KR20180033888A - Pattern Recognization Method and Systerm Thereof - Google Patents

Abstract

Disclosed are a location-based pattern recognition method and a system thereof. The location-based pattern recognition method is realized through a process of allowing a management server to store various collected information of restaurants and sightseeing places in a server. According to the present invention, sightseeing information can be provided to a MICARE traveler by utilizing a location-based technology and an image pattern recognition application technology of an object.

Description

{Pattern Recognization Method and Systerm Thereof}

The present invention relates to a location-based pattern recognition method and system, and more particularly, to a location-based pattern recognition method and system that can provide tourist information to a MICARE traveler by utilizing location- And a system.

Various location-based services based on the user's location information confirmation system through the rapid expansion of smart phones have been provided, but the present invention does not provide location-based sign recognition technology.

Accordingly, it is an object of the present invention to provide a location-based pattern recognition method and system that can provide tourist information to a MICARE traveler by utilizing location-based technology and application technology of image pattern recognition of objects.

According to another aspect of the present invention, there is provided a method for recognizing a location-based pattern, comprising the steps of: a management server storing various information of a restaurant and a sightseeing spot in a server.

According to the present invention, there is provided a location-based pattern recognition method and system for providing tourism information to a MICARE traveler by utilizing a location-based technology and an application technique of image pattern recognition of objects.

1 to 32 are diagrams for explaining an execution procedure of a method for recognizing a location-based pattern according to an embodiment of the present invention.

Hereinafter, the present invention will be described in detail with reference to the drawings. It is to be noted that the same elements among the drawings are denoted by the same reference numerals whenever possible. In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail.

The location-based sign recognition technology is expected to be applied to various areas such as accommodation, tourist information, foreign language support, detailed information including moving images of traffic milestones, etc., It is predicted that it can be used for learning. In addition, it is possible to utilize it variously in a location-based active field in which a player can go to the field such as treasure hunting or orientation in the experiential game field in the future and find a target object and play various types of games based on the object.

8 is a system configuration diagram for providing a service.

Admin Web

- A program for storing and managing various information of collected restaurants and sightseeing places on the server

- restaurant signboard photos, restaurant introduction, representative menu, location information, food information, presence of parking etc.

- Photos of sightseeing spots, introduction of sightseeing spots, location information, presence of parking, presence of restrooms, etc.

Client

- A program (smartphone app) that transmits the photos taken by the user (traveler) to the server, receives information about the photographed photos from the server, and displays them to the user.

Server

- A program that searches places and various information about pictures received from users from collected data and provides them to users

- Storage configuration and storage program based on location information about image when storing data through management program

- A program that classifies and stores information other than images in DataBase when storing data through management program

Server construction

1.API server construction

   (1) Platforms: Java SE (Standard Edition) 1.8

   (2) Web server: tomcat 8

   (3) Application framework: spring boot 1.3.5

   (4) Build system: Gradle 2.12

2. Image search server

   (1) Platforms: Java SE (Standard Edition) 1.8

   (2) Web server: tomcat 8

   (3) Application framework: spring boot 1.3.6

   (4) Build system: Gradle 2.12

3. Database

(1) MariaDB 10.1.14

Developed Admin Web

The system administrator stores the store information collected through the administrator program in the database of the server's database, mysql, and the images are stored in Lucene Image Retrieval (LIRE), which is a Lucene-based image search engine.

The GPS coordinates 33.481416 and 126.497126 of the shop shown in the example in Fig. 10 are converted into the vcyqx0 geocode code as shown in Fig. 11 using the geocoding algorithm.

Images are stored in a directory called wvcyqx0 under the image storage directory, and the open source image search engine, LIRE, is pre-indexed using the Color and Edge Directivity Descriptor (CEDD) algorithm. This value is used to judge the similarity and retrieve it at the time of image retrieval in the future.

Database construction

12, a database table is constructed to store and manage store information.

