• 제목/요약/키워드: texture features

검색결과 495건 처리시간 0.027초

비교사 블록-기반 군집에 의한 다중 텍스쳐 영상 인식 (Multiple Texture Image Recognition with Unsupervised Block-based Clustering)

  • 이우범;김욱현
    • 정보처리학회논문지B
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    • 제9B권3호
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    • pp.327-336
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    • 2002
  • 텍스쳐 분석은 표면, 물체, 모양, 깊이 인식 등의 많은 영상 이해 분야에서 활용되는 가장 중요한 인식 기술 중의 하나이다. 그러나 기존의 방법들은 다중 텍스쳐 영상에 내재된 텍스쳐 성분의 인식 정보를 활용할 수 없는 분할만을 목적으로 하고 있으며, 내재된 텍스쳐 인식을 기반으로 하는 비교사적인 방법에 관한 연구는 거의 이루어지고 있지 않은 실정이다. 따라서 본 논문에서는 텍스쳐 성분을 방향장(orientation-field) 특징 정보인 방향각과 방향강도로 정의하고 블록-기반 자기조직화 신경회로망에 의해서 비교사적으로 영상 내에 존재하는 텍스쳐 영역을 군화(clustering) 및 통합(merging) 처리에 의해서 식별한다. 또한 제안된 알고리즘의 성능 평가를 위해서는 다양한 형태의 다중 텍스쳐 영상을 생성하여 블록 기반의 불림(dilation) 및 윤곽 검출 과정을 통해서 영상에 내재하는 텍스쳐 영역을 분할함으로써 그 유효성을 보인다.

Texture Analysis and Classification Using Wavelet Extension and Gray Level Co-occurrence Matrix for Defect Detection in Small Dimension Images

  • Agani, Nazori;Al-Attas, Syed Abd Rahman;Salleh, Sheikh Hussain Sheikh
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2004년도 ICCAS
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    • pp.2059-2064
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    • 2004
  • Texture analysis is an important role for automatic visual insfection. This paper presents an application of wavelet extension and Gray level co-occurrence matrix (GLCM) for detection of defect encountered in textured images. Texture characteristic in low quality images is not to easy task to perform caused by noise, low frequency and small dimension. In order to solve this problem, we have developed a procedure called wavelet image extension. Wavelet extension procedure is used to determine the frequency bands carrying the most information about the texture by decomposing images into multiple frequency bands and to form an image approximation with higher resolution. Thus, wavelet extension procedure offers the ability to robust feature extraction in images. Then the features are extracted from the co-occurrence matrices computed from the sub-bands which performed by partitioning the texture image into sub-window. In the detection part, Mahalanobis distance classifier is used to decide whether the test image is defective or non defective.

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Classification of Seabed Physiognomy Based on Side Scan Sonar Images

  • Sun, Ning;Shim, Tae-Bo
    • The Journal of the Acoustical Society of Korea
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    • 제26권3E호
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    • pp.104-110
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    • 2007
  • As the exploration of the seabed is extended ever further, automated recognition and classification of sonar images become increasingly important. However, most of the methods ignore the directional information and its effect on the image textures produced. To deal with this problem, we apply 2D Gabor filters to extract the features of sonar images. The filters are designed with constrained parameters to reduce the complexity and to improve the calculation efficiency. Meanwhile, at each orientation, the optimal Gabor filter parameters will be selected with the help of bandwidth parameters based on the Fisher criterion. This method can overcome some disadvantages of the traditional approaches of extracting texture features, and improve the recognition rate effectively.

Facial Expression Classification through Covariance Matrix Correlations

  • Odoyo, Wilfred O.;Cho, Beom-Joon
    • Journal of information and communication convergence engineering
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    • 제9권5호
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    • pp.505-509
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    • 2011
  • This paper attempts to classify known facial expressions and to establish the correlations between two regions (eye + eyebrows and mouth) in identifying the six prototypic expressions. Covariance is used to describe region texture that captures facial features for classification. The texture captured exhibit the pattern observed during the execution of particular expressions. Feature matching is done by simple distance measure between the probe and the modeled representations of eye and mouth components. We target JAFFE database in this experiment to validate our claim. A high classification rate is observed from the mouth component and the correlation between the two (eye and mouth) components. Eye component exhibits a lower classification rate if used independently.

