ARIHANT TECHNO SOLUTIONS

MATLAB IMAGE PROCESSING AND WIRELESS COMMUNICATION - 2016-2017

ATS_MAT16_001 - Dynamic Facial Expression Recognition with Atlas Construction and Sparse Representation

                   In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison.

ATS_MAT16_002 - Lossless Compression of JPEG Coded Photo Collections

                   The explosion of digital photos has posed a significant challenge to photo storage and transmission for both personal devices and cloud platforms. In this paper, we propose a novel lossless compression method to further reduce the size of a set of JPEG coded correlated images without any loss of information. The proposed method jointly removes inter/intra image redundancy in the feature, spatial, and frequency domains. For each collection, we first organize the images into a pseudo video by minimizing the global prediction cost in the feature domain. We then present a hybrid disparity compensation method to better exploit both the global and local correlations among the images in the spatial domain. Furthermore, the redundancy between each compensated signal and the corresponding target image is adaptively reduced in the frequency domain. Experimental results demonstrate the effectiveness of the proposed lossless compression method. Compared with the JPEG coded image collections, our method achieves average bit savings of more than 31%.

ATS_MAT16_003 - Pixel modeling using histograms based on fuzzy partitions for dynamic background subtraction

                   We propose a novel pixel-modeling approach for background subtraction using histograms based on strong uniform fuzzy partitions. In the proposed method, the temporal distribution of pixel values is represented by a histogram based on a triangular partition. The threshold for background segmentation is set adaptively according to the shape of the histogram. Histogram accumulation is controlled adaptively by a fuzzy controller under a supervised learning framework. Benefiting from the adaptive scheme, with no parameter tuning, the proposed algorithm functions well across a wide spectrum of challenging environments. The performance of the proposed method is evaluated against more than 20 state-of-the-art methods in complex outdoor environments, particularly in those consisting of highly dynamic backgrounds and camouflaged foregrounds. Experimental results confirm that the proposed method performs effectively in terms of both the true positive rate and the noise suppression ability. Further, it outperforms other state-of-the-art methods by a significant margin.

ATS_MAT16_004 - Layer-Based Approach for Image Pair Fusion

                   Recently, image pairs, such as noisy and blurred images or infrared and noisy images, have been considered as a solution to provide high-quality photographs under low lighting conditions. In this paper, a new method for decomposing the image pairs into two layers, i.e., the base layer and the detail layer, is proposed for image pair fusion. In the case of infrared and noisy images, simple naive fusion leads to unsatisfactory results due to the discrepancies in brightness and image structures between the image pair. To address this problem, a local contrast-preserving conversion method is first proposed to create a new base layer of the infrared image, which can have visual appearance similar to another base layer, such as the denoised noisy image. Then, a new way of designing three types of detail layers from the given noisy and infrared images is presented. To estimate the noise-free and unknown detail layer from the three designed detail layers, the optimization framework is modeled with residual-based sparsity and patch redundancy priors. To better suppress the noise, an iterative approach that updates the detail layer of the noisy image is adopted via a feedback loop. This proposed layer-based method can also be applied to fuse another noisy and blurred image pair. The experimental results show that the proposed method is effective for solving the image pair fusion problem.

ATS_MAT16_005 - Adaptive Pairing Reversible Watermarking

                   This letter revisits the pairwise reversible watermarking scheme of Ou et al., 2013. An adaptive pixel pairing that considers only pixels with similar prediction errors is introduced. This adaptive approach provides an increased number of pixel pairs where both pixels are embedded and decreases the number of shifted pixels. The adaptive pairwise reversible watermarking outperforms the state-of-the-art low embedding bit-rate schemes proposed so far.

