clustering, objects are often then assigned to the Gaussian distribution they most likely belong to; for soft clusterings, this

1260-400: A greater tonality range over conventional methods by varying the contrast of the film to fit the print contrast capability. Digital cameras can achieve similar results ( high dynamic range ) by combining several different exposures (varying shutter or diaphragm) made in quick succession. Today, most cameras automatically determine the correct exposure at the time of taking a photograph by using

1350-521: A hierarchical result related to that of linkage clustering . DeLi-Clu, Density-Link-Clustering combines ideas from single-linkage clustering and OPTICS, eliminating the ε {\displaystyle \varepsilon } parameter entirely and offering performance improvements over OPTICS by using an R-tree index. The key drawback of DBSCAN and OPTICS is that they expect some kind of density drop to detect cluster borders. On data sets with, for example, overlapping Gaussian distributions –

1440-472: A loss of highlight detail, that is, when important bright parts of an image are "washed out" or effectively all white, known as "blown-out highlights" or " clipped whites ". A photograph may be described as underexposed when it has a loss of shadow detail, that is, when important dark areas are "muddy" or indistinguishable from black, known as "blocked-up shadows" (or sometimes "crushed shadows", "crushed blacks", or "clipped blacks", especially in video). As

1530-508: A photo where information is lost due to extreme brightness are described as having "blown-out highlights" or "flared highlights". In digital images this information loss is often irreversible, though small problems can be made less noticeable using photo manipulation software . Recording to RAW format can correct this problem to some degree, as can using a digital camera with a better sensor. Film can often have areas of extreme overexposure but still record detail in those areas. This information

1620-407: A reciprocally smaller aperture is required to reduce the amount of light hitting the film to obtain the same exposure. For example, the photographer may prefer to make his sunny-16 shot at an aperture of f /5.6 (to obtain a shallow depth of field). As f /5.6 is 3 stops "faster" than f /16 , with each stop meaning double the amount of light, a new shutter speed of (1/125)/(2·2·2) = 1/1000 s

1710-458: A scene with strong or harsh lighting, the ratio between highlight and shadow luminance values may well be larger than the ratio between the film's maximum and minimum useful exposure values. In this case, adjusting the camera's exposure settings (which only applies changes to the whole image, not selectively to parts of the image) only allows the photographer to choose between underexposed shadows or overexposed highlights; it cannot bring both into

1800-414: A shutter speed of 1/100 of a second. This is called the sunny 16 rule : at an aperture of f /16 on a sunny day, a suitable shutter speed will be one over the film speed (or closest equivalent). A scene can be exposed in many ways, depending on the desired effect a photographer wishes to convey. An important principle of exposure is reciprocity . If one exposes the film or sensor for a longer period,

1890-580: A single device that produces the same result as a small circuit in other event cameras. Retinomorphic sensors have to-date only been studied in a research environment. Image reconstruction from events has the potential to create images and video with high dynamic range, high temporal resolution and reduced motion blur. Image reconstruction can be achieved using temporal smoothing, e.g. high-pass or complementary filter. Alternative methods include optimization and gradient estimation followed by Poisson integration . The concept of spatial event-driven convolution

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1980-422: A specified region. An "exposure" is a single shutter cycle . For example, a long exposure refers to a single, long shutter cycle to gather enough dim light, whereas a multiple exposure involves a series of shutter cycles, effectively layering a series of photographs in one image. The accumulated photometric exposure ( H v ) is the same so long as the total exposure time is the same. Radiant exposure of

2070-401: A trivial task, as it is done by the sensor on-chip. However, these tasks are difficult, because events carry little information and do not contain useful visual features like texture and color. These tasks become further challenging given a moving camera, because events are triggered everywhere on the image plane, produced by moving objects and the static scene (whose apparent motion is induced by