- Configuration of the manager information related table

1.tb_users: administrator account table

   (1) id: user number

   (2) email: user ID (email address)

   (3) email_crc: The value of the crc32 email address (used to search by email)

   (4) password: password, stored encrypted with sha256

   (5) created: account creation date

   (6) updated: Account last modified

2. tb_roles: tables with administrator privileges

   (1) id: Administrator role number

   (2) role: Administrator role (super admin, admin, viewer, etc.)

   (3) description (administrator authority description)

   (4) created: creation date

   (5) updated: Last modified

3. tb_user_authorities: the authorization management table assigned to the administrator

   (1) user_id: administrator number

(2) role_id: authority id

- Store information related table

1.tb_stores: store information table

   (1) id: store number

   (2) name: store name

   (3) summary: Introduction to the store

   (4) addr: store address

   (5) gps_lat: GPS Latitude

   (6) gps_lng: GPS longitude

   (7) geohash: geohash value of hen coordinates

   (8) operating_time: business hours

   (9) toilet_shared_inside: Public toilet room

   (10) toilet_shared_outside: Shared toilet outside

   (11) toilet_inside: bathroom interior

   (12) toilet_outside: toilet outside

   (13) toilet_stool: toilet toilet seat

   (14) toilet_direction: flush toilet

   (15) toilet_urinal: toilet urinal

   (16) toilet_tissue: toilet toilet paper

   (17) toilet_clean: Toilet cleanliness

   (18) wifi: Wi-Fi availability

   (19) korean_food: Korean food

   (20) chinese_food: Chinese

   (21) american_food: Form

   (22) japanese_food: Japanese food

   (23) cafe_food: Cafe

   (24) global_food: world food

   (25) flour_based_food: Fruit

   (26) cate_solo: 1 person

   (27) cate_couple: couple

   (28) cate_family: Family

   (29) cate_team: group

   (30) cate_health: Healthy diet

   (31) cate_vegetable: vegetarian

   (32) main_image: store main image

   (33) created: creation date

   (34) updated: last modified

(35) in_use: Whether to use store information

2. tb_store_images: store image information

   (1) id: store image number

   (2) path: image path

   (3) gps_lat: gps latitude stored in the image

   (4) gps_lng: gps longitude stored in the image

   (5) illumination: the value of the cell phone illuminance

   (6) orientation: The horizontal and vertical directions of the mobile phone

   (7) magnetic: The direction the mobile phone points at the time of photography

   (8) main_image: whether the store main image

(9) tb_stores_id: Store number

3. tb_store_menus: About store menu

   (1) id: Menu number

   (2) menu: The menu name

   (3) pungency: about spicy

   (4) soumess: about sour

   (5) sweetness: sweetness

   (6) saltiness: salty taste

   (7) price

   (8) image: menu picture

   (9) created: creation date

   (10) updated: last modified

(11) tb_stores_id: store number

Store information and images are converted into geo code based on the GPS coordinates of the shop, and grouped and stored.

Store information search server

The store information search server searches the store for the pictures transmitted by the user using the photos received from the user's smartphone, the GPS coordinates, the compass direction, and the illuminance sensor value.

If the user's current location GPS coordinates are 33.481426, 126.497226, changing to a character using the geo-hash algorithm will result in a "vcyqx0" geo code.

Using the geocode code value, the user requests the similar image search between the user's transmitted image and the collected image to the image search server LIRE.

The image search server retrieves similar pictures using the CEDD value of the pre-built image and the CEDD value sent by the current user.

The shop information is retrieved from the database using the address of the retrieved picture and transmitted to the user.

If the position is slightly different due to the GPS error, it means that the values starting with the geocode code value wvcyq- * are adjacent. This is used to further search for 8 squares of GPS values.

The geo code value (8 values) of the example shop in the above figure is adjacent. Therefore, additional images are retrieved from eight adjacent positions.

Geohash

Geohash relates to a system for encoding geographic coordinates.

It is a geocoding system developed by Gustavo Niemeyer and placed in the public domain.

