Fuzzy Color Research Articles

Detection of Moving Objects Using Fuzzy Color Difference Histogram Based Background Subtraction

Detection of moving objects in the presence of complex scenes such as dynamic background (e.g, swaying vegetation, ripples in water, spouting fountain), illumination variation, and camouflage is a very challenging task. In this context, we propose a robust background subtraction technique with three contributions. First, we present the use of color difference histogram (CDH) in the background subtraction algorithm. This is done by measuring the color difference between a pixel and its neighbors in a small local neighborhood. The use of CDH reduces the number of false errors due to the non-stationary background, illumination variation and camouflage. Secondly, the color difference is fuzzified with a Gaussian membership function. Finally, a novel fuzzy color difference histogram (FCDH) is proposed by using fuzzy c-means (FCM) clustering and exploiting the CDH. The use of FCM clustering algorithm in CDH reduces the large dimensionality of the histogram bins in the computation and also lessens the effect of intensity variation generated due to the fake motion or change in illumination of the background. The proposed algorithm is tested with various complex scenes of some benchmark publicly available video sequences. It exhibits better performance over the state-of-the-art background subtraction techniques available in the literature in terms of classification accuracy metrics like $MCC$ and $PCC$ .

Detection of venous needle dislodgement during haemodialysis using fractional order shape index ratio and fuzzy colour relation analysis.

Venous needle dislodgement (VND) is a life-threatening complication during haemodialysis (HD) treatment. When VND occurs, it only takes a few minutes for blood loss in an adult patient. According to the ANNA (American Nephrology Nurses' Association) VND survey reports, VND is a concerning issue for the nephrology nurses/staff and patients. To ensure HD care and an effective treatment environment, this Letter proposes a combination of fractional order shape index ratio (SIR) and fuzzy colour relation analysis (CRA) to detect VND. If the venous needle drops out, clinical examinations show that both heart pulses and pressure wave variations have a low correlation at the venous anatomic site. Therefore, fractional order SIR is used to quantify the differences in transverse vibration pressures (TVPs) between the normal condition and meter reading. Linear regression shows that the fractional order SIR has a high correlation with the TVP variation. Fuzzy CRA is designed in a simple and visual message manner to identify the risk levels. A worst-case study demonstrated that the proposed model can be used for VND detection in clinical applications.

Stereo-camera-based object detection using fuzzy color histograms and a fuzzy classifier with depth and shape estimations

This paper proposes a new method of detecting an object containing multiple colors with non-homogeneous distributions in complex backgrounds and subsequently estimating the depth and shape of the object using a stereo camera. To extract features for object detection, this paper proposes fuzzy color histograms (FCHs) based on the self-splitting clustering (SSC) of the hue-saturation (HS) color space. For each scanning window in a pyramid of scaled images, the FCH is obtained by accumulating the fuzzy degrees of all of the pixels belonging to each cluster. The FCH is fed to a fuzzy classifier to detect an object in the left image captured by the stereo camera. To find the matched object region in the right image, the left and right images are first segmented using the SSC-partitioned HS space. The depth of the object is then found by performing stereo matching on the segmented images. To find the shape of the object, a disparity map is built using the estimated object depth to automatically determine the stereo matching window size and disparity search range. Finally, the shape of the object is segmented from the disparity map. The experimental results of the detection of different objects with depth and shape estimations are used to verify the performance of the proposed method. Comparisons with different detection and disparity map construction methods are performed to demonstrate the advantage of the proposed method.

Multiclass SVM based adaptive filter for removal of high density impulse noise from color images

This paper proposes a multiclass support vector machine (SVM) based adaptive filter for removal of impulse noise from color images. The quality of the image gets degraded due to the presence of impulse noise. As a result, the homogeneity amongst the pixels gets distorted that needs to be restored. The feature set comprising of prediction error, difference between the median value and the center pixel; the median value in the kernel under operation has been used during this study. The pixel of test image is processed using adaptive window based filter that depends on the associated class assigned at the testing phase. The baseline system has been designed using modified histogram based fuzzy color filter (MHFC) technique. Four set of experiments have been carried out on a large database to validate the proposed method. The performance of the technique have been evaluated using peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). The results suggest that for fixed valued impulse noise, the proposed filter performs better than the MHFC in case of high density impulse noise (>45%). However, for random valued impulse noise the proposed filter outperforms the MHFC based method for both low and high density of noise. The objective analysis suggests that there is ∼3dB improvement in PSNR as compared to the MHFC based method for high density of impulse noise. The results of SSIM along with visual observations indicate that the image details are maintained significantly in the proposed technique as compared to existing methods.