2160-587: A working knowledge of exposure values , the APEX system and/or the Zone System . A camera in automatic exposure or autoexposure (usually initialized as AE ) mode automatically calculates and adjusts exposure settings to match (as closely as possible) the subject's mid-tone to the mid-tone of the photograph. For most cameras, this means using an on-board TTL exposure meter . Aperture priority (commonly abbreviated as A , or Av for aperture value ) mode gives

2250-427: Is Marina Meilă's variation of information metric; another provides hierarchical clustering. Using genetic algorithms, a wide range of different fit-functions can be optimized, including mutual information. Also belief propagation , a recent development in computer science and statistical physics , has led to the creation of new types of clustering algorithms. Evaluation (or "validation") of clustering results

2340-471: Is adequate for real data, or only on synthetic data sets with a factual ground truth, since classes can contain internal structure, the attributes present may not allow separation of clusters or the classes may contain anomalies . Additionally, from a knowledge discovery point of view, the reproduction of known knowledge may not necessarily be the intended result. In the special scenario of constrained clustering , where meta information (such as class labels)

2430-446: Is as difficult as the clustering itself. Popular approaches involve " internal " evaluation, where the clustering is summarized to a single quality score, " external " evaluation, where the clustering is compared to an existing "ground truth" classification, " manual " evaluation by a human expert, and " indirect " evaluation by evaluating the utility of the clustering in its intended application. Internal evaluation measures suffer from

2520-502: Is biased towards algorithms that use the same cluster model. For example, k-means clustering naturally optimizes object distances, and a distance-based internal criterion will likely overrate the resulting clustering. Therefore, the internal evaluation measures are best suited to get some insight into situations where one algorithm performs better than another, but this shall not imply that one algorithm produces more valid results than another. Validity as measured by such an index depends on

2610-436: Is changed, but otherwise remain in equilibrium. When a photosensitive capacitor is placed in series with a resistor , and an input voltage is applied across the circuit, the result is a sensor that outputs a voltage when the light intensity changes, but otherwise does not. Unlike other event sensors (typically a photodiode and some other circuit elements), these sensors produce the signal inherently. They can hence be considered

2700-418: Is controlled in a camera by shutter speed , and the illuminance depends on the lens aperture and the scene luminance . Slower shutter speeds (exposing the medium for a longer period of time), greater lens apertures (admitting more light), and higher-luminance scenes produce greater exposures. An approximately correct exposure will be obtained on a sunny day using ISO 100 film, an aperture of f /16 and

2790-414: Is fast and has low computational complexity. There are two types of grid-based clustering methods: STING and CLIQUE. Steps involved in grid-based clustering algorithm are: In recent years, considerable effort has been put into improving the performance of existing algorithms. Among them are CLARANS , and BIRCH . With the recent need to process larger and larger data sets (also known as big data ),

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2880-435: Is intended to allow the photographer to simply offset the exposure level from the internal meter's estimate of appropriate exposure. Frequently calibrated in stops, also known as EV units , a "+1" exposure compensation setting indicates one stop more (twice as much) exposure and "–1" means one stop less (half as much) exposure. Exposure compensation is particularly useful in combination with auto-exposure mode, as it allows

2970-577: Is key to understanding the differences between the various algorithms. Typical cluster models include: A "clustering" is essentially a set of such clusters, usually containing all objects in the data set. Additionally, it may specify the relationship of the clusters to each other, for example, a hierarchy of clusters embedded in each other. Clusterings can be roughly distinguished as: There are also finer distinctions possible, for example: As listed above, clustering algorithms can be categorized based on their cluster model. The following overview will only list

3060-429: Is lost during capture. The photographer may carefully overexpose or underexpose the photograph to eliminate "insignificant" or "unwanted" detail; to make, for example, a white altar cloth appear immaculately clean, or to emulate the heavy, pitiless shadows of film noir . However, it is technically much easier to discard recorded information during post processing than to try to 're-create' unrecorded information. In

