Natural Color Images Research Articles

FRACTAL DIMENSION ESTIMATION OF RGB COLOR IMAGES USING MAXIMUM COLOR DISTANCE

Natural images exhibit a high degree of complexity, randomness and irregularity in color and texture, however fractal can be an effective tool to describe various irregular phenomena in nature. Fractal dimensions are important because they can be defined in connection with real-world data, and they can be measured approximately by means of experiments. In this paper, we proposed a fractal dimension estimation method for RGB color images. In the proposed method, we present a hyper-surface partition method which considers the hyper-surface as continuous and divide the image into nonoverlapped blocks. We also defined a counting method in color domain. To validate the proposed method, experiments were carried on two types of color images: synthesized fractal images and natural RGB color images. The experimental results demonstrate that the proposed method is effective and efficient. The behaviors of the proposed method on the rescale images are also shown in the paper. And it can be performed as a reliable FD estimation approach for the RGB color images.

Identifying Spatio-Temporal Landslide Hotspots on North Island, New Zealand, by Analyzing Historical and Recent Aerial Photography

Accurate mapping of landslides and the reliable identification of areas most affected by landslides are essential for advancing the understanding of landslide erosion processes. Remote sensing data provides a valuable source of information on the spatial distribution and location of landslides. In this paper we present an approach for identifying landslide-prone “hotspots” and their spatio-temporal variability by analyzing historical and recent aerial photography from five different dates, ranging from 1944 to 2011, for a study site near the town of Pahiatua, southeastern North Island, New Zealand. Landslide hotspots are identified from the distribution of semi-automatically detected landslides using object-based image analysis (OBIA), and compared to hotspots derived from manually mapped landslides. When comparing the overlapping areas of the semi-automatically and manually mapped landslides the accuracy values of the OBIA results range between 46% and 61% for the producer’s accuracy and between 44% and 77% for the user’s accuracy. When evaluating whether a manually digitized landslide polygon is only intersected to some extent by any semi-automatically mapped landslide, we observe that for the natural-color images the landslide detection rate is 83% for 2011 and 93% for 2005; for the panchromatic images the values are slightly lower (67% for 1997, 74% for 1979, and 72% for 1944). A comparison of the derived landslide hotspot maps shows that the distribution of the manually identified landslides and those mapped with OBIA is very similar for all periods; though the results also reveal that mapping landslide tails generally requires visual interpretation. Information on the spatio-temporal evolution of landslide hotspots can be useful for the development of location-specific, beneficial intervention measures and for assessing landscape dynamics.

Case study of dust event sources from the Gobi and Taklamakan deserts: An investigation of the horizontal evolution and topographical effect using numerical modeling and remote sensing

A severe dust event occurred from April 23 to April 27, 2014, in East Asia. A state-of-the-art online atmospheric chemistry model, WRF/Chem, was combined with a dust model, GOCART, to better understand the entire process of this event. The natural color images and aerosol optical depth (AOD) over the dust source region are derived from datasets of moderate resolution imaging spectroradiometer (MODIS) loaded on a NASA Aqua satellite to trace the dust variation and to verify the model results. Several meteorological conditions, such as pressure, temperature, wind vectors and relative humidity, are used to analyze meteorological dynamic. The results suggest that the dust emission occurred only on April 23 and 24, although this event lasted for 5days. The Gobi Desert was the main source for this event, and the Taklamakan Desert played no important role. This study also suggested that the landform of the source region could remarkably interfere with a dust event. The Tarim Basin has a topographical effect as a “dust reservoir” and can store unsettled dust, which can be released again as a second source, making a dust event longer and heavier.

An Identification of Chlorophyll Content using Image Processing Technique and Fuzzy Mamdani Method

Background/Objectives: To improve an identification of chlorophyll content in leaf, this paper presents an implementation of a supervised learning method based on membership function training in the context of mamdani fuzzy models. Methods/Statistical Analysis: this paper presents a fuzzy rule based algorithm; natural color image in leaf (Red, Green and Blue) is used as the input to the fuzzy mamdani models. The output of the fuzzy mamdani models is value of chlorophyll content. Results: The proposed approach was superior to identification of chlorophyll content in leaf using image processing technique and mamdani fuzzy method has higher identification accuracy. Conclusion/Application: Finally, the basic difference of value of chlorophyll content between fuzzy mamdani models and the actual was less than 3,1 % on average.

