Debashis Sen

Generalized Rough Sets, Entropy, and Image Ambiguity Measures

Quantifying ambiguities in images using fuzzy set theory has been of utmost interest to researchers in the field of image processing. In this paper, we present the use of rough set theory and its certain generalizations for quantifying ambiguities in images and compare it to the use of fuzzy set theory. We propose classes of entropy measures based on rough set theory and its certain generalizations, and perform rigorous theoretical analysis to provide some properties which they satisfy. Grayness and spatial ambiguities in images are then quantified using the proposed entropy measures. We demonstrate the utility and effectiveness of the proposed entropy measures by considering some elementary image processing applications. We also propose a new measure called average image ambiguity in this context.

Automatic Exact Histogram Specification for Contrast Enhancement and Visual System Based Quantitative Evaluation

Histogram equalization, which aims at information maximization, is widely used in different ways to perform contrast enhancement in images. In this paper, an automatic exact histogram specification technique is proposed and used for global and local contrast enhancement of images. The desired histogram is obtained by first subjecting the image histogram to a modification process and then by maximizing a measure that represents increase in information and decrease in ambiguity. A new method of measuring image contrast based upon local band-limited approach and center-surround retinal receptive field model is also devised in this paper. This method works at multiple scales (frequency bands) and combines the contrast measures obtained at different scales using Lp-norm. In comparison to a few existing methods, the effectiveness of the proposed automatic exact histogram specification technique in enhancing contrasts of images is demonstrated through qualitative analysis and the proposed image contrast measure based quantitative analysis.

Gradient histogram: Thresholding in a region of interest for edge detection

Selecting a threshold from the gradient histogram, a histogram of gradient magnitudes, of an image plays a crucial role in a gradient based edge detection system. This paper presents a methodology to determine the threshold from a gradient histogram generated using any kind of linear gradient operator on an image. We consider the image as a random process with dependent samples, model the gradient histogram using theories of random process and random input to a system, and determine a region of interest in the gradient histogram using certain properties of a probability density function. Standard histogram thresholding techniques are then used within the region of interest to get the threshold value. To obtain the edges, this threshold value is then used as the upper threshold of the hysteresis thresholding technique that follows the non-maximum suppression operation applied on the gradient magnitude image. The proposed methodology of determining a threshold in a gradient histogram is deduced through rigorous analysis and hence it helps in achieving consistently appreciable edge detection performance. Experimental results using different real-life and benchmark images are shown to demonstrate the effectiveness of the proposed technique.

Histogram Thresholding Using Fuzzy and Rough Measures of Association Error

This paper presents a novel histogram thresholding methodology using fuzzy and rough set theories. The strength of the proposed methodology lies in the fact that it does not make any prior assumptions about the histogram unlike many existing techniques. For bilevel thresholding, every element of the histogram is associated with one of the two regions by comparing the corresponding errors of association. The regions are considered ambiguous in nature, and, hence, the error measures are based on the fuzziness or roughness of the regions. Multilevel thresholding is carried out using the proposed bilevel thresholding method in a tree structured algorithm. Segmentation, object/background separation, and edge extraction are performed using the proposed methodology. A quantitative index to evaluate image segmentation performance is also proposed using the median of absolute deviation from median measure, which is a robust estimator of scale. Extensive experimental results are given to demonstrate the effectiveness of the proposed methods in terms of both qualitative and quantitative measures.

Image Segmentation using Global and Local Fuzzy Statistics

In this paper, criterion optimization based image thresholding techniques to perform segmentation using global and local fuzzy statistics are presented. The global and local fuzzy statistics considered for an image are the fuzzy histogram and fuzzy co-occurrence matrix of the image, respectively. A novel way of adapting the membership function, which is required to calculate the fuzzy statistics, to the local nature of the corresponding crisp first-order statistic (histogram) is suggested. The fuzzy statistics of an image obtained using such an adaptive membership function are called adaptive fuzzy statistics. Experimental results of various image segmentation techniques using crisp, fuzzy and the proposed adaptive fuzzy statistics are given. A comparative study demonstrating the usefulness of fuzzy statistics in image segmentation and the effectiveness of adapting the membership function in order to determine the fuzzy statistics is presented

