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In this paper, we propose a novel color-texture image segmentation method based on local histograms. Starting with clustering-based color quantization, we extract a sufficient number of representative colors. For each pixel, through counting the number of pixels with each representative color within a circular neighborhood, a local histogram is obtained. After the circular neighborhood is extended to several scales, a local histogram with an appropriate scale is adopted as a color-texture descriptor at the corresponding pixel for image segmentation. Further, we correct the color-texture features near boundaries and obtain a initial segmentation by a clustering method with the color-texture descriptors. Finally, in order to obtain a better segmentation result, we merge the over segmented regions guided by the obtained boundaries. Experiments are performed on both synthetic and natural color-texture images, and the results show that our proposed method performs much better compared with state-of-the-art methods on image segmentation, particularly in textured areas.

Crowdsensing has been recognized as a promising data collection paradigm, in which a platform outsources sensing tasks to a large number of users. However, requesting users to report raw data may give rise to many practical concerns, such as a significant overhead of communication and central processing, besides users’ privacy concerns. In many scenarios (e.g, advertising and recommendation), the data collector directly benefits from statistical aggregation of raw data. Thus motivated, we consider the data collection problem based on user's local histograms, which is intimately related to the fundamental trade-off between the platform's accuracy and users’ privacy. Because of users’ social relationship, their data are often correlated, indicating that users’ privacy may be leaked from others’ data. To tackle this challenge, we first utilize Gaussian Markov random fields to model the correlation structure embedded in users’ data. The data collection is modeled as a Stackelberg game where the platform decides its reward policy and users decide their noise levels while taking into account the social coupling among users. For the reward policy design, we first establish the relationship between users’ Nash equilibrium and the payment mechanism, and then optimize the platform's accuracy under a budget constraint. Further, since the noise levels are users’ private information, they may use falsified noise levels to achieve higher payoffs, which in turn impairs the crowdsensing performance. It turns out that with the insight into the correlation structure among users’ data, the information asymmetry can be overcome based on peer prediction. We revisit the payment mechanism to guarantee dominant truthfulness of each user's strategy. Theoretical analysis and numerical results demonstrate the effectiveness of the proposed mechanism. [ABSTRACT FROM AUTHOR]

• We deduce the distribution function of artifact noise. • We use the method based on local histogram matching to detect artifact noise. • The artifact noise detection is useful for designing an adaptive bilateral filter. • The proposed algorithm can reduce artifact noise and increase the tissue structure. • Simulation and phantom testing show that the proposed method can improve the quality of strain imaging. Strain imaging in medical ultrasound is the imaging modality of elastic properties of biological tissue. In general, strain image will suffer from artifacts noise, which degrades lesion detectability and increases the likelihood of misdiagnosis. How to both suppress artifacts effectively and preserve the structure is vital for diagnosis and also for image post-processing. The bilateral filtering can reduce artifact noise and, at the same time, maintain the tissue structure. However, the balance between noise suppression and edge preservation often makes the threshold selection difficult. This paper is to solve the problem of difficult threshold selection in bilateral filtering. The probability distribution function of amplitude modulation noise in this paper is derived from the statistics of uncompressed speckle. The statistical model of artifact formation is useful for designing an adaptive fast bilateral filter for artifact reduction in ultrasound strain imaging. Both simulation and phantom testing show that the proposed method can improve the quality of ultrasonic strain imaging. Furthermore, the elastographic signal-to-noise ratio was increased by 129.91% and 52.36% for simulated and phantom strain images. The elastographic contrast-to-noise ratio was increased by 521.42% and 218.07% for simulated and phantom strain images, respectively. As indicated by the profiles, the proposed method produces a better result for the purpose of visualization. [ABSTRACT FROM AUTHOR]

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In this paper, we present a video application example and its implementation in a reconfigurable system-on- chip (SOC) platform. The proposed platform employs the benefits of field programmable gate array (FPGA) technology. A prototype based on a Xilinx Virtex-5 FPGA is developed. The application includes a video shot boundary detection module based on the local histogram (LH) technique. Diverse hardware module versions corresponding to different quantization levels and architectural solutions for an LH-based shot detection system are presented. The developed modules have different hardware resource occupations and can be used in a dynamic way to allow flexible management of the target hardware system. They also show high execution time efficiency and can reach an important bandwidth that can support the most recent high-resolution video formats with a high rate of frames per second. A complete SOC-based demonstration for video summarization based on the LH is also developed. It includes the LH extraction, shot boundary detection, and key frame visualization. [ABSTRACT FROM AUTHOR]

According to the problem of slow license plate detection and low detection accuracy under different conditions, this paper proposed an improved adaptive morphological closed and open operation to improve the license plate detection algorithm. The algorithm first used the local histogram to equalize the license plate image, and used the adaptive morphological closed operation to smooth all grayscale regions, and then introduced the local adaptive threshold processing to get the smooth image and the separated license plate. Finally, it adopted the morphological open operation to separate the connection parts between the external region and the number of the license plates. The experimental results show that the detection accuracy of the proposed method is higher than other algorithms. At the same time, the average detection time is less than other algorithms. The proposed method is suitable for real-time license plate detection under different conditions. [ABSTRACT FROM AUTHOR]