* geocoding: Specify location information. A software process that matches road addresses or other geographic elements to road data material to derive geographical coordinates of addresses or associated road segments

It is a hierarchical spatial data structure that subdivides the space into grid-shaped buckets and is one of many applications known as Z-order (Z-order) curves, generally space-filling curves.

Geohash provides features such as arbitrary precision and the ability to gradually remove features from the end of the code to reduce its size (and gradually lose precision).

As a result of gradual degradation of precision, nearby places will often (but not always) provide a similar prefix. A long shared prefix can be two near.

Service

It is the only way to provide a short URL that identifies your location on the Earth so that it is more convenient for you to reference it on your website.

To obtain Geohash, the user provides geographic information encoding or latitude and longitude coordinate addresses (most commonly the format used for latitude and longitude pairs are accepted) in one field and performs the request.

In addition to showing that latitude and longitude correspond to a given geohash, users navigating to geohash at geohash.org can also submit embedded maps, download GPX files, or directly transfer waypoints to a GPS receiver . Links are also provided on external sites that can supply more details around a particular place.

For example, the coordinate pair 57.64911,10.40744 (near the end of the Jutland peninsula, Denmark) produces a somewhat shorter hash of u4pruydqqvj.

Uses

The main uses of Geohash are as follows.

- By unique identifier

- For example, to represent a decimal point data in a database

Geohash has also been suggested for use for geotagging.

When used in a database, the data structure of geohashed has two advantages. First, the data indexed by geohash will have all the key points for the specified rectangular area in the adjacent slice (the number of slices depends on the required precision and the existence of the "fault line" with geohash) Queries are much easier or faster in database systems than multi-index queries. Second, this index structure can be used for fast and easy proximity searches: the closest points are often among the nearest geohash.

Design

The first step is decoding it from base 32 using the following character map.

Decimal 0 One 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Base 32 0 One 2 3 4 5 6 7 8 9 b c d e f g

Decimal 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Base 32 h j k m n p q r s t u v w x y z

01101 11111 11000 00100 Operation result of 00010 (ezs42) bits. It is assumed that the counting for the even bit longitude code (0111110000000) starts from the left 0 while the odd bit is performed for the latitude code (101111001001).

Each binary code is again used for successive partitions while considering one bit from left to right at the same time. For latitude values, create two segments by dividing -90 to +90 by 2: -90 to 0 and 0 to 90

Since the first bit is 1, the higher interval is selected and the current interval. The procedure is repeated for all the bits of the code. Finally, it is the center of the latitude value generation interval. The hardness is treated in an equivalent way, with the initial spacing being between -180 and +180.

At the end of the procedure, calculate approximately latitude 42.6 and longitude -5.6.

Here is an example of decoding 42.6 to 101111001001. First we know that latitude is somewhere in the range -90 to 90. Without any bits, we should assume that the latitude with an error of + -90 was zero. With one bit we can determine that it is in the range from -90 to 0 or from 0 to 90. Because the first bit is high, we know that our latitude is somewhere between 0 and 90 degrees. Without any more bits, we assume we have a latitude of 45, giving us an error of -45.

Each subsequent bit bisects this error. This table shows the effect of each bit. The appropriate half of the range for each step is highlighted in green; The low bit selects the range below, the high bit selects the upper range. The last column shows the latitude, simply the average value of the range. Each subsequent bit makes this value more accurate.

bit min mid max val err One -90,000 0.000 90,000 45,000 45,000 0 0.000 45,000 90,000 22.500 22.500 One 0.000 22.500 45,000 33.750 11.250 One 22.500 33.750 45,000 39.375 5.625 One 33.750 39.375 45,000 42.188 2.813 One 39.375 42.188 45,000 43.594 1.406 0 42.188 43.594 45,000 42.891 0.703 0 42.188 42.891 43.594 42.539 0.352 One 42.188 42.539 42.891 42.715 0.176 0 42.539 42.715 42.891 42.627 0.088 0 42.539 42.627 42.715 42.583 0.044 One 42.539 42.583 42.627 42.605 0.022

The numbers in Table 3 are rounded to 32 decimal places for clarity.

