Showing papers on "Histogram equalization published in 2001"

An advanced contrast enhancement using partially overlapped sub-block histogram equalization

Abstract: An advanced histogram-equalization algorithm for contrast enhancement is presented. Histogram equalization is the most popular algorithm for contrast enhancement due to its effectiveness and simplicity. It can be classified into two branches according to the transformation function used: global or local. Global histogram equalization is simple and fast, but its contrast-enhancement power is relatively low. Local histogram equalization, on the other hand, can enhance overall contrast more effectively, but the complexity of computation required is very high due to its fully overlapped sub-blocks. In this paper, a low-pass filter-type mask is used to get a nonoverlapped sub-block histogram-equalization function to produce the high contrast associated with local histogram equalization but with the simplicity of global histogram equalization. This mask also eliminates the blocking effect of nonoverlapped sub-block histogram-equalization. The low-pass filter-type mask is realized by partially overlapped sub-block histogram-equalization (POSHE). With the proposed method, since the sub-blocks are much less overlapped, the computation overhead is reduced by a factor of about 100 compared to that of local histogram equalization while still achieving high contrast. The proposed algorithm can be used for commercial purposes where high efficiency is required, such as camcorders, closed-circuit cameras, etc.

Contrast Enhancement by Multi-scale Adaptive Histogram Equalization

Abstract: An approach for contrast enhancement utilizing multi-scale analysis is introduced. Sub-band coefficients were modified by the method of adaptive histogram equalization. To achieve optimal contrast enhancement, the sizes of sub-regions were chosen with consideration to the support of the analysis filters. The enhanced images provided subtle details of tissues that are only visible with tedious contrast/brightness windowing methods currently used in clinical reading. We present results on chest CT data, which shows significant improvement over existing state-of-the-art methods: unsharp masking, adaptive histogram equalization (AHE), and the contrast limited adaptive histogram equalization (CLAHE). A systematic study on 109 clinical chest CT images by three radiologists suggests the promise of this method in terms of both interpretation time and diagnostic performance on different pathological cases. In addition, radiologists observed no noticeable artifacts or amplification of noise that usually appears in traditional adaptive histogram equalization and its variations.

A Robust CBIR Approach Using Local Color Histograms

Abstract: Global color histograms are well-known as a simple and often way to perform color-based image retrieval. However, it lacks spatial information about the image colors. The use of a grid of cells superimposed on the images and the use of local color histograms for each such cell improves retrieval in the sense that some notion of color location is taken into account. In such an approach however, retrieval becomes sensitive to image rotation and translation. In this thesis we present a new way to model image similarity, also using colors and a superimposing grid, via bipartite graphs. As a result, the technique is able to take advantage of color location but is not sensitive to rotation and translation. Experimental results have shown the approach to be very effective. If one uses global color histograms as a filter then our approach, named Harbin, becomes quite effcient as well (i.e., it imposes very little overhead over the use of global color histograms).

Adaptive-neighborhood histogram equalization of color images

Abstract: Histogram equalization (HE) is one of the simplest and most effective techniques for enhancing gray-level images. For color images, HE becomes a more difficult task, due to the vectorial nature of the data. We propose a new method for color image enhancement that uses two hierarchical levels of HE: global and local. In order to preserve the hue, equalization is only applied to intensities. For each pixel (called the ‘‘seed’’ when being processed) a variable-sized, variable-shaped neighborhood is determined to contain pixels that are ‘‘similar’’ to the seed. Then, the histogram of the region is stretched to a range that is computed with respect to the statistical parameters of the region (mean and variance) and to the global HE function (of intensities), and only the seed pixel is given a new intensity value. We applied the proposed color HE method to various images and observed the results to be subjectively ‘‘pleasant to the human eye,’’ with emphasized details, preserved colors, and with the histogram of intensities close to the ideal uniform one. The results compared favorably with those of three other methods (histogram explosion, histogram decimation, and three-dimensional histogram equalization) in terms of subjective visual quality.

