Foreground Area Research Articles

EXAMPLES OF USE OF THE CERAMIC MATERIALS ON THE BASIS OF THE INNOVATIVE TECHNOLOGIES

The author considers the latest techniques of using the ceramic finishing agents on the basis of improvement of the technological, operational and aesthetic characteristics of the material. The author also identifies the foreground areas in the field of development and application of the ceramic tile.

Linear colour correction for multiple illumination changes and non‐overlapping cameras

Many image processing methods, such as techniques for people re-identification, assume photometric constancy between different images. This paper addresses the correction of photometric variations based upon changes in background areas to correct foreground areas. We assume a multiple light source model where all light sources can have different colors and will change over time. In training mode we learn per-location relations between foreground and background color intensities. In correction mode we apply a double linear correction model based on learned relations. This double linear correction includes a dynamic local illumination correction mapping as well as an inter-camera mapping. We evaluate our illumination correction by computing the similarity between two images based on the Earth Mover’s Distance. We compare the results to a representative auto-exposure (AE) algorithm found in the recent literature plus a color correction one based on the inverseintensity chromaticity. Especially in complex scenarios our method outperforms these state-of-the-art algorithms.

Object-based image segmentation and retrieval for texture images

In this paper, object retrieval techniques based on texture segmentation are proposed for use in local region texture images. In order to perform the segmentation of a local region texture image in this study, three texture features were first extracted: coarseness, contrast and directionality. For image retrieval, texture features for visual perception (TFVP), colour histogram for K-mean (CHKM) and shape features for principal moments of inertia (SFPMI) features of the object were extracted after segmentation. TFVP, CHKM and SFPMI are the texture, colour features and shape features, respectively, of the object region in an image; they are used for image retrieval. In order to more precisely represent the accuracy of this proposed method, the image segmentation/image retrieval results are compared with other methods. It was shown that the segmentation performance of the proposed method evaluated using misclassification error (ME), relative foreground area error (RAE), modified Hausdorff distance (MHD) and AVE is better than other methods. The results clearly show that the performances of the proposed method for local region texture image retrieval are significantly superior to those of the other methods for global region texture image retrieval. The object segmentation in this study can accurately segment the image object, thereby obtaining image retrieval results with much higher precision.

Object Separation Using Saliency Algorithm

In this paper we are examining saliency algorithm with utilization of a straight forward image signature or image descriptor. With the assistance of image signature we can summarize the foreground area of an image inside the zone of signal mixing. At this point with the assistance of experimentation we are going to figure out whether the rough foreground area and the areas which are not outwardly clear overlap with one another. The proposed work will be fulfilled with the assistance of saliency algorithm utilizing image signature. The procedure includes division of all the item scenes and the dark image, this partition methodology is called Figure Ground Separation or FGS. Human intelligence can decipher image and can perform reckoning effectively yet doing so with the assistance of electrical gadgets will be a difficult task for analysts. Using saliency algorithm we are recreating brain tumor discovery.Keywords: Brain Tumor, Image Signature, Saliency, Saliency Method

GPGPU를 이용한 Grabcut의 수행 속도 개선 방법에 관한 연구

Abstract In this paper, the processing speed of Grabcut algorithm in order to efficiently improve the GPU (Graphics Processing Unit) for processing the data from the method. Grabcut algorithm has excellent performance object detection algorithm. Grabcut existing algorithms to split the foreground area and the background area, and then background and foreground K-cluster is assigned a cluster. And assigned to gradually improve the results, until the process is repeated. But Drawback of Grabcut algorithm is the time consumption caused by the repetition of clustering. Thus GPGPU (General-Purpose computing on Graphics Processing Unit) using the repeated operations in parallel by processing Grabcut algorithm to effectively improve the processing speed of the method. We proposed method of execution time of the algorithm reduced the average of about 95.58%. Key Words : GPU, GPGPU, Parallel transaction, Graphcut, GMM, Grabcut * 본 논문은 2014년 광운대학교 교내 학술 연구비 지원에 의하여 연구되었음Received 18 September 2014, Revised 25 October 2014Accepted 20 November 2014Corresponding Author: Sang Hun Lee(Kwangwoon University) Email: leesh58@kw.ac.krⒸ The Society of Digital Policy & Management. All rights reserved. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is ISSN: 1738-1916 properly cited.