Efficient Image Retrieval Based on Fuzzy Color Feature Extraction

Efficient Image Retrieval Based on Fuzzy Color Feature Extraction

Deciphering an RGB color image cryptosystem based on Choquet fuzzy integral

For the purpose of designing better color image cryptosystems, it is necessary to perform a mathematically rigorous security analysis for the existing ones. In this study, a Choquet fuzzy integral-based color image cryptosystem with shift---diffusion architecture is cryptanalyzed. Firstly, it is demonstrated that the color image cryptosystem is irreversible, since the encryption algorithm cannot be decrypted correctly. Meanwhile, a simple improvement is presented to guarantee the reversibility. Moreover, a cryptanalysis of the improved scheme is proposed and results show the improved color image cryptosystem can be broken by a known plain/cipher image pair. The shift---diffusion architecture should alter the external key every time for security consideration.

Survey on Content based Image Retrieval to Deal with Rapid Growth of Digital Images

in image retrieval systems has increased in large part due to the rapid growth of the digital images produced by World Wide Web and high capacity of digital data storage devices available in the human race domain. A desired image from network and storage media is shared by citizens belonging to various field, including education, business, government agencies, journalism, and advertising agencies. But due to generation of large collection of digital images, users are not satisfied with the traditional information retrieval techniques. As the elaboration of multimedia technologies are becoming more trendy, so these days the content based image retrieval are becoming a foundation of exact and fast retrieval. This survey paper deals with the techniques of content based image retrieval using both local features and global features. One of them is conventional color histogram and fuzzy color histogram. Further Support vector machine (SVM) with optimized feature sub set selection using radial bias network can also be used to improve retrieval performance.

Improving Performance of Image Retrieval Based on Fuzzy Colour Histograms by Using Hybrid Colour Model and Genetic Algorithm

A hybrid colour model is a colour descriptor formed by combining channels from several different colour models. Although rarely used in computer graphics applications due to redundancy, hybrid colour models may be of interest for the Content-Based Image Retrieval (CBIR). Best features of each colour model can be combined to obtain optimal retrieval performance. This paper evaluates several approaches to the construction of a hybrid colour model that is used to construct a fuzzy colour histogram of image as a compact feature for retrieval. By evaluating each channel separately, a colour model named HSY is proposed. Various parameters of fuzzy histogram are further improved using Genetic algorithm (GA). Using standard data sets and the Average Normalized Modified Retrieval Rank (ANMRR) as a metric for retrieval performance, it is shown that this novel approach can give an improved retrieval performance.

Fuzzy colouring of fuzzy graphs

Colouring of fuzzy graphs has several applications in real world. In this paper, a new concept of colouring of fuzzy graphs has been introduced. Also, some important terms like strength cut graphs, fuzzy colour, chromatic number of fuzzy graphs have been described. Some relevant results are proved. This technique is used to colour world political map mentioning the strength of relationship among the countries. Also, a new kind of traffic light system has been proposed.

Parallel weighted semantic fusion for cross-media retrieval

Efficiency and effectiveness of image retrieval IR system are based on the good interpretation and integration of multimodal information. Research results in the recent years show that combining the two modalities text based and content based even with simple fusion strategies alleviates the image retrieval results and also reduces the semantic gap. In this paper, we deploy parallel computing in weighted semantic similarity technique for IR using both text and content. This technique gives the weightage to the annotations associated with the query image based upon their semantic similarity with user's query and then establishes the semantics with database images. The semantic similarity has been measured using WordNet. For content matching, colour feature is extracted and is represented using fuzzy colour histogram FCH. Furthermore, to fuse the two modalities, image reordering with late fusion strategy is used. Parallel processing is done at data level using the single program multiple data SPMD programming model that focuses on parallel execution of semantic similarity matching computations. The proposed approach shows that the parallel computing largely reduces the response time of the system. Whereas, semantics learned at an early stage helps in reducing the semantic gap. Experiments performed on two standard datasets reveal the good efficiency and effectiveness of the proposed approach.

