Color Image

Extraction of text from image and video is an important step in building efficient indexing and retrieval systems for multimedia databases. We adopt a hybrid approach for such text extraction by exploiting a number of characteristics of text blocks in color images and video frames. Our system detects both caption text as well as scene text of different font, size, color and intensity. We have developed an application for on-line extraction and recognition of texts from videos. Such texts are used for retrieval of video clips based on any given keyword. The application is available on the web for the readers to repeat our experiments and also to try text extraction and retrieval from their own videos.

This paper presents a novel approach to recognizing driver activities using a multi-perspective (i.e., four camera views) multi-modal (i.e., thermal infrared and color) video-based system for robust and real-time tracking of important body parts. The multi-perspective characteristics of the system provides redundant trajectories of the body parts, while the multi-modal characteristics of the system provides robustness and reliability of feature detection and tracking. The combination of a deterministic activity grammar (called 'operationtriplet') and a Hidden Markov model-based classifier provides semantic-level analysis of human activity. The application context for this research is that of intelligent vehicles and driver assistance systems. Experimental results in real-world street driving demonstrate effectiveness of the proposed system.

In standard digital color imaging, each pixel position acquires data for only one color plane and the remaining two color planes must be inferred through a process known as demosaicking. Furthermore, the image is susceptible to blurring artifacts due to a moving camera or fast moving subject. In this work we develop a robust framework to demosaick the color filter array (CFA) image while reducing the blur corrupting the image. We begin by defining a color motion blur model that describes the motion blur artifacts affecting color images. We then integrate the motion blur model in the demosaicking algorithm to obtain a computationally efficient framework for deblurring while demosaicking.

Objective: To compare the impedance to uterine artery blood flow, during the midluteal phase, in women with recurrent spontaneous abortion (RSA) as compared to normal fertile controls. Design: A prospective study involving women with a history of RSA and fertile controls. Setting: University of Rome "Tor Vergata." Patient(s): Two hundred thirty women with RSA and 50 fertile controls referred for routine examination. Intervention(s): Transvaginal Doppler examination of uterine arteries during the midluteal phase of untreated cycles. Main Outcome Measure(s): The mean pulsatility index (PI) of uterine arteries.

The aim of salient point detection is to find distinctive events in images. Salient features are generally determined from the local differential structure of images. They focus on the shape saliency of the local neighborhood. The majority of these detectors is luminance based which has the disadvantage that the distinctiveness of the local color information is completely ignored. To fully exploit the possibilities of color image salient point detection, color distinctiveness should be taken into account next to shape distinctiveness.

Facial skin detection is an important step in facial surgical planning like as many other applications. There are many problems in facial skin detection. One of them is that the image features can be severely corrupted due to illumination, noise, and occlusion, where, shadows can cause numerous strong edges. Hence, in this papHence, in this pappper, we present an automatic Expectation-Maximization (EM) algorithm for facial skin color segmentation that uses knowledge of chromatic space and varying illumination conditions to correct and segment frontal and lateral facial color images, simultaneously. The proposed EM algorithm leads to a method that allows for more robust and accurate segmentation of facial images. The initialization of the model parameters is very important in convergence of algorithm. For this purpose, we use a method for robust parameter estimation of Gaussian mixture components. Also, we use an additional class, which includes all pixels not modeled explicitly by Gaussian with small variance, by a uniform probability density, and amending the EM algorithm appropriately, in order to obtain significantly better results. Experimental results on facial color images show that accurate estimates of the Gaussian mixture parameters are computed. Also, other results on images presenting a wide range of variations in lighting conditions, demonstrate the efficiency of the proposed color skin segmentation algorithm compared to conventional EM algorithm.

