In this letter, a novel decolorization method is proposed to convert color images into grayscale. The proposed method, called CorrC2G, estimates the three global linear weighting parameters of the color to gray conversion by correlation. These parameters are estimated directly from the correlations between each channel of the RGB image and a contrast image. The proposed method works directly on the RGB channels; it does not use any edge information nor any optimization or training. The objective and subjective experimental results on three available benchmark datasets of color to gray conversion, e.g., Cadik, CSDD, and Color250, show that the proposed decolorization method is highly efficient and comparable to recent state-of-the-art decolorization methods. The MATLAB source code of the proposed method is available at: http://www.synchromedia.ca/system/files/CorrC2G.m.

In this letter, a very simple no-reference image quality assessment (NR-IQA) model for JPEG compressed images is proposed. The proposed metric called median of unique gradients (MUG) is based on the very simple facts of unique gradient magnitudes of JPEG compressed images. MUG is a parameterless metric and does not need training. Unlike other NR-IQAs, MUG is independent to block size and cropping. A more stable index called MUG$^+$is also introduced. The experimental results on six benchmark datasets of natural images and a benchmark dataset of synthetic images show that MUG is comparable to the state-of-the-art indices in the literature. In addition, its performance remains unchanged for the case of the cropped images in which block boundaries are not known. The MATLAB source code of the proposed metrics is available athttps://dl.dropboxusercontent.com/u/74505502/MUG.mandhttps://dl.dropboxusercontent.com/u/74505502/MUGplus.m.

In this paper, we propose a new unsupervised change detection method designed to analyze multispectral remotely sensed image pairs. It is formulated as a segmentation problem to discriminate the changed class from the unchanged class in the difference images. The proposed method is in the category of the committee machine learning model that utilizes an ensemble of classifiers (i.e., the set of segmentation results obtained by several thresholding methods) with a dynamic structure type. More specifically, in order to obtain the final “change/no-change” output, the responses of several classifiers are combined by means of a mechanism that involves the input data (the difference image) under an iterative Bayesian-Markovian framework. The proposed method is evaluated and compared to previously published results using satellite imagery.

The first competition on the MultiSpectral Text Extraction (MS-TEx) from historical document images has been organized in conjunction with the ICDAR 2015 conference. The goal of this contest is evaluation of the most recent advances in text extraction from historical document images captured by a multispectral imaging system. The MS-TEx 2015 dataset contains 10 handwritten and machine-printed historical document images along with eight spectral images for each image. This paper provides a report on the methodology and performance of the five submitted algorithms by various research groups across the world. The objective evaluation and ranking was performed by using well-known evaluation metrics of binarization and classification.

In this work, based on the local phase information of images, an objective index, called the feature similarity index for tone-mapped images (FSITM), is proposed. To evaluate a tone mapping operator (TMO), the proposed index compares the locally weighted mean phase angle map of an original high dynamic range (HDR) to that of its associated tone-mapped image calculated using the output of the TMO method. In experiments on two standard databases, it is shown that the proposed FSITM method outperforms the state-of-the-art index, the tone mapped quality index (TMQI). In addition, a higher performance is obtained by combining the FSITM and TMQI indices.

This paper presents a novel preprocessing method of color-to-gray document image conversion. In contrast to the conventional methods designed for natural images that aim to preserve the contrast between different classes in the converted gray image, the proposed conversion method reduces as much as possible the contrast (i.e., intensity variance) within the text class. It is based on learning a linear filter from a predefined data set of text and background pixels that: 1) when applied to background pixels, minimizes the output response and 2) when applied to text pixels, maximizes the output response, while minimizing the intensity variance within the text class. Our proposed method (called learning-based color-to-gray) is conceived to be used as preprocessing for document image binarization. A data set of 46 historical document images is created and used to evaluate subjectively and objectively the proposed method. The method demonstrates drastically its effectiveness and impact on the performance of state-of-the-art binarization methods. Four other Web-based image data sets are created to evaluate the scalability of the proposed method.

In this paper, a phase-based binarization model for ancient document images is proposed, as well as a postprocessing method that can improve any binarization method and a ground truth generation tool. Three feature maps derived from the phase information of an input document image constitute the core of this binarization model. These features are the maximum moment of phase congruency covariance, a locally weighted mean phase angle, and a phase preserved denoised image. The proposed model consists of three standard steps: 1) preprocessing; 2) main binarization; and 3) postprocessing. In the preprocessing and main binarization steps, the features used are mainly phase derived, while in the postprocessing step, specialized adaptive Gaussian and median filters are considered. One of the outputs of the binarization step, which shows high recall performance, is used in a proposed postprocessing method to improve the performance of other binarization methodologies. Finally, we develop a ground truth generation tool, called PhaseGT, to simplify and speed up the ground truth generation process for ancient document images. The comprehensive experimental results on the DIBCO'09, H-DIBCO'10, DIBCO'11, H-DIBCO'12, DIBCO'13, PHIBD'12, and BICKLEY DIARY data sets show the robustness of the proposed binarization method on various types of degradation and document images.

For the purpose of facilitating benchmark contributions for binarization methods, a new fast ground truthing approach, called the PhaseGT, is proposed. This approach is used for building the 1stgroundtruthed Persian Heritage Image Binarization Dataset (PHIBD 2012). The PhaseGT is a semiautomatic approach to ground truthing of images of any language, especially designed for historical document images. The main goal of the PhaseGT is to accelerate the ground truthing process and reduce the manual ground truthing effort. It uses the phase congruency features to preprocess the input image and to provide a more accurate initial binarization to the human expert who performs the manual part. This preprocessing is in turn based on a priori knowledge that is provided by human user. The PHIBD 2012 dataset contains 15 historical document images with their corresponding ground truth binary images. The historical images in the dataset suffer from various types of degradation. It has been also divided into two subsets of training and testing images for those binarization methods that use learning approaches.

Preprocessing and post processing steps significantly improve the performance of binarization methods, especially in the case of severely-degraded historical documents. In this paper, an unsupervised post processing method is introduced based on the phase-preserved denoised image and also phase congruency features extracted from the input image. The core of the method consists of two robust mask images that can be used to cross out false positive pixels on the output of the binarization method. First, a mask with a high recall value is obtained from the denoised image using morphological operations. In parallel, a second mask is obtained based on phase congruency features. Then, a median filter is used to remove noise on these two masks, which then are used to correct the output of any binarization method. This approach was tested along with several state-of the-art binarization methods on the DIBCO'09, H-DIBCO'10, DIBCO'11 and H-DIBCO'12 datasets with promising and robust results. Furthermore, the high performance of the proposed masks shows their potential use as unsupervised semi-ground truth generator for learning-based binarization methods.