Quantile based histogram equalization for noise robust speech recognition

Abstract: This paper describes an approach to increase the noise robustness of automatic speech recognition systems by, transforming the signal after Mel scaled filtering, to make the cumulative density functions of the signal’s values in recognition match the ones that where estimated on the training data. The cumulative density functions are approximated using a small number of quantiles. Recognition tests on several databases showed significant reductions of the word error rates. On a real life database recorded in driving cars with a large mismatch between the training and testing conditions the relative reductions of the word error rates where over 60%.

Estimating text color and segmentation of images

Abstract: In some embodiments, the invention includes receiving a digital image including text and background. The method includes vector quantizing the digital image such that the digital image is divided into certain colors, and creating a text color histogram from a portion of the text and a first portion of the background. The method also includes creating at least one background color histogram from a second portion of the background, and creating a difference color histogram from a difference between the text color histogram and the at least one background color histogram, and wherein an estimated color of the text is derived from the difference color histogram. In other embodiments, the invention includes receiving a text object including bounding boxes of multiple frames of a video signal. The method further includes estimating a color of text of the bounding boxes and aligning blocks representing the bounding boxes through a best displacement search in which only pixels having a color within a threshold of an estimated color are considered. Some embodiments of the invention also include receiving digital images in text bounding boxes and in preparation for a segmentation process, adjusting sizes of the digital images to a fixed height.

An Extendible Hash for Multi-Precision Similarity Querying of Image Databases

Abstract: We propose multi-precision similarity matching where the image is divided into a number of subblocks, each with its associated color histogram. We present experimental results showing that the spatial distribution information recorded by multiprecision color histograms helps to make similarity matching more precise. We also show that sub-image queries are much better supported with multi-precision color histograms. To minimize the overhead, we employ a filtering scheme based on the 3-dimensional average color vectors. We provide a formal result proving that filtering with multi-precision color histograms is complete. Finally, we develop a novel extendible hashing structure for indexing the average color vectors. We give experimental results showing that the proposed structure significantly outperforms the SR-tree.

Family histogram based techniques for detection of commercials and other video content

Abstract: Techniques are disclosed for detecting commercials or other particular types of video content in a video signal. In an illustrative embodiment, color histograms are extracted from frames of the video signal. For each of at least a subset of the extracted color histograms, the extracted color histogram is compared to a family histogram. If the extracted color histogram falls within a specified range of the family histogram, the family histogram is updated to include the extracted color histogram as a new member. If the extracted color histogram does not fall within the specified range of the family histogram, the family histogram is considered complete and the extracted color histogram is utilized to generate a new family histogram for use in processing subsequent extracted color histograms. The resulting family histograms are utilized to detect commercials or other particular type of video content in the video signal.

Membership function modification of fuzzy logic controllers with histogram equalization

Abstract: In most fuzzy logic controllers (FLCs), initial membership functions (MFs) are normally laid evenly all across the universes of discourse (UD) that represent fuzzy control inputs. However, for evenly distributed MFs, there exists a potential problem that may adversely affect the control performance; that is, if the actual inputs are not equally distributed, but instead concentrate within a certain interval that is only part of the entire input area, this will result in two negative effects. On one hand, the MFs staying in the dense-input area will not be sufficient to react precisely to the inputs, because these inputs are too close to each other compared to the MFs in this area. The same fuzzy control output could be triggered for several different inputs. On the other hand, some of the MFs assigned for the sparse-input area are "wasted". In this paper we argue that, if we arrange the placement of these MFs according to a statistical study of feedback errors in a closed-loop system, we can expect a better control performance. To this end, we introduce a new mechanism to modify the evenly distributed MFs with the help of a technique termed histogram equalization. The histogram of the errors is actually the spatial distribution of real-time errors of the control system. To illustrate the proposed MF modification approach, a computer simulation of a simple system that has a known mathematical model is first analyzed, leading to our understanding of how this histogram-based modification mechanism functions. We then apply this method to an experimental laser tracking system to demonstrate that in real-world applications, a better control performance can he obtained by using this proposed technique.

