Image Recognition Technology

Image Recognition Algorithm ImplementationFavorites

In the past, image processing functions were mostly for image handles. Complex image files must be operated for algorithm implementation.

However, the algorithm implementation and debugging cycle is longer than the period. The matrix library I inserted in the middle to accelerate the external access. Since most image processing algorithms target matrices, implementation and debugging are faster than compaction.

 

 

 

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Basic Principles of fingerprint image recognition algorithms

In some countries, fingerprints belong to personal privacy and cannot be processed as manually. Therefore, many biometric identification technologies do not directly store fingerprint images. Over the years, companies and research institutions have produced many different digital algorithms. Even though they are different, these algorithms finally come down to finding and comparing fingerprint features on the fingerprint image. We define two fingerprint features for fingerprint verification: overall features and local features.

A. Overall features: The overall features are those that can be directly observed with the naked eye, including:

1. Pattern

Other fingerprint patterns are based on these three basic patterns. It is far from enough to identify fingerprints by means of lines. This is just a rough classification. More specific classification makes searching for fingerprints in large databases more convenient and convenient.

2. Mode Zone

The mode area refers to the area where the fingerprint contains the overall features, that is, the fingerprint can be identified from the mode area. Some fingerprint recognition algorithms only use data in the mode area. Securetouch's fingerprint recognition algorithm uses the obtained full fingerprint instead of the pattern area for analysis and recognition.

3. core points

The core point is located in the progressive center of the fingerprint pattern. It serves as a benchmark test point when reading and comparing fingerprints. Many algorithms are based on core points. They can only process and recognize fingerprints with core points. The core point is very important for securetouch's fingerprint recognition algorithm, but it can still be processed without the core point fingerprint.

4. Triangle

The triangle points are located at the first forks or breakpoints starting from the core point, or two lines of convergence, isolated points, turns, or points to these mysterious points. The triangle provides the starting point for counting and tracking of fingerprint pattern.

5. Number of lines

The number of fingerprint links in the mode area. When calculating the number of prints of a fingerprint, it is generally first connected to the core point and triangle point. The number of lines that match the fingerprint pattern can be considered as the number of prints.

B. Local Features

Local Features refer to the features of nodes on the fingerprint. These nodes with certain features are called feature points. The two fingerprints often share the same overall characteristics, but they do not have the same local features-feature points. The fingerprint pattern is not continuous, smooth, straight, but often interrupted, forked, or discounted. These breakpoints, forks, and turning points are called "feature points ". These feature points provide fingerprint uniqueness confirmation information. Nodes on the fingerprint have four different features:

1. Classification of Feature Points: There are the following types, the most typical of which are endpoints and forks.

Endpoint

A line ends here.

Forks

One line is separated into two or more other lines.

Differences

Two parallel lines are separated here

Isolated point

A very short line, so it becomes a little bit.

Cycle

After a line is separated into two lines, it is immediately merged into one. A small ring formed in this way is called a ring point.

Short Grain

One end is short but not a bit of texture.

2. direction: the node can be in a certain direction.

3. curvature: Describes the speed at which the texture direction changes.

4. position: the position of a node is described by (x, y) coordinates. It can be absolute or relative to a triangle or feature point.
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Design of forest fire prevention system based on image recognition algorithm traditional forest fire surveillance technology includes artificial forest monitoring, aircraft flight monitoring, satellite monitoring, etc. in this paper, a forest fire surveillance system based on ordinary CCD cameras and Short-Wave Wireless Communication devices on the tower is proposed. Based on the difference of real-time images and entrance exam images and the results of wavelet decomposition, when an exception occurs, extract the flame and smoke area to determine whether the extracted area has dynamic characteristics of smoke and flame. if a fire is detected, the compressed image is sent back to the command center through a short-wave communication device. //////////////////////////////////////// ///////////////////////// Study and Implementation of Image Recognition Algorithms for vehicle license plates Chapter 1 Introduction 1
1.1 research background 1
1.2 principle of Vehicle License Plate Recognition System 1
1.3 Current Situation of vehicle license recognition at Home and Abroad 2
1.4 Main Work and content 3
Chapter 4 positioning of vehicle licenses 4
2.1 pre-processing of Vehicle License Image 4
2.1.1 256 grayscale color bitmap 4
2.1.2 binarization of grayscale images 5
2.1.3 remove background noise 6
2.2 brief introduction to locating methods of vehicle licenses 6
2.3 Positioning Method for system compaction 7
2.3.1 horizontal positioning of vehicle licenses 7
2.3.2 vertical positioning of vehicle licenses 7
2.3.3 Positioning Algorithm Implementation 10
2.4 Experiment Result Analysis 12
Chapter 13 character cutting of vehicle licenses 13
3.1 license plate preprocessing 13
3.1.1 border removal 13
3.1.2 Noise Removal 13
3.1.3 gradient sharpening 15
3.1.4 tilt adjustment 16
Brief Introduction to the 3.2 character Cutting Method 17
3.3 Cutting Method for system compaction 19
3.3.1 algorithm Introduction 19
3.3.2 Algorithm Implementation 20
3.4 character Cutting Experiment Result 21
Chapter 2 Feature Extraction and character recognition 22
Feature Extraction with 4.1 characters 22
Brief Introduction to the 4.2 Character Recognition Method 23
4.3 system token recognition method 24
4.3.1 Artificial Neural Network Overview 24
4.3.2 BP neural network recognition license plate 25
4.3.3 BP Neural Network Recognition Algorithm Implementation 28
4.4 Experiment Result Analysis 29
Conclusion 32
Thank you 33
Exam 34
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