IMAGE PROCESSING APPARATUS AND FEATURE DETECTION METHOD

• US 20150287160A1
• Filed: 06/19/2015
• Published: 10/08/2015
• Est. Priority Date: 12/28/2012
• Status: Active Grant

• Current Assignee: Fujitsu Limited
• Original Assignee: Fujitsu Limited
• Inventors: NAGATO, Tsuyoshi, KOEZUKA, Tetsuo
• Granted Patent: US 9,710,877 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/2111 by using evolutionary computational techniques, e.g. genetic algorithms
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06T 1/20 Processor architectures; Processor configuration, e.g. pipelining
• CPC Class Codes: G06T 2207/20072 Graph-based image processing
• CPC Class Codes: G06T 2207/30164 Workpiece; Machine component
• CPC Class Codes: G06T 3/00 Geometric image transformations in the plane of the image
• CPC Class Codes: G06T 7/0004 Industrial image inspection
• CPC Class Codes: G06V 10/451 with interaction between the filter responses, e.g. cortical complex cells
• CPC Class Codes: G06V 10/457 by analysing connectivity, e.g. edge linking, connected component analysis or slices
• CPC Class Codes: G06V 10/771 Feature selection, e.g. selecting representative features from a multi-dimensional feature space

Abstract

An image processing apparatus is disclosed. A processor performs a process that arranges multiple image filters in a tree structure and acquires features of multiple input images captured in different environments. A storage part stores a filter table that maintains information concerning the multiple image filters. In the process, an image filter having a different type for each of layers, in which multiple image processes are hierarchized, is selected from the filter table. A population, which includes the multiple image filters in the tree structure, is generated.

Claims

1. An image processing apparatus, comprising:

a processor configured to perform a process that arranges multiple image filters in a tree structure and acquires features of multiple input images captured in different environments; and

a storage part configured to store a filter table that maintains information concerning the multiple image filters,

wherein the process includes

selecting an image filter having a different type for each of layers in which multiple image processes are hierarchized, from the filter table; and

generating a population that includes the multiple image filters in the tree structure.

2. The image processing apparatus as claimed in claim 1, wherein the generating the population generates the population, which the multiple image filters are arranged in the tree structure, by using genetic programming.

3. The image processing apparatus as claimed in claim 2, wherein the generating the population arranges the multiple image filters in the tree structure in which the target image is set as a root, and selects the input images to an end node in a predetermined layer.

4. The image processing apparatus as claimed in claim 1, further comprising:

conducting an evolutionary process using genetic programming in which a crossover process is conducted with respect to the population based on a first restriction rule of a crossover pair which restricts selection of the crossover pair for each of the layers.

5. The image processing apparatus as claimed in claim 4, wherein the conducting the evolutionary process conducts a mutation process with respect to the population based on a second restriction rule of mutation which restricts types of the mutation for each of the layers.

6. A feature detection method performed in a computer, the method comprising:

performing, by the computer, a feature detection process for arranging multiple image filters in a tree structure and acquiring features of multiple input images captured in different environments,

wherein the feature detection process includes

selecting an image filter having a different type for each of layers in which multiple image processes are hierarchized, from a filter table stored in a storage part, the filter table maintaining information concerning the multiple image filters; and

generating a population that includes the multiple image filters in the tree structure.

7. A non-transitory computer-readable recording medium storing a program which, when executed by a computer, causes the computer to perform a feature detection process for arranging multiple image filters in a tree structure and acquiring features of multiple input images captured in different environments, the feature detection process comprising:

selecting an image filter having a different type for each of layers in which multiple image processes are hierarchized, from a filter table stored in a storage part, the filter table maintaining information concerning the multiple image filters; and

generating a population that includes the multiple image filters in the tree structure.

8. An image processing apparatus for executing a process program, in which multiple filters are formed in a tree structure, with respect to multiple images captured by an imaging device, the apparatus comprising:

a storage part; and

a processor configured to perform a process,

wherein the process includes

selecting learning data based on feature amounts concerning the multiple images at an initial learning stage; and

generating the process program through learning employing genetic programming.

9. The image processing apparatus as claimed in claim 8, wherein the feature amounts are acquired based on an average luminance, a luminance histogram, and space frequency information for each of the multiple images.

10. The image processing apparatus as claimed in claim 8, wherein during a learning process using the genetic programming, the feature amounts are calculated by the process program of the tree structure in middle of generation, and the learning data is re-selected.

11. The image processing apparatus as claimed in claim 10, wherein the average luminance is acquired as the feature amount from each of the filters forming the process program of the tree structure.

12. The image processing apparatus as claimed in claim 8, wherein clustering is performed based on the feature amounts with respect to the learning data, representative samples are selected respectively from sample groups, and the learning data is updated.

13. An image processing method performed in a computer, the method comprising:

performing, by the computer, a feature detection process for executing a process program, in which multiple filters are formed in a tree structure, with respect to multiple images captured by an imaging device,

wherein the feature detection process includes

selecting learning data based on feature amounts concerning the multiple images at an initial learning stage; and

generating the process program through learning employing genetic programming.

14. A non-transitory computer-readable recording medium storing a program which, when executed by a computer, causes the computer to perform a process for executing a process program, in which multiple filters are formed in a tree structure, with respect to multiple images captured by an imaging device, the process comprising:

selecting learning data based on feature amounts concerning the multiple images at an initial learning stage; and

generating the process program through learning employing genetic programming.