ANALYSIS OF VISUAL INTERPRETATION OF SATELLITE DATA

Millions of people of all ages and expertise are using satellite and aerial data as an important input for their work in many different fields. Satellite data are also gradually finding a new place in education, especially in the fields of geography and in environmental issues. The article presents the results of an extensive research in the area of visual interpretation of image data carried out in the years 2013 - 2015 in the Czech Republic. The research was aimed at comparing the success rate of the interpretation of satellite data in relation to a) the substrates (to the selected colourfulness, the type of depicted landscape or special elements in the landscape) and b) to selected characteristics of users (expertise, gender, age). The results of the research showed that (1) false colour images have a slightly higher percentage of successful interpretation than natural colour images, (2) colourfulness of an element expected or rehearsed by the user (regardless of the real natural colour) increases the success rate of identifying the element (3) experts are faster in interpreting visual data than non-experts, with the same degree of accuracy of solving the task, and (4) men and women are equally successful in the interpretation of visual image data.

ANALYSIS OF VISUAL INTERPRETATION OF SATELLITE DATA

Abstract. Millions of people of all ages and expertise are using satellite and aerial data as an important input for their work in many different fields. Satellite data are also gradually finding a new place in education, especially in the fields of geography and in environmental issues. The article presents the results of an extensive research in the area of visual interpretation of image data carried out in the years 2013 - 2015 in the Czech Republic. The research was aimed at comparing the success rate of the interpretation of satellite data in relation to a) the substrates (to the selected colourfulness, the type of depicted landscape or special elements in the landscape) and b) to selected characteristics of users (expertise, gender, age). The results of the research showed that (1) false colour images have a slightly higher percentage of successful interpretation than natural colour images, (2) colourfulness of an element expected or rehearsed by the user (regardless of the real natural colour) increases the success rate of identifying the element (3) experts are faster in interpreting visual data than non-experts, with the same degree of accuracy of solving the task, and (4) men and women are equally successful in the interpretation of visual image data.

A Manifold Alignment Approach for Hyperspectral Image Visualization With Natural Color

The trichromatic visualization of hundreds of bands in a hyperspectral image (HSI) has been an active research topic. The visualized image shall convey as much information as possible from the original data and facilitate easy image interpretation. However, most existing methods display HSIs in false color, which contradicts with user experience and expectation. In this paper, we propose a new framework for visualizing an HSI with natural color by the fusion of an HSI and a high-resolution color image via manifold alignment. Manifold alignment projects several data sets to a shared embedding space where the matching points between them are pairwise aligned. The embedding space bridges the gap between the high-dimensional spectral space of the HSI and the RGB space of the color image, making it possible to transfer natural color and spatial information in the color image to the HSI. In this way, a visualized image with natural color distribution and fine spatial details can be generated. Another advantage of the proposed method is its flexible data setting for various scenarios. As our approach only needs to search a limited number of matching pixel pairs that present the same object, the HSI and the color image can be captured from the same or semantically similar sites. Moreover, the learned projection function from the hyperspectral data space to the RGB space can be directly applied to other HSIs acquired by the same sensor to achieve a quick overview. Our method is also able to visualize user-specified bands as natural color images, which is very helpful for users to scan bands of interest.

Color image quality assessment based on sparse representation and reconstruction residual

Image quality assessment (IQA) is a fundamental problem in image processing. While in practice almost all images are represented in the color format, most of the current IQA metrics are designed in gray-scale domain. Color influences the perception of image quality, especially in the case where images are subject to color distortions. With this consideration, this paper presents a novel color image quality index based on Sparse Representation and Reconstruction Residual (SRRR). An overcomplete color dictionary is first trained using natural color images. Then both reference and distorted images are represented using the color dictionary, based on which two feature maps are constructed to measure structure and color distortions in a holistic manner. With the consideration that the feature maps are insensitive to image contrast change, the reconstruction residuals are computed and used as a complementary feature. Additionally, luminance similarity is also incorporated to produce the overall quality score for color images. Experiments on public databases demonstrate that the proposed method achieves promising performance in evaluating traditional distortions, and it outperforms the existing metrics when used for quality evaluation of color-distorted images.