The final rounding method should be carefully performed in the following manner.

Min ≤ round (value) ≤ max

So at 42.605, 42.61 or 42.6 is the correct rounding, not rounding to 43.

Geohash
length Lat bits Lng bits Lat error Lng error Km error One 2 3 ± 23 ± 23 ± 2500 2 5 5 ± 2.8 ± 5.6 ± 630 3 7 8 ± 0.70 ± 0.7 ± 78 4 10 10 ± 0.087 ± 0.18 ± 20 5 12 13 ± 0.022 ± 0.022 ± 2.4 6 15 15 ± 0.0027 ± 0.0055 ± 0.61 7 17 18 ± 0.00068 ± 0.00068 ± 0.076 8 20 20 ± 0.000085 ± 0.00017 ± 0.019

One limitation of the Geohash algorithm is that it utilizes it in an attempt to find points close to each other based on a common prefix. In the case of the edges, the position information is separated from each other, but will result in a geohash code without any common prefix on either side of the 180 degree meridian (different hardnesses for the surrounding physical locations). Points are very different geohash (Different hardnesses for the surrounding physical locations).

Two close places on either side of the equator (or Greenwich meridian) will not have a long common prefix because they belong to another 'half' of the world. Simply put, the binary latitude (or longitude) of one position is 011111 ... And the other 100000 .., so they will not have a common prefix and most of the bits will be inverted. This can also be seen as a result of relying on the Z-order curve (which is more appropriate, in this case referred to as N-order visit) to order the points, as two nearby points may be visited at very different times have. However, two points with a long common prefix will be contiguous.

For proximity probing, one can calculate the southwest corner (low geohash with low latitude and longitude) and the northeast corner (high geohash with high latitude and longitude) and search for a geohash between them . This will search all points in the z-order curve between the two corners, which can be too many points. It also breaks down at the 180 meridian and pole. Solr uses the filter list of the prefix by calculating the closest square prefix close to the geohash.

Third, because geohash (in this implementation) is based on longitude and latitude coordinates, the distance between the two geohashes can be thought of as the distance between two points in latitude / longitude being equalized, Do not interpret it as actual distance.

Examples of nonlinearities for latitude-longitude systems:

- The length of the longitude is 111.320 km at equator (0 degree), while the latitude is 110.574km, 0.67% error

At -30 degrees (latitude and longitude), the error is 110.852 / 96.486 = 14.89%.

The error reaches 111.412 / 55.800 = 99.67% while reaching infinite pole at -60 degrees (high north pole).

Note: This restriction is not due to geohashing or latitude-longitude coordinates. However, this is due to the difficulty of mapping coordinates on a sphere for two-dimensional coordinates (wrapping non-linear and modulo-similar values) and the difficulty of studying two-dimensional space uniformly. The first relates to Hilbert curves and z-order curves with geographic coordinates and map projection and others. Once we find a coordinate system that can point to a distance linearly and surround the edges and be studied uniformly, applying geohashing to that coordinate will not suffer from the above limitations.

When geohashing is enabled to be applied to an area with a Cartesian coordinate system, it will then be applied to the area to which the coordinate system is applied.

Despite these problems, there are possible solutions and algorithms have been used successfully in Elasticsearch, MongoDB, HBase, and accumulo to implement proximity search.

The alternative to storing geohashes as a string in the database is a localized code, also called a space key and similar to QuadTiles.

theorem

- Geohash is a system for encoding (geocoding) geographic spatial coordinates

- Hierarchical spatial data structure that subdivides space into grid type

   Divide the earth into 32 cells (4 rows, 8columns)

- Launched in February 2008. Provides short URLs for e-mail, forums, and websites to locate geographical coordinates

For latitude / longitude coordinate pairs 57.64911 and 10.40744, expressed as a geohash value of u4pruydqqvj

As shown in FIG. 14, the entire earth is divided into 4 rows and 8 columns. To represent the current user's position, each subdivided segment is further subdivided into 4 rows and 8 columns. The above procedure can be repeated up to 12 times to express the geographical coordinates. That is, it is represented by 12-digit code.