Accurate tissue classification is a crucial prerequisite to MRI morphometry. Automated methods based on intensity histograms constructed from the entire volume are challenged by regional intensity variations due to local radiofrequency artifacts as well as disparities in tissue composition, laminar architecture and folding patterns. Current work proposes a novel anatomy-driven method in which parcels conforming cortical folding were regionally extracted from the brain. Each parcel is subsequently classified using nonparametric mean shift clustering. Evaluation was carried out on manually labeled images from two datasets acquired at 3.0 Tesla ( n = 15) and 1.5 Tesla ( n = 20). In both datasets, we observed high tissue classification accuracy of the proposed method (Dice index >97.6% at 3.0 Tesla, and >89.2% at 1.5 Tesla). Moreover, our method consistently outperformed state-of-the-art classification routines available in SPM8 and FSL-FAST, as well as a recently proposed local classifier that partitions the brain into cubes. Contour-based analyses localized more accurate white matter-gray matter (GM) interface classification of the proposed framework compared to the other algorithms, particularly in central and occipital cortices that generally display bright GM due to their highly degree of myelination. Excellent accuracy was maintained, even in the absence of correction for intensity inhomogeneity. The presented anatomy-driven local classification algorithm may significantly improve cortical boundary definition, with possible benefits for morphometric inference and biomarker discovery. Hum Brain Mapp 36:3563-3574, 2015. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Elastography in medical ultrasound is an imaging technique that displays information about tissue stiffness. However, elastography suffers from artefact noise that may come from two dominant sources: decorrelation error and amplitude modulation error. In order to reduce artefact and improve the quality of ultrasonic elastography, a fast bilateral filter is proposed in this study based on local histogram. The presented filter is derived from a conventional bilateral filter, and a local histogram is introduced to speed up the filter. The proposed algorithm can reduce artefact noise and, at the same time, maintain the tissue structure. Both simulation and phantom testing show that the proposed method can improve the quality of ultrasonic elastography in terms of tissue elastographic signal-to-noise ratio and elastographic contrast-to-noise ratio values. [ABSTRACT FROM PUBLISHER]

This paper presents a unifying approach for calculating a wide range of popular, but seemingly very different, similarity measures. Our domain is the registration of n-dimensional images sampled on a regular grid, and our approach is well suited for gradient-based optimization algorithms. Our approach is based on local intensity histograms and built upon the technique of Locally Orderless Images. Histograms by Locally Orderless Images are well posed and offer explicit control over the three inherent and unavoidable scales: the spatial resolution, intensity levels, and spatial extent of local histograms. Through Locally Orderless Images, we offer new insight into the relations between these scales. We demonstrate our unification by developing a Locally Orderless Registration algorithm for two quite different similarity measures, namely, Normalized Mutual Information and Sum of Squared Differences, and we compare these variations both theoretically and empirically. Finally, using our algorithm, we explain the empirically observed differences between two popular joint density estimation techniques used in registration: Parzen Windows and Generalized Partial Volume. [ABSTRACT FROM AUTHOR]

Abstract: The local histogram transform of an image is a data cube that consists of the histograms of the pixel values that lie within a fixed neighborhood of any given pixel location. Such transforms are useful in image processing applications such as classification and segmentation, especially when dealing with textures that can be distinguished by the distributions of their pixel intensities and colors. We, in particular, use them to identify and delineate biological tissues found in histology images obtained via digital microscopy. In this paper, we introduce a mathematical formalism that rigorously justifies the use of local histograms for such purposes. We begin by discussing how local histograms can be computed as systems of convolutions. We then introduce probabilistic image models that can emulate textures one routinely encounters in histology images. These models are rooted in the concept of image occlusion. A simple model may, for example, generate textures by randomly speckling opaque blobs of one color on top of blobs of another. Under certain conditions, we show that, on average, the local histograms of such model-generated-textures are convex combinations of more basic distributions. We further provide several methods for creating models that meet these conditions; the textures generated by some of these models resemble those found in histology images. Taken together, these results suggest that histology textures can be analyzed by decomposing their local histograms into more basic components. We conclude with a proof-of-concept segmentation-and-classification algorithm based on these ideas, supported by numerical experimentation. [Copyright &y& Elsevier]

Abstract: This paper presents an automatic method for iris localization based on image statistics. The proposed method localizes the iris in two stages. In the first stage, a circular moving window is used to localize the pupil by finding the range of grey levels that has the highest probability of enclosing the pupil. The window with the grey level peak having the minimum standard deviation of x- and y-coordinates is selected as the region enclosing the pupil. In the second stage, effect of the eyelashes is reduced by using median filtering and the iris boundary is estimated by taking the gradient of the rows within pupil. The proposed method has been tested on three public databases: CASIA-IrisV1, CASIA-IrisV3-Lamp and MMU version 1.0. Experimental results demonstrate the superiority of the proposed method in comparison with some of the existing methods. [Copyright &y& Elsevier]

With the enormous growth in digital audiovisual (AV) information in our life, there is an important need for tools which enable describing the AV content information. In this context, the MPEG-7 standard was developed in order to provide a set of standardized description tools which generate metadata about AV content. However, before any content-based manipulations, the hierarchical structure of video must be determined. This process is known as shot boundary detection or in other case scene change detection. In this paper, an old and reliable method based on local histogram has been used to implement shot cut detector for real-time applications. Since software implementation on PC is not suitable for this algorithm due to the sequential treatments of the processor, we have used an FPGA-based platform. [Copyright &y& Elsevier]