Shervin Minaee

Screen content image segmentation using least absolute deviation fitting

We propose an algorithm for separating the foreground (mainly text and line graphics) from the smoothly varying background in screen content images. The proposed method is designed based on the assumption that the background part of the image is smoothly varying and can be represented by a linear combination of a few smoothly varying basis functions, while the foreground text and graphics create sharp discontinuity and cannot be modeled by this smooth representation. The algorithm separates the background and foreground using a least absolute deviation method to fit the smooth model to the image pixels. This algorithm has been tested on several images from HEVC standard test sequences for screen content coding, and is shown to have superior performance over other popular methods, such as k-means clustering based segmentation in DjVu and shape primitive extraction and coding (SPEC) algorithm. Such background/foreground segmentation are important pre-processing steps for text extraction and separate coding of background and foreground for compression of screen content images.

Screen content image segmentation using sparse-smooth decomposition

Sparse decomposition has been extensively used for different applications including signal compression and denoising and document analysis. In this paper, sparse decomposition is used for image segmentation. The proposed algorithm separates the background and foreground using a sparse-smooth decomposition technique such that the smooth and sparse components correspond to the background and foreground respectively. This algorithm is tested on several test images from HEVC test sequences and is shown to have superior performance over other methods, such as the hierarchical k-means clustering in DjVu. This segmentation algorithm can also be used for text extraction, video compression and medical image segmentation.

Screen Content Image Segmentation Using Robust Regression and Sparse Decomposition

This paper considers how to separate text and/or graphics from smooth background in screen content and mixed document images and proposes two approaches to perform this segmentation task. The proposed methods make use of the fact that the background in each block is usually smoothly varying and can be modeled well by a linear combination of a few smoothly varying basis functions, while the foreground text and graphics create sharp discontinuity. The algorithms separate the background and foreground pixels by trying to fit background pixel values in the block into a smooth function using two different schemes. One is based on robust regression, where the inlier pixels will be considered as background, while remaining outlier pixels will be considered foreground. The second approach uses a sparse decomposition framework where the background and foreground layers are modeled with a smooth and sparse components respectively. These algorithms have been tested on images extracted from HEVC standard test sequences for screen content coding, and are shown to have superior performance over previous approaches. The proposed methods can be used in different applications such as text extraction, separate coding of background and foreground for compression of screen content, and medical image segmentation.

Iris recognition using scattering transform and textural features

Iris recognition has drawn a lot of attention since the mid-twentieth century. Among all biometric features, iris is known to possess a rich set of features. Different features have been used to perform iris recognition in the past. In this paper, two powerful sets of features are introduced to be used for iris recognition: scattering transform-based features and textural features. PCA is also applied on the extracted features to reduce the dimensionality of the feature vector while preserving most of the information of its initial value. Minimum distance classifier is used to perform template matching for each new test sample. The proposed scheme is tested on a well-known iris database, and showed promising results with the best accuracy rate of 99.2%.

A geometric approach to fully automatic chromosome segmentation

A fundamental task in human chromosome analysis is chromosome segmentation. Segmentation plays an important role in chromosome karyotyping. The first step in segmentation is to remove intrusive objects such as stain debris and other noises. The next step is detection of touching and overlapping chromosomes, and the final step is separation of such chromosomes. Common methods for separation between touching chromosomes are interactive and require human intervention for correct separation between touching and overlapping chromosomes. In this paper, a geometric-based method is used for automatic detection of touching and overlapping chromosomes and separating them. The proposed scheme performs segmentation in two phases. In the first phase, chromosome clusters are detected using three geometric criteria, and in the second phase, chromosome clusters are separated using a cut-line. Most of earlier methods did not work properly in case of chromosome clusters that contained more than two chromosomes. Our method, on the other hand, is quite efficient in separation of such chromosome clusters. At each step, one separation will be performed and this algorithm is repeated until all individual chromosomes are separated. Another important point about the proposed method is that it uses the geometric features of chromosomes which are independent of the type of images and it can easily be applied to any type of images such as binary images and does not require multispectral images as well. We have applied our method to a database containing 62 touching and partially overlapping chromosomes and a success rate of 91.9% is achieved.