The 32-base code is arranged by Z-link Order method.

In FIG. 14, the arrangement of the 32-base code is the result set by the Z-link Order as shown in FIG.

Geohash can not represent a precise point because it expresses spatial geographic coordinates (latitude / longitude pairs) into 32 buckets. However, by increasing the number of buckets divided into 32 buckets, the error range can be reduced sufficiently.

The table in FIG. 16 shows the bucket size of spatial geographic coordinates according to the number of classification of Geohash.

When expressed in Level 12, the 3.7 cm x 1.8 cm bucket can be of sufficient accuracy to represent spatial geographical coordinates in which a person is located.

The algorithm according to the present invention is a method of encoding / decoding GPS values (latitude / longitude) into Geohash, and the basic idea is the same as a method of constructing a binary tree.

First, set the range of latitude and longitude as follows.

Latitude: -90 to 90 (-90 to 0, 0 to 90 Classify into two sections based on the median 0)

Longitude: -180 ~ 180 (-180 ~ 0, 0 ~ 180).

Select the interval that contains the latitude / longitude value that you want to convert. In this case, the interval including a small number is set to a lower section ('0'), and the interval including a large number is set to an upper section ('1').

ex) In the case of -90 ~ 90 interval, set the 90 ~ 0 interval as the lower interval ('0') and the interval of 0 ~ 90 as the upper interval ('1'). In the same way, 0 to 45 are set to the lower section ('0') and 45 to 90 are set to the upper section ('1') for 0 to 90 sections.

After dividing the set interval into two sections, the section including the latitude / longitude value to be converted is selected. Repeat the above procedure to create a binary value until it is the closest to the latitude / longitude value.

Generate binary values for latitude and longitude and then rearrange latitude / longitude values. The rearrangement method arranges the latitude to odd bits and the hardness to even bits.

ex) Assuming binary for latitude is '00000' and binary for hardness is '11111', rearrangement is 1010101010.

After rearranging the latitude / longitude, create code by grouping 5 bits to represent Geohash according to the Base32 code table.

The following figure shows the result of converting the GPS value of Sohchunhyang Multimedia Center into Geohash. And we explain the process of converting 25 bits into Geohash according to the above algorithm.

FIG. 17 shows the result of converting the GPS value of the multimedia center of Soonchunhyang University into Geohash. And we explain the process of converting 25 bits into Geohash according to the above algorithm.

The above figure shows the value converted from the geohash.org site to the geohash value after obtaining the GPS value for one point of Soonchunhyang University Multimedia Hall on google map.

In the initial screen of geohash.org site, enter the GPS value (36.769033, 126.934728) of the Soonchunhyang University Multimedia Hall on the Coordinates and press the Go button to display the geohash value (wyd30mx51s93) as shown above.

encoding algorithm is used to convert the GPS value (latitude: 36.769033, longitude: 126.934728) of the multimedia center of Soonchunhyang University into the geohash value step by step. In the above site, we convert from 12Level to 5Level to 5-digit code so that we can understand how the algorithm is proceeded and transformed.

The encoding process for Sohn Chunhyang University Multimedia Center latitude 36.769033 and longitude 126.934728, set to precision = 25 (25 bits)