Context-Based Predictor Blending for Lossless Color Image Compression

Images are typically nonstationary signals. If prediction is applied in a linear fashion, it must be combined with a technique that takes this characteristic into account. In general, images can either be regarded as piecewise 2-D autoregressive processes or they are handled in a blockwise manner. This paper presents a novel prediction technique, which treats the image data as an interleaved sequence generated by multiple sources. The challenge is to deinterleave the sequence and to compute prediction weights for each subsource separately. The proposed approach adaptively determines the subsources based on the textures surrounding the pixels. The new linear prediction technique is combined with template-matching prediction and a blending method that considers the correlation between the predictors’ estimates is proposed. The prediction method is incorporated in a framework for lossless color image compression. In combination with an adaptive color transform and a dedicated coding algorithm, the proposed approach shows a competitive compression performance for a wide range of natural color images.

Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor colour images

A fast normalized cross correlation (FNCC) based machine vision algorithm was proposed in this study to develop a method for detecting and counting immature green citrus fruit using outdoor colour images toward the development of an early yield mapping system. As a template matching method, FNCC was used to detect potential fruit areas in the image, which was the very basis for subsequent false positive removal. Multiple features, including colour, shape and texture features, were combined in this algorithm to remove false positives. Circular Hough transform (CHT) was used to detect circles from images after background removal based on colour components. After building disks centred in centroids resulted from both FNCC and CHT, the detection results were merged based on the size and Euclidian distance of the intersection areas of the disks from these two methods. Finally, the number of fruit was determined after false positive removal using texture features. For a validation dataset of 59 images, 84.4 % of the fruits were successfully detected, which indicated the potential of the proposed method toward the development of an early yield mapping system.

Switching non-local vector median filter

This paper describes a novel image filtering method that removes random-valued impulse noise superimposed on a natural color image. In impulse noise removal, it is essential to employ a switching-type filtering method, as used in the well-known switching median filter, to preserve the detail of an original image with good quality. In color image filtering, it is generally preferable to deal with the red (R), green (G), and blue (B) components of each pixel of a color image as elements of a vectorized signal, as in the well-known vector median filter, rather than as component-wise signals to prevent a color shift after filtering. By taking these fundamentals into consideration, we propose a switching-type vector median filter with non-local processing that mainly consists of a noise detector and a noise removal filter. Concretely, we propose a noise detector that proactively detects noise-corrupted pixels by focusing attention on the isolation tendencies of pixels of interest not in an input image but in difference images between RGB components. Furthermore, as the noise removal filter, we propose an extended version of the non-local median filter, we proposed previously for grayscale image processing, named the non-local vector median filter, which is designed for color image processing. The proposed method realizes a superior balance between the preservation of detail and impulse noise removal by proactive noise detection and non-local switching vector median filtering, respectively. The effectiveness and validity of the proposed method are verified in a series of experiments using natural color images.

A New Hybrid Order Approach to Morphological Color Image Processing Based on Reduced Order with Adaptive Absolute Reference

Mathematical morphology can process the binary and grayscale image successfully. This theory cannot be extended to the color image directly. In color space, a vector represents a pixel, so in order to compare vectors, vectoriel orderings must be defined first. This paper addresses the question of the extension of morphological operator to the case of color images. The proposed method used the order by bit mixing to replace the conditional order. Our order is based on a combination of reduced and bit mixing ordering of the underlying data. Additionally it is a total ordering. Since it not only solves the problems of false color generated by the marginal order but also those of multiple extrema generated by reduced order. The performance of the introduced operators is illustrated by means of different applications: color gradients for segmenting, image smoothing (noise suppression) by median filter operator and Laplacian operators. Examples of natural color images and synthetic color images are given. Experimental results show the improvement brought by this new method.

