A complete accuracy evaluation, comparing the results obtained with the geometric-based method given in Clady et al. The corner events parameters spatial location and velocity and the 11 corners ones obtained with the ground-truth are compared using different measures of errors. Each corner event is associated to the spatially closest ground-truth corner's trajectory. Figure 9. Precision evaluation of the corner detectors; the green plain curves correspond to the results obtained with the algorithm proposed in Clady et al. The blue and red dotted curves correspond to the respective feature-based approaches but without speed-tuned temporal kernels.
The left figure A represents the spatial location errors of the corner events compared to the manually-obtained ground-truth trajectories of the corners; the middle one B the relative error about the intensity of the estimated speed and the right one C its error in direction. Accuracies X-axis fo the Figures are given related to the considered percent Y-axis of the population of corner events detected with the different methods; e. In order to propose a fair evaluation, the thresholds used in the different methods have been set in order to detect the same number of corner events and other algorithms' parameters have been set as the ones proposed in Clady et al.
The distribution of the corner events per corner's trajectory is shown in Figure 11A. We can observe that the distributions using the geometric-based and the 2-maxima decision based methods are closely similar. However, the one obtained with the velocity-constraint decision based method is unbalanced, with a great number close to the third of the corner events of detections around a particular corner, corner number 5. This can be explained by the fact that the proposed method is less spatially precise than the geometric-based one cf.
It is not the case for the corners number 1, 7, and 11, for example; the high speed of the cube close to pix. Figure Snapshots of the results obtained for the three compared detectors, projecting in a frame the visual events black dots and corner events circles, associated to vectors representing the estimated speeds over two short time periods 1 ms. Distributions of the detected event corners related to the labeled corners. A Comparison between the three evaluated detectors.
B Comparison with or without black speed-tuned temporal kernels. Remark 7.
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Note that accuracy results in Figure 9 concern median evaluations over the 11 ground-truth corners. Each corner is associated to the spatially closest ground-truth corners trajectory. Each set of corner events associated to a ground-truth corner is sorted according to one of the evaluation criteria type of errors. Finally, the accuracy median value for this evaluation criterion is computed over all ground-truths corners. So these evaluations are a priori not or weakly biased by these differences in distributions. We can observe that the detectors proposed in this article are influenced by the quantification of the grid; especially in the Figure 9C representing the angular precision of the estimated speed direction.
The velocity-constrainst based decision method is less clearly influenced because it takes into account more elements in the feature not only the elements with the maximal values, but also their neighboring elements to estimate the speed. In addition, Figure 11B shows the detections distribution for both feature-based methods, with or without speed-tuned temporal kernels. Without speed-tuning, some corners are not or not often detected, in particular corners number 6 and 8. They correspond to X-junctions between two intersecting edges with quite different dynamics, because generated by front and back wires.
Furthermore, the accuracy performances for the approaches without speed-tuned temporal kernels are significantly lower than the ones with speed-tuned kernels, as shown in Figure 9. We have demonstrated that the proposed feature can be used in its local approach to detect corners in event streams. Even if the detectors are slightly less precise and more sensitive to the quality of the event streams than the other method proposed in the literature, our feature-based approaches are more efficient in terms of memory and computation loads.
Indeed the method in Clady et al. In the approach presented in this article, the visual motion events are integrated directly in the neighboring features, and corner detection related computations are operated only using the feature at the spatiotemporal location of the current event. We have measured important differences in terms of computation time between their different implementations; e. Table 1 presents the distribution of mean computation times obtained with the different approaches and over 10 repetitions for detections.
But as the method in Clady et al. Measuring the computation time without code lines dedicated to memory management which is a crucial part of the method in Clady et al. While the geometric-based method is only envisioned in Clady et al. Table 1. Distribution of mean computation times CT with the different approaches estimated on Matlabb. Beyond this operational asset, the greatest strength of the proposed feature-based approaches lies in fact that they lead to a solution of the corner detection issue on event streams based on classical event-based neural network models leaky integrate-and-fire neural network, coincidence detectors, etc.
Human movement analysis is an area of study that has been quickly expanding since the 's see Moeslund et al. The evolution and miniaturization of both computers and motion capturing sensors have made motion analysis possible in a growing set of environments. They have enabled numerous applications in robotics, control, surveillance, medical purposes Zhou and Hu, or even in video-games with the Microsoft's Kinect Han et al.
However, the available technologies and methods still present numerous limitations, discouraging their use in embedded systems. Conventional time-sampled acquisition is very problematic when implemented in mobile devices because the embedded cameras usually operate at a frame-rate of 30 to 60 Hz: normal speed gesture movements can not be properly captured.