3150-472: Is measured on a scale published by the International Organization for Standardization (ISO). Faster film, that is, film with a higher ISO rating, requires less exposure to make a readable image. Digital cameras usually have variable ISO settings that provide additional flexibility. Exposure is a combination of the length of time and the illuminance at the photosensitive material. Exposure time

3240-448: Is moved to the densest area in its vicinity, based on kernel density estimation . Eventually, objects converge to local maxima of density. Similar to k-means clustering, these "density attractors" can serve as representatives for the data set, but mean-shift can detect arbitrary-shaped clusters similar to DBSCAN. Due to the expensive iterative procedure and density estimation, mean-shift is usually slower than DBSCAN or k-Means. Besides that,

3330-407: Is needed. Once the photographer has determined the exposure, aperture stops can be traded for halvings or doublings of speed, within limits. The true characteristic of most photographic emulsions is not actually linear (see sensitometry ), but it is close enough over the exposure range of about 1 second to 1/1000 of a second. Outside of this range, it becomes necessary to increase the exposure from

3420-456: Is not necessary. Distribution-based clustering produces complex models for clusters that can capture correlation and dependence between attributes. However, these algorithms put an extra burden on the user: for many real data sets, there may be no concisely defined mathematical model (e.g. assuming Gaussian distributions is a rather strong assumption on the data). In density-based clustering, clusters are defined as areas of higher density than

3510-409: Is often necessary to modify data preprocessing and model parameters until the result achieves the desired properties. Besides the term clustering , there is a number of terms with similar meanings, including automatic classification , numerical taxonomy , botryology (from Greek : βότρυς ' grape ' ), typological analysis , and community detection . The subtle differences are often in

3600-424: Is represented by a central vector, which is not necessarily a member of the data set. When the number of clusters is fixed to k , k -means clustering gives a formal definition as an optimization problem: find the k cluster centers and assign the objects to the nearest cluster center, such that the squared distances from the cluster are minimized. The optimization problem itself is known to be NP-hard , and thus

3690-545: Is the task of grouping a set of objects in such a way that objects in the same group (called a cluster ) are more similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis , and a common technique for statistical data analysis , used in many fields, including pattern recognition , image analysis , information retrieval , bioinformatics , data compression , computer graphics and machine learning . Cluster analysis refers to

Event camera - Misplaced Pages Continue

3780-582: Is to estimate the subject's mid-tone luminance and indicate the camera exposure settings required to record this as a mid-tone. In order to do this it has to make a number of assumptions which, under certain circumstances, will be wrong. If the exposure setting indicated by an exposure meter is taken as the "reference" exposure, the photographer may wish to deliberately overexpose or underexpose in order to compensate for known or anticipated metering inaccuracies. Cameras with any kind of internal exposure meter usually feature an exposure compensation setting which

3870-516: Is underway, it is not yet convenient for use with applications requiring color sensing. Exposure (photography) In photography , exposure is the amount of light per unit area reaching a frame of photographic film or the surface of an electronic image sensor . It is determined by shutter speed , lens f-number , and scene luminance . Exposure is measured in units of lux - seconds (symbol lx ⋅ s), and can be computed from exposure value (EV) and scene luminance in

3960-409: Is used already in the clustering process, the hold-out of information for evaluation purposes is non-trivial. A number of measures are adapted from variants used to evaluate classification tasks. In place of counting the number of times a class was correctly assigned to a single data point (known as true positives ), such pair counting metrics assess whether each pair of data points that is truly in

4050-667: Is usually somewhat recoverable when printing or transferring to digital. A loss of highlights in a photograph is usually undesirable, but in some cases can be considered to "enhance" appeal. Examples include black and white photography and portraits with an out-of-focus background. Areas of a photo where information is lost due to extreme darkness are described as "crushed blacks". Digital capture tends to be more tolerant of underexposure, allowing better recovery of shadow detail, than same-ISO negative print film. Crushed blacks cause loss of detail, but can be used for artistic effect. Cluster analysis Cluster analysis or clustering