Lossless Color Image Compression using Double Level RCT in BBWCA

A new lossless image compression scheme for natural color images which is based on double level Reversible Color Transform(RCT) and Bi-level Burrows Wheeler Compression Algorithm(BBWCA) is proposed. The first level RCT from RGB to HSV provide higher reduction in number of unique hue components, thus gives higher compression in case of natural images. The first level RCT followed by the second level RCT from HSV to YUV helps to yield small number of unique Y component values. DC level shifting and twos complement operation will be applied as preprocessing steps to second level RCT. The result of double level RCT is used as the input to BBWCA, in which row-wise BWT followed by column-wise BWT is applied. The compressed image data is formed by using move-to-front(MTF), Run-length-encoding and Entropy coding. The proposed method using double level RCT with BBWCA results in high compression by taking advantage of reduction in hue components of natural images. Among the different color space compared, the proposed method achieves better compression and is well suited for small and large size natural images. Proposed method make use of a double level RCT on the existing BBWCA algorithm and resulted in improving the compression ratio by 46 percentage.

An Underwater Color Image Quality Evaluation Metric.

Quality evaluation of underwater images is a key goal of underwater video image retrieval and intelligent processing. To date, no metric has been proposed for underwater color image quality evaluation (UCIQE). The special absorption and scattering characteristics of the water medium do not allow direct application of natural color image quality metrics especially to different underwater environments. In this paper, subjective testing for underwater image quality has been organized. The statistical distribution of the underwater image pixels in the CIELab color space related to subjective evaluation indicates the sharpness and colorful factors correlate well with subjective image quality perception. Based on these, a new UCIQE metric, which is a linear combination of chroma, saturation, and contrast, is proposed to quantify the non-uniform color cast, blurring, and low-contrast that characterize underwater engineering and monitoring images. Experiments are conducted to illustrate the performance of the proposed UCIQE metric and its capability to measure the underwater image enhancement results. They show that the proposed metric has comparable performance to the leading natural color image quality metrics and the underwater grayscale image quality metrics available in the literature, and can predict with higher accuracy the relative amount of degradation with similar image content in underwater environments. Importantly, UCIQE is a simple and fast solution for real-time underwater video processing. The effectiveness of the presented measure is also demonstrated by subjective evaluation. The results show better correlation between the UCIQE and the subjective mean opinion score.

MToS: A Tree of Shapes for Multivariate Images.

The topographic map of a gray-level image, also called tree of shapes, provides a high-level hierarchical representation of the image contents. This representation, invariant to contrast changes and to contrast inversion, has been proved very useful to achieve many image processing and pattern recognition tasks. Its definition relies on the total ordering of pixel values, so this representation does not exist for color images, or more generally, multivariate images. Common workarounds, such as marginal processing, or imposing a total order on data, are not satisfactory and yield many problems. This paper presents a method to build a tree-based representation of multivariate images, which features marginally the same properties of the gray-level tree of shapes. Briefly put, we do not impose an arbitrary ordering on values, but we only rely on the inclusion relationship between shapes in the image definition domain. The interest of having a contrast invariant and self-dual representation of multivariate image is illustrated through several applications (filtering, segmentation, and object recognition) on different types of data: color natural images, document images, satellite hyperspectral imaging, multimodal medical imaging, and videos.

Color image enhancement with brightness preservation using a histogram specification approach

Enhancement of image quality is a primary requirement before visual based information can be used in various real-world and engineering applications. With regard to a faithful reproduction of the scene information, degradations resulting in losses of contrast have to be rectified where the efficient histogram equalization is frequently employed. Moreover, it is also necessary to maintain the brightness of the corrected image in order to truly represent the scene characteristics. In this work, an approach is proposed to specify a proper histogram profile such that image intensity values are adjusted accordingly and the output brightness is maintained close to the input image. Specifically, the equalization profile is shaped by finding a balancing control threshold through carrying out an integration of rectangular and triangular sections. Experiments are conducted using a large number of natural color images and compared to other available histogram based image enhancement methods. Results have shown that the proposed approach is able to accomplish a wide set of performance goals including information content, sharpness and color quality.

Binocular Goggle Augmented Imaging and Navigation System provides real-time fluorescence image guidance for tumor resection and sentinel lymph node mapping.

