Sub-pattern texture recognition using intelligent focal-plane imaging sensor of small window-size

Szirányi, Tamás; Hanis, A.

doi:10.1016/s0167-8655(99)00080-x

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…For texture analysis by using CNN based upon GAs, some relevant and representative studies as [19] and [42]- [47] have to be taken for further discussions. Like [19], a strategic approach was proposed to provide a simple but complete methodology for texture classification and segmentation.…”

Section: Design Of Cnn Templates For Screening Texture Classificamentioning

confidence: 99%

“…In [44], the hardware implementation for texture segmentation was developed, which sped up the research and discovery of texture analysis and other related applications, and meanwhile made texture-specific filtering and evaluation processes facilitated with parallel handling capability and consecutive template training of CNNs. The uses of gray level histograms or other statistical methods in [19], [42]- [44] also modulate the decision procedure after texture classification. In addition to filtering textures in some orientation, having a process of cross-correlation between the state and input, and using the halftone-like output of CNN as in [47], this paper turns the statistical analysis of CNN and the classification of screening patterns in advance for descreening on a differently breaking view.…”

Section: Design Of Cnn Templates For Screening Texture Classificamentioning

confidence: 99%

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Image Descreening by GA-CNN-Based Texture Classification

Shou

Lin

2004

IEEE Trans. Circuits Syst. I

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This paper proposes a new image-descreening technique based on texture classification using a cellular neural network (CNN) with template trained by genetic algorithm (GA), called GA-CNN. Instead of using the fixed filters for image descreening, we are equipped with a more pliable mechanism for classifications in screening patterns. Using CNN makes it possible to get an accurate texture classification result in a faster speed by its superiority of implementable hardware and the flexible choices of templates. The use of the GA here helps us to look for the most appropriate template for CNNs more adaptively and methodically. The evolved parameters in the template for CNNs can not only provide a quicker classification mechanism but also help us with a better texture classification for screening patterns. After the class of screening patterns in the querying images is determined by the trained GA-CNN-based texture classification system, the recommendatory filters are induced to solve the screening problems. The induction of the classification in screening patterns has simplified the choice of filters and made it valueless to determine a new structured filter. Eventually, our comprehensive methodology is going to be topped off with more desirable results and the indication for the decrease in time complexity.Index Terms-Cellular neural network (CNN), genetic algorithm (GA), image descreening, texture classification.

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Section: Design Of Cnn Templates For Screening Texture Classificamentioning

confidence: 99%

Section: Design Of Cnn Templates For Screening Texture Classificamentioning

confidence: 99%

Image Descreening by GA-CNN-Based Texture Classification

Shou

Lin

2004

IEEE Trans. Circuits Syst. I

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“…Above we reported on the development of a single-chip texture classifier smart-sensor system, which is very fast hut it has a limited window-size. We can see in [14] that this architecture can effectively recognize textures of periodicity larger than the window-size, when using statistical evaluation of the filtering output of then scanning CNN-window. As a result, we recognized 15 Brodatz-textures by using a 20'22 CNN chip with a success of 0.4% error-rate.…”

Section: Ox22bw Cnnchipmentioning

confidence: 97%

“…We have found that this CNN chip with a simple 3 * 3 CNN kernel can reliably classify 5 textures. In [14] we found that statistical evaluation of random scanning of textures by using a filtering CNN chip can result in a high-precision recognition ofup to 15 very different textures. Texture segmentation methods can be grouped into four main classes [8]:…”

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confidence: 97%

Texture segmentation by the 64×64 CNN chip

Szirányi

Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications

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CNN's fast image processing technology helps us to nm high-speed filtering t a s k for image mhaneement, recognition or segmentation. Texme analysis is a spsifie task, since the whole image is pmcessed massively parallel while we b v e B limited number of tame-specific filming and evaluariaa steps. Former resulu of simulations and recognition remlu of simple CNN chips show that the CNN is an appropriate tmI for this imag-prprocessing task. Now we see what the gray-scale image processor CNN chip at i@ limited memory capability and data-handling/-proeetsing accuracy can complele for multi-texm images. We demonstrate and compare some of OUT earlier CNNrelated texhlre analysis methods. Some methods to improve CNN configuration are proposed.

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Image Analysis and Computer Vision: 1999

Rosenfeld

2000

Computer Vision and Image Understanding

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Sub-pattern texture recognition using intelligent focal-plane imaging sensor of small window-size

Cited by 3 publications

References 9 publications

Image Descreening by GA-CNN-Based Texture Classification

Image Descreening by GA-CNN-Based Texture Classification

Texture segmentation by the 64×64 CNN chip

Image Analysis and Computer Vision: 1999

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