4140-422: The adjacent image shows, these terms are technical ones rather than artistic judgments; an overexposed or underexposed image may be "correct" in the sense that it provides the effect that the photographer intended. Intentionally over- or underexposing (relative to a standard or the camera's automatic exposure) is casually referred to as " exposing to the right " or "exposing to the left" respectively, as these shift

4230-430: The advantages of providing principled statistical answers to questions such as how many clusters there are, what clustering method or model to use, and how to detect and deal with outliers. While the theoretical foundation of these methods is excellent, they suffer from overfitting unless constraints are put on the model complexity. A more complex model will usually be able to explain the data better, which makes choosing

4320-427: The advantages the event camera possesses, compared to conventional image sensors, it is considered fitting for applications requiring low power consumption, low latency, and difficulty to stabilize camera line of sight. These applications include the aforementioned autonomous systems, but also space imaging, security, defense and industrial monitoring. It is notable that while research into color sensing with event cameras

4410-403: The applicability of the mean-shift algorithm to multidimensional data is hindered by the unsmooth behaviour of the kernel density estimate, which results in over-fragmentation of cluster tails. The grid-based technique is used for a multi-dimensional data set. In this technique, we create a grid structure, and the comparison is performed on grids (also known as cells). The grid-based technique

4500-402: The appropriate spectral sensitivity , the exposure is still measured in radiometric units (joules per square meter), rather than photometric units (weighted by the nominal sensitivity of the human eye). Only in this appropriately weighted case does the H measure the effective amount of light falling on the film, such that the characteristic curve will be correct independent of the spectrum of

4590-418: The appropriate model complexity inherently difficult. Standard model-based clustering methods include more parsimonious models based on the eigenvalue decomposition of the covariance matrices, that provide a balance between overfitting and fidelity to the data. One prominent method is known as Gaussian mixture models (using the expectation-maximization algorithm ). Here, the data set is usually modeled with

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4680-439: The beholder." The most appropriate clustering algorithm for a particular problem often needs to be chosen experimentally, unless there is a mathematical reason to prefer one cluster model over another. An algorithm that is designed for one kind of model will generally fail on a data set that contains a radically different kind of model. For example, k-means cannot find non-convex clusters. Most traditional clustering methods assume

4770-404: The best of multiple runs, but also restricting the centroids to members of the data set ( k -medoids ), choosing medians ( k -medians clustering ), choosing the initial centers less randomly ( k -means++ ) or allowing a fuzzy cluster assignment ( fuzzy c-means ). Most k -means-type algorithms require the number of clusters – k – to be specified in advance, which is considered to be one of

4860-412: The biggest drawbacks of these algorithms. Furthermore, the algorithms prefer clusters of approximately similar size, as they will always assign an object to the nearest centroid. This often leads to incorrectly cut borders of clusters (which is not surprising since the algorithm optimizes cluster centers, not cluster borders). K-means has a number of interesting theoretical properties. First, it partitions

4950-471: The calculated value to account for this characteristic of the emulsion. This characteristic is known as reciprocity failure . The film manufacturer's data sheets should be consulted to arrive at the correction required, as different emulsions have different characteristics. Digital camera image sensors can also be subject to a form of reciprocity failure. The Zone System is another method of determining exposure and development combinations to achieve

5040-542: The camera reports events with microsecond resolution, the actual temporal resolution (or, alternatively, the bandwidth for sensing) is in the order of tens of microseconds to a few miliseconds - depending on signal contrast, lighting conditions and sensor design. range (dB) framerate (fps) resolution (MP) consumption (mW) Temporal contrast sensors (such as DVS (Dynamic Vision Sensor), or sDVS (sensitive-DVS)) produce events that indicate polarity (increase or decrease in brightness), while temporal image sensors indicate