ATS_MAT16_006 - Adaptive Part-Level Model Knowledge Transfer for Gender Classification 

                   In this letter, we propose an adaptive part-level model knowledge transfer approach for gender classification of facial images based on Fisher vector (FV). Specifically, we first decompose the whole face image into several parts and compute the dense FVs on each face part. An adaptive transfer learning model is then proposed to reduce the discrepancies between the training data and the testing data for enhancing classification performance. Compared to the existing gender classification methods, the proposed approach is more adaptive to the testing data, which is quite beneficial to the performance improvement. Extensive experiments on several public domain face data sets clearly demonstrate the effectiveness of the proposed approach.

ATS_MAT16_007 - Patch-Based Video Denoising With Optical Flow Estimation

                   A novel image sequence denoising algorithm is presented. The proposed approach takes advantage of the selfsimilarity and redundancy of adjacent frames. The algorithm is inspired by fusion algorithms, and as the number of frames increases, it tends to a pure temporal average. The use of motion compensation by regularized optical flow methods permits robust patch comparison in a spatiotemporal volume. The use of principal component analysis ensures the correct preservation of fine texture and details. An extensive comparison with the state-of-the-art methods illustrates the superior performance of the proposed approach, with improved texture and detail reconstruction.

ATS_MAT16_008 - Fusion of Quantitative Image and Genomic Biomarkers to Improve Prognosis Assessment of Early Stage Lung Cancer Patients

                   This study aims to develop a new quantitative image feature analysis scheme and investigate its role along with 2 genomic biomarkers namely, protein expression of the excision repair cross-complementing 1 (ERCC1) genes and a regulatory subunit of ribonucleotide reductase (RRM1), in predicting cancer recurrence risk of Stage I non-small-cell lung cancer (NSCLC) patients after surgery. Methods: By using chest computed tomography images, we developed a computer-aided detection scheme to segment lung tumors and computed tumor-related image features. After feature selection, we trained a Naïve Bayesian network based classifier using 8 image features and a Multilayer Perceptron classifier using 2 genomic biomarkers to predict cancer recurrence risk, respectively. Two classifiers were trained and tested using a dataset with 79 Stage I NSCLC cases, a synthetic minority oversampling technique and a leave-one-case-out validation method. A fusion method was also applied to combine prediction scores of two classifiers. Results: AUC (areas under ROC curves) values are 0.78±0.06 and 0.68±0.07 when using the image feature and genomic biomarker based classifiers, respectively. AUC value significantly increased to 0.84±0.05 (p<0.05) when fusion of two classifier-generated prediction scores using an equal weighting factor. Conclusion: A quantitative image feature based classifier yielded significantly higher discriminatory power than a genomic biomarker based classifier in predicting cancer recurrence risk. Fusion of prediction scores generated by the two classifiers further improved prediction performance. Significance: We demonstrated a new approach that has potential to assist clinicians in more effectively managing Stage I NSCLC patients to reduce cancer recurrence risk.

ATS_MAT16_009 - Multivideo Object Cosegmentation for Irrelevant Frames Involved Videos

                   Even though there have been a large amount of previous work on video segmentation techniques, it is still a challenging task to extract the video objects accurately without interactions, especially for those videos which contain irrelevant frames (frames containing no common targets). In this essay, a novel multivideo object cosegmentation method is raised to cosegment common or similar objects of relevant frames in different videos, which includes three steps: 1) object proposal generation and clustering within each video; 2) weighted graph construction and common objects selection; and 3) irrelevant frames detection and pixel-level segmentation refinement. We apply our method on challenging datasets and exhaustive comparison experiments demonstrate the effectiveness of the proposed method.

ATS_MAT16_010 - Multi-Viewpoint Panorama Construction with Wide-Baseline Images

                   We present a novel image stitching approach, which can produce visually plausible panoramic images with input taken from different viewpoints. Unlike previous methods, our approach allows wide baselines between images and non-planar scene structures. Instead of 3D reconstruction, we design a mesh based framework to optimize alignment and regularity in 2D. By solving a global objective function consisting of alignment and a set of prior constraints, we construct panoramic images, which are locally as perspective as possible and yet nearly orthogonal in the global view. We improve composition and achieve good performance on misaligned area. Experimental results on challenging data demonstrate the effectiveness of the proposed method.