Dynamic histogram equalization for high dynamic range images

Abstract: Dynamic range equalization by histogram modification. A histogram is analyzed to determine locations of peaks. A mapping function is formed which relates to the locations of the peaks in a histogram. That mapping function may have areas of highest slope near the peaks. The mapping function is used to form a compressed histogram, which has the required number of levels to display on a display device.

Floating-gate adaptation for focal-plane online nonuniformity correction

Abstract: Stochastic adaptive algorithms are investigated for online correction of spatial nonuniformity in random-access addressable imaging systems. The adaptive architecture is implemented in analog VLSI, integrated with the photosensors on the focal plane. Random sequences of address locations selected with controlled statistics are used to adaptively equalize the intensity distribution at variable spatial scales. Through a logarithm transformation of system variables, adaptive gain correction is achieved through offset correction in the log-domain. This idea is particularly attractive for compact implementation using translinear floating-gate MOS circuits. Furthermore, the same architecture and random addressing provide for oversampled binary encoding of the image with equalized intensity histogram. The techniques apply to a variety of solid-state imagers, such as artificial retinas, active pixel sensors, and IR sensor arrays. Experimental results confirm gain correction and histogram equalization in a 64/spl times/64 pixel adaptive array integrated on a 2.2-mm/spl times/2.25-mm chip in 1.2-/spl mu/m CMOS technology.

Content based image retrieval and information theroy: a general approach

Abstract: A fundamental aspect of content-based image retrieval (CBIR) is the extraction and the representation of a visual feature that is an effective discriminant between pairs of images. Among the many visual features that have been studied, the distribution of color pixels in an image is the most common visual feature studied. The standard representation of color for content-based indexing in image databases is the color histogram. Vector-based distance functions are used to compute the similarity between two images as the distance between points in the color histogram space. This paper proposes an alternative real valued representation of color based on the information theoretic concept of entropy. A theoretical presentation of image entropy is accompanied by a practical description of the merits and limitations of image entropy compared to color histograms. Specifically, the L1 norm for color histograms is shown to provide an upper bound on the difference between image entropy values. Our initial results suggest that image entropy is a promising approach to image description and representation.

Robust histogram construction from color invariants

Abstract: A simple and effective object recognition scheme is to represent and march images on the basis of color histograms. To obtain robustness against varying imaging circumstances (e.g. a change in illumination, object pose, and viewpoint), color histograms are constructed from color invariants. However in general, color invariants are negatively affected by sensor noise due to the instabilities of these color invariant transforms at many RGB values. To suppress the effect of noise blow-up for unstable color invariant values, in this paper color invariant histograms are computed using variable kernel density estimators. To apply variable kernel density estimation in a principled way, models are proposed for the propagation of sensor noise through color invariants. As a result the associated uncertainty is known for each color invariant value. The associated uncertainty is used to derive the parameterization of the variable kernel density estimator during histogram construction. It is empirically verified that the proposed method compares favorably to traditional color histograms for object recognition.

Content-based multimedia retrieval system and method thereof

Abstract: The present invention relates to a multimedia retrieval system and a method thereof which is capable of performing a multimedia data retrieval among different systems using color histograms constructed with different color spaces and color quantization methods, in particular to a content-based multimedia retrieval system and a method thereof which is capable of retrieving multimedia data among different systems, by extracting or converting a color histogram of a query image or a color histogram of an image to be retrieved into a color histogram of the same color space and color quantization method each other, and using the extracted or converted color histogram.

Evolutionary image enhancement with user behavior modeling

Abstract: In this paper we present a novel method for image enhancement of gray-scale images based on the simulation of evolution. Our method employs Genetic Algorithms to evolve the shape of the contrast curve in the image, while attempting to partially automate the subjective process of image evaluation (e.g. user behavior) by performing multiple regression on fitness values. Results obtained show the robustness and efficiency of the evolutive method for image enhancement. For several images in the test set our method obtains better results than the classical histogram equalization technique. Extensive statistics performed, shows that multiple regression can be effectively applied to model the user behavior.