Increasing the frame rate would result in the overload of the recognition algorithm, only displacing the bottleneck from acquisition to post-processing. Furthermore, conventional cameras and infrared-based methods are perturbed by dynamic lighting and infra-red radiations emitted by the sun cf. Because they both require light-controlled environments, those technologies are unsuitable for outdoor use.
Asynchronous event-based sensing technology is expected to overcome several limitations encountered by state-of-the-art gesture recognition systems, in particular for battery-powered, mobile devices.
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These vision sensors, due to their near continuous-time operation, allow capturing the complete and true dynamics of human motion during the whole gesture duration. Due to the pixel-individual style of acquisition and pre-processing of the visual information, and in contrast to practically all existing technologies, they will be also able to support device operation under uncontrolled lighting conditions, particularly in outdoor scenarios cf. Simon-Chane et al.
Native redundancy suppression performed in event-based sensing and processing will ensure that computation can be performed in real time, while at the same time saving energy, decreasing system complexity. Gesture recognition using neuromorphic camera has already been investigated by Lee et al. A stereo pair of DVS allows them to compute disparity in order to cluster the hand. Then, they use a tracking algorithm to extract the 2D trajectory of the movement. Finally the trajectory is sampled into directions, and the obtained sequence of directions is fed to a HMM classifier.
This approach uses event-based information only during the first step extraction of the location of the hand.
In addition, with this type of multi-steps architecture, a failure in a step could result in the failure of the whole system. Here we propose to demonstrate that our feature can be used to detect and recognize more directly gestures. Hoof-like features see Section 4. This transformation consists in summing the intensities of the optical flow vectors with respect to their directions.
In the global approach, normalization to sum to 1 makes the hoof-like feature globally speed- and scale-invariant. Figure 4D represents the histogram of oriented optical flows computed globally on an event stream capturing a walking human Figure 4A. A state g 0 is added in G , in order to consider the not-considered gestures or the instants while the user is not performing a hand gesture.
The camera observes the user's action and at each occurring feature estimates a distribution over the current state g t k i :. To estimate this probability, a time update and a measurement update are performed alternately. The time update updates the belief that the user is performing a specific gesture given previous information:.
The time update includes a transition probability from the previous state to the current state. As no-contextual information is available here, we assume that an user is likely to perform the same gesture, and at each timestamp has a large probability of transitioning to the same state:. This assumption means that the gesture's certainty slowly decays over time, in the absence of corroborating information, converging to a uniform distribution even if no event is observed.
The measurements update combines the previous belief with the newest observation to update each belief state, such as:. It is decomposed into two steps:. Then a k-means algorithm is applied on them in order to compute N candidate models, noted m g i. This selection is processed through a discrete Adaboost classifier. Adaboost Freund and Schapire, is an iterative algorithm that finds, from a feature set, some weak but discriminative classification functions and combines them in a strong classification function:. T is the threshold of the strong classifier B. The principle of the Adaboost algorithm is to select, at each iteration, a new weak classifier in favor of the instances or features misclassified by previous classifiers, through a weighting process attributing more influence to misclassified instances.
During the learning step, its default value is 1; this means a classification frontier at the middle of the margin see Schapire et al. Increasing or reducing its value correspond to moving the frontier closer or further to the positive class, respectively. In literature, discriminative training of generative models, as we propose here, has been shown as efficient learning methods in numerous applications as object or human detection Holub et al. The proposed classifier based on the training and the selection of generative models in a discriminative way, combines indeed the main characteristics of discriminative and generative approaches: discriminative power and generalization ability, respectively.
The latter is in particular very important in our application, when a weak amount of labeled training data is available, see Section 4. Following the framework described in Jing et al. During training, Adaboost based algorithm tends to select iteratively the most discriminative and complementary models for each gesture.
We limit the number of selected models, such as the relative difference between F-measure computed on training database, see Section 4. Optimizing it means also to determine a number of models for which an acceptable compromise between precision the ratio of positive detections to instances belonging to performed gestures and recall the ratio of positive detections to all instances detected as belonging to gestures is reached. The probability associated to not-considered gesture or no-gesture , noted g 0 , is then defined as:.
Back to the basics of pattern recognition and machine learning
Figure 12 presents the obtained classification architecture. Finally a gesture's class G t k at each time is attributed from the distribution of probabilities, defined as:. Remark 8. Even if our implementation is based on a learning process not directly related to neural approaches essentially due to the limited size of the database , we can observe that the resulting classification architecture could be fully implemented in an event-based framework.
Through a rate-coding model, hoof-like features could be computed and transmitted from the leaky integrate-and-fire neural network, corresponding to the feature computation, as evoked in Section 2. The other operations, in particular involved in Bayes filters, would correspond to feedback lines and basic mathematical operations that can be modeled using precise timing and event-based paradigms as demonstrated in Lagorce and Benosman The protocol assumes that the users performed gestures in front of the camera.