ATS_MAT16_011 - A Security-Enhanced Alignment-Free Fuzzy Vault-Based Fingerprint Cryptosystem Using Pair-Polar Minutiae Structures

Alignment-free fingerprint cryptosystems perform matching using relative information between minutiae, e.g., local minutiae structures, is promising, because it can avoid the recognition errors and information leakage caused by template alignment/registration. However, as most local minutiae structures only contain relative information of a few minutiae in a local region, they are less discriminative than the global minutiae pattern. Besides, the similarity measures for trivially/coarsely quantized features in the existing work cannot provide a robust way to deal with nonlinear distortions, a common form of intra-class variation. As a result, the recognition accuracy of current alignment-free fingerprint cryptosystems is unsatisfying. In this paper, we propose an alignment-free fuzzy vault-based fingerprint cryptosystem using highly discriminative pair-polar (P-P) minutiae structures. The fine quantization used in our system can largely retain information about a fingerprint template and enables the direct use of a traditional, well-established minutiae matcher. In terms of template/key protection, the proposed system fuses cancelable biometrics and biocryptography. Transforming the P-P minutiae structures before encoding destroys the correlations between them, and can provide privacy-enhancing features, such as revocability and protection against cross-matching by setting distinct transformation seeds for different applications. The comparison with other minutiae-based fingerprint cryptosystems shows that the proposed system performs favorably on selected publicly available databases and has strong security.

ATS_MAT16_012 - Microwave Unmixing With Video Segmentation for Inferring Broadleaf and Needleleaf Brightness Temperatures and Abundances From Mixed Forest Observations

Passive microwave sensors have better capability of penetrating forest layers to obtain more information from forest canopy and ground surface. For forest management, it is useful to study passive microwave signals from forests. Passive microwave sensors can detect signals from needleleaf, broadleaf, and mixed forests. The observed brightness temperature of a mixed forest can be approximated by a linear combination of the needleleaf and broadleaf brightness temperatures weighted by their respective abundances. For a mixed forest observed by an N-band microwave radiometer with horizontal and vertical polarizations, there are 2 N observed brightness temperatures. It is desirable to infer 4 N + 2 unknowns: 2 N broadleaf brightness temperatures, 2 N needleleaf brightness temperatures, 1 broadleaf abundance, and 1 needleleaf abundance. This is a challenging underdetermined problem. In this paper, we devise a novel method that combines microwave unmixing with video segmentation for inferring broadleaf and needleleaf brightness temperatures and abundances from mixed forests. We propose an improved Otsu method for video segmentation to infer broadleaf and needleleaf abundances. The brightness temperatures of needleleaf and broadleaf trees can then be solved by the nonnegative least squares solution. For our mixed forest unmixing problem, it turns out that the ordinary least squares solution yields the desired positive brightness temperatures. The experimental results demonstrate that the proposed method is able to unmix broadleaf and needleleaf brightness temperatures and abundances well. The absolute differences between the reconstructed and observed brightness temperatures of the mixed forest are well within 1 K.

ATS_MAT16_013 - 2D Orthogonal Locality Preserving Projection for Image Denoising

Sparse representations using transform-domain techniques are widely used for better interpretation of the raw data. Orthogonal locality preserving projection (OLPP) is a linear technique that tries to preserve local structure of data in the transform domain as well. Vectorized nature of OLPP requires high-dimensional data to be converted to vector format, hence may lose spatial neighborhood information of raw data. On the other hand, processing 2D data directly, not only preserves spatial information, but also improves the computational efficiency considerably. The 2D OLPP is expected to learn the transformation from 2D data itself. This paper derives mathematical foundation for 2D OLPP. The proposed technique is used for image denoising task. Recent state-of-the-art approaches for image denoising work on two major hypotheses, i.e., non-local self-similarity and sparse linear approximations of the data. Locality preserving nature of the proposed approach automatically takes care of self-similarity present in the image while inferring sparse basis. A global basis is adequate for the entire image. The proposed approach outperforms several state-of-the-art image denoising approaches for gray-scale, color, and texture images.