Improving feature space based image segmentation via density modification

Feature space based approaches have been popularly used to perform low-level image analysis. In this paper, a density modification framework that enhances density map based discriminability of feature values in a feature space is proposed in order to aid feature space based segmentation in images. The framework embeds a position-dependent property associated with each sample in the feature space of an image into the corresponding density map and hence modifies it. The property association and embedding operations in the framework is implemented using a fuzzy set theory based system devised with cues from beam theory of solid mechanics and the appropriateness of this approach is established. Qualitative and quantitative experimental results of segmentation in images are given to demonstrate the effectiveness of the proposed density modification framework and the usefulness of feature space based segmentation via density modification.

Integrating Geometric and Textural Features for Facial Emotion Classification Using SVM Frameworks.

In this paper, we present a fast facial emotion classification system that relies on the concatenation of geometric and texture-based features. For classification, we propose to leverage the binary classification capabilities of a support vector machine classifier to a hierarchical graph-based architecture that allows multi-class classification. We evaluate our classification results by calculating the emotion-wise classification accuracies and execution time of the hierarchical SVM classifier. A comparison between the overall accuracies of geometric, texture-based, and concatenated features clearly indicates the performance enhancement achieved with concatenated features. Our experiments also demonstrate the effectiveness of our approach for developing efficient and robust real-time facial expression recognition frameworks.

Incorporating local image structure in normalized cut based graph partitioning for grouping of pixels

Abstract Graph partitioning for grouping of image pixels has been explored a lot, with normalized cut based graph partitioning being one of the popular ones. In order to have a credible allegiance to the perceptual grouping taking place in early human vision, we propose and study in this paper the incorporation of local image structure/context in normalized cut based graph partitioning for grouping of image pixels. Similarity and proximity, which have been studied earlier for grouping of image pixels, are only two among many perceptual cues that act during grouping in early human vision. In addition to the said two cues, we study three other such cues, namely, common fate, common region and continuity, and find indications of local image structure utilization during grouping of image pixels. Appropriate incorporation of local image structure/context is achieved by representing it using neighborhood in the form of histogram and fuzzy set. We demonstrate both qualitatively and quantitatively through experimental results that the incorporation of local image structure improves performance of grouping of image pixels.

Thresholding for Edge Detection in SAR Images

Selecting a threshold from the gradient histogram, a histogram of gradient magnitudes, of an image plays a crucial role in a gradient based edge detection system. In this paper, we propose a methodology to determine this threshold value when the edge detection system is applied to synthetic aperture radar (SAR) images. We consider a SAR image as a random process, perform a transformation, model the gradient histogram of the transformed image using theories of random process and then determine a region of interest in the gradient histogram using certain properties of a probability density function. Standard histogram thresholding techniques are then used within the region of interest to get the threshold value. The proposed methodology provides a systematic solution to the thresholding problem in gradient based edge detection systems for SAR images and hence results in consistently appreciable performance. Extensive experimental results are shown to demonstrate the effectiveness of the proposed methodology.

Visible watermarking based on importance and just noticeable distortion of image regions

Visible watermarking is the process of embedding data (watermark) into a multimedia object (video/image) such that the embedded watermark is perceptible to a human observer. Many times, visible watermarks occlude important portion of multimedia objects. This paper introduces a visible watermarking algorithm to embed a binary logo watermark at N non-overlapping positions in an image such that important portions of the image are not occluded. The important portions are found through visual saliency computation or available human eye fixation density maps. In the proposed visible watermarking, just noticeable distortion is used to adaptively filter the watermark embedding energy based on the image content. A mathematical model in terms of information-content-weighted-structural-similarity-index and visual importance is proposed to find optimal watermark embedding strength. We tested the algorithm on several color images of different sizes and on several binary watermarks of different sizes and found the results to be very promising as per the requirements in visible watermarking. When compared to the state-of-the-art, we also found that the proposed technique does better in not hiding the details of any test image.