Moving Shadow Removal Algorithm Based on HSV Color Space

In order to accurately separate a moving object from its shadow in a monitoring scene, this paper proposes a algorithm, which combines Multi-frame Average method for building background and HSV color space. First, the multi-frame average is used for setting up the background model. Second, the current frame and the background model are converted to HSV color space. In the foreground area, the values of brightness and saturation are smaller than that of the background, while the colors basically remain same. Just using these characters, the shadow is detected and eliminated. Finally, the algorithm is applied in videos with different monitoring scenes from the standard video library, such as highway, laboratory and campus, etc, to verify its effectiveness. The experiment results show that the algorithm has better accuracy, reliability and robustness than the compared algorithm. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4991

Depth-Assisted Frame Rate Up-Conversion for Stereoscopic Video

In this letter, we propose a depth-assisted frame rate up-conversion (DA-FRUC) scheme which finds applications in 3D video processing. By considering the depth cue in video plus depth representation, we categorize the blocks of the interpolated frame as depth-continuous and depth-discontinuous groups. The motion vector (MV) outliers of the depth continuous blocks are then detected and corrected by layer-constrained MV refinement method. Moreover, a depth-based adaptive interpolation and block segmentation method is proposed to deal with the disocclusion and occlusion at the boundary of the foreground area. Experimental results show that the proposed method achieves better subjective and objective performance for the interpolated color frames. Additionally, the interpolated depth frames obtained by the proposed method is more accurate, which benefit the video quality in view synthesis.

Background Subtraction With Video Coding

The classic Gaussian mixture model is based on the statistical information of every pixel; it is not robust to light changes. Before analysing every pixel in videos, it must be decoded to raw videos. In this letter, the method combining video coding and the Gaussian mixture model together is proposed. We use intra mode and motion vectors to find the foreground macroblock, then add one overhead flag in the compressed video to indicate it. In the decoder, we just decode possible foreground areas and detect moving objects in these areas. In our experiments, we test this method on two datasets, both of them with unique, dynamic, illumination conditions. Results show that the proposed method is effective to detect moving objects and easily assemble to current automated video surveillance systems.

A Simple Hole Filling Algorithm for Binary Cell Images

Filling holes in binary images is often required during medical image processing and analysis. However, traditional hole filling algorithms for medical images expose some disadvantages such as possible edge degradations and relatively low efficiency. To overcome such limits, a hole filling algorithm for binary cell images based on largest connected region extraction is proposed in this paper. Since there are less pixels for foreground areas in usual binary cell images, the holes in the binary images can be simply filled by extracting & filling the largest connected region in correspondent negative images together with a following OR operation between the obtained result image and the original binary image. Such an algorithm based on largest connected region extraction nearly has no effect on edge information of the ROI(region of interested) with high accuracy and efficiency. The final experiments show its validity and advantage over the traditional morphology methods for hole filling in binary cell images. Thus the presented method is of a considerable reference value for binary image processing.

Inland Ship Recognition Algorithm Based on Neighboring Static Region Substitution for Background Updating

Considering the particular scene that the moving ships in Inland River are surrounded by the nearly static water, this paper presents a method that updates the background by adopting the neighboring static region to replace the dynamic areas. By making the difference between the current frame and the background image to get the foreground area, and using the region growing algorithm to shape a complete ship region, we can get the coordinate, the center point and the direction of its movement. To obtain its mean gray value to replace the foreground dynamic region, we choose a static adjacent background at front-end of the ship. Then we adopt a variable weight method to update background in order to prepare for the next image proceeding background difference calculation. As the loop goes on, we can make fast and accurate ship image identification from the surveillance streaming media.

Moving object segmentation in Daubechies complex wavelet domain

Motion segmentation is a crucial step in video analysis and is associated with a number of computer vision applications. This paper introduces a new method for segmentation of moving object which is based on double change detection technique applied on Daubechies complex wavelet coefficients of three consecutive frames. Daubechies complex wavelet transform for segmentation of moving object has been chosen as it is approximate shift invariant and has a better directional selectivity as compared to real valued wavelet transform. Double change detection technique is used to obtain video object plane by inter-frame difference of three consecutive frames. Double change detection technique also provides automatic detection of appearance of new objects. The proposed method does not require any other parameter except Daubechies complex wavelet coefficients. Results of the proposed method for segmentation of moving objects are compared with results of other state-of-the-art methods in terms of visual performance and a number of quantitative performance metrics viz. Misclassification Penalty, Relative Foreground Area Measure, Pixel Classification Based Measure, Normalized Absolute Error, and Percentage of Correct Classification. The proposed method is found to have high degree of segmentation accuracy than the other state-of-the-art methods.