ATS_MAT16_014 - Exploring the Usefulness of Light Field Cameras for Biometrics : An Empirical Study on Face and Iris Recognition

A light field sensor can provide useful information in terms of multiple depth (or focus) images, holding additional information that is quite useful for biometric applications. In this paper, we examine the applicability of a light field camera for biometric applications by considering two prominently used biometric characteristics: 1) face and 2) iris. To this extent, we employed a Lytro light field camera to construct two new and relatively large scale databases, for both face and iris biometrics. We then explore the additional information available from different depth images, which are rendered by light field camera, in two different manners: 1) by selecting the best focus image from the set of depth images and 2) combining all the depth images using super-resolution schemes to exploit the supplementary information available within the set elements. Extensive evaluations are carried out on our newly constructed database, demonstrating the significance of using additional information rendered by a light field camera to improve the overall performance of the biometric system.

ATS_MAT16_015 - Spectral–Spatial Adaptive Sparse Representation for Hyperspectral Image Denoising

In this paper, a novel spectral-spatial adaptive sparse representation (SSASR) method is proposed for hyperspectral image (HSI) denoising. The proposed SSASR method aims at improving noise-free estimation for noisy HSI by making full use of highly correlated spectral information and highly similar spatial information via sparse representation, which consists of the following three steps. First, according to spectral correlation across bands, the HSI is partitioned into several nonoverlapping band subsets. Each band subset contains multiple continuous bands with highly similar spectral characteristics. Then, within each band subset, shape-adaptive local regions consisting of spatially similar pixels are searched in spatial domain. This way, spectral-spatial similar pixels can be grouped. Finally, the highly correlated and similar spectral-spatial information in each group is effectively used via the joint sparse coding, in order to generate better noise-free estimation. The proposed SSASR method is evaluated by different objective metrics in both real and simulated experiments. The numerical and visual comparison results demonstrate the effectiveness and superiority of the proposed method.

ATS_MAT16_016 - Robust Sclera Recognition System With Novel Sclera Segmentation and Validation Techniques

Sclera blood veins have been investigated recently as a biometric trait which can be used in a recognition system. The sclera is the white and opaque outer protective part of the eye. This part of the eye has visible blood veins which are randomly distributed. This feature makes these blood veins a promising factor for eye recognition. The sclera has an advantage in that it can be captured using a visible-wavelength camera. Therefore, applications which may involve the sclera are wide ranging. The contribution of this paper is the design of a robust sclera recognition system with high accuracy. The system comprises of new sclera segmentation and occluded eye detection methods. We also propose an efficient method for vessel enhancement, extraction, and binarization. In the feature extraction and matching process stages, we additionally develop an efficient method, that is, orientation, scale, illumination, and deformation invariant. The obtained results using UBIRIS.v1 and UTIRIS databases show an advantage in terms of segmentation accuracy and computational complexity compared with state-of-the-art methods due to Thomas, Oh, Zhou, and Das.

ATS_MAT16_017 - Enhancing Sketch-Based Image Retrieval by Re-Ranking and Relevance Feedback

A sketch-based image retrieval often needs to optimize the tradeoff between efficiency and precision. Index structures are typically applied to large-scale databases to realize efficient retrievals. However, the performance can be affected by quantization errors. Moreover, the ambiguousness of user-provided examples may also degrade the performance, when compared with traditional image retrieval methods. Sketch-based image retrieval systems that preserve the index structure are challenging. In this paper, we propose an effective sketch-based image retrieval approach with re-ranking and relevance feedback schemes. Our approach makes full use of the semantics in query sketches and the top ranked images of the initial results. We also apply relevance feedback to find more relevant images for the input query sketch. The integration of the two schemes results in mutual benefits and improves the performance of the sketch-based image retrieval.

