What is Pattern Recognition? It is generally easy for a person to differentiate the sound of a human voice, from that of a violin; a handwritten numeral "3," from an "8"; and the aroma of a rose, from that of an onion. However, it is difficult for a programmable computer to solve these kinds of perceptual problems. These problems are difficult because each pattern usually contains a large amount of information, and the recognition problems typically have an inconspicuous, high-dimensional, structure. Pattern recognition is the science of making inferences from perceptual data, using tools from statistics, probability, computational geometry, machine learning, signal processing, and algorithm design. Thus, it is of central importance …show more content…
Optical character recognition may be a classic example of the appliance of a pattern classifier, see OCR-example. the tactic of language one's name was captured with stylus and overlay beginning in 1990.[citation needed] The strokes, speed, relative min, relative liquid ecstasy, acceleration and pressure is employed to unambiguously establish and make sure identity. Banks were initial offered this technology, however were content to gather from the corp for any bank fraud and failed to need to inconvenience customers..[citation needed] Artificial neural webworks (neural net classifiers) and Deep Learning have several real-world applications in image process, many
Martin, K. A. (1994). A brief history of the "feature detector". Cerebral Cortex, 4, 1-7.
...means and become familiar with K-means clustering and its usage. Then, we finish this part by different method of clustering. The K-nearest- neighbors is also discussed in this chapter. The KNN is simple for implication, programming, and one of the oldest techniques of data clustering as well. There are many applications existing for KNN and it is still growing. The PCA also discussed in this chapter as a method for dimension reduction, and then discrete wavelet transform is discussed. For the next chapter the combination of PCA and DWT, which can be useful in de-noising, come about. In this study, we have examined the neural network structure and modeling that is most of usage these days. The backpropagation is one of the common methods of training neural networks and for the last model, we discussed autoregressive model and the strategies to choose a model order.
Critical Evaluation of a Theoretical Approach Used to Describe Pattern/Object Recognition Pattern/object recognition is concerned with the processes involved in the identification of images and objects. This essentially involves taking information that enters the visual system and comparing this with information stored in memory, and finding a match. There are three approaches within pattern recognition; template and prototype theories, feature comparison theories and structural theories. The focus of this essay is feature comparison theories, their advantages and disadvantages and their overall success in pattern/object recognition. Feature comparison models were developed in response to the many problems found with template and prototype theories.
Stergiou, C., & Siganos, D. (2011, August 6). Neural Networks. Retrieved August 6, 2011, from
[5] W.Zhang, S.Shan, ”Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition,” ICCV, vol. 1, pp.786-791, 2005.
Fingerprints are the very basis for criminal identification and conviction in every police agency on earth. Fingerprint evidence represents one of the most important pieces of evidence found at the scene of a crime, and can be used to determine the steps that the suspect took while committing the crime, but also has the ability to rule out suspects, or to eventually lead to the offender. The idea that no two individuals can have identical fingerprints is accepted by the courts and can lead to a fingerprint being the single piece of evidence in a crime that will lead to a conviction. Although, in recent years, the reliability and validity of using finger printing as evidence has been questioned by a variety scientists and also the media.
This approach includes two processes, training and classification (Chelali, Djeradi & Dejradi, 2009). In the training process, a subspace will be established by using the training samples, and then the training faces will be projected onto the same subspace. In the classification process, the input face image will be measured by Euclidean Distance to the subspace, and a decision will be made, either accept or reject.
Emergence networks mimics biological nervous system unleash generations of inventions and discoveries in the artificial intelligent field. These networks have been introduced by McCulloch and Pitts and called neural networks. Neural network’s function is based on principle of extracting the uniqueness of patterns through trained machines to understand the extracted knowledge. Indeed, they gain their experiences from collected samples for known classes (patterns). Quick development of neural networks promotes concept of the pattern recognition by proposing intelligent systems such as handwriting recognition, speech recognition and face recognition. In particular, Problem of handwriting recognition has been considered significantly during the last decades in the academic and industrial fields by employing types of direct matching. Performance of this recognition has been paying strong attention through developing several schemas and algorithms to learn the machines. In the light of that development, David Shepard invented first modern OCR’s version to read texts in 1951. After few years, this innovation is followed by originating a prototype machine to read upper case characters with speed of a character per minutes (Srihari & Lam 1995).In the same way, many companies, such as IBM, have continued in developing reader systems to challenge problems of character recognition(Lianwen, Kwokping & Bingzheng 1995). The competition between the realised systems concentrated on improving accuracy and speed of the intelligent machine.
Pattern recognition is when you look for similarities among and within small, decomposed problems that help solve complex problems more efficiently. An example of this would be drawing a dog, if we wanted to draw a dog we wouldn’t have to think too long because we know all dogs have 4 legs, eyes and a tail so knowing that it would make it easier and quicker to complete many different drawings. Finding patterns in problems makes problem solving a lot easier and it gives you a place to start when fixing a new problem. Pattern recognition is a process based on 5 key
Artificial intelligence works in a similar way by the use of a system referred to as ANN which stands for Artificial Neuron Networks. The idea for ANN came from brain and the neurological system and is the commonly used of the 4 systems in healthcare. It is made up of computer system networks enhanced by inter-linked computer processors and side by side calculations for the processing of information. This allows for the artificial intelligence network to be taught to predict the next set of information needed by analysis and thinking ahead. Its use in medicine is owed to Baxt because he was able to create a network of neurons that correctly and precisely diagnosed acute myocardial infarction. This led to the doors opening for Artificial Intelligent use in all areas of medicine as the potential increased for accuracy and better patient treatment. Artificial Intelligence is especially beneficial in radiology and analysis of images by possibly using “both human observations and direct digitised images as inputs to the networks. ANNs have been used to interpret plain radiographs, ultrasound, CT, MRI, and radioisotope scans” (Ramesh, Kambhampati, Monson and Drew, 2004).
[Jain, 2004] Jain, A.K.;Ross, A.;Prabhakar, S.;"An introduction to biometric recognition", Volume: 14 Issue: 1 Issue Date: Jan. 2004, on page(s): 4 - 20
Data is collected and the patterns are recognized, in order to understand the physical properties, and further to visualize the data as
This is similar to the life of any computer. Humans gain information through the senses. Computers gain similar information through a video camera, a microphone, a touch pad or screen, and it is even possible for computers to analyze scents and chemicals. Humans also gain information through books, other people, and even computers, all of which computers can access through software, interfacing, and modems. For the past year, speech recognition software products have become mainstream(Lyons,176).
...fman R. A. - "Data Mining and Knowledge Discovery" - A Review of issues and Multi- strategy Approach". Reports of the Machine Learning and Inference Laboratory, MCI 97-2, George Mason University, Fairfax, V.A. 1997. http://www.mli.gmu.edu/~kaufman/97-1.ps
Handwritten signatures happens in different patterns and there is a great deal of dissimilarity signatures of people even of the same area with same language. Some used to just write their name while others follow certain pattern to represent their signature. Signatures done with complexity are however less vulnerable to forgery effects [7]. Also, the signatures are very much influenced by the thinking panorama of a person. There is a particular process on how a signature s generated. Signature has three attributes at minimum . They are pattern form, movement and variation, and since the signatures are produced by moving a pen on a paper, movement of pattern is the most important