Earticle

본 논문에서는 기존의 그랩컷 알고리즘을 이용한 객체 검출의 정확도를 향상시키기 위하여 샤프닝 필터를 이용한 영상의 화질을 개선하는 방법을 제안한다. 그랩컷 알고리즘은 사각 윈도우 범위 내에서 객체 추출에 뛰어난 성능을 보이지만, 객체와 배경의 구분이 뚜렷하지 않은 영상에서는 성능이 떨어지는 단점이 있다. 따라서 본 논문에서는 히스토그램 평활화를 통해 밝기 및 선명도를 보강하고, 샤프닝 필터를 이용하여 객체의 경계를 강화하여 기존의 그랩컷 알고리즘보다 객체와 배경의 색상이 비슷한 영상에서 향상된 객체 추출 결과를 보인다. 개선된 그랩컷 알고리즘을 토대로 문자인식, 실시간 객체추적 등 영상처리 융합 기술에서 향상된 결과를 얻을 수 있다.

In this paper, we proposed image enhancement method using sharpening filter for improving the accuracy of object detection using the existing Grabcut algorithm. GrabCut algorithm is the excellent performance extracting an object within a rectangular window range, but it has the drawback of the inferior performance in image with no clear distinction between background and objects. So, in this paper, reinforcing the brightness and clarity through histogram equalization, and tightening the border of the object using the sharpening filter look better than that extracted result of existing GrabCut algorithm in a similar image of the object and the background. Based on improved Grabcut algorithm, it is possible to obtain an improved result in the image processing convergence technique of character recognition, real-time object tracking and so on.

논문은 영상의 주요한 색채들을 기반으로 하는 histogram-based 영상 검색을 위한 변형된 히스토그램 방법(SHM)을 제안한다. 히스토그램을 기초로 하는 방법은 이행이나 로테이션과 같은 이미지의 기하학적 변화에 영향을 받지 않기 때문에 컬러 영상 검색에 있어 매우 적합하다. 동일하고 비주얼한 정보를 지녔지만 컬러 강도가 변화된 영상의 경우, 전통적인 히스토그램 인터섹션(HIM)을 이용할 경우에는 현저히 성능이 떨어질 수도 있다. 이 문제를 해결하기 위해 변형된 히스토그램 방법(SHM)을 사용하였다. 실험 결과 변형된 히스토그램 방법(SHM)은 기존의 히스토그램 방식에 비해 더 높은 영상 검색 성능을 보였다

This paper proposes the shifted histogram method (SHM), for histogram-based image retrieval based on the dominant colors in images. The histogram-based method is very suitable for color image retrieval because retrievals are unaffected by geometrical changes in images, such as tran-slation and rotation. Images with the same visual information, but with shifted color intensity, may significantly degrade if the conventional histogram intersection method (HIM) is used. To solve this problem, we use the shifted histogram method (SHM). Our experimental results show that the shifted histogram method has significant higher retrieval performance than the standard histogram method.

From the 90's, the image information retrieval methods have been on progress. As good examples of the methods, Conventional histogram method and merged-color histogram method were introduced. They could get good result in image retrieval. However, Conventional histogram method has disadvantages if the histogram is shifted as a result of intensity change. Merged-color histogram, also, causes more process so, it needs more time to retrieve images. In this paper, we propose an improved new method using Adaptive Color Histogram (ACH) in image retrieval. The proposed method has been tested and verified through a number of simulations using hundreds of images in a database. The simulation results have quickly yielded the highly accurate candidate images in comparison to other retrieval methods. We show that ACH's can give superior results to color histograms for image retrieval.

The fire should extinguish as soon as possible because it causes economic loss and loses precious life. In this study, we propose a new atypical fire and smoke detection algorithm using deep learning and color histogram of fire and smoke. First, input frame images obtain from the ONVIF surveillance camera mounted in factory search motion candidate frame by motion detection algorithm and mean square error (MSE). Second deep learning (Faster R-CNN) is used to extract the fire and smoke candidate area of motion frame. Third, we apply a novel algorithm to detect the fire and smoke using color histogram algorithm with local area motion, similarity, and MSE. In this study, we developed a novel fire and smoke detection algorithm applied the local motion and color histogram method. Experimental results show that the surveillance camera with the proposed algorithm showed good fire and smoke detection results with very few false positives.

This paper describes a novel color image enhancement method using fuzzy membership functions. We use three well-known fuzzy membership functions smf, sigmamf,and trapezmf for fuzzy logic. The shape of the membership functions used in fuzzy logic is formed in an versatile fashion in accord with the given parameters. By applying fuzzy process and defuzzification, we acquire a relationship between input and output values. Simulation results indicate that the proposed method yields satisfactory results in all test images.

This paper presents a luminosity conserving and contrast enhancing histogram equalization method for color images. The histogram equalization is one of the ordinary methods employed for enhancing contrast in TV and images for consumer electronics where unwanted subjective deterioration are frequently occur. Although there have been many solutions to overcome the drawback of histogram equalization, however the method is for RGB color space, which is not well suited for different color spaces. To do this, we use fuzzy set to improve histogram equalization. All RGB images are firstly transformed into different color spaces, and particular channels are applied histogram equalization process. From our 20 test LC images show that HSV color space yields the favorable results in MSE by giving the luminosity conserving ability.