Latitude 36.769033

step 1: range = [-90, 90], middle = 0, return 1

step 2: range = [0, 90], middle = 45, return 0

step 3: range = [0, 45], middle = 22.5, return 1

step 4: range = [22.5, 45], middle = 33.75, return 1

step 5: range = [33.75, 45], middle = 39.375, return 0

step 6: range = [33.75, 39.375], middle = 36.5625, return 1

step 7: range = [36.5625, 39.375], middle = 37.96875, return 0

step 8: range = [36.5625, 37.96875], middle = 37.265625, return 0

step 9: range = [36.5625, 37.265625], middle = 36.9140625, return 0

step 10: range = [36.5625, 36.9140625], middle = 36.73828125, return 1

step 11: range = [36.73828125, 36.9140625], middle = 36.826171875, return 0

step 12: range = [36.73828125, 36.826171875], middle = 36.73822265625, return 0

Latitude binary = 101101000100

Hardness 126.934728

setp 1: range = [-180, 180], middle = 0, return 1

setp 2: range = [0, 180], middle = 90, return 1

setp 3: range = [90, 180], middle = 135, return 0

setp 4: range = [90, 135], middle = 112.5, return 1

setp 5: range = [112.5, 135], middle = 123.75, return 1

setp 6: range = [123.75, 135], middle = 129.375, return 0

setp 7: range = [123.75, 129.375], middle = 126.5625, return 1

setp 8: range = [126.5625, 129.375], middle = 127.96875, return 0

setp 9: range = [126.5625, 127.96875], middle = 127.265625, return 0

setp 10: range = [126.5625, 127.265625], middle = 126.9140625, return 1

setp 11: range = [126.9140625, 127.265625], middle = 127.08984375, return 0

setp 12: range = [126.9140625, 127.08984375], middle = 127.001953125, return 0

setp 13: range = [126.9140625, 127.001953125], middle = 126.9580078125, return 0

Longitude binary = 1101101001000

Longitude = even number array, latitude = odd number array (longitude latitude longitude latitude ...)

11100 11110 01100 00011 0000 -> Convert to Base32 code table => wyd30

The converted geohash value from geohash.org is wyd30mx51s93, and the result of the hand-converted process of the above algorithm can be confirmed that wyd30 is converted to 5level. It is confirmed that the same result can be obtained by calculating up to 12level, that is, 60 bits (latitude 30 steps, hardness 30 steps).

The decode process is the same as above. If there is a difference, the encoding process is the process of constructing the bits with GPS coordinates. The decoding process extracts the middle value of the final stage by using each bit and restores the GPS coordinates. Process.

As we have seen in the concept of Geohash, geohash takes an intermediate value separated by 32 buckets, so decoding may cause some errors. However, when the encoding is performed up to level 12, the error is in the range of 3.8 cm x 1.8 cm, so it can be judged that the error range is sufficiently within the range of representing the human position information.

GPS coordinate values and various information (restaurants overview, restaurant signboard image, restaurant representative menu image, presence of toilet, presence of parking, taste information, sightseeing spot image, etc.) of collected restaurants and tourist sites are compared with images taken by a user on a smart phone Provide information on restaurants and tourist attractions to your smartphone. At this time, it is necessary to find the information of restaurants and sightseeing places that are closest to each other by using the GPS coordinate value of the user smart phone.

There is an error between the GPS coordinate value of the collected restaurant and the tourist spot and the GPS coordinate value of the user smartphone, so that there is a problem in searching with the same GPS coordinate value. This is expressed in cm when GPS coordinates are expressed in full resolution. Finding the same GPS coordinate value may be close to impossible. Therefore, in order to solve this problem, GPS coordinates can be converted into Geohash codes and classified into buckets (zones) ranging from several centimeters to several tens of meters in size. In this case, the bucket indicates that the GPS value of the user's smartphone is the same as the GPS value of the collected restaurant and tourist spot. The search time can be minimized by comparing the image information taken by the user among the information of restaurants and sightseeing places existing in the same bucket.

The problem also occurs here. It is most important to optimize the size of the bucket because the accuracy of the smartphone GPS of the general user, not the military purpose, is error-prone.

In order to test which size of bucket size would be advantageous, some smart phones should be used to determine the error range of the GPS.

The following figure shows how to test the GPS error range of smartphone.

If you measure the GPS coordinates only at one reference point, you can not know how much error occurs with the GPS coordinates on the actual map. However, measuring the GPS coordinates of 4 points from the reference point to the point 2, 3, 4, and 5, and measuring the distance from the reference point, the error range of the corresponding smartphone will be known.