5130-517: The camera’s ego-motion). Some of the recent approaches to solving this problem include the incorporation of motion-compensation models and traditional clustering algorithms . Potential applications include most tasks classically fitting conventional camera, but with emphasis on machine vision tasks (such as object recognition, autonomous vehicles, and robotics.). The US military is considering infrared and other event cameras because of their lower power consumption and reduced heat generation. Considering

5220-409: The claim that this kind of structure exists in the data set. An algorithm designed for some kind of models has no chance if the data set contains a radically different set of models, or if the evaluation measures a radically different criterion. For example, k-means clustering can only find convex clusters, and many evaluation indexes assume convex clusters. On a data set with non-convex clusters neither

5310-399: The cluster. At different distances, different clusters will form, which can be represented using a dendrogram , which explains where the common name " hierarchical clustering " comes from: these algorithms do not provide a single partitioning of the data set, but instead provide an extensive hierarchy of clusters that merge with each other at certain distances. In a dendrogram, the y-axis marks

5400-407: The clusters exhibit a spherical, elliptical or convex shape. Connectivity-based clustering, also known as hierarchical clustering , is based on the core idea of objects being more related to nearby objects than to objects farther away. These algorithms connect "objects" to form "clusters" based on their distance. A cluster can be described largely by the maximum distance needed to connect parts of

5490-423: The common approach is to search only for approximate solutions. A particularly well-known approximate method is Lloyd's algorithm , often just referred to as " k-means algorithm " (although another algorithm introduced this name ). It does however only find a local optimum , and is commonly run multiple times with different random initializations. Variations of k -means often include such optimizations as choosing

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5580-422: The complete data set and dividing it into partitions). These methods will not produce a unique partitioning of the data set, but a hierarchy from which the user still needs to choose appropriate clusters. They are not very robust towards outliers, which will either show up as additional clusters or even cause other clusters to merge (known as "chaining phenomenon", in particular with single-linkage clustering ). In

5670-511: The data space into a structure known as a Voronoi diagram . Second, it is conceptually close to nearest neighbor classification, and as such is popular in machine learning . Third, it can be seen as a variation of model-based clustering, and Lloyd's algorithm as a variation of the Expectation-maximization algorithm for this model discussed below. Centroid-based clustering problems such as k -means and k -medoids are special cases of

5760-424: The data to be clustered. This makes it possible to apply the well-developed algorithmic solutions from the facility location literature to the presently considered centroid-based clustering problem. The clustering framework most closely related to statistics is model-based clustering , which is based on distribution models . This approach models the data as arising from a mixture of probability distributions. It has

5850-408: The database – and that it will discover essentially the same results (it is deterministic for core and noise points, but not for border points) in each run, therefore there is no need to run it multiple times. OPTICS is a generalization of DBSCAN that removes the need to choose an appropriate value for the range parameter ε {\displaystyle \varepsilon } , and produces

5940-425: The distance at which the clusters merge, while the objects are placed along the x-axis such that the clusters don't mix. Connectivity-based clustering is a whole family of methods that differ by the way distances are computed. Apart from the usual choice of distance functions , the user also needs to decide on the linkage criterion (since a cluster consists of multiple objects, there are multiple candidates to compute

6030-462: The distance) to use. Popular choices are known as single-linkage clustering (the minimum of object distances), complete linkage clustering (the maximum of object distances), and UPGMA or WPGMA ("Unweighted or Weighted Pair Group Method with Arithmetic Mean", also known as average linkage clustering). Furthermore, hierarchical clustering can be agglomerative (starting with single elements and aggregating them into clusters) or divisive (starting with

6120-459: The effect the photographer intended. A more technical approach recognises that a photographic film (or sensor) has a physically limited useful exposure range , sometimes called its dynamic range . If, for any part of the photograph, the actual exposure is outside this range, the film cannot record it accurately. In a very simple model, for example, out-of-range values would be recorded as "black" (underexposed) or "white" (overexposed) rather than