Fuzzy color video filtering technique for sequences corrupted by additive Gaussian noise

In this paper, a novel framework is presented for the denoising of color video sequences corrupted by additive Gaussian noise. The proposed technique consists of three filtering stages: spatial, spatio-temporal, and spatial postprocessing. During the first spatial stage, the gradient values in eight directions for pixels located in the vicinity of a central pixel, as well as the interchannel correlation between the analogous pixels in different color bands (RGB), are taken into account. These gradient values that estimate the level of noise contamination are employed using the designed fuzzy rules to preserve the image features (e.g., textures, edges, sharpness, and chromatic properties). In the spatio-temporal denoising stage, two consecutive video frames are filtered together, thereby yielding more information. Additionally, small local motions between consecutive frames are estimated using block matching procedure in different directions, gathering interframe samples with similar features for efficient denoising. In the final stage, the edge and plain areas in a current frame are separated for different spatial postprocessing denoising. Two variants of proposed fuzzy filter, depending on sliding windows, are proposed. Additionally, a hybrid fuzzy-Wiener denoising technique is performed employing the proposed filtering approach. Numerous simulation results confirm that these novel fuzzy frameworks outperform other state-of-the-art techniques in terms of objective criteria, as well as subjective visual perception in the various color sequences.

Integrated color defect detection method for polysilicon wafers using machine vision

For the typical color defects of polysilicon wafers, i.e., edge discoloration, color inaccuracy and color non-uniformity, a new integrated machine vision detection method is proposed based on an HSV color model. By transforming RGB image into three-channel HSV images, the HSV model can efficiently reduce the disturbances of complex wafer textures. A fuzzy color clustering method is used to detect edge discoloration by defining membership function for each channel image. The mean-value classifying method and region growing method are used to identify the other two defects, respectively. A vision detection system is developed and applied in the production of polysilicon wafers.

A modified fuzzy color histogram using vision perception variation of pixels at different location

The fuzzy linking color histogram considers not only the similarity of different colors from different bins but also the dissimilarity of those colors assigned to the same bin. Moreover, it projects the three-dimension histogram onto the one single-dimension histogram, which reduces the complexity of computation. Spatial fuzzy linking color histogram (SFLCH) combines fuzzy linking color histogram with spatial information that describes the color distribution of pixels in different regions. Meanwhile, the concept "color complexity" is defined in histogram similarity measure in order to add the influence of human vision perception to image retrieval. Compared with other methods, the modified fuzzy color histogram is proved to be more accurate and effective for the content-based image retrieval from the experimental results.

Improved nonlocal fuzzy color segmentation-based color reconstruction hybrid approach for white-RGB imaging systems

Conventional local-based color reconstruction for a white-RGB (WRGB) imaging system relies excessively on reference pixels. Therefore, it is sensitive to noisy interference. To address this issue, we propose an improved nonlocal fuzzy color segmentation-based color reconstruction hybrid approach for a WRGB imaging system. Unlike local-based approaches, we attempt to reproduce color information based on the statistical color distribution of the raw sensor data. According to the distribution analysis, the color distribution (histogram and cumulative distribution function) is close to that of the full-resolution image. However, brief histogram matching gives rise to zipper artifacts, which result from the multicombination of the red, green, and blue corresponding to one white. Therefore, a hybrid color segmentation is proposed to address this issue. The first step is a brief sorting-based color segmentation in the hue channel. Fuzzy-based color segmentation is then utilized to acquire more subregions in the proposed saturation space. Finally, fast histogram matching is carried out to obtain the full-color information for the white pixel for each region. Compared with state-of-the-art approaches, the proposed nonlocal hybrid approach is capable of significantly reducing the influence of noise with higher peak signal-to-noise ratios. Furthermore, according to the hybrid color segmentation, zipper artifacts are successfully avoided.

Retrieval Model Based on Image Content Features

This paper presents fuzzy color histogram feature-based image retrieval method and texture spectrum fuzzy histogram feature analyzes the image database indexing techniques and the introduction of the experimental system for an improved method of fuzzy indexes. Algorithm reflects the underlying characteristics of high-level concepts and integration, relevance feedback and machine learning mechanism combining ideas. In this paper, the algorithm full processing power of computer systems, has a certain reference value and practical significance.