The selection of “proper similarity measure” of color histograms is an essential consideration for the success of many methods. The Histogram Quadratic Distance Measure (HQDM) is a metric distance. Till today, this method is supposed to be the better choice, But it holds a disadvantage that it can compute the cross similarity between all elements of histograms. Therefore, computationally it is more expensive. This paper proposes a method that is known as Integrated Histogram Bin Matching (IHBM) which is also a metric method, and overcomes the disadvantages of the HQDM. The proposed IHBM first matches the closest Histogram Bin Pair according to the distance matrix determined from color histograms, which satisfies the Monge condition. After matching histogram bins, the similarity measure is computed as a weighed sum of the similarity between histogram bin pairs, with weights determined by the matching scheme. The proposed IHBM is experimented on 1000 color images and results are compared with the existing methods.

In this paper, we present a method to define a color similarity between color images using Octree-based quantization and similar color integration. The proposed method defines major colors from each image using Octree-based quantization. Two color palettes to consist of major colors are compared based on Euclidean distance and similar color bins between palettes are matched. Multiple matched color bins are integrated and major colors are adjusted. Color histogram based on the color palette is constructed for each image and the difference between two histograms is computed by the weighted Euclidean distance between the matched color bins in consideration of the frequency of each bin. As an experiment to validate the usefulness, we discriminated the same clothing from CCD camera images based on the proposed color similarity analysis. We retrieved the same clothing images with the success rate of 88 % using only color analysis without texture analysis.

Because face has non-rigid structure and is influenced by illumination, we need robust face detection algorithm with the variations of external environments (orientation of lighting and face, complex background, etc.). In this paper we develop a new face detection algorithm to achieve robustness. First we transform RGB color into other color space, in which we can reduce lighting effect much. Second, hierarchical image segmentation technique is used for dividing a image into homogeneous regions. This process uses not only color information, but also spatial information. One of them is used in segmentation by histogram analysis, the other is used in segmentation by grouping. And we can select face region among the homogeneous regions by using facial features.

In this paper, we present a non-static color histogram method to retrieve clothing images that are similar to a query clothing. Given clothing area, our method automatically extracts major colors by using the octree-based quantization approach[16]. Then, a color palette that is composed of the major colors is generated. The feature of each clothing, which can be either a query or a database clothing image, is represented as a color histogram based on its color palette. We define the match color bins between two possibly different color palettes, and unify the color palettes by merging or deleting some color bins if necessary. The similarity between two histograms is measured by using the weighted Euclidean distance between the match color bins, where the weight is derived from the frequency of each bin. We compare our method with previous histogram matching methods through experiments. Compared to HSV cumulative histogram-based approach, our method improves the retrieval precision by 13.7 % with less number of color bins.

In this paper, a novel vehicle detection algorithm is proposed that utilizes the color histogram of the image. The color histogram is used to search the image for regions with shadow, block symmetry, and block non-homogeneity, thereby detecting the vehicle region. First, an integral histogram of the input image is computed to decrease the amount of required computation time for the block color histograms. Then, shadow detection is performed and the block symmetry and block non-homogeneity are checked in a cascade manner to detect the vehicle in the image. Finally, the proposed scheme is applied to both still images taken in a parking lot and an on-road video sequence to demonstrate its effectiveness.

In this study, we propose the new color-based image retrieval technique using the representative colors of images and their ratios to a total image size obtained through color segmentation in HSV color space. Color information of an image is described by reconstructing the color histogram of an image through Gaussian modelling to its representative colors and ratios. And the similarity between two images is measured by histogram intersection. The proposed method is compared with the existing methods by performing retrieval experiments for various 1280 trademark image database.

Since the details in quasi-homogeneous region will be destroyed from the conventional global image enhancement method such as histogram equalization. This defect is caused by the saturation of gray level in equalization process. So the local histogram equalization for each quasi-homogeneous region will be used in order to improve the details in the region itself. To obtain the quasi- homogeneous regions, the original image must be segmented. Here we applied the watershed transform to the interesting image. Since the watershed transform is based on mathematical morphology, therefore, the regions touch can be effectively separated. Hence two adjacent regions which have the similar gray pixels will be split off. The process will be independently applied to three different spectral images. Then three different colors are assigned to each processed image in order to produce a color composite image. By the proposed algorithm, the result image shows the better perception on image details. Therefore, the high efficiency of image classification can be obtained by using this color image.

This paper presents a method for hand segmentation using depth information, and adaptive threshold by means of histogram analysis and color clustering in HSV color model. We consider hand area as a nearer object to the camera than background on depth information. And the threshold of hand color is adaptively determined by clustering using the matching of color values on the input image with one of the regions of hue histogram. Experimental results demonstrate 95% accuracy rate. Thus, we confirmed that the proposed method is effective for hand segmentation in variations of hand color, scale, rotation, pose, different lightning conditions and any colored background.

