The Future of Computer Vision

Continued payment of developer community see limits to what we can achieve. While we can produce its own vehicles that drive themselves on a highway, we will have difficulty producing reliable means to work on small roads, especially if bad marking on the road. Even in the highway environment, though, we have a legal issue, such as who is responsible if the car crashes? It is clear that developing countries technology doesn't think she should have and that instead of the driver should be responsible for anything bad. Responsibility for this matter difficult and originates with many real-world vision applications. Take another example, if we were in a medical imaging system for cancer diagnosis, what will happen when he accidentally not diagnosed?Although this system may be more reliable than any individual rays, get into a legal mine field. Therefore, now, is the simplest solution to address non-critical problems or only for systems development, assistants, rather than the replacement of the current human experts.

Another problem with dissemination of computer vision systems. In some countries, install and use video cameras infringed on our fundamental right to privacy. This varies significantly from country to country and from company to company, and even from individual to individual. While most people involved in technology see potential benefits of camera systems, a lot of people don't trust the nature of digital video cameras and videos that can be used in. Among other things, they fear (probably justified) big brother scenario, where constantly monitors the movements and actions. However, a growing number of cameras very quickly, as there are cameras in almost every new computer, every new phone, every new games unit, and so on.

Moving forward, we expect to see computer vision processing progressively harder problems; problems in more complex environments with less restrictions. We expect to see the computer start to be able to recognize more objects of different types and start extracting descriptions more reliable and powerful in the world. For example,we expect to see the computer to

Qualified to become an integral part of the general computer interfaces;
  • provide higher levels of security through a biological analysis;
  • provide reliable diagnoses of medical conditions from medical images and medical records;
  • Qualified vehicles allowed to be driven independently;
  • Qualified automatically identifying criminals through forensic analysis of the video.



A Human Vision System

If we can replicate the human visual system and to solve the problem of developing a computer vision system. So why can't we? The main difficulty that we do not understand what human vision system over time.

If you look at your eyes, maybe not clear to you that your color vision (by 6 million cones in the eye) is concentrated in the center of the Visual field (known as spot). The rest of your retina consists of about 120 million rod cells (sensitive to visible light of any wavelength or color). In addition, each eye big blind instead where the optic nerve attaches to the retina. Somehow, we believe we see continuously (no blind spot) with color everywhere, but even this minimum level of processing is clear on how that impression within the brain.

The visual cortex has been studied (in the back of the brain) and show contains cells that sort of edge detection, but most of them we know what brain sections done based on localized brain damage. For example, the number of people with damage to a section of the brain can no longer faces (a condition known as prosobagnosia). Other people have lost the ability to sense moving objects (a condition known as akinitobsia). These conditions inspired us to develop separate units recognition and motion detection of the object.

Can also look in the brain using functional magnetic resonance imaging, which allows us to see that the focus of the electrical activity in different parts of the brain as subjects perform various activities. Again, this may tell us what the major parts of the brain, but she could not provide us with algorithms for solving the problem of interpreting the huge arrays of numbers that provide video cameras.

Machine Vision

Machine vision (MV) is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance in industry. The scope of MV is broad. MV is related to, though distinct from, computer vision.

The primary uses for machine vision are automatic inspection and industrial robot guidance. Other machine vision applications include:



·         Automated Train Examiner (ATEx) Systems
·         Automatic PCB inspection
·         Wood quality inspection
·         Final inspection of sub-assemblies
·         Engine part inspection
·         Label inspection on products
·         Checking medical devices for defects
·         Final inspection cells
·         Robot guidance and checking orientation of components
·         Packaging Inspection
·         Medical vial inspection
·         Food pack checks
·         Verifying engineered components
·         Wafer Dicing
·         Reading of Serial Numbers
·         Inspection of Saw Blades
·         Inspection of Ball Grid Arrays (BGAs)
·         Surface Inspection
·         Measuring of Spark Plugs
·         Molding Flash Detection
·         Inspection of Punched Sheets
·         3D Plane Reconstruction with Stereo
·         Pose Verification of Resistors
·         Classification of Non-Woven Fabrics

Computer Vision : Representational and Control

Computer Vision : Representational and control requirements 


Image-understanding systems (IUS) include three levels of abstraction as follows: Low level includes image primitives such as edges, texture elements, or regions; intermediate level includes boundaries, surfaces and volumes; and high level includes objects, scenes, or events. Many of these requirements are really topics for further research. 



The representational requirements in the designing of IUS for these levels are: representation of prototypical concepts, concept organization, spatial knowledge, temporal knowledge, scaling, and description by comparison and differentiation. 

While inference refers to the process of deriving new, not explicitly represented facts from currently known facts, control refers to the process that selects which of the many inference, search, and matching techniques should be applied at a particular stage of processing. Inference and control requirements for IUS are: search and hypothesis activation, matching and hypothesis testing, generation and use of expectations, change and focus of attention, certainty and strength of belief, inference and goal satisfaction.