A common problem in fruit production systems is sorting and classification. A usual procedure to carry out this task is based on human visual inspection considering general fruit attributes like color and size. Color contains important information about fruit status and in some cases it is decisive for fruit quality differentiation. An adequate color classification can improve system accuracy and productivity. Large-scale utilization of automatic classification system for this purpose demands a robust color classification even under different color saturation, variations of environment lighting and light reflections. This paper provides an investigation on the applicability of color classification using an artificial neural network in the fruit-sorting domain. Using the well-known network generalization property we investigate the applicability of this approach to the segmentation of colored images represented by the RGB color system. Jointly with color analysis, we also use some shape analysis to generate a robust and real time system that was tested for orange classification according to a Brazilian standard and which was able to provide fruit classification under less restricted visual conditions.

In this paper, we present an efficient coarse-tofine multiresolution framework for multidimensional scaling and demonstrate its performance on a large-scale nonlinear dimensionality reduction and embedding problem in a texture feature extraction step for the unsupervised image segmentation problem. We demonstrate both the efficiency of our multiresolution algorithm and its real interest to learn a nonlinear low-dimensional representation of the texture feature set of an image which can then subsequently be exploited in a simple clustering-based segmentation algorithm. The resulting segmentation procedure has been successfully applied on the Berkeley image database, demonstrating its efficiency compared to the best existing state-ofthe-art segmentation methods recently proposed in the literature.

This paper discusses a new approach for detecting edges with sub-pixel accuracy in color images using the principal axis analysis and the moment-preserving principle. First, the principal axis of the color vectors in a three-dimensional space is obtained analytically for each image block. Based on the projection scores, the direction of the line edge is obtained by analyzing the three-dimensional principal axis of a color image block. Next, by preserving some spatial color moments of the pixels of each square block, analytic formulas are derived to extract the edge feature. Experiments on both synthetic and real images show that the accuracy of the proposed method is comparable to other color edge detectors. The proposed algorithm can be performed very fast for real-time applications with no need for special hardware.

Relative spectral reflectance is an illumination invarient image feature that is related to many ecological phenomena that are difficult to measure, such as plant CO2 uptake. We describe a procedure to estimate the relative spectral reflectance of known subject using ...

The conventional histogram intersection (HI) algorithm computes the intersected section of the corresponding color histograms in order to measure the matching rate between two color images. Since this algorithm is strictly based on the matching between bins of identical colors, the final matching rate can be easily affected by color variation caused by various environment changes. In this paper, a Gaussian weighted histogram intersection (GWHI) algorithm is proposed to facilitate the histogram matching via taking into account matching of both identical and similar colors. The weight is determined by the distance between two colors. The algorithm is applied to license plate classification. Experimental results show that the proposed algorithm produces a much lower intra-class distance and a much higher inter-class distance than previous HI algorithms for tested images which are captured under various illumination conditions.

We present novel algorithms to suppress impulsive noise in video color sequences. They use order statistics, directional and adaptive processing techniques. Extensive simulation results in known reference video color sequences have demonstrated that the proposed filters consistently outperform other filters by balancing the tradeoff between noise suppression, detail preservation, and chromaticity characteristics. The criteria used to compare the performance or various filters were the PSNR, MAE, and NCD.

Abstract: We introduce a new classification method that is applicable to classify image pixels. This work was motivated by the test-based classification (TBC) introduced by Liao and Akritas (2007). We found that direct application of TBC on image pixel classification ...

Morphological associative memories (MAMs) are a special type of associative memory which exhibit optimal absolute storage capacity and one-step convergence. This associative model substitutes the additions and multiplications used in the classical models by additions/subtractions and maximums/minimums depending on the proposed model. MAMs have been applied to different pattern recognition problems including face localization and gray scale image restoration. Despite of his power, it has not been applied in problems that involve true-color patterns. In this paper we show how a Morphological Auto-associative Memory (MAAM) can be applied to restore true-color patterns. We present a study of the behavior of this associative model with a benchmark of 14400 images altered by different type of noises.