In this paper, we present a technique to extract the title areas from book cover images. A typical book cover image may contain text, pictures, diagrams as well as complex and irregular background. In addition, the high variability of character features such as thickness, font, position, background and tilt of the text also makes the text extraction task more complicated. Therefore, we propose a two steps efficient method that uses Histogram of Oriented Gradients and color information to find the title areas. Firstly, text localization is carried out to find the title candidates. Finally, refinement process is performed to find the sufficient components of title areas. To obtain the best result, we also use other constraints about the size, ratio between the length and width of the title. We achieve encouraging results of extracted title regions from book cover images which prove the advantages and efficiency of the proposed method.

칼라 히스토그램 인터섹션 방법은 칼라 분포간의 유사도를 측정하는데 널리 사용된다 하지만 이 방법은 칼라 공간을 고정된 칼라수로 양자화시킨 경우에만 유효하므로 칼라 공간에 대한 분할 문제와 양자화 레벨의 결정 문제를 내포하고 있다. 이에, 본 논문에서는 고정 양자화된 칼라 분포뿐만 아니라 적응적 양자화되어 상이한 칼라분포를 갖는 영상간의 정합에 적용 가능한 확장 칼라 히스토그램 인터섹션 방법을 제안한다. 제안된 방법은 생산자가 생산된 상품을 소비자에게 공급하는 동안 생산효율을 계산하여 경제적 이익을 최대화 시키기 위한 생산자-소비자 모델로 간주되어질 수 있다 실험을 통해 우리는 제안된 방법이 두 칼라 분포간의 유사도를 효과적으로 측정할 수 있음을 확인하였다

본 논문에서는 컬러 히스토그램과 MPEG-7의 EHD(Edge Histogram Descriptor)를 이용한 영상 검색 기법을 제안한다. EHD 알고리즘은 에지의 기울기 분포를 수집하여 유사 영상을 검색하는데 사용할 수 있다. 하지만 영상의 색상 정보는 고려하지 않고 에지의 기울기만으로 검색하면 색상 정보에는 취약한 면을 보인다. 이를 보완하기 위해서 컬러 히스토그램을 이용해 특징을 추출하여 유사 영상인지 판단한다. 기존 EHD의 취약점을 보이고 컬러 히스토그램을 이용하여 이를 보완할 수 있는 기법을 제안한다.

In this paper, we propose an image retrieval method using an EHD (Edge Histogram Descriptor) of MPEG-7 and the color histogram. The EHD algorithm can be used to collect the gradient of edge distribution and to find a similar image. However, if you only search the edge gradient without considering the image color, the color shows a weakness. In order to overcome this problem, we use the color histogram and extract the feature to determine whether a similar image. The proposed method shows that the weakness of existing EHD can be overcome by using the color histogram.

장면 전환 검출은 비디오를 구조화하고 비디오 연산을 수행하는데 필수적인 요소이다. 본 문에서는 기존에 제시된 컬러 히스토그램과 x2 히스토그램을 합성한 새로운 방법의 장면 전환 검출 방법을 제시한다. 특히 이 방법은 비디오 프레임들의 차이값 추출 방법들의 단점을 극복하고 장점을 최대한 활용한 방법이다. 그리고 매우 빠르게 화면이 지나가는 급진적 장면 전환 검출에서 느리게 화면이 진행하는 점진적 장면 전환 검출까지 모두 검출할 수 있다. 실험을 통해서 본 방법이 기존의 방법보다 우수하다는 것을 보여주고 있다.

본 논문은 컬러 히스토그램과 ‘컬러 텍스쳐’을 이용하는 새로운 내용기반 영상 검색 기법을 제안한다. 제안한는 방법은 영상의 컬러 히스토그램을 k-means 군집화하여 얻은 컬러 벡터로 히스토그램을 대표하고, 각 대표 컬러 벡터를 중심으로 화소 색상과의 거리를 이용해 컬러 텍스처를 만든다. 그러므로, 컬러 텍스처란 영상의 컬러 히스토그램에 의해 두드러지는 텍스처 성분을 의미하며 본 논문에서는 컬러 텍스처를 Gaussian Markov Random Field (GMRF) 모델로 해석한다. 제안하는 알고리듬은 영역화와 같은 기하학적 정보를 추출하는 과정이 없으므로 고속의 검색에 적합하며, 기존의 컬러 히스토그램만을 이용한 기법이나 영상의 밝기 성분에서 나타나는 텍스처를 이용한 방법에 비해 효과적인 검색 결과를 나타낸다.

In this paper, a color image retrieval algorithm is proposed based on color histogram and color texture. The representative color vectors of a color image are made from k-means clustering of its color histogram, and color texture is generated by centering around the color of pixels with its color vector. Thus the color texture means texture properties emphasized by its color histogram, and it is analyzed by Gaussian Markov Random Field (GMRF) model. The proposed algorithm can work efficiently because it does not require any low level image processing such as segmentation or edge detection, so it outperforms the traditional algorithms which use color histogram only or texture properties come from image intensity.