Event streams using the ATIS camera have been collected with nine users young and middle-aged people working in the laboratory. All users are right-handed but the database could be extended to left-handed users by mirroring the sequences horizontally. The hand is moving at a distance around 30 cm from the camera, approximatively. Note that this distance has been determined to ensure that the hand is fully viewed by the camera see Figure 13A considering the current optic lens this distance should be reduced when a wider-angle lens will be implemented. Each gesture is repeated five times by each user, varying the hand speed.
Illustration of the targeted human-machine interaction. A Example of a hand gesture performed in front of the camera. B ATIS camera embedded on a smartphone. Six gestures have been defined and correspond to a dictionary of coarse gestures; the gesture is defined by the global motion of the hand hand moving to the left, to the right, upward, downward, opening, or closing. Furthermore, they constitute a dictionary for more complex gestures, successively combining these movements.
In Figure 14 , an iconic representation of these coarse gestures is presented in the second column. Iconic representations second column of the gestures first column and corresponding models selected by the Adaboost-based machine learning process. The training database is composed of the event streams collected with five users and the test database with the four other ones. During the evaluations see next Section , a cross-validation is performed ten times presented evaluations are the obtained mean values , putting randomly the users in the training or test databases.
An equal quantity is again randomly selected for the F-measure based optimization process and the selection of the number of models. Six hundred candidate models per gesture have been computed using k-means algorithm. The characteristics of the hoof-like features are the same as described in Section 2. This detection is counted as positive if this time period overlaps the manually labeled ground truth with an overlap ratio superior to 0.
Figure 14 represents the considered gestures and the models selected by Adaboost during a learning process see Section 4. We can observe that the number of selected models is relatively weak 3 or 4. This means that the hoof-like features are able to represent well the gestures despite their speed- and user-related variability, mostly thanks to its speed- and scale-invariance property.
For most of them, they match well to the iconic representation of the corresponding motion; for example, for the motions to the left and to the right, most speed vectors are oriented to these respective directions, etc. However, some singularities have to be explained considering not only the global motion but also the directions of the principal contours of the human parts hand, finger and arm involved in the hand movement.
For the opening hand motion, models obtained at iterations 1 and 2 highlight the motion of the thumb, for which the moving contours are prevalent in the feature. For the downward motion, the contours of the arm are too prevalent see models obtained at iterations 2 and 3 because the camera viewed the user's bust see Figure Note that the F-measures obtained during the optimization to determine th B and the number of models are around 0. The greater value obtained at the final output highlights the filtering action of the Bayes filters. Finally the confusion matrix given in Figure 15 shows us the recognized gestures among the positive detections.
The downward and closing hand gestures are obviously a little confused because the similarity of the hand's and the fingers' motions, respectively. The confusion of other gestures with the opening hand is probably due to the fact that the gesture is hard to detect, probably because the larger proportion of the movement involved the other fingers than the thumb and their moving contours generated few visual events because in folded positions; the finger-skin vs.
Back to the basics of pattern recognition and machine learning
Indeed, in order to optimize the F-measure, the proposed process tends to select a low threshold compared to others 3 or 4 times lower ; this means that classification frontier defined for this gesture tends to include other gestures. Hence, these gestures are sometimes misclassified as opening hand. Confusion matrix expressed in percent showing the recognized gestures columns related to the performed gestures lines , among the positive detections.
In further developments, we expect to improve these performances combining this global feature with locally computed ones, taking into account their relative spatio-temporal relationships. This should help us to better distinct the global motion of the hand and the local motions of the fingers, and hence better detect and categorize gestures. In this article, we have proposed a motion-based feature for event-based vision. It consists in encoding the local or global visual information provided a neuromorphic camera in a grid-sampled map of optical flow.
Collecting optical flow or visual motion events computed around each visual event in a neighborhood or in the entire retina, this map represents their current probabilistic distribution in a speed- and direction-coordinates frame. Two event-based pattern recognition frameworks have been developed in order to demonstrate its usefulness for such tasks. The first one is dedicated to detection of specific interest points, corners. Two feature-based approaches have been developed and evaluated.
Formulated as an intersection of constraints issue, this fundamental task in computer vision can be resolved operating with the information encoded in the proposed local feature. The second one consists in a hand gesture recognition system for human-machine interaction, in particular with mobile devices. More compact and scale-invariant representations called hoof-like features of the motion observed in the visual scene, are extracted directly from the global version of the proposed feature, and feed a classification architecture, based on a discriminative learning schema of gestures' generative models and framed as a Bayes filter.