Nonlinear enhancement and segmentation algorithm for the detection of age-related macular degeneration (AMD) in human eye's retina

Abstract: Assessment of the risk for the development of age related macular degeneration requires reliable detection of retinal abnormalities that are considered as precursors of the disease. A typical sign for the latter are the so-called drusen, which appear as abnormal white-yellow deposits on the retina. This paper presents a novel segmentation algorithm for automatic detection of abnormalities in images of the human eye's retina, acquired from a depth-vision camera. Conventional image processing techniques are sensitive to non-uniform illumination and nonhomogeneous background, which obstructs the derivation of reliable results for a large set of different images. Homomorphic filtering and a multilevel variant of histogram equalization are used for non-uniform illumination compensation and enhancement. We develop a novel segmentation technique, the histogram-teased adaptive local thresholding (HALT), to detect drusen in retina images by extracting the useful information without being affected by the presence of other structures. We provide experimental results from the application of our technique to real images, where certain abnormalities (drusen) have slightly different characteristics from the background and are hard to be segmented by other conventional techniques.

Artificial landmark tracking based on the color histogram

Abstract: For the fast and accurate self-localization of mobile robots, landmarks can be used very efficiently in the complex workspace. In this paper, we propose a simple color landmark model for self-localization and a fast landmark detection and tracking algorithm based on the proposed landmark model. We develop a color landmark with symmetric and repetitive structures, which shows invariant color histogram characteristics under some geometric distortions. Detection and tracking of the model are accomplished by a factored sampling technique in which color similarity is estimated by the color histogram intersection. We also use the color similarity to update the color histogram model of the landmark model for robust tracking under illumination change. We demonstrate the feasibility of the proposed technique through experiments in cluttered indoor environments.

Apparatus and method for image processing and storage medium for the same

Abstract: An apparatus and a method as well as a storage device of the present invention transform intensity levels with consideration of both features of the intensity levels in an input image and human visual characteristics, wherein the present invention includes the steps of inputting wider dynamic range digital image data, generating a histogram of intensity levels of the image data, generating a histogram equalization LUT by cumulating the histogram, generating a visual characteristics LUT with reference to the histogram equalization LUT, generating a combined LUT by combining the histogram equalization LUT and the visual characteristics LUT, and correcting the image data.

PCA-based representation of color distributions for color-based image retrieval

Abstract: In many color based image retrieval systems the color properties of an image are described by the histogram of the colors in the image. Color histograms are insensitive to small object distortions and easy to compute. Histogram based searches are however often inefficient for the large histogram sizes needed to represent color distributions. Therefore we introduce several new, PCA-based methods that provide efficient representations of color histograms and differences between two color histograms. We also investigate distance measures in the space of histograms which are defined by quadratic forms and which take into account the geometric structure of the underlying color space. We show that the combination of the quadratic forms based distance measure and the compression of the histogram information by difference based PCA-approximations provide new powerful and efficient retrieval algorithms for color based image retrieval.

Color histogram correction for panoramic images

Abstract: Colors need to be corrected to minimize the discrepancy in the appearance of adjacent images such as in the applications of panoramic images. In this paper, we proposed a color correction technique that constructs a mapping from the registered overlapped regions based on a color histogram. Since the color histogram can reflect the overall color distribution, experiments prove that it can efficiently correct the color difference between the two overlapping images.

Color image segmentation by fuzzy morphological transformation of the 3D color histogram

Abstract: Summary form only given. We present a color image segmentation method using fuzzy mathematical morphology operators on the 3D color histogram. Segmentation consists in detecting the different modes which are present in the 3D color histogram and associated to homogeneous regions. In order to detect these modes, we show how a color image can be considered as a fuzzy set of pixels characterised by its membership function to a mode. It is defined by the concavity analyses of the histogram. Then, a fuzzy morphological transformation is applied to this membership function in order to enhance the modes. The efficiency of our proposed fuzzy morphological approach is then illustrated using a color image.

