• Asian Pacific Journal of Cancer Prevention
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• 제17권11호
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• pp.4869-4873
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• 2016

Colonoscopy is currently the best technique available for the detection of colon cancer or colorectal polyps or other precursor lesions. Computer aided detection (CAD) is based on very complex pattern recognition. Local binary patterns (LBPs) are strong illumination invariant texture primitives. Histograms of binary patterns computed across regions are used to describe textures. Every pixel is contrasted relative to gray levels of neighbourhood pixels. In this study, colorectal polyp detection was performed with colonoscopy video frames, with classification via J48 and Fuzzy. Features such as color, discrete cosine transform (DCT) and LBP were used in confirming the superiority of the proposed method in colorectal polyp detection. The performance was better than with other current methods.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.698-701
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• 2006

Remotely sensed data have been utilized efficiently for damage detection immediately after the natural disaster since they provide valuable information on land cover change due to spatial synchronization and multitemporal observation over large areas. Damage information obtained at an early stage is important for rapid emergency response and recovery works. Many useful techniques to analyze the characteristics of the pre- and post-event satellite images in large-scale damage detection have been successfully investigated for emergency management. Since high-resolution satellite images provide a wealth of information on damage occurred in urban areas, they are successfully utilized for damage detection in urban areas. In this research, a method to perform automated damage detection is proposed based on the differences of the textural characteristics in pre- and post- high resolution satellite images.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.85-88
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• 2009

In this paper, we propose a DRF-based object detection method using the object adaptive patch in the satellite imagery. It is a Discriminative Random Fields (DRF) based work, so the detection is done by labeling to the possible patches in the image. For the feature information of each patch, we use the multi-scale and object adaptive patch and its texton histogram, instead of using the single scale and fixed grid patch. So, we can include contextual layout of texture information around the object. To make object adaptive patch, we use "superpixel lattice" scheme. As a result, each group of labeled patches represents the object or object's presence region. In the experiment, we compare the detection result with a fixed grid scheme and shows our result is more close to the object shape.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2014년도 추계학술발표대회
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• pp.520-522
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• 2014

Due to the availability of easy-to-use and powerful image editing tools, the authentication of digital images cannot be taken for granted and it gives rise to non-intrusive forgery detection problem because all imaging devices do not embed watermark. Forgery detection plays an important role in this case. In this paper, an effective framework for passive-blind method for copy-move image forgery detection is proposed, based on Gabor filter which is robust to illumination, rotation invariant, robust to scale. For the detection, the suspicious image is selected and Gabor wavelet is applied from whole scale space and whole direction space. We will extract the mean and the standard deviation as the texture features and feature vectors. Finally, a distance is calculated between two textures feature vectors to determine the forgery, and the decision will be made based on that result.

• Investigative Magnetic Resonance Imaging
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• 제25권2호
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• pp.93-100
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• 2021

Purpose: (1) To evaluate the trabecular pattern at the femoral attachment of the posterior cruciate ligament (PCL) in patients with a PCL injury; (2) to analyze bone microarchitecture by applying gray level co-occurrence matrix (GLCM)-based texture analysis; and (3) to determine if there is a significant relationship between bone microarchitecture and posterior instability. Materials and Methods: The study included 96 patients with PCL tears. Trabecular patterns were evaluated on T2-weighted MRI qualitatively, and were evaluated by GLCM texture analysis quantitatively. The grades of posterior drawer test (PDT) and the degrees of posterior displacement on stress radiographs were recorded. The 96 patients were classified into two groups: acute and chronic injury. And 27 patients with no PCL injury were enrolled for control. Pearson's correlation coefficient and one-way ANOVA with Bonferroni test were conducted for statistical analyses. This protocol was approved by the Institutional Review Board. Results: A thick and anisotropic trabecular bone pattern was apparent in normal or acute injury (n = 57/61;93.4%), but was not prominent in chronic injury and posterior instability (n = 31/35;88.6%). Grades of PDT and degrees of posterior displacement on stress radiograph were not correlated with texture parameters. However, the texture analysis parameters of chronic injury were significantly different from those of acute injury and control groups (P < 0.05). Conclusion: The trabecular pattern and texture analysis parameters are useful in predicting posterior instability in patients with PCL injury. Evaluation of the bone microarchitecture resulting from altered biomechanics could advance the understanding of PCL function and improve the detection of PCL injury.