6210-485: The existing methods fail due to the curse of dimensionality , which renders particular distance functions problematic in high-dimensional spaces. This led to new clustering algorithms for high-dimensional data that focus on subspace clustering (where only some attributes are used, and cluster models include the relevant attributes for the cluster) and correlation clustering that also looks for arbitrary rotated ("correlated") subspace clusters that can be modeled by giving

6300-661: The general case, the complexity is O ( n 3 ) {\displaystyle {\mathcal {O}}(n^{3})} for agglomerative clustering and O ( 2 n − 1 ) {\displaystyle {\mathcal {O}}(2^{n-1})} for divisive clustering , which makes them too slow for large data sets. For some special cases, optimal efficient methods (of complexity O ( n 2 ) {\displaystyle {\mathcal {O}}(n^{2})} ) are known: SLINK for single-linkage and CLINK for complete-linkage clustering. In centroid-based clustering, each cluster

6390-512: The histogram of the image to the right or left. In manual mode, the photographer adjusts the lens aperture and/or shutter speed to achieve the desired exposure. Many photographers choose to control aperture and shutter independently because opening up the aperture increases exposure, but also decreases the depth of field , and a slower shutter increases exposure but also increases the opportunity for motion blur . "Manual" exposure calculations may be based on some method of light metering with

6480-461: The instantaneous intensity with each event. The DAVIS (Dynamic and Active-pixel Vision Sensor) contains a global shutter active pixel sensor (APS) in addition to the dynamic vision sensor (DVS) that shares the same photo sensor array . Thus, it has the ability to produce image frames alongside events. Many event cameras additionally carry an inertial measurement unit (IMU). Another class of event sensors are so-called retinomorphic sensors. While

6570-654: The light. Many photographic materials are also sensitive to "invisible" light, which can be a nuisance (see UV filter and IR filter ), or a benefit (see infrared photography and full-spectrum photography ). The use of radiometric units is appropriate to characterize such sensitivity to invisible light. In sensitometric data, such as characteristic curves, the log exposure is conventionally expressed as log 10 ( H ). Photographers more familiar with base-2 logarithmic scales (such as exposure values ) can convert using log 2 ( H ) ≈ 3.32 log 10 ( H ) . "Correct" exposure may be defined as an exposure that achieves

6660-461: The most prominent examples of clustering algorithms, as there are possibly over 100 published clustering algorithms. Not all provide models for their clusters and can thus not easily be categorized. An overview of algorithms explained in Misplaced Pages can be found in the list of statistics algorithms . There is no objectively "correct" clustering algorithm, but as it was noted, "clustering is in the eye of

6750-488: The other hand, the labels only reflect one possible partitioning of the data set, which does not imply that there does not exist a different, and maybe even better, clustering. Neither of these approaches can therefore ultimately judge the actual quality of a clustering, but this needs human evaluation, which is highly subjective. Nevertheless, such statistics can be quite informative in identifying bad clusterings, but one should not dismiss subjective human evaluation. When

6840-477: The photographer manual control of the aperture, whilst the camera automatically adjusts the shutter speed to achieve the exposure specified by the TTL meter. Shutter priority (often abbreviated as S , or Tv for time value ) mode gives manual shutter control, with automatic aperture compensation. In each case, the actual exposure level is still determined by the camera's exposure meter. The purpose of an exposure meter

6930-401: The photographer to bias the exposure level without resorting to full manual exposure and losing the flexibility of auto exposure. On low-end video camcorders, exposure compensation may be the only manual exposure control available. An appropriate exposure for a photograph is determined by the sensitivity of the medium used. For photographic film, sensitivity is referred to as film speed and

7020-406: The precisely graduated shades of colour and tone required to describe "detail". Therefore, the purpose of exposure adjustment (and/or lighting adjustment) is to control the physical amount of light from the subject that is allowed to fall on the film, so that 'significant' areas of shadow and highlight detail do not exceed the film's useful exposure range. This ensures that no 'significant' information

