Hybrid Features of Mask Generated with Gabor Filter for Texture Analysis and Sobel Operator for Image Regions Segmentation Using K-Means Technique

To make the image easily represented for more analysis and processing the segmentation procedure is required, where the image is portioned into its formed regions using some segmentation techniques based on features extraction. In this paper, a proposed procedure for finding the regions that formed the image is achieved based on hybrid features in two different components of different two colors spaces L*a*b* and RGB segmented by the k-means method. The hybrid features which comprise the mask segmentation are a combination of texture image characterization extracted by the Gabor filter and gradient image intensity by the Sobel operator after image quality enhancement by applying wiener filter noise reduction and contrast enhancement using Contrast limited adaptive equalization (CLAHE). Some statistical metrics are used for evaluating the performance of the proposed work stages

Aplikasi Metode Analisis Fraktal dan K-Means Clustering untuk Identifikasi Retinopati Diabetik dan Retinopati Hipertensi menggunakan Citra Fundus Mata

Penyakit retinopati diabetik dan retinopati hipertensi dapat menyebabkan terjadinya kelainan vaskular pada pembuluh darah retina. Kelainan ini menimbulkan pola-pola unik yang muncul pada citra fundus mata dan memberikan ciri pada dimensi fraktal dan lakunaritas citra tersebut. Dalam studi ini, nilai dimensi fraktal dan lakunaritas citra fundus mata ditelaah dan dijadikan sebagai parameter pengelompokan data dengan metode k-means clustering untuk identifikasi penyakit retinopati diabetik dan retinopati hipertensi. Sebelum analisis dilakukan, citra fundus mata terlebih dahulu melalui tahap preprocessing agar dapat diolah lebih lanjut. Penghitungan dimensi fraktal dilakukan dengan metode box counting dan lakunaritas dihitung dengan metode gliding box. Pengelompokan data dengan metode k-means dilakukan dengan algoritma Lloyd. Hasil yang diperoleh menunjukkan bahwa metode yang digunakan memiliki akurasi sebesar 96.67%, dengan 29 identifikasi benar dari 30 data masukan yang diberikan. Hasil ini menunjukkan bahwa metode yang digunakan prospektif untuk diaplikasikan dalam identifikasi penyakit retinopati diabetik dan retinopati hipertensi

Human Tracking and Profiling for Risk Management

Infectious viruses are conveyed via respiratory droplets produced by an infected person when they speak, sneeze, or cough. So, to combat virus transmission, the World Health Organization (WHO) has imposed severe regulations such as mandatory face mask use and social segregation in public spaces. The Human Tracking and Profiling for Risk Management System (HTPRM) is an online application that identifies the risk associated with failing to follow proper health practices. This proposed approach, which is divided into four components, utilizes You Only Live Once YOLO (V3) to detect facemask danger, which would be determined based on two factors: wearing the face mask properly and the type of mask (Surgical, k95, homemade, and bare). The second phase is to use Open CV and SSDMobilenet to evaluate the value of a one-meter space (Social Distance) between people. The system recognizes the maximum number of individuals that can be in the vicinity of the specific hall that uses YOLO( V3) and image processing as the third procedure. In the last processing, the system identifies each persons behavior, classifies it as uncommon or not, and calculates the risk associated with each category. Finally, the system computes the overall risk and generates a warning alarm to notify the user that they are in a dangerous scenario

Image Processing Algorithm for Virtual Chromoendoscopy (Tone Enhancement) in Clinical Decision Support System

Virtual chromoendoscopy is one of the demanded modern direction for increasing the diagnostic value of medical images. The most famous technics are I-SCAN and FICE, and image-processing algorithms that can leverage the unique characteristics of different spectral response in the endoscopic image are actual and relevant, such as TRI-SCAN. The new method of virtual chromoendoscopy based on digital processing of images obtained in the white light was proposed. The main feature of method is local nonlinear processing each color plane. Proposed method was tested on open database of endoscopic images KVASIR. Experiment shows that method can effectively improve color contrast. Proposed method realizes visual effect corresponding visual effect of images obtaining with modern technologies of virtual chromoendoscopy (I-SCAN and FICE) and give possibility to get visual effect superior than modern method of tone enhancement TRI-SCAN

A Single Image Defogging Method Using Dark Channel Prior and

이 논문은 단일 영상에서 안개를 제거하는 기법(defogging method)을 연구한 논문이다. 기존의 DCP (dark channel prior) 기법에 히스토그램 분석을 도입하여 비용함수를 제안하였다. 또한 안개 제거 과정 에서 발생할 수 있는 색정보의 왜곡을 방지하기 위해 HSI 색공간에서 색상(hue)와 채도(saturation)를 계산하여 비용함수에 적용하였다. 제안한 안개 제거 기법은 DCP 비용과 지역적 히스토그램 균등화 비용을 이용하여 전달량(transmiss -ion) 추정 함수를 모델링하였다. 이를 통해 기존의 DCP 기법에서 볼 수 있는 안개 제거 결과의 저대 비 현상 및 후광 현상(halo effect)을 개선할 수 있었다. 또한 안개 제거 전후 색공간 채널별 변화 특성 을 HSI 색공간에서 분석하여 얻은 색상 및 채도 가중치를 적용함으로써 안개 제거 영상에서 발생하는 색상 및 채도의 왜곡을 방지할 수 있었다. 이 논문에서 제안한 방법은 기존의 방법들에 비해 전달량 추정 성능이 우수하며, 특히 안개 제거 과 정에서 안개 아닌 무채색 물체의 영역에서 전달량이 필요 이상으로 크게 추정됨으로써 발생하는 색정 보 왜곡을 방지할 수 있음을 보여준다.제 1 장 서 론 ............................................................. 1 제 2 장 안개 모델링을 이용한 안개 제거 방법 .................. 4 2.1 안개 모델링 ....................................................... 4 2.2지역적 대비 향상 방법 .......................................... 5 2.3DCP를 이용한 방법 ............................................. 11 제 3 장 제안한 안개 제거 방법 ..................................... 17 3.1 안개값 추정 ...................................................... 17 3.2 채도 보정 가중치 ............................................... 20 3.3 안개 제거를 위한 비용 함수 ................................. 24 3.3.1 DCP 비용함수 항 ....................................... 24 3.3.2 히스토그램 균등화 비용함수 항 .................... 24 3.3.3 색상 가중치 .............................................. 24 3.4 전달량 정제 ..................................................... 26 제 4 장 실험 및 고찰 ................................................. 29 제 5 장 결 론 .......................................................... 35 참고 문헌 ................................................................ 3

Low-Light Image Enhancement Based on Guided Image Filtering in Gradient Domain

We propose a novel approach for low-light image enhancement. Based on illumination-reflection model, the guided image filter is employed to extract the illumination component of the underlying image. Afterwards, we obtain the reflection component and enhance it by nonlinear functions, sigmoid and gamma, respectively. We use the first-order edge-aware constraint in the gradient domain to achieve good edge preserving features of enhanced images and to eliminate halo artefact effectively. Moreover, the resulting images have high contrast and ample details due to the enhanced illumination and reflection component. We evaluate our method by operating on a large amount of low-light images, with comparison with other popular methods. The experimental results show that our approach outperforms the others in terms of visual perception and objective evaluation

Improved DCP Haze Removal Method Using Entropy of Depth Information

It is important to haze removal in image processing because it makes it difficult to analyze color information and edge information in marine and aeronautical fields, which are sensitive to meteorological conditions. The DCP(Dark Channel Prior), which estimates the haze using the minimum values of R, G, B information, is the most widely used algorithm to remove haze from the current image information. The DCP algorithm is a method for estimating the amount of haze by using the minimum value of R, G, B information on a local area selected stepwise from a given fog image, and estimating the transmission map to remove the haze. At this time, the haze is estimated from the edge of the boundary to the local area, so that the block artifact inevitably occurs. Therefore, the image analysis performance is not high near the edge. This paper proposes a haze removal method using an improved transmission map to reduce the block artifact occurred during DCP process which is a representative algorithm for haze removal. The proposed method estimates depth information and edge information in a dark channel using entropy, which are stochastic properties, and predicts the part where block artifact occurs. Using the adaptive window according to the entropy value in the predicted part, new transmission map is obtained, which can reduce the block artifact in the edge of the boundary containing the depth information. In the conclusion, we can obtain improved fog removal image than transmission map the existing DCP algorithm by using the new transmission map.Abstract 1. 서론 1 1.1 연구 배경 1 1.2 논문의 구성 3 2. DCP를 기반으로 한 안개제거 4 2.1 안개 모델링 4 2.2 DCP 기반의 단일영상 안개 제거 방법 6 2.3 기존 DCP 안개제거 알고리즘의 문제점 12 3. 제안하는 안개제거 방법 16 3.1 엔트로피를 이용한 심도가 반영된 경계선 정보 추출 18 3.2 적응적인 윈도우를 적용한 전달량 추정에 의한 안개제거 23 4. 실험 및 고찰 29 5. 결론 37 참고 문헌Maste