Therefore, the distances of 5M, 10M, 30M, and 50M from the reference point to the points 2, 3, 4, and 5 are measured and GPS coordinates at each point are measured to test the error range.

As a result of the experiment according to the present invention, there is a tendency that the error range is small in the measurement results at a short distance and the only GPS mode (only the GPS measurement value is used) rather than the high accuracy setting in the GPS mode of the smartphone And that the error range is smaller than that when it is used.

As a result of the simple test, if the smartphone's only GPS mode is used, it will be possible to set up a bucket of about 30M x 30M with a 10M error range for quick search.

If you look at the bucket size according to the level of Geohash, it is expected that search will be done mostly in the same area if you set it to Level 8 (38.2M x 19.1M) size.

The above distance test will be best if it is possible to do all the phones that are available in the market, but it will not be practically possible, so you will have to use more phones and test it in a variety of places with plenty of times and different environments.

In order to compare the images of restaurants and sightseeing places in Cheju Island, images of restaurants and sightseeing sites collected in advance are necessary.

DSLR (Digital Single-Lens Reflex) camera or general digital camera, etc., and additional equipment is required when editing. To extract GPS coordinate values, we use GPS-specific equipment to collect information on restaurants and sightseeing spots. It is troublesome to record the process of matching the collected GPS value with the name of the restaurant and the sightseeing place by using a device capable of taking notes or memos.

Although it is not included in the development contents of this research project, the collection app is planned as follows for easy data collection.

The following items are indispensable for the entire development scope of the collection app (taking, taking, and taking pictures).

19 is a service flow chart of the collected application development guide.

- GPS and geomagnetic sensor are executed automatically when the app is running.

- You need to be able to run the camera for shooting, shoot immediately, and set the camera.

- In camera setting, it should be able to turn on / off the information that can be additionally provided to the photographed pictures.

- The information to be provided to the photographed image is as follows.

GPS coordinate value (select latitude / longitude)

Ambient light sensor value (range 0 ~ 1023)

Geomagnetic sensor operation (direction angle display selection)

Flash operation (On / Off selection)

- It is necessary to be able to shoot multiple images at various locations and then select the same objects when shooting objects for a restaurant or sightseeing spot. (At least one image must be available)

- It is necessary to be able to set the exact location because there may be errors in the GPS value of the smartphone that photographs things about restaurant and sightseeing spot.

- Basically, you should be able to see the GPS value of your smartphone on the map.

- If the current location is different from the GPS location of the smartphone, the map should be enlarged / reduced, moved, and the current location should be selected and saved.

20 is a design content of the initial screen for the collected application.

 Part specifies the name of the app to be displayed in the name of the app that is displayed when the app is executed. In the img area, you can use related illustrations and images to intuitively tell your collection app users what they are doing.

Section specifies the company name or copywriter.

When the app is executed with the intro screen, it will be displayed on the screen for a minute or two and then moved to the main screen.

21 is a design content of the main screen of the collected app.

Fig. 21 is a camera activation step of the collection app, and is divided into a menu for taking a standard board of a restaurant and a menu for shooting a tourist destination object.

 Section displays the top status bar where the name of the collection app is displayed.

 Part is a menu for photographing a signboard of a restaurant, and when the area is touched, it moves to a screen for photographing a signboard.

 Is a menu for shooting objects on tourist spots in Jeju Island. If you touch the area, it moves to the screen for shooting tourist spots.

FIG. 22 is a design content of a restaurant signboard photographing of a collection app.

FIG. 23 shows a screen configuration of a step of photographing a signboard of a restaurant.

It is a step of collecting data of objects through camera shooting, and signs are made in landscape and portrait form. Therefore, when the smartphone is rotated 90 degrees / 180 degrees according to the form of the signboard, it must rotate automatically according to the angle of the smartphone. Signage area of signboard is important in signboard shooting

 The compass is the area where you can see the compass position, the compass is always displayed, and the top is always to the north of the compass. The needle moves to show the azimuth.