That is the least of computer vision representional and control requirement

Implementation of Machine Learning

Here some implementation of machine learning. A portion of the undertakings that have actualized in these territories include: 

Mechanical substance process control 


This arrangement predicts the right substance process definition taking into account past history and current procedure and ecological properties. The framework was actualized for the excellence care division of one of the world's biggest buyer items organizations. 


Forecast of money related records from printed news streams 


In this application news streams from 20 amazing sources are digitized and utilized as the contribution to a neural system indicator of a money related time arrangement. The innovation supplements conventional figures construct exclusively with respect to time arrangement history accommodating a more hearty general estimate. 

High explosives indicator for air terminal security checkpoints 


A microwave sign is utilized to recognize the nearness of hazardous material, however the sign experiences various variable impacts, for example, body conditions, mass and state of the explosives, reflections from the encompassing environment, and so forth. A neural system was utilized to discover certain non-straight mixes between various recurrence readings which are most steady under these differing annoyances. This is an extremely troublesome and exceedingly non-direct undertaking, yet with adequate preparing a neural system gives an amazing arrangement. 

Oil pipeline deformity acknowledgment and measurement 


Neural systems and probabilistic trees were utilized to find peculiarities in attractive flux spillage and ultrasonic sensor signals. Via painstakingly "educating" the framework on test tests of imperfections with known sizes, the virtual detecting innovation is presently ready to anticipate deformity size and profundity for mechanical estimations in pipelines, permitting the framework to rapidly describe deformities as basic or non-basic. 

Versatile execution control of iterative straight solver 


A neural system motor with nearby memory advanced by a hereditary calculation can track the execution of an iterative direct solver progressively, amid calculations, and suggest ideal arrangements of solver parameters for the following emphasis accommodating quick, exact meeting.

That is some examples of implementation of machine learning.

Free eBook of Computer Vision, Image Processing and Pattern Recognition

Here is some free eBooks of Computer Vision, Image Processing, and Pattern Recognition. If it possible we also upload eBook Machine learning and Data Mining.

If you can not download it, just tell me on the comment box. Thanx.

Index of eBooks

#1

A Practical Introduction to Computer Vision with OpenCV (Kenneth Dawson-Howe)
Adaptive image processing: a computational intelligence perspective

Efficient Image Retrieval (L.V. Tran)
Mathematical Image Processing (M. Bergounioux)
Pattern Recognition (C.M. Bishop)



Long Short Term Memory

Various specialists now utilize variations of a profound learning RNN called the Long transient memory (LSTM) system distributed by Hochreiter and Schmidhuber in 1997. It is a framework that not at all like conventional RNNs doesn't have the vanishing slope issue. LSTM is ordinarily expanded by repetitive entryways called overlook gates. LSTM RNNs keep backpropagated blunders from vanishing or exploding. Instead mistakes can stream in reverse through boundless quantities of virtual layers in LSTM RNNs unfurled in space. That is, LSTM can learn "Profound Learning" tasks that require recollections of occasions that happened thousands or even a great many discrete time steps prior. Issue particular LSTM-like topologies can be evolved. LSTM works notwithstanding when there are long defers, and it can deal with signs that have a blend of low and high recurrence segments. 



Today, numerous applications use heaps of LSTM RNNs and train them by Connectionist Temporal Classification (CTC) to discover a RNN weight framework that expands the likelihood of the mark arrangements in a preparation set, given the relating information groupings. CTC accomplishes both arrangement and acknowledgment. In 2009, CTC-prepared LSTM was the main RNN to win design acknowledgment challenges, when it won a few rivalries in associated penmanship recognition Already in 2003, LSTM began to wind up focused with customary discourse recognizers on certain tasks. In 2007, the blend with CTC accomplished first great results on discourse data. Since then, this methodology has upset discourse acknowledgment. In 2014, the Chinese inquiry goliath Baidu utilized CTC-prepared RNNs to break the Switchboard Hub5'00 discourse acknowledgment benchmark, without utilizing any conventional discourse handling methods. LSTM likewise enhanced huge vocabulary discourse recognition, content to-discourse synthesis, additionally for Google Android, and photograph genuine talking heads. In 2015, Google's discourse acknowledgment purportedly encountered an emotional execution hop of 49% through CTC-prepared LSTM, which is presently accessible through Google Voice to billions of cell phone users.

LSTM has additionally turned out to be exceptionally prominent in the field of Natural Language Processing. Not at all like past models taking into account HMMs and comparative ideas, LSTM can figure out how to perceive setting touchy languages. LSTM enhanced machine translation, Language modeling and Multilingual Language Processing. LSTM joined with Convolutional Neural Networks (CNNs) likewise enhanced programmed picture captioning and a plenty of different applications.