That is why it is necessary for us to know what kind of data to expect , and what are some values that are the result of some measurement errors, faulty data, erroneous procedures , or simply what are the areas where a certain theory might not be valid. So, to improve the model and gain better results of our applications, we must recognize and deal with outliers in the data.
In statistics, an outlier is a data point that differs significantly from other observations.
statistical learning and pattern analysis for image and video process…
Outliers in the data can be very dangerous , since they change the classical data statistics, such as mean value and variance of the data. This affects the results of an algorithm of any kind image processing, machine learning, deep learning algorithm…. So, when modeling, it is extremely important to clean the data sample to ensure that the observations best represent the problem. We just know that we must stop them messing with our results. But how? Maybe one thinks that a simple way to handle outliers is to detect them and remove them from the data set. Deleting an outlier, although better than doing nothing, still poses a number of problems :.
It is very common to assume the Gaussian distribution in different kinds of an engineering problems. The most widely used model formalization is the assumption that the observed data have a normal Gaussian distribution. This assumption has been present in statistics as well as engineering for two centuries and has been the framework for all the classical methods in regression, analysis of variance and multivariate analysis. The main justification for assuming a normal distribution is that it gives an approximate representation to many real data sets, and at the same time is theoretically quite convenient because it allows one to derive explicit formulas for optimal statistical methods such as maximum likelihood, likelihood ratio tests, etc.
We refer to such methods as classical statistical methods and note that they rely on the assumption that normality holds exactly. The classical statistics are by modern computing standards quite easy to compute. Unfortunately, theoretical and computational convenience does not always deliver an adequate tool for the practice of statistics and data analysis.
It often happens in practice that an assumed normal distribution model e. Now, we know that such atypical data are called outliers , and even a single outlier can have a large distorting influence on a classical statistical method that is optimal under the assumption of normality or linearity. One might naively expect that if such approximate normality holds, then the results of using a normal distribution theory would also hold approximately. This is unfortunately not the case. The robust approach to statistical modeling and data analysis aims at deriving methods that produce reliable parameter estimates and associated tests and confidence intervals, not only when the data follow a given distribution exactly, but also when this happens only approximately in the sense just described.
As a consequence of fitting the bulk of the data well, robust methods provide a very reliable method of detecting outliers, even in high-dimensional multivariate situations. We note that one approach to dealing with outliers is the diagnostic approach. Diagnostics are statistics generally based on classical estimates that aim at giving numerical or graphical clues for the detection of data departures from the assumed model. There is a considerable literature on outlier diagnostics, and a good outlier diagnostic is clearly better than doing nothing.
However, these methods present two drawbacks. One is that they are in general not as reliable for detecting outliers as examining departures from a robust fit to the data. Robust methods have a long history that can be traced back at least to the end of the nineteenth century. But the first great steps forward occurred in the s, and the early s with the fundamental work of John Tukey , , Peter Huber , and Frank Hampel , The applicability of the new robust methods proposed by these researchers was made possible by the increased speed and accessibility of computers.
In this post we will not talk about Robust Statistics any more. If you want to find out more, a new post will be published soon, or you can get some information from the references given at the end. This was just a beginning and a warm up for those who need more information about getting started in designing more robust applications. If you are a beginner in the area of the image and video processing, you may often hear the term real time processing. In this post, we will try to explain the term and list some typical concerns related to this term.
Real time image processing is related with typical frame rate. Current standard for capture is typically 30 frames per second. Real time processing would require processing all the frames as soon as they are captured. So broadly speaking, if capture rate is 30 FPS then 30 frames needs to be processed in one second.
Similar calculation can be done for any frame rate to get required processing time per frame. In image and video processing, the source of our signal is a camera. So, what real time image processing really means is: produce output simultaneously with the input. What is actually meant is that the algorithm will run at the rate of the source e.
The first thing to understand is that we perceive different aspects of vision differently. Detecting motion is not the same as detecting light. Another thing is that different parts of the eye perform differently. The center of vision is good at different stuff than the periphery. And another thing is that there are natural , physical limits to what we can perceive. It takes time for the light that passes through your cornea to become information on which your brain can act, and our brains can only process that information at a certain speed.
Another important concept : the whole of what we perceive is greater than what any one element of our visual system can achieve. This point is fundamental to understanding our perception of vision.
The temporal sensitivity and resolution of human vision varies depending on the type and characteristics of visual stimulus , and it differs between individuals. The human visual system can process 10 to 12 images per second and perceive them individually, while higher rates are perceived as motion. Modulated light such as a computer display is perceived as stable by the majority of participants in studies when the rate is higher than 50 Hz through 90 Hz.