 The part recognizes the signboard. The main recognition area is treated as transparent. The area other than the signboard is treated as black translucent, and guides the signboard to the transparent area. It has a white border outside the transparent area so that the signboard can be placed in the border area in full when shooting.

 , The same picture selection button (moving to a kind of gallery screen), a camera shooting button (photographing), and a flash operation button (flashing on / off) are arranged so that a function necessary for photographing can be selected.

 The part that displays the GPS value and the illumination value of the smartphone is basically displayed in the horizontal direction, and in the case of vertical, there is no need to change the vertical character representation

Fig. 24 is a design content for the same picture selection menu.

24 is a screen when the same picture selection button is selected in the standard picture taking menu.

It is a function that allows you to take pictures from multiple locations for a single subject and then view the captured pictures in one place.

 A name of the same picture selection is displayed, a back button, a delete button, and a writing button are located.

- The back button functions to return to the standard shooting menu.

- Press the Delete button to create the part. After selecting the picture in multiple, press OK to delete the selected picture. If you do not want to delete the selected picture, press Cancel to return to the same picture selection screen.

- Writing button is a button to enter the title of the photographed pictures. When writing button is selected, the part is created. After selecting multiple pictures, press OK to go to the title input window. Press Cancel to return to the same picture selection screen.

 Part is a part showing thumbnail images of photographs taken at various positions with respect to the same object. When the delete button is selected, the multi-select icon is displayed.

 Section displays the latitude / longitude, illuminance, and direction value of the photographed picture

 The part is not the default part. Delete part of the item, when the Write button is selected.

The photo is located in a folder that is separate from the collection app, not the gallery folder

Fig. 25 shows design contents for GPS confirmation and title input.

FIG. 25 is displayed when the signboard shooting> select the same picture> write button is selected as a screen for confirming the GPS position information and the title of the photographed picture.

 Section displays the title of the current page and a back button is placed. Pressing the Back button will take you to the standard picture taking> picture selection screen.

 Portion displays the first of the multiple selected pictures in the previous step.

 Portion displays the GPS value of several photographed photographs.

  - Basically, read the GPS value of the smartphone that photographed and display it on the map.

  - If the GPS value of the smartphone is different from the current position on the map, the point of interest (POI) is moved by pressing the corresponding position manually on the map.

  - If you do not see the current location on the map, you can zoom in / out / move the map.

  - The GPS information of all images selected in the previous step is changed to the same GPS information confirmed here.

 Part is a part for inputting a title for the selected photographs. For a restaurant, enter the name of the restaurant, and the multiple selected photos are numbered after (1), (2), and (3). For example, it is numbered in the following order: (1) water, (2) water, (3) water.

 Section can save or cancel the entered title and revised results in a batch.

  - When saving / canceling, open the warning window and check again to avoid mistakes.

Fig. 26 is a design content of a collection app for a tourist spot photographing.

FIG. 27 is a screen configuration of a photographing step of a tourist destination in Jeju Island.

The basic design contents are the same as the design contents for the restaurant signboard photography. As in the case of the standard picture taking step, the object should be automatically rotated when taking pictures of the object in the horizontal and vertical directions of the smartphone.

 Portion displays the azimuth at the position where the photograph is taken as a compass image.

 Part is an image recognition area for a sightseeing object.

  - The central area of the object is important for the photographing of the tourist destination object. In the case of standard photography, it is important to have a rectangular box area to recognize a signboard, while in the case of a sightseeing object, it is important to cover the entire area shown on a smartphone, but to keep a central point of the object well.

 Part places a button for camera shooting and a button for operating the flash.

 - The same picture selection part placed on the standard picture photography menu can be added to the menu like the picture taking of the signboard if necessary for the implementation of the application, and this will be selected during the implementation.

 Part is a part for displaying GPS value and illuminance value. The text displays only on a horizontal basis.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In the present application, the terms "comprises" or "having" and the like are used to specify that there is a feature, a number, a step, an operation, an element, a component or a combination thereof described in the specification, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention.

Claims (1)

And the management server stores various information of the collected restaurant and sightseeing spot in a server.

Images