Fingerprint recognition using translation invariant scattering network

Fingerprint recognition has drawn a lot of attention during the last few decades. Different features and algorithms have been used for fingerprint recognition in the past. In this paper, a powerful image representation called scattering transform/ network is used for recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. The first layer of scattering representation is similar to SIFT descriptors and the higher layers capture higher frequency content of the signal. After extracting the scattering features, their dimensionality is reduced by applying principal component analysis (PCA). In the end, multi-class SVM is used to perform template matching for the recognition task. The proposed algorithm in this paper is one of the first works which explores the application of deep architecture for fingerprint recognition. The proposed scheme is tested on a well-known fingerprint database and has shown promising results with the best accuracy rate of 98%.

Prediction of longterm outcome of neuropsychological tests of MTBI patients using imaging features

Mild Traumatic Brain Injury (MTBI) is a growing public health problem with an underestimated incidence of over one million people annually in the U.S. Neuropsychological tests are used to both assess the patient condition and to monitor patient progress. This work aims to use features extracted from MR images taken shortly after injury to predict the performance of MTBI patients on neuropsychological tests one year after injury. Successful prediction can enable early patient stratification and proper treatment planning. The goal of this project is to determine the most effective features and corresponding prediction method for such prediction. The main challenge is that we have only limited training data, from which we need to develop the prediction method that can be expected to provide accurate prediction results for unseen data. In the machine learning literature, feature selection is usually done to minimize the cross validation error, which still has a chance for overfitting if the available data set is small and noisy. We propose a novel criterion for feature selection, which considers both the cross validation error and the prediction model variance, to further reduce the chance for overfitting. The algorithm is applied to a data set of 15 MTBI patients. The proposed method was able to determine a subset of MR image features for predicting each neuropsychological test to yield both small prediction error and prediction variance.

An experimental study of deep convolutional features for iris recognition

Iris is one of the popular biometrics that is widely used for identity authentication. Different features have been used to perform iris recognition in the past. Most of them are based on hand-crafted features designed by biometrics experts. Due to tremendous success of deep learning in computer vision problems, there has been a lot of interest in applying features learned by convolutional neural networks on general image recognition to other tasks such as segmentation, face recognition, and object detection. In this paper, we have investigated the application of deep features extracted from VGG-Net for iris recognition. The proposed scheme has been tested on two well-known iris databases, and has shown promising results with the best accuracy rate of 99.4%, which outperforms the previous best result.

Screen content image segmentation using sparse decomposition and total variation minimization

Sparse decomposition has been widely used for different applications, such as source separation, image classification, image denoising and more. This paper presents a new algorithm for segmentation of an image into background and foreground text and graphics using sparse decomposition and total variation minimization. The proposed method is designed based on the assumption that the background part of the image is smoothly varying and can be represented by a linear combination of a few smoothly varying basis functions, while the foreground text and graphics can be modeled with a sparse component overlaid on the smooth background. The background and foreground are separated using a sparse decomposition framework regularized with a few suitable regularization terms which promotes the sparsity and connectivity of foreground pixels. This algorithm has been tested on a dataset of images extracted from HEVC standard test sequences for screen content coding, and is shown to have superior performance over some prior methods, including least absolute deviation fitting, k-means clustering based segmentation in DjVu and shape primitive extraction and coding (SPEC) algorithm.

On the power of joint wavelet-DCT features for multispectral palmprint recognition

Biometric-based identification has drawn a lot of attention in the recent years. Among all biometrics, palmprint is known to possess a rich set of features. In this paper we have proposed to use DCT-based features in parallel with wavelet-based ones for palmprint identification. PCA is applied to the features to reduce their dimensionality and the majority voting algorithm is used to perform classification. The features introduced here result in a near-perfectly accurate identification. This method is tested on a well-known multispectral palmprint database and an accuracy rate of 99.97-100% is achieved, outperforming all previous methods in similar conditions.