Content-based image retrieval (CBIR) is a collection of techniques for retrieving images on the basis of features, such as color, texture and shape. An efficient tool, which is widely used in CBIR, is that of color image histograms. The classic method of color histogram creation results in very large histograms with large variations between neighboring bins. Thus, small changes in the image might result in great changes in the histogram. Moreover, the fact that each color space consists of three components leads to 3-dimensional histograms. Manipulating and comparing 3D histograms is a complicated and computationally expensive procedure. The need, therefore, for reduction of the three dimensions to one could lead to efficient approaches. This procedure of projecting the 3D histogram onto one single-dimension histogram is called histogram linking. In this paper, a new fuzzy linking method of color histogram creation is proposed based on the L*a*b* color space and provides a histogram which contains only 10 bins. The histogram creation method in hand was assessed based on the performances achieved in retrieving similar images from a widely diverse image collection. The experimental results prove that the proposed method is less sensitive to various changes in the images (such as lighting variations, occlusions and noise) than other methods of histogram creation.

Large area hydrogenated amorphous silicon single and stacked p-in structures with low conductivity doped layers are proposed as monochrome and color image sensors. The layers of the structures are based on amorphous silicon alloys (a-Si(x)C(1-x):H). The current-voltage characteristics and the spectral sensitivity under different bias conditions are analyzed. The output characteristics are evaluated under different read-out voltages and scanner wavelengths. To extract information on image shape, intensity and color, a modulated light beam scans the sensor active area at three appropriate bias voltages and the photoresponse in each scanning position ("sub-pixel") is recorded. The investigation of the sensor output under different scanner wavelengths and varying electrical bias reveals that the response can be tuned, thus enabling color separation. The operation of the sensor is exemplified and supported by a numerical simulation.

Demosaicking is the process of reconstructing a full-resolution color image from the sampled data acquired by a digital camera that apply a color filter array to a single sensor. This paper discusses the need of a color filter array and presents a survey of several techniques proposed to demosaicking. A comparison between the different methods is also provided, discussing their performances.

This paper addresses the problem of compensating for lateral chromatic aberration in digital images through colour plane realignment. Two main contributions are made: the derivation of a model for lateral chromatic aberration in images, and the subsequent calibration of this model from a single view of a chess pattern. These advances lead to a practical and accurate alternative for the compensation of lateral chromatic aberrations. Experimental results validate the proposed models and calibration algorithm. The effects of colour channel correlations resulting from the camera colour filter array interpolation is examined and found to have a negligible magnitude relative to the chromatic aberration. Results with real data show how the removal of lateral chromatic aberration significantly improves the colour quality of the image.

The superficial appearance and color of food are the first parameters of quality evaluated by consumers, and are thus critical factors for acceptance of the food item by the consumer. Although there are different color spaces, the most used of these in the measuring of color in food is the L*a*b* color space due to the uniform distribution of colors, and because it is very close to human perception of color. In order to carry out a digital image analysis in food, it is necessary to know the color measure of each pixel on the surface of the food item. However, there are at present no commercial L*a*b* color measures in pixels available because the existing commercial colorimeters generally measure small, non-representative areas of a few square centimeters. Given that RGB digital cameras obtain information in pixels, this article presents a computational solution that allows the obtaining of digital images in L*a*b* color units for each pixel of the digital RGB image. This investigat...

Steganography is a technique to hide secret information in some other data (we call it a vessel) without leaving any apparent evidence of data alteration. All of the traditional steganographic techniques have limited information-hiding capacity. They can hide only 10% (or less) of the data amounts of the vessel. This is because the principle of those techniques was either to replace a special part of the frequency components of the vessel image, or to replace all the least significant bits of a multivalued image with the secret information.

In this paper, after an overview of the literature concerning the imaging technologies applied to skin wounds assessment, we present an original approach to build 3-D models of skin wounds from color images. The method can deal with uncalibrated images acquired with a handheld digital camera with free zooming. Compared with the cumbersome imaging systems already proposed, this novel solution uses a low-cost and user-friendly image acquisition device suitable for widespread application in health care centers. However, this method entails the development of a robust image processing chain. An original iterative matching scheme is used to generate a dense estimation of the surface geometry from two widely separated views. The best configuration for taking photographs lies between 15 and 30 for the vergency angle. The metric reconstruction of the skin wound is fully automated through self-calibration. From the 3-D model of the skin wound, accurate volumetric measurements are achieved. The accuracy of the inferred 3-D surface is validated by registration to a ground truth and repetitive tests on volume. The global precision around 3% is in accordance with the clinical requirement of 5% for assessing the healing process.