Evaluations show that this feature has sufficient descriptive power to solve such pattern recognition problems. Other extensions or derivations of the proposed feature can be also envisioned in further developments, in order to address other pattern recognition issues. For example, summing the elements of the feature, with respect to their directions and without weighting them by corresponding speed, will result into another compact form, similar to the hog histogram of oriented gradients feature proposed by Dalal and Triggs This feature and its derivations have been demonstrated as very efficient for many pattern recognition tasks in frame-based vision.
To evaluate it in event-based vision would required to design event-based and dedicated classification architecture s. All required information for both tasks are provided by a local computation of optical flow; this information is precisely encoded in the primary area V1 of the visual cortex via the selectivity of V1 neurons. We underline also that the proposed frameworks are fully incremental and could be implemented as event-based neural networks, in particular thanks to speed and direction coordinates frame based representation of the visual motion information. Such polar coordinate frame based representations have been already investigated for computer vision; e.
Works about natural image statistics Hyvarinen et al. Recently, a work in Chandrapala and Shi encoding more directly local event streams as local spatiotemporal surfaces Lagorce et al. Moreover, other works Cedras and Shah, ; Chaudhry et al. In addition, the work presented in this article supports the proposition that optical flow's speed and direction based grid is not only a powerful manner for encoding visual information in pattern recognition tasks, but it plays also a key role at a computational level when dealing with asynchronous event-based streams.
Indeed we have shown that, to compute the distribution of optical flow along current edges, we need to take into account their respective dynamics, in order to ensure that the moving edges are equitably represented in the feature whatever their own dynamics. The discretization of the visual motion information into the proposed speed- and direction-based grid allows us to incorporate directly the required speed-tuned temporal kernels in the structure of the computational architecture computing the feature.
We have in addition proposed that this architecture can be implemented as a leaky integrate-and-fire neural layer, wherein neurons have then speed-tuned integration times; so it could be further integrated as the first layer in a spiking neural network using back-propagation based deep learning technique, as the one recently proposed by Lee et al. Manufacturer warranty may not apply Learn more about Amazon Global Store. Review From the reviews: "This interesting book provides a concise and simple exposition of principal topics in pattern recognition using an algorithmic approach, and is intended mainly for undergraduate and postgraduate students.
An application to handwritten digit recognition is described at the end of the book. Many examples and exercises are proposed to make the treatment clear. A 'further reading' section and a bibliography are presented at the end of each chapter. See all Product description. No customer reviews.
Share your thoughts with other customers. Write a customer review. Most helpful customer reviews on Amazon. February 28, - Published on Amazon. Verified Purchase. I found this book a lot different from what I expected. The topics are poorly covered and don't give any insight whatsoever. It is used in various algorithms of speech recognition which tries to avoid the problems of using a phoneme level of description and treats larger units such as words as pattern.
Over a million developers have joined DZone. Let's be friends:. Machine Learning and Pattern Recognition. DZone 's Guide to. What's the difference between ML and pattern recognition? Free Resource. Like 3. Join the DZone community and get the full member experience. Join For Free. Introduction In very simple language, Pattern Recognition is a type of problem while Machine Learning is a type of solution. Machine Learning The goal of Machine Learning is never to make "perfect" guesses because Machine Learning deals in domains where there is no such thing. Pattern Recognition Pattern recognition is the process of recognizing patterns by using a Machine Learning algorithm.
Features of Pattern Recognition: Pattern recognition completely rely on data and derives any outcome or model from data itself Pattern recognition system should recognise familiar pattern quickly and accurate Recognize and classify unfamiliar objects very quickly Accurately recognize shapes and objects from different angles Identify patterns and objects even when partly hidden Recognise patterns quickly with ease, and with automaticity Pattern recognition always learn from data Training and Learning Models in Pattern Recognition Training and Learning is the building block model of Pattern Recognition.
Pattern recognition is used in Terrorist Detection Credit Fraud Detection Credit Applications Finger print identification: The fingerprint recognition technique is a dominant technology in the biometric market. Advantages of Pattern Recognition Pattern recognition can interpret DNA Sequences Pattern recognition has extensive application in astronomy, medicine, robotics, and remote sensing by satellites Pattern recognition solves classification problems Pattern recognition solves the problem of fake bio metric detection It is useful for cloth pattern recognition for visually impaired blind people We can recognise particular object from different angle Pattern recognition helps in forensic Lab Difference Between Machine Learning and Pattern Recognition Differences Between Machine Learning and Pattern Recognition: Machine Learning Pattern Recognition Machine Learning is a method of data analysis that automates analytical model building.
Pattern recognition is the engineering application of various algorithms for the purpose of recognition of patterns in data. Pattern Recognition may be outside the machine. Like This Article?
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