ATS_MAT16_018 - Detection of Moving Objects Using Fuzzy Color Difference Histogram Based Background Subtraction

Detection of moving objects in the presence of complex scenes such as dynamic background (e.g, swaying vegetation, ripples in water, spouting fountain), illumination variation, and camouflage is a very challenging task. In this context, we propose a robust background subtraction technique with three contributions. First, we present the use of color difference histogram (CDH) in the background subtraction algorithm. This is done by measuring the color difference between a pixel and its neighbors in a small local neighborhood. The use of CDH reduces the number of false errors due to the non-stationary background, illumination variation and camouflage. Secondly, the color difference is fuzzified with a Gaussian membership function. Finally, a novel fuzzy color difference histogram (FCDH) is proposed by using fuzzy c-means (FCM) clustering and exploiting the CDH. The use of FCM clustering algorithm in CDH reduces the large dimensionality of the histogram bins in the computation and also lessens the effect of intensity variation generated due to the fake motion or change in illumination of the background. The proposed algorithm is tested with various complex scenes of some benchmark publicly available video sequences. It exhibits better performance over the state-of-the-art background subtraction techniques available in the literature in terms of classification accuracy metrics like MCC and PCC.

ATS_MAT16_019 - A Decomposition Framework for Image Denoising Algorithms

In this paper, we consider an image decomposition model that provides a novel framework for image denoising. The model computes the components of the image to be processed in a moving frame that encodes its local geometry (directions of gradients and level lines). Then, the strategy we develop is to denoise the components of the image in the moving frame in order to preserve its local geometry, which would have been more affected if processing the image directly. Experiments on a whole image database tested with several denoising methods show that this framework can provide better results than denoising the image directly, both in terms of Peak signal-to-noise ratio and Structural similarity index metrics.

ATS_MAT16_020 - Distance-Based Encryption: How to Embed Fuzziness in Biometric-Based Encryption

We introduce a new encryption notion called distance-based encryption (DBE) to apply biometrics in identity-based encryption. In this notion, a ciphertext encrypted with a vector and a threshold value can be decrypted with a private key of another vector, if and only if the distance between these two vectors is less than or equal to the threshold value. The adopted distance measurement is called Mahalanobis distance, which is a generalization of Euclidean distance. This novel distance is a useful recognition approach in the pattern recognition and image processing community. The primary application of this new encryption notion is to incorporate biometric identities, such as face, as the public identity in an identity-based encryption. In such an application, usually the input biometric identity associated with a private key will not be exactly the same as the input biometric identity in the encryption phase, even though they are from the same user. The introduced DBE addresses this problem well as the decryption condition does not require identities to be identical but having small distance. The closest encryption notion to DBE is the fuzzy identity-based encryption, but it measures biometric identities using a different distance called an overlap distance (a variant of Hamming distance) that is not widely accepted by the pattern recognition community, due to its long binary representations. In this paper, we study this new encryption notion and its constructions. We show how to generically and efficiently construct such a DBE from an inner product encryption (IPE) with reasonable size of private keys and ciphertexts. We also propose a new IPE scheme with the shortest private key to build DBE, namely, the need for a short private key. Finally, we study the encryption efficiency of DBE by splitting our IPE encryption algorithm into offline and online algorithms.

ATS_MAT16_021 - Scalable Feature Matching by Dual Cascaded Scalar Quantization for Image Retrieval

In this paper, we investigate the problem of scalable visual feature matching in large-scale image search and propose a novel cascaded scalar quantization scheme in dual resolution. We formulate the visual feature matching as a range-based neighbor search problem and approach it by identifying hyper-cubes with a dual-resolution scalar quantization strategy. Specifically, for each dimension of the PCA-transformed feature, scalar quantization is performed at both coarse and fine resolutions. The scalar quantization results at the coarse resolution are cascaded over multiple dimensions to index an image database. The scalar quantization results over multiple dimensions at the fine resolution are concatenated into a binary super-vector and stored into the index list for efficient verification. The proposed cascaded scalar quantization (CSQ) method is free of the costly visual codebook training and thus is independent of any image descriptor training set. The index structure of the CSQ is flexible enough to accommodate new image features and scalable to index large-scale image database. We evaluate our approach on the public benchmark datasets for large-scale image retrieval. Experimental results demonstrate the competitive retrieval performance of the proposed method compared with several recent retrieval algorithms on feature quantization.