This paper presents a fast color-to-gray conversion algorithm which robustly reproduces the visual appearance of a color image in grayscale. The conversion preserves feature discriminability and reasonable color ordering, while respecting the original lightness of colors, by simple optimization of a nonlinear global mapping. Experimental results show that our method produces convincing results for a variety of color images. We further extend the method to temporally coherent color-to-gray video conversion.

Several efficient component and vector filters applicable for processing multichannel and color images are compared quantitatively. The cases of Gaussian and mixed Gaussian and impulsive noise are considered. It is shown that in situations typical for practice the best results are provided by a vector doublewindow modified trimmed mean filter and by a modified sigma filter applied to each component.

We investigate the general problem of reproducing color images on an offset press using custom inks in any combination and number. While this problem has been explored previously for the case of two inks, there are a number of new mathematical and algorithmic challenges that arise as the number of inks increases. These challenges include more complex gamut mapping strategies, more efficient ink selection strategies, and fast and numerically accurate methods for computing ink separations in situations that may be either over-or under-constrained. In addition, the demands of high-quality color printing require an accurate physical model of the colors that result from overprinting multiple inks using halftoning, including the effects of trapping, dot gain, and the interreflection of light between ink layers. In this paper, we explore these issues related to printing with multiple custom inks, and address them with new algorithms and physical models. Finally, we present some printed examples demonstrating the promise of our methods.

A Record in Stone is a descriptive presentation of stone-tool types from the continent of Australia, which combines Australian prehistory with lithic typology from a tutorial perspective. A unique and welcome feature is a supplemental compact disc (CD-ROM) which stores color images of the black and white illustrations in the text. The CD contains nearly 500 images of various stone tools, data tables, and images of flintknapping. The images are high-quality and include both JPEG and HTML formats; in many of the photographs, diagnostic morphological features are labeled. Such a visual format in books about prehistory is rare; it provides the reader with a quick-reference source for color images that often are too expensive to print in the text. A CD is a suitable alternative and should be considered by other authors in paleoanthropology and related disciplines.

The JPEG baseline standard for image compression employs a block Discrete Cosine Transform (DCT) and uniform quantization. For a monochrome image, a single quantization matrix is allowed, while for a color image, distinct matrices are allowed for each color channel.. Here we describe a method, called DCTune, for design of color quantization matrices that is based on a model of the visibility of quantization artifacts. The model describes artifact visibility as a function of DCT frequency, color channel, and display resolution and brightness. The model also describes summation of artifacts over space and frequency, and masking of artifacts by the image itself. The DCTune matrices are different from the de facto JPEG matrices, and appear to provide superior visual quality at equal bit-rates.

Abstract. In this contribution we present experiments on color image enhancement for several dierent non-linear lters which originally were de ned for gray-level images. We disturb sample images by dierent types of noise and measure performance of the lters. We provide signal-to-noise measurements as well as perceived color dierence in E as dened by the CIE. All images and test programs are provided online on the internet so that experiments can be validated by arbitrary users on any image data.

This paper presents a novel histogram thresholding – fuzzy C-means hybrid (HTFCM) approach that could find different application in pattern recognition as well as in computer vision, particularly in color image segmentation. The proposed approach applies the histogram thresholding technique to obtain all possible uniform regions in the color image. Then, the Fuzzy C-means (FCM) algorithm is utilized to improve

We present an approach to color image segmentation by applying it to recognition and vectorization of geo-images (satellite, cartographic). This is a simultaneous segmentation-recognition system when segmented geographical objects of interest (alphanumeric, punctual, linear, and area) are labeled by the system in same, but are different for each type of objects, gray-level values. We exchange the source image by a number of simplified images. These images are called composites. Every composite image is associated with certain image feature. Some of the composite images that contain the objects of interest are used in the following object detection-recognition by means of association to the segmented objects corresponding "names" from the user-defined subject domain. The specification of features and object names associated with perspective composite representations is regarded as a type of knowledge domain, which allows automatic or interactive system's learning. The results of gray-level and color image segmentation-recognition and vectoriztion are shown.