Counting moving people in crowds using motion statistics of feature-points

Reliable people counting is a crucial task in video surveillances. Among the available techniques, map-based approaches have shown a good performance in estimating the number of people in crowds. These approaches generally subtract the background, and then map the number of people to some features such as foreground area, texture features or edge count. However, in complex scenes, they suffer from inaccurate foreground/background segmentations, erroneous image features, and require large amount of training data to capture the wide variations in crowd distribution. This paper proposes a method using motion statistics of feature-points to estimate the number of moving people in a crowd. Simple feature-points are tracked within the scene. Then moving feature-points are partitioned into clusters corresponding to separate groups of people. For each group, three statistical features are calculated from related feature-points. The amount of moving feature-points is used to provide a rough estimate of group size. Furthermore, motion trajectories of feature-points are utilized to extract two other features related with the amount of occlusions present in groups. The extracted data are used to estimate the number of people in each group, so that the total crowd size is the sum of all group estimates. The experimental results show that the proposed method outperforms the state of the art approaches, e.g., with MSE of 2.357 and MAE of 1.093 for the benchmark video clip "Peds1". The proposed approach is good for estimating the number of people in public places, such as pedestrian walkways and parks, where people are moving and partial occlusions present in the scene.

Foreground Extraction and Depth Map Creation Method based on Analyzing Focus/Defocus for 2D/3D Video Conversion

In this paper, depth of foreground is analysed by focus and color analysis grouping for 2D/3D video conversion and depth of foreground progressing method is preposed by using focus and motion information. Candidate foreground image is generated by estimated movement of image focus information for extracting foreground from 2D video. Area of foreground is extracted by filling progress using color analysis on hole area of inner object existing candidate foreground image. Depth information is generated by analysing value of focus existing on actual frame for allocating depth at generated foreground area. Depth information is allocated by weighting motion information. Results of previous proposed algorithm is compared with proposed method from this paper for evaluating the quality of generated depth information.

입체영상에서 특징의 군집화를 통한 대상객체 분할

다양한 영상으로부터 사용자가 원하는 대상 물체를 정확하게 분할하는 기존의 기법은 2차원적인 특징을 위주로 사용하므로 3차원적인 정보가 부족하여 여러 제한사항이 존재한다. 따라서 본 논문에서는 연속적으로 입력되는 3차원의 스테레오 입체 영상으로부터 2차원과 3차원의 특징을 결합하여 군집화함으로써 대상 물체를 보다 강건하게 분할하는 기법을 제안한다. 제안된 방법에서는 먼저 촬영된 장면의 좌우 스테레오 영상으로부터 스테레오 정합 알고리즘을 이용해 영상의 각 화소별로 카메라와 물체 사이의 거리를 나타내는 깊이 특징을 추출한다. 그런 다음, 깊이 특징과 색상 특징을 효과적으로 군집화하여 배경에 해당하는 영역을 제외하고, 전경에 해당하는 대상 물체를 감지한다. 실험에서는 본 논문에서 제안된 방법을 여러 가지 영상에 적용하여 테스트를 해 보았으며, 제안된 방법이 기존의 2차원 기반의 물체 분리 방법에 비해 보다 강건하게 대상물체를 분할함을 확인하였다. Since the existing methods of segmenting target objects from various images mainly use 2-dimensional features, they have several constraints due to the shortage of 3-dimensional information. In this paper, we therefore propose a new method of accurately segmenting target objects from three dimensional stereoscopic images using 2D and 3D feature clustering. The suggested method first estimates depth features from stereo images by using a stereo matching technique, which represent the distance between a camera and an object from left and right images. It then eliminates background areas and detects foreground areas, namely, target objects by effectively clustering depth and color features. To verify the performance of the proposed method, we have applied our approach to various stereoscopic images and found that it can accurately detect target objects compared to other existing 2-dimensional methods.

Fast and robust skew estimation in document images through bilinear filtering model

Skew estimation of scanned document is important for document analysis and recognition. Owing to the complexity inherent in the document, some methods only process the documents with a small skew angle or specified content, layout and have high computational cost. A fast and robust skew estimation method is proposed based on a bilinear filtering model, which is used to detect edges existing in the document. Some foreground areas in the document have been extracted without considering document layouts or contents. The proposed approach enhances the structure of the document and reduces the effects of the noises in the document. It combines filtering operators into a single approach, so noise filtering which is an unavoidable pre-processing in the previous skew detection methods has been overcome. A dominant angle has been estimated based on the detected edges. According to the estimated dominant angle, a skew angle can be determined efficiently without confining the search space or making assumptions including skew angle range and layout of the document in advance. The proposal greatly reduces the computational time and works in an unsupervised style. Comparative tests with the state-of-the-art skew estimation methods indicate the superior performance of the developed approach.

An Improved Leaf Area Measurement Based on Image Processing

This paper concerns the plant leaf area measurement based on improved image processing. Firstly, the referenced rectangle was detected with 2-side scanning method. Then the leaf region was segmented according to 2G-R-B of every pixel with two different thresholds, and by using of dilatation operation, the trimap of leaf image was got. Next the pixels in unknown area were classified to the foreground or background area with improved knockout method and the exact leaf was segmented. Lastly, the leaf area was calculated according to the pixels proportion between leaf region and the referenced rectangle. Experiment results show this method has good accuracy and rapid speed.