CT Image Analysis of Hepatic Lesions Using CAD ; Fractal Texture Analysis

  • Hwang, Kyung-Hoon;Cheong, Ji-Wook;Lee, Jung-Chul;Lee, Hyung-Ji;Choi, Duck-Joo;Choe, Won-Sick
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2007년도 춘계학술발표대회
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    • pp.326-327
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    • 2007
  • We investigated whether the CT images of hepatic lesions could be analyzed by computer-aided diagnosis (CAD) tool. We retrospectively reanalyzed 14 liver CT images (10 hepatocellular cancers and 4 benign liver lesions; patients who presented with hepatic masses). The hepatic lesions on CT were segmented by rectangular ROI technique and the morphologic features were extracted and quantitated using fractal texture analysis. The contrast enhancement of hepatic lesions was also quantified and added to the differential diagnosis. The best discriminating function combining the textural features and the values of contrast enhancement of the lesions was created using linear discriminant analysis. Textural feature analysis showed moderate accuracy in the differential diagnosis of hepatic lesions, but statistically insignificant. Combining textural analysis and contrast enhancement value resulted in improved diagnostic accuracy, but further studies are needed.

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계층 구조와 텍스쳐 특징을 이용한 위성 영상의 분류 (Classification of satellite image using pyramid structure and texture features)

  • 엄기문;김정호;김정기;이쾌희
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1992년도 하계학술대회 논문집 A
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    • pp.449-452
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    • 1992
  • Before performing an adaptive stereo matching using satellite images, classification is needed as a preprocessing step. This paper describes that classification of three land cover types : river, mountain, and agricultural fields. We proposed that classification algorithm using pyramid structure and texture features. Results of applying the proposed algorithm to satellite image improved classification accuracy.

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변형된 지역 Gabor Feature를 이용한 VQ 기반의 영상 검색 (Image Retrieval using VQ based Local Modified Gabor Feature)

  • 신대규;김현술;박상희
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2001년도 하계학술대회 논문집 D
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    • pp.2634-2636
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    • 2001
  • This paper proposes a new method of retrieving images from large image databases. The method is based on VQ(Vector Quantization) of local texture information at interest points automatically detected in an image. The texture features are extracted by Gabor wavelet filter bank, and rearranged for rotation. These features are classified by VQ and then construct a pattern histogram. Retrievals are performed by just comparing pattern histograms between images. Experimental results have shown the robustness of the proposed method to image rotation, small scale change, noise addition and brightness change and also shown the possibility of the retrieval by a partial image.

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PATN: Polarized Attention based Transformer Network for Multi-focus image fusion

  • Pan Wu;Zhen Hua;Jinjiang Li
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제17권4호
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    • pp.1234-1257
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    • 2023
  • In this paper, we propose a framework for multi-focus image fusion called PATN. In our approach, by aggregating deep features extracted based on the U-type Transformer mechanism and shallow features extracted using the PSA module, we make PATN feed both long-range image texture information and focus on local detail information of the image. Meanwhile, the edge-preserving information value of the fused image is enhanced using a dense residual block containing the Sobel gradient operator, and three loss functions are introduced to retain more source image texture information. PATN is compared with 17 more advanced MFIF methods on three datasets to verify the effectiveness and robustness of PATN.

Efficient Content-Based Image Retrieval Methods Using Color and Texture

  • Lee, Sang-Mi;Bae, Hee-Jung;Jung, Sung-Hwan
    • ETRI Journal
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    • 제20권3호
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    • pp.272-283
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    • 1998
  • In this paper, we propose efficient content-based image retrieval methods using the automatic extraction of the low-level visual features as image content. Two new feature extraction methods are presented. The first one os an advanced color feature extraction derived from the modification of Stricker's method. The second one is a texture feature extraction using some DCT coefficients which represent some dominant directions and gray level variations of the image. In the experiment with an image database of 200 natural images, the proposed methods show higher performance than other methods. They can be combined into an efficient hierarchical retrieval method.

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Fast Video Fire Detection Using Luminous Smoke and Textured Flame Features

  • Ince, Ibrahim Furkan;Yildirim, Mustafa Eren;Salman, Yucel Batu;Ince, Omer Faruk;Lee, Geun-Hoo;Park, Jang-Sik
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제10권12호
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    • pp.5485-5506
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    • 2016
  • In this article, a video based fire detection framework for CCTV surveillancesystems is presented. Two novel features and a novel image type with their corresponding algorithmsareproposed for this purpose. One is for the slow-smoke detection and another one is for fast-smoke/flame detection. The basic idea is slow-smoke has a highly varying chrominance/luminance texture in long periods and fast-smoke/flame has a highly varying texture waiting at the same location for long consecutive periods. Experiments with a large number of smoke/flame and non-smoke/flame video sequences outputs promising results in terms of algorithmic accuracy and speed.