Improved medical image modality classification using a combination of visual and textual features

In this paper, we present the approach that we applied to the medical modality classification tasks at the ImageCLEF evaluation forum. More specifically, we used the modality classification databases from the ImageCLEF competitions in 2011, 2012 and 2013, described by four visual and one textual types of features, and combinations thereof. We used local binary patterns, color and edge directivity descriptors, fuzzy color and texture histogram and scale-invariant feature transform (and its variant opponentSIFT) as visual features and the standard bag-of-words textual representation coupled with TF-IDF weighting. The results from the extensive experimental evaluation identify the SIFT and opponentSIFT features as the best performing features for modality classification. Next, the low-level fusion of the visual features improves the predictive performance of the classifiers. This is because the different features are able to capture different aspects of an image, their combination offering a more complete representation of the visual content in an image. Moreover, adding textual features further increases the predictive performance. Finally, the results obtained with our approach are the best results reported on these databases so far.

VIDEO COPY DETECTION USING SEGMENTATION METHOD AND MPEG-7 DESCRIPTORS

There are a number of methods available for video copy detection. Some of the methods were employing the application of local and global descriptors which were found to be ineffective in detections involving complex transformations. In order to overcome the above specified inefficiency, Scale Invariant Feature Transform (SIFT) descriptor came into picture but was found to have a high computational cost. The method proposed in this paper involving five different types of MPEG-7 descriptors namely Color and Edge Directivity Descriptor (CEDD), Fuzzy Color and Texture Histogram (FCTH), Scalable Color Descriptor (SCD), Edge Histogram Descriptor (EHD), Color Layout Descriptor (CLD) for extracting the features of the frames in the selected video is found to be cost effective and efficient even in case of high level of transformations. This paper also throws light on certain improvements in graphbased video sequence matching method which is used to overcome the level of noise, to detect videos with different frame rates and optimal sequence matching is found automatically from the disordered video sequences by applying spatial features during copy detection. Experimental results have showed that the proposed method is far effective than the previously existing video detection scenarios.

Color differences based fuzzy filter for extremely corrupted color images

In this paper, a color difference based fuzzy filter is presented for fix and random-valued impulse noise. Noise detection scheme of two stages was applied to detect noise efficiently whereas for noise removal an improved Histogram based Fuzzy Color Filter (HFC) is presented. Pixels detected as noisy by the noise detection scheme are deliberated as candidate for the removal of noise. Candidate noisy pixels are then processed using a modified Histogram based Fuzzy Color Filter to estimate their non-noisy values. The idea of using multiple fuzzy membership functions is presented, so that best suitable membership function for local image statistics can be used automatically. In the proposed technique we have used three different types of fuzzy membership functions (bell-shaped, trapezoidal-shaped, and triangular-shaped) and their fuzzy number construction algorithms are proposed. Experimentation is also performed with three, five, and seven membership functions. Type and number of suitable fuzzy membership functions are then identified to remove noise. Comparison with the existing filtering techniques is established on the basis of objective quantitative measures including structural similarity index measure (SSIM) and peak-signal-to-noise-ratio (PSNR). Simulations show that this filter is superior to that of the existing state-of-the-art filtering techniques in removing fix and random-valued impulse noise whereas retaining the details of the image contents.

Detection of Moving Objects with Fuzzy Color Coherence Vector

Background subtraction is a popular method for detecting foreground that is widely adopted as the fundamental processing for advanced applications such as tracking and surveillance. Color coherence vector (CCV) includes both the color distribution information (histogram) and the local spatial relationship information of colors. So it overcomes the weakness of the conventional color histogram for the representation of an object. In this paper, we introduce a fuzzy color coherence vector (FCCV) based background subtraction method. After applying the fuzzyc-means clustering to color coherence subvectors and color incoherence subvectors, we develop a region-based fuzzy statistical feature for each pixel based on the fuzzy membership matrices. The features are extracted from consecutive frames to build the background model and detect the moving objects. The experimental results demonstrate the effectiveness of the proposed approach.

Fuzzy Set Theoretical Approach to the Tone Triangle System

The present study considers a fuzzy color system in which three fuzzy sets are constructed on the tone triangle. This system processes a fuzzy input and outputs a color on the color triangle system. Two fuzzy sets (not black and white) are applied to the tone triangle relationship. By evaluating the attributes of chromaticness, whiteness, and blackness on the tone triangle, a target color can be easily obtained as the center of gravity of the resulting fuzzy set. The output of the system is a tone triangle, which includes a compound vector with three weights (scalars) in color space. The differences between a fuzzy input and the resulting inference output is shown by the input-output characteristic (linear shape and right triangle shape) between the chromaticness, the whiteness, and the blackness of the input and the chromaticness of the output.