Segmentation of HeLa cells in phase-contrast images with and without DAPI stained cell nuclei

Microscopy is an essential tool for the observation of microbial effectors in host cells. Manual evaluation of the acquired image data is elaborate and error prone. Thus, in order to increase objectivity and reproducibility of results, an automated evaluation of the experiment is performed. The present study evaluates methods to delineate HeLa cells depicted in phase-contrast (PC) micrographs, with and without the additional use of the DAPI stained cell nuclei. For initial cell detection a variance filter is applied to the PC micrographs. Based on this foreground cells are segmented with an intensity based k-means clustering. In a second step, the foreground areas are refined to match the cell boarders by region growing on the background. These foreground areas are used to study the performance of cell separation methods, namely seeded watershed (SW) and graph cuts (GC). Either the maxima of the distance transform of the foreground regions in the PC images (“dist”) or alternatively regions of the cell nuclei (“nuc”) obtained from the DAPI channel are utilized to initialize the cell separation methods. For the detailed evaluation of segmentation quality the cells were categorized to four classes, namely isolated cells, touching cells, overlapping and overlaying cells. With regards the rating of the obtained performance using the Jaccard overlap measure, an intra-observer variance was determined as the median of performance values of all cells with p=0.79. The performance using seeds of type “dist” resulted in pSW(dist)=0.59 for watershed transform and pGC(dist)=0.60 for graph cuts. Switching seeds to type “nuc” performance improved to pSW(nuc)=0.72 for watershed transform and pGC(nuc)=0.69 for graph cuts.

Automatic Segmentation of Phalanx and Epiphyseal/Metaphyseal Region by Gamma Parameter Enhancement Algorithm

The performance of bone age assessment is highly correlated with the extraction of bony tissue from soft tissues, and the key problem is how to successfully separate epiphyseal/metaphyseal region of interests (EMROIs) from the background and soft tissue. In our experiment, a series of image preprocessing procedures are used to exclude the background and locate the EMROIs of left-hand radiographs. Subsequently, automatic gamma parameter enhancement is applied to test the two segmentation methods (adaptive two-means clustering algorithm and gradient vector flow snake) among children of different age (the age from 2 to 16 years for 80 girls and boys). Four error measurements of misclassification error, relative foreground area error, modified Hausdorff distances, and edge mismatch, are included to evaluate the segmentation performance. The result shows that the two segmentation algorithms are corresponding to different ranges of optimal gamma parameters. Furthermore, the margin of EMROIs can be obtained more precisely by developing an automatic bone age assessment method with the gamma parameter enhancement. Keywords: Bone age, Epiphyseal/metaphyseal, Segmentation, Gamma parameter enhancement, Adaptive two-means clustering

Crowded pedestrian counting at bus stops from perspective transformations of foreground areas

Automated bus fleet scheduling and dispatch require an accurate measurements of current passenger demand. This study presents an effective holistic approach for estimating the number of people waiting at regular open bus stops by means of image processing. This is a non-trivial problem because of several varying conditions that complicate the detection process, such as illumination, crowdedness and different people poses, to name a few. The proposed method estimates the pedestrian count using measurements of foreground areas corrected by perspective. Four approaches are evaluated to find the best mapping between the area measurements and the people count. These mappings include two parametric (standard linear regression model, linear discriminant analysis) and two non-parametric (probabilistic neural network, k-nearest neighbours) approaches. This study also evaluates the performance of the algorithm when thermal and panoramic catadioptric cameras are used instead of standard perspective colour cameras. The proposed method is shown to yield better pedestrian count estimates than those obtained using milestone detectors, and requires model fitting procedures than can be easily implemented without requiring very large datasets for proper classifier training. The approach can also be employed to count people in other public spaces, such as buildings and crosswalks.

Evaluation of single and multi-thresholdentropy-based algorithms for folded substrate analysis

This paper presents a detailed evaluation of two variants of Maximum Entropy image segmentation algorithm (single and multi-thresholding) with respect to their performance on segmenting test images showing folded substrates. The segmentation quality was determined by evaluating values of four different measures: misclassification error, modified Hausdorff distance, relative foreground area error and positive-negative false detection ratio. New normalization methods were proposed in order to combine all parameters into a unique algorithm evaluation rating. The segmentation algorithms were tested on images obtained by three different digitalisation methods covering four different surface textures. In addition, the methods were also tested on three images presenting a perfect fold. The obtained results showed that Multi-Maximum Entropy algorithm is better suited for the analysis of images showing folded substrates.