7110-637: The problem that they represent functions that themselves can be seen as a clustering objective. For example, one could cluster the data set by the Silhouette coefficient; except that there is no known efficient algorithm for this. By using such an internal measure for evaluation, one rather compares the similarity of the optimization problems, and not necessarily how useful the clustering is. External evaluation has similar problems: if we have such "ground truth" labels, then we would not need to cluster; and in practical applications we usually do not have such labels. On

7200-476: The remainder of the data set. Objects in sparse areas – that are required to separate clusters – are usually considered to be noise and border points. The most popular density-based clustering method is DBSCAN . In contrast to many newer methods, it features a well-defined cluster model called "density-reachability". Similar to linkage-based clustering, it is based on connecting points within certain distance thresholds. However, it only connects points that satisfy

7290-494: The same cluster is predicted to be in the same cluster. As with internal evaluation, several external evaluation measures exist, for example: One issue with the Rand index is that false positives and false negatives are equally weighted. This may be an undesirable characteristic for some clustering applications. The F-measure addresses this concern, as does the chance-corrected adjusted Rand index . To measure cluster tendency

7380-410: The term retinomorphic has been used to describe event sensors generally, in 2020 it was adopted as the name for a specific sensor design based on a resistor and photosensitive capacitor in series. These capacitors are distinct from photocapacitors, which are used to store solar energy , and are instead designed to change capacitance under illumination. They charge/discharge slightly when the capacitance

7470-424: The uncapacitated, metric facility location problem , a canonical problem in the operations research and computational geometry communities. In a basic facility location problem (of which there are numerous variants that model more elaborate settings), the task is to find the best warehouse locations to optimally service a given set of consumers. One may view "warehouses" as cluster centroids and "consumer locations" as

7560-467: The use of k -means, nor of an evaluation criterion that assumes convexity, is sound. More than a dozen of internal evaluation measures exist, usually based on the intuition that items in the same cluster should be more similar than items in different clusters. For example, the following methods can be used to assess the quality of clustering algorithms based on internal criterion: In external evaluation, clustering results are evaluated based on data that

7650-461: The use of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. Cluster analysis originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Joseph Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology . The notion of

7740-524: The useful exposure range at the same time. Methods for dealing with this situation include: using what is called fill lighting to increase the illumination in shadow areas; using a graduated neutral-density filter , flag, scrim, or gobo to reduce the illumination falling upon areas deemed too bright; or varying the exposure between multiple, otherwise identical, photographs ( exposure bracketing ) and then combining them afterwards in an HDRI process. A photograph may be described as overexposed when it has

7830-443: The willingness to trade semantic meaning of the generated clusters for performance has been increasing. This led to the development of pre-clustering methods such as canopy clustering , which can process huge data sets efficiently, but the resulting "clusters" are merely a rough pre-partitioning of the data set to then analyze the partitions with existing slower methods such as k-means clustering . For high-dimensional data , many of

7920-416: Was limited to 100 pixels. A later entry reached 640x480 resolution in 2019. Because individual pixels fire independently, event cameras appear suitable for integration with asynchronous computing architectures such as neuromorphic computing . Pixel independence allows these cameras to cope with scenes with brightly and dimly lit regions without having to average across them. It is important to note that while

8010-435: Was not used for clustering, such as known class labels and external benchmarks. Such benchmarks consist of a set of pre-classified items, and these sets are often created by (expert) humans. Thus, the benchmark sets can be thought of as a gold standard for evaluation. These types of evaluation methods measure how close the clustering is to the predetermined benchmark classes. However, it has recently been discussed whether this

8100-554: Was postulated in 1999 (before the DVS), but later generalized during EU project CAVIAR (during which the DVS was invented) by projecting event-by-event an arbitrary convolution kernel around the event coordinate in an array of integrate-and-fire pixels. Extension to multi-kernel event-driven convolutions allows for event-driven deep convolutional neural networks . Segmentation and detection of moving objects viewed by an event camera can seem to be

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