ATS_MAT16_022 - ACE–An Effective Anti-forensic Contrast Enhancement Technique

Detecting Contrast Enhancement (CE) in images and anti-forensic approaches against such detectors have gained much attention in multimedia forensics lately. Several contrast enhancement detectors analyze the first order statistics such as gray-level histogram of images to determine whether an image is CE or not. In order to counter these detectors various anti-forensic techniques have been proposed. This led to a technique that utilized second order statistics of images for CE detection. In this letter, we propose an effective anti-forensic approach that performs CE without significant distortion in both the first and second order statistics of the enhanced image. We formulate an optimization problem using a variant of the well known Total Variation (TV) norm image restoration formulation. Experiments show that the algorithm effectively overcomes the first and second order statistics based detectors without loss in quality of the enhanced image.

ATS_MAT16_023 - Visualization of Tumor Response to Neoadjuvant Therapy for Rectal Carcinoma by Nonlinear Optical Imaging

The continuing development of nonlinear optical imaging techniques has opened many new windows in biological exploration. In this study, a nonlinear optical microscopy-multiphoton microscopy (MPM) was expanded to detect tumor response in rectal carcinoma after neoadjuvant therapy; especially normal tissue, pre- and post-therapeutic cancerous tissues were investigated in order to present more detailed information and make comparison. It was found that the MPM has ability not only to directly visualize histopathologic changes in rectal carcinoma, including stromal fibrosis, colloid response, residual tumors, blood vessel hyperplasia, and inflammatory reaction, which had been proven to have important influence on estimation of the prognosis and the effect of neoadjuvant treatment, but also to provide quantitative optical biomarkers including the intensity ratio of SHG over TPEF and collagen orientation index. These results show that the MPM will become a useful tool for clinicians to determine whether neoadjuvant therapy is effective or treatment strategy is approximate, and this study may provide the groundwork for further exploration into the application of MPM in a clinical setting.

ATS_MAT16_024 - Robust Edge-Stop Functions for Edge-Based Active Contour Models in Medical Image Segmentation

Edge-based active contour models are effective in segmenting images with intensity inhomogeneity but often fail when applied to images containing poorly defined boundaries, such as  in medical images. Traditional edge-stop functions (ESFs) utilize only gradient information, which fails to stop contour evolution at such boundaries because of the small gradient magnitudes. To address this problem, we propose a framework to construct a group of ESFs for edge-based active contour models to segment objects with poorly defined boundaries. In our framework, which incorporates gradient information as well as probability scores from a standard classifier, the ESF can be constructed from any classification algorithm and applied to any edge-based model using a level set method. Experiments on medical images using the distance regularized level set for edge-based active contour models as well as the k-nearest neighbors and the support vector machine confirm the effectiveness of the proposed approach.

ATS_MAT16_025 - A Combined KFDA Method and GUI Realized for Face Recognition

Traditional face recognition methods such as Principal Components Analysis(PCA), Independent Component Analysis(ICA) and Linear Discriminant Analysis(LDA) are linear discriminant methods, but in the real situation, a lot of problems can't be linear discriminated; therefore, researchers proposed face recognition method based on kernel techniques which can transform the nonlinear problem of inputting space into the linear problem of high dimensional space. In this paper, we propose a recognition method based on kernel function which combines kernel Fisher Discriminant Analysis(KFDA) with kernel Principle Components Analysis(KPCA) and use typical ORL(Olivetti Research Laboratory) face database as our experimental database. There are four key steps: constructing feature subspace, image projection, feature extraction and image recognition. We found that the recognition accuracy has been greatly improved by using nonlinear identification method and combined feature extraction methods. We use MATLAB software as the platform, and use the GUI to demