• 반도체디스플레이기술학회지
• /
• 제18권4호
• /
• pp.110-115
• /
• 2019

Blur detection and segmentation play vital role in many computer vision applications. Among various methods, local binary pattern based methods provide reasonable blur detection results. However, in conventional local binary pattern based methods, the blur map is computed by using a fixed threshold irrespective of the type and level of blur. It may not be suitable for images with variations in imaging conditions and blur. In this paper we propose an effective method based on local binary pattern with adaptive threshold for blur detection. The adaptive threshold is computed based on the model learned through the multinomial logistic regression. The performance of the proposed method is evaluated using different datasets. The comparative analysis not only demonstrates the effectiveness of the proposed method but also exhibits it superiority over the existing methods.

• 반도체디스플레이기술학회지
• /
• 제19권3호
• /
• pp.7-11
• /
• 2020

Enormous methods have been proposed for the detection and segmentation of blur and non-blur regions of the images. Due to the limited available information about the blur type, scenario and the level of blurriness, detection and segmentation is a challenging task. Hence, the performance of the blur measure operators is an essential factor and needs improvement to attain perfection. In this paper, we propose an effective blur measure based on the local binary pattern (LBP) with the adaptive threshold for blur detection. The sharpness metric developed based on LBP uses a fixed threshold irrespective of the blur type and level which may not be suitable for images with large variations in imaging conditions and blur type and level. Contradictory, the proposed measure uses an adaptive threshold for each image based on the image and the blur properties to generate an improved sharpness metric. The adaptive threshold is computed based on the model learned through the support vector machine (SVM). The performance of the proposed method is evaluated using a well-known dataset and compared with five state-of-the-art methods. The comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all the methods.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2019년도 춘계학술발표대회
• /
• pp.431-434
• /
• 2019

In the case of blur processing, it is common to use a tool such as Photoshop to perform processing manually. However, it can be considered very efficient if the blur is processed at one time in the object detection process. Based on this point, we can use the object detection model to blur the objects during the process. The object detection is performed by using the YOLO [3] model. If such blur processing is used, it may be additionally applied to streaming data of video or image.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2021년도 추계학술대회
• /
• pp.109-111
• /
• 2021

최근 라이트 필드 카메라를 통한 컴퓨터 비전 연구가 활발히 진행되고 있다. 라이트 필드 카메라에서는 공간정보를 가지고 있기 때문에, depth map estimation, super resolution, 3D object detection 과 같은 분야에서 다양한 연구가 진행되고 있다. 본 논문에서는 라이트필드 카메라를 통해 취득되는 7×7 배열의 이미지를 통해 blur 영상에서 객체를 검출하기 위한 방법을 제안한다. 기존의 카메라에서 취약한 blur 영상을 라이트 필드 카메라를 통하여 검출한다. 제안하는 방법은 SSD 알고리즘을 사용하여 실제 라이트 필드 카메라에서 취득한 blur 영상을 사용하여 성능평가를 수행한다

• Journal of Information Technology Applications and Management
• /
• 제25권2호
• /
• pp.41-52
• /
• 2018

This paper presents a method for accurate camera self-calibration based on SIFT Feature Detection and image quality assessment. We performed image quality assessment to select high quality images for the camera self-calibration process. We defined high quality images as those that contain little or no blur, and have maximum contrast among images captured within a short period. The image quality assessment includes blur detection and contrast assessment. Blur detection is based on the statistical analysis of energy and standard deviation of high frequency components of the images using Discrete Cosine Transform. Contrast assessment is based on contrast measurement and selection of the high contrast images among some images captured in a short period. Experimental results show little or no distortion in the perspective view of the images. Thus, the suggested method achieves camera self-calibration accuracy of approximately 93%.

• 방송공학회논문지
• /
• 제25권1호
• /
• pp.58-66
• /
• 2020

영상 촬영 시 객체의 움직임, 탈초점(Out-of-focus) 등의 이유로 영상 흐려짐 현상이 빈번하게 발생하며, 이 과정에서 선명한 영역의 고주파 성분이 급격하게 감소하게 된다. 이러한 성질을 바탕으로, 본 논문에서는 질감 특징 표현자를 사용하여 별도의 주파수 변환 과정 없이 고주파 성분을 추정하고, 이를 바탕으로 흐려진 영역을 검출하는 방법을 제안한다. 제안하는 방법은 학습 기반 질감 표현자와 유역(Watershed) 기반 질감 표현자를 함께 이용하여 다양한 환경에서도 흐려진 영역을 검출할 수 있다. 또한, 흐려짐을 검출하는 최소 단위를 화소 단위에서 영역 단위로 확장하여 처리 속도를 향상시키고, 영상 보정 기법을 이용하여 흐려짐 검출 성능을 개선하였다. 실험 결과는 제안하는 방법이 기존의 흐려짐 검출 방법 대비 성능이 향상되었음을 보여준다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
• /
• pp.177-179
• /
• 2005

This paper suggests a high-speed blurred blob image inspection algorithm. When we inspect some products using high-resolution camera, the detected blob images usually have severe blur. And the blur makes it hard to detect an object. There are many blur-processing algorithms, but most of them have no real-time property for high-speed applications at all. In this paper, an MMX technology based algorithm is suggested. The suggested algorithm was found to be effective to detect the blurred blob images via many simulations and long time real-plant experiments.

• ETRI Journal
• /
• 제42권3호
• /
• pp.411-419
• /
• 2020

In real-world intelligent transportation systems, accuracy in vehicle license plate detection and recognition is considered quite critical. Many algorithms have been proposed for still images, but their accuracy on actual videos is not satisfactory. This stems from several problematic conditions in videos, such as vehicle motion blur, variety in viewpoints, outliers, and the lack of publicly available video datasets. In this study, we focus on these challenges and propose a license plate detection and recognition scheme for videos based on a temporal matching prior network. Specifically, to improve the robustness of detection and recognition accuracy in the presence of motion blur and outliers, forward and bidirectional matching priors between consecutive frames are properly combined with layer structures specifically designed for plate detection. We also built our own video dataset for the deep training of the proposed network. During network training, we perform data augmentation based on image rotation to increase robustness regarding the various viewpoints in videos.

• ETRI Journal
• /
• 제38권3호
• /
• pp.502-509
• /
• 2016

Feature detection and description are key ingredients of common image processing and computer vision applications. Most existing algorithms focus on robust feature matching under challenging conditions, such as inplane rotations and scale changes. Consequently, they usually fail when the scene is blurred by camera shake or an object's motion. To solve this problem, we propose a new feature description algorithm that is robust to image blur and significantly improves the feature matching performance. The proposed algorithm builds a feature descriptor by considering the integral projection along four angular directions ($0^{\circ}$, $45^{\circ}$, $90^{\circ}$, and $135^{\circ}$) and by combining four projection vectors into a single highdimensional vector. Intensive experiment shows that the proposed descriptor outperforms existing descriptors for different types of blur caused by linear motion, nonlinear motion, and defocus. Furthermore, the proposed descriptor is robust to intensity changes and image rotation.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2023년도 제67차 동계학술대회논문집 31권1호
• /
• pp.93-95
• /
• 2023

본 논문에서는 장편의 드라마나 영화에서 스토리 기반의 축약된 요약본을 자동으로 제작하기 위해 미디엄 숏(medium shot) 크기의 숏(shot)들을 추출하기 위한 방법을 고려한다. 미디엄 숏 정도의 크기는 보통 인물에 중심을 둔 숏들로 인물들 간의 관계에서 특히 대사나 표정으로 내용을 전달하기 위한 목적으로 적극 권장된다. 비디오 검색을 위한 인덱싱에서 신(scene) 전환 검출 및 숏 경계 검출, 그리고 이미지에서 심도와 초점기반의 화질 및 피사체 추출 등을 위해 전통적인 신호/영상처리 기법의 활용에서부터 최근의 기계학습 접목 등 다양한 연구들이 진행되고 있다. 영상문법에 근거하여 편집된 영상물에서 미디엄 숏 정도 크기의 숏들을 추출하여 배열한다면 어느 정도 원본 내용을 충실히 전달할 수 있는 축약된 요약본을 제작할 수 있다는 가정하에 해당 샷들을 블러(blur) 기반으로 검출하기 위해 이와 관련된 키워드들을 기반으로 기존 연구들을 살펴보고 적용 방법을 모색한다.