Color Space Quantization for Color-Content-Based Query Systems

Abstract: Color histograms are widely used in most of color content-based image retrieval systems to represent color content. However, the high dimensionality of a color histogram hinders efficient indexing and matching. To reduce histogram dimension with the least loss in color content, color space quantization is indispensable. This paper highlights and emphasizes the importance and the objectives of color space quantization. The color conservation property is examined by investigating and comparing different clustering techniques in perceptually uniform color spaces and for different images. For studying color spaces, perceptually uniform spaces, such as the Mathematical Transformation to Munsell system (MTM) and the C.I.E. Laaaba, are investigated. For evaluating quantization approaches, the uniform quantization, the hierarchical clustering, and the Color-Naming-System (CNS) supervised clustering are studied. For analyzing color loss, the error bound, the quantized error in color space conversion, and the average quantized error of 400 color images are explored. A color-content-based image retrieval application is shown to demonstrate the differences when applying these clustering techniques. Our simulation results suggest that good quantization techniques lead to more effective retrieval.

Fluorescent image classification by major color histograms and a neural network.

Abstract: Efficient image classification of microscopic fluorescent spheres is demonstrated with a supervised backpropagation neural network (NN) that uses as inputs the major color histogram representation of the fluorescent image to be classified Two techniques are tested for the major color search: (1) cluster mean (CM) and (2) Kohonen’s self-organizing feature map (SOFM) The method is shown to have higher recognition rates than Swain and Ballard’s Color Indexing by histogram intersection Classification with SOFM-generated histograms as inputs to the classifier NN achieved the best recognition rate (90%) for cases of normal, scaled, defocused, photobleached, and combined images of AMCA (7-Amino-4-Methylcoumarin-3-Acetic Acid) and FITC (Fluorescein Isothiocynate)-stained microspheres

Preventing doming phenomena

Abstract: In a method of preventing doming phenomena, luminance histogram distribution values are obtained (10) from a luminance signal, the various luminance histogram distribution values are processed and normalized (12), the luminance histogram distribution values are processed (16) to correct for at least actual contrast and brightness settings to obtain corrected histogram data, a beam current is predicted (18) from the corrected histogram data, and a correction signal is derived (20) from the predicted beam current and making said correction signal effective in the processing and normalizing step (12).

Image authentication by exact histogram specification

Abstract: The paper proposes a spatial domain watermarking scheme for image authentication and, in case image content has been changed, for localization of the altered area. A pseudo-random set of pixels is selected by using a secret key and is marked with a visually meaningful watermark by exact histogram specification. The exact recovery of the watermark authenticates the image. Specially designed watermarks are used to locate the altered area. Image visual aspect as well as image histogram are preserved. The approach addresses gray level and color images as well.

Image enhancement using the modified cosine function and semi-histogram equalization for gray-scale and color images

Abstract: The paper outlines two image enhancement operators for gray scale and color images. A modified cosine function was developed for image enhancement, in which some enhanced images may appear a little darker if the average pixel value in the image generally falls below the intermediate value (which is 128 in most images used in the experiments (256 gray-level)). A Semi-Histogram-Equalization (SHE) method is then proposed for enhancement so that regardless of how dark or bright the image is, it would give a good contrast enhancement. Experimental results show that the proposed operators can enhance gray scale and color images effectively.

Soft color signatures for image retrieval by content.

Abstract: Content-based image retrieval primarily used color distributions as descriptors of the image content; researches have since focused on the use of various color representation spaces, color and illumination invariance, color quantization and color matching. In order to overcome the many limitations of the description by a first-order distribution, several higher-order distributions have been introduced since (like autoconelogram or color coherence vectors). Although they can perform better, their computational complexity is prohibitive and they require parameter setting. We propose to upgrade the first order color distribution (color histogram) by embedding for each color additional information about its perceptual or statistical relevance. Such information is obtained by using local activity measures such as the Laplacian, the entropy and others. Histograms computed on windows and combined by different ways of accumulation improve the information on geometric repartition of colors. We prove that the new color distribution family is compact, robust and easy to compute and provides a superior retrieval performance, independent with respect to the color representation.