The inability to identify microscopic tumors and assess surgical margins in real-time during oncologic surgery leads to incomplete tumor removal, increases the chances of tumor recurrence, and necessitates costly repeat surgery. To overcome these challenges, we have developed a wearable goggle augmented imaging and navigation system (GAINS) that can provide accurate intraoperative visualization of tumors and sentinel lymph nodes in real-time without disrupting normal surgical workflow. GAINS projects both near-infrared fluorescence from tumors and the natural color images of tissue onto a head-mounted display without latency. Aided by tumor-targeted contrast agents, the system detected tumors in subcutaneous and metastatic mouse models with high accuracy (sensitivity = 100%, specificity = 98% ± 5% standard deviation). Human pilot studies in breast cancer and melanoma patients using a near-infrared dye show that the GAINS detected sentinel lymph nodes with 100% sensitivity. Clinical use of the GAINS to guide tumor resection and sentinel lymph node mapping promises to improve surgical outcomes, reduce rates of repeat surgery, and improve the accuracy of cancer staging.

Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation.

Fuzzy c-means (FCM) clustering with spatial constraints has attracted great attention in the field of image segmentation. However, most of the popular techniques fail to resolve misclassification problems due to the inaccuracy of their spatial models. This paper presents a new unsupervised FCM-based image segmentation method by paying closer attention to the selection of local information. In this method, region-level local information is incorporated into the fuzzy clustering procedure to adaptively control the range and strength of interactive pixels. First, a novel dissimilarity function is established by combining region-based and pixel-based distance functions together, in order to enhance the relationship between pixels which have similar local characteristics. Second, a novel prior probability function is developed by integrating the differences between neighboring regions into the mean template of the fuzzy membership function, which adaptively selects local spatial constraints by a tradeoff weight depending upon whether a pixel belongs to a homogeneous region or not. Through incorporating region-based information into the spatial constraints, the proposed method strengthens the interactions between pixels within the same region and prevents over smoothing across region boundaries. Experimental results over synthetic noise images, natural color images, and synthetic aperture radar images show that the proposed method achieves more accurate segmentation results, compared with five state-of-the-art image segmentation methods.

Pairwise-Distance-Analysis-Driven Dimensionality Reduction Model with Double Mappings for Hyperspectral Image Visualization

This paper describes a novel strategy for the visualization of hyperspectral imagery based on the analysis of image pixel pairwise distances. The goal of this approach is to generate a final color image with excellent interpretability and high contrast at the cost of distorting a few pairwise distances. Specifically, the principle of equal variance is introduced to divide all hyperspectral bands into three subgroups and to ensure the energy is distributed uniformly between them, as in natural color images. Then, after detecting both normal and outlier pixels, these three subgroups are mapped into three color components of the output visualization using two different mapping (i.e., dimensionality reduction) schemes for the two types of pixels. The widely-used multidimensional scaling (MDS) is used for normal pixels and a new objective function, taking into account the weighting of pairwise distances, is presented for the outlier pixels. The pairwise distance weighting is designed such that small pairwise distances between the outliers and their respective neighbors are emphasized and large deviations are suppressed. This produces an image with high contrast and good interpretability while retaining the detailed information content. The proposed algorithm is compared with several state-of-the-art visualization techniques and evaluated on the well-known AVIRIS hyperspectral images. The effectiveness of the proposed strategy is substantiated both visually and quantitatively.

Automatic Method for Contrast Enhancement of Natural Color Images

The contrast enhancement is great challenge in the image processing when images are suffering from poor contrast problem. Therefore, in order to overcome this problem an automatic method is proposed for contrast enhancement of natural color images. The proposed method consist of two stages: in first stage lightness component in YIQ color space is normalized by sigmoid function after the adaptive histogram equalization is applied on Y component and in second stage automatic color contrast enhancement algorithm is applied on output of the first stage. The proposed algorithm is tested on different NASA color images, hyperspectral color images and other types of natural color images. The performance of proposed algorithm is evaluated and compared with the other existing contrast enhancement algorithms in terms of colorfulness metric and color enhancement factor. The higher values of colorfulness metric and color enhancement factor imply that the visual quality of the enhanced image is good. Simulation results demonstrate that proposed algorithm provides higher values of colorfulness metric and color enhancement factor as compared to other existing contrast enhancement algorithms. The proposed algorithm also provides better visual enhancement results as compared with the other existing contrast enhancement algorithms.