The goal of this work is to propose a set of techniques based on Mathematical Morphology to simplify or segment color images. Such simplification is done spatially or by quantization. The proposed methods apply the connected operators and morphological segmentation theories or extend them to color images.

Demosaicking is an estimation process to determine missing color values when a single-sensor digital camera is used for color image capture. In this paper, we propose a number of new methods based on the application of Taylor series and cubic spline interpolation for color filter array demosaicking. To avoid the blurring of an edge, interpolants are first estimated in four opposite directions so that no interpolation is carried out across an edge. A weighted median filter, whose filter coefficients are determined by a classifier based on an edge orientation map, is then used to produce an output from the four interpolants to preserve edges. Using the proposed methods, the original color can be faithfully reproduced with minimal amount of color artifacts even at edges.

The main objective of image enhancement is to improve some characteristic of an image to make it visually better one. This paper proposes a method for enhancing the color images based on nonlinear transfer function and pixel neighborhood by preserving details. In the proposed method, the image enhancement is applied only on the V (luminance value) component of the HSV color image and H and S component are kept unchanged to prevent the degradation of color balance between HSV components. The V channel is enhanced in two steps. First the V component image is divided into smaller overlapping blocks and for each pixel inside the block the luminance enhancement is carried out using nonlinear transfer function. In the second step, each pixel is further enhanced for the adjustment of the image contrast depending upon the center pixel value and its neighborhood pixel values. Finally, original H and S component image and enhanced V component image are converted back to RGB image. The subjective and objective performance evaluation shows that the proposed enhancement method yields better results without changing image original color in comparison with the conventional methods.

This paper presents the results of some partitional clustering algorithms applied to the segmentation of color images in the RGB space. As more information is involved in the algorithm, and the distance measure is more flexible, the better the results. The selected algorithms for this work are the K-means, the FCM, the GK-B, and the GKPFCM. The GKPFCM gives the better results when all the algorithms are applied to the segmentation of two images, an image of bananas and the other one of tomates at different stages of ripeness in both cases. The results are interesting as it is possible to identify the objects, to determine the degree of ripeness, and to estimate the amount and proportion of ripe objects for a possible decision-making.

This paper proposes an edge-directed interpolation algorithm for natural images. The basic idea is to first estimate local covariance coefficients from a low-resolution image and then use these covariance estimates to adapt the interpolation at a higher resolution based on the geometric duality between the low-resolution covariance and the high-resolution covariance. The edge-directed property of covariance-based adaptation attributes to its capability of tuning the interpolation coefficients to match an arbitrarily oriented step edge. A hybrid approach of switching between bilinear interpolation and covariance-based adaptive interpolation is proposed to reduce the overall computational complexity. Two important applications of the new interpolation algorithm are studied: resolution enhancement of grayscale images and reconstruction of color images from CCD samples. Simulation results demonstrate that our new interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional linear interpolation.

This article proposes an original method for grading the colours between different images or shots. The first stage of the method is to find a one-to-one colour mapping that transfers the palette of an example target picture to the original picture. This is performed using an original and parameter free algorithm that is able to transform any N-dimensional probability density function into another one. The proposed algorithm is iterative, non-linear and has a low computational cost. Applying the colour mapping on the original picture allows reproducing the same 'feel' as the target picture, but can also increase the graininess of the original picture, especially if the colour dynamic of the two pictures is very different. The second stage of the method is to reduce this grain artefact through an efficient post-processing algorithm that intends to preserve the gradient field of the original picture.