This perception of modulated light as steady is known as the flicker fusion threshold. However, when the modulated light is non-uniform and contains an image, the flicker fusion threshold can be much higher, in the hundreds of hertz. Regarding image recognition , people have been found to recognize a specific image in an unbroken series of different images, each of which lasts as little as 13 milliseconds. Persistence of vision sometimes accounts for very short single-millisecond visual stimulus having a perceived duration of between ms and ms. Multiple stimuli that are very short are sometimes perceived as a single stimulus, such as a 10 ms green flash of light immediately followed by a 10 ms red flash of light perceived as a single yellow flash of light.
With the increasing capabilities of imaging systems like cameras with very high-density captures having 16 or more megapixels, it is extremely difficult to get real time performance for many applications. That is why we will talk about online real time and offline processing. Offline processing is processing already recorded video sequence or image. So, digital video stabilization, video enhancement, video coloring, or any application can work with already prepared video. These applications can be found in marketing, industry, medical imaging, film industry or in some ordinary commercial applications, such as a user that wants to stabilize and enhance some video from the phone library.
Offline processing enables using more complex and computationally demanding algorithms , therefore usually gives better results than real time processing. That is why offline processing tools are used a lot in academic research and in some kinds of challenges. On the other hand, some applications have a demand for real time processing.
For example, traffic monitoring, target tracking in military applications, surveillance and monitoring, real time video games, etc. The algorithms that work in real time do not have the luxury of high complexity , since the processing time for each frame is determined by source frame rate and resolution. New hardware solutions nowadays offer better processing speeds, but there are still limitations , depending of the specific application.
Sometimes the application demands multiple complex algorithms working in parallel. That is the time when not only the complexity of the algorithms is considered, but also which algorithm will be processed first and how this affects the desired performance of the application. One good example is when video enhancement and digital video stabilization algorithm work in parallel.
Video stabilization and video dehazing algorithms in the same video processing pipeline can affect the results of each other. This interesting topic is described in a paper [Dehazing Algorithms Influence on Video Stabilization Performance] given in references at the end of the post. When there is no severe haze, noise or low contrast in the scene, it is important to perform video stabilization algorithm prior to video dehazing algorithm.
On the other hand, when the feature level in the scene is low, which happens because of severe haze or low contrast in image, the stabilization algorithm cannot perform well, since it cannot calculate global motion accurately. That is why, for the sake of the better stabilization performance, the proposed pipeline performs video dehazing algorithm prior to video stabilization.certum.wecan-group.com/us-rules-for-financial-success-basic.php
Ai image processing
At the end, we will mention some of the possibilities for real time image processing platforms :. Processing of complex data can now be done on-board edge devices. This means you can count on fast, accurate inference in everything from robots and drones to enterprise collaboration devices and intelligent cameras. Bringing AI to the edge unlocks huge potential for devices in network-constrained environments.
Pre-processing represents the stage where we prepare our images for some future applications from preparation to post on Instagram or showing on surveillance monitors , to preparation to use them in some complex applications , such as tracking, video stabilization…. This is the stage where we actually use the information from images to artificially create a content and add meaning to group of pixels. Here we will explain the terms object classification, object localization, object detection and image segmentation :.
Object detection is VERY popular topic nowadays in scientific community, so several datasets have been released for object detection challenges:.
Authors and titles for recent submissions
Video processing actually represents image processing on the set of frames which represent the sequence. This actually means the sequence can be processed in real time real time processing , frame by frame and we should monitor the processing time between frames , or the sequence can be recorded and then processed , so there is no need for taking care of processing time.
In surveillance and monitoring, it is necessary to have real time video processing , which affects the complexity of the algorithm used. On the other hand, when we need post-processing in, for example, our recorded videos, complexity of the algorithms can rise. This would be all for the post today, but if you want to study the topic more, here are some scientific papers that talk about the mentioned algorithms. His work received several awards, including the H.
From to , he was a Lecturer, Assistant Professor at the same University. From to , he was a temporary lecturer in the Department of Informatics, University of Thessaloniki. From to , he was a researcher and teaching assistant in the Department of Informatics, University of Thessaloniki. Tefas participated in 15 research projects financed by national and European funds. He has co-authored 94 journal papers, papers in international conferences and contributed 8 chapters to edited books in his area of expertise.
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Over citations have been recorded to his publications and his H-index is 33 according to Google scholar. His current research interests include computational intelligence, deep learning, pattern recognition, statistical machine learning, digital signal and image analysis and retrieval and computer vision. Search in:. Submit Your Paper. Supports Open Access. View Articles.
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