• ETRI Journal
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• 제45권5호
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• pp.847-861
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• 2023

Dynamic object detection is essential for ensuring safe and reliable autonomous driving. Recently, light detection and ranging (LiDAR)-based object detection has been introduced and shown excellent performance on various benchmarks. Although LiDAR sensors have excellent accuracy in estimating distance, they lack texture or color information and have a lower resolution than conventional cameras. In addition, performance degradation occurs when a LiDAR-based object detection model is applied to different driving environments or when sensors from different LiDAR manufacturers are utilized owing to the domain gap phenomenon. To address these issues, a sensor-fusion-based object detection and classification method is proposed. The proposed method operates in real time, making it suitable for integration into autonomous vehicles. It performs well on our custom dataset and on publicly available datasets, demonstrating its effectiveness in real-world road environments. In addition, we will make available a novel three-dimensional moving object detection dataset called ETRI 3D MOD.

• 한국멀티미디어학회논문지
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• 제16권2호
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• pp.169-179
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• 2013

변화 검출 알고리즘은 두 영상 프레임을 비교하여 변화를 가져온 유입된 외부 객체의 영역을 검출 하는 것이다. 본 논문은 두 영상 간의 밝기 변화 요인을 유입 객체에 의한 요인, 객체의 표면에서 주변으로 반사된 반사광 및 그림자에 의한 요인, 내재된 잡음에 의한 요인 등으로 분류하고, 이를 바탕으로 유입된 객체의 영역을 정확하게 검출하는 새로운 변화 검출 방법을 제안한다. 변화 요인의 분류와 객체 영역의 검출을 위하여, 우선 두 영상 간의 밝기 변화 값의 히스토그램을 분석하여 다수의 임계값을 추정한다. 다음으로 유입 객체의 경계 후보 화소를 추정하고, 추정된 객체 경계 후보 화소와 임계값을 이용하여 영상을 다수의 영역으로 분할한다. 최종적으로 분할된 각 영역의 텍스처를 두 영상에서 각각 구하고 이들 간의 유사도를 이용하여 각 영역이 외부 유입 객체 영역인지 아닌지를 판단한다. 다른 조명 변화 특성을 가지는 다수의 영상에서 수행된 실험을 통하여 제안한 방법이 유입된 객체의 영역을 강인하게 검출함을 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권4호
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• pp.1807-1823
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• 2016

Local Binary Pattern (LBP) and its variants have powerful discriminative capabilities but most of them just consider each LBP code independently. In this paper, we propose sub oriented histograms of LBP for smoke detection and image classification. We first extract LBP codes from an image, compute the gradient of LBP codes, and then calculate sub oriented histograms to capture spatial relations of LBP codes. Since an LBP code is just a label without any numerical meaning, we use Hamming distance to estimate the gradient of LBP codes instead of Euclidean distance. We propose to use two coordinates systems to compute two orientations, which are quantized into discrete bins. For each pair of the two discrete orientations, we generate a sub LBP code map from the original LBP code map, and compute sub oriented histograms for all sub LBP code maps. Finally, all the sub oriented histograms are concatenated together to form a robust feature vector, which is input into SVM for training and classifying. Experiments show that our approach not only has better performance than existing methods in smoke detection, but also has good performance in texture classification.

• Journal of information and communication convergence engineering
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• 제4권4호
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• pp.174-179
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• 2006

In this paper we present a text detection method inspired by wavelet packet analysis and improved fuzzy clustering algorithm(IAFC).This approach assumes that the text and non-text regions are considered as two different texture regions. The text detection is achieved by using wavelet packet analysis as a feature analysis. The wavelet packet analysis is a method of wavelet decomposition that offers a richer range of possibilities for document image. From these multi scale features, we adapt the improved fuzzy clustering algorithm based on the unsupervised learning rule. The results show that our text detection method is effective for document images scanned from newspapers and journals.

• Journal of Multimedia Information System
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• 제1권2호
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• pp.119-126
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• 2014

We propose a bicycle detection system riding on people based on modified projected local binary pattern(PLBP) for vision based intelligent vehicles. Projection method has robustness for rotation invariant and reducing dimensionality for original image. The features of Local binary pattern(LBP) are fast to compute and simple to implement for object recognition and texture classification area. Moreover, We use uniform pattern to remove the noise. This paper suggests that modified LBP method and projection vector having different weighting values according to the local shape and area in the image. Also our system maintains the simplicity of evaluation of traditional formulation while being more discriminative. Our experimental results show that a bicycle and motorcycle riding on people detection system based on proposed PLBP features achieve higher detection accuracy rate than traditional features.