Most multi-camera systems assume a well structured environment to detect and track objects across cameras. Cameras need to be fixed and calibrated, or only objects within a training data can be detected (e.g. pedestrians only). In this work, a master-slave system is presented to detect and track any objects in a network of uncalibrated fixed and mobile cameras. Cameras can have non-overlapping field-of-views. Objects are detected with the mobile cameras (the slaves) given only observations from the fixed cameras (the masters). No training stage and data are used. Detected objects are correctly tracked across cameras leading to a better understanding of the scene.

This paper presents an automatic road-sign detection and recognition system based on support vector machines (SVMs). In automatic traffic-sign maintenance and in a visual driverassistance system, road-sign detection and recognition are two of the most important functions. Our system is able to detect and recognize circular, rectangular, triangular, and octagonal signs and, hence, covers all existing Spanish traffic-sign shapes. Road signs provide drivers important information and help them to drive more safely and more easily by guiding and warning them and thus regulating their actions. The proposed recognition system is based on the generalization properties of SVMs. The system consists of three stages: 1) segmentation according to the color of the pixel; 2) traffic-sign detection by shape classification using linear SVMs; and 3) content recognition based on Gaussian-kernel SVMs. Because of the used segmentation stage by red, blue, yellow, white, or combinations of these colors, all traffic signs can be detected, and some of them can be detected by several colors. Results show a high success rate and a very low amount of false positives in the final recognition stage. From these results, we can conclude that the proposed algorithm is invariant to translation, rotation, scale, and, in many situations, even to partial occlusions.

A picture-logic puzzle is a game that takes the form of an N Â M grid, with numbers situated on the left of its rows and on the top of its columns, which give the clues for solving the puzzle. These puzzles have gained popularity in the last years all over the world, and there are companies involved in the commercialization of products related to them, mainly magazines, on-line puzzles via the web and games for mobile phones. The main problem with selling picture-logic puzzles is the need to generate a large number of picture-logic puzzles per month, or even per week, so automating the generation process of such puzzles is an important task. In this paper we propose algorithms for the automated generation of picture-logic puzzles (both black and white and color puzzles), starting from any digital RGB color image. We also present a visualization interface which provides several useful tools for finishing the puzzle generation process, like the possibility of changing colors and displaying the process of puzzle solution. We show the performance of the algorithms for generating picture-logic puzzles with several examples. r

In modern endoscopy, wide field of view and full color are considered necessary for navigating inside the body, inspecting tissue for disease and guiding interventions such as biopsy or surgery. Current flexible endoscope technologies suffer from reduced resolution when device diameter shrinks. Endoscopic procedures today, using coherent fiber-bundle technology on the scale of 1 mm, are performed with such poor image quality that the clinician's vision meets the criteria for legal blindness. Here, we review a new and versatile scanning fiber-imaging technology and describe its implementation for ultrathin and flexible endoscopy. This scanning fiber endoscope (SFE) or catheterscope enables high-quality, laser-based, video imaging for ultrathin clinical applications, while also providing new options for in vivo biological research of subsurface tissue and high resolution fluorescence imaging.

This paper proposes an edge-directed interpolation algorithm for natural images. The basic idea is to first estimate local covariance coefficients from a low-resolution image and then use these covariance estimates to adapt the interpolation at a higher resolution based on the geometric duality between the low-resolution covariance and the high-resolution covariance. The edge-directed property of covariance-based adaptation attributes to its capability of tuning the interpolation coefficients to match an arbitrarily oriented step edge. A hybrid approach of switching between bilinear interpolation and covariance-based adaptive interpolation is proposed to reduce the overall computational complexity. Two important applications of the new interpolation algorithm are studied: resolution enhancement of grayscale images and reconstruction of color images from CCD samples. Simulation results demonstrate that our new interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional linear interpolation.

Nowadays photoshop crosses all of the restrictions to editing images. Anyone should be an excellent designer if a specialist in photoshop.  In 2020 Photoshop added some amazing features. That’s really good news for each Photoshop lovers. It creates things so easier. The thing selection tool is among the best equipment in Photoshop 2020. A graphic artist can simply select the object. Today’s I’m likely to share “how exactly to Change the colour In Photoshop 2020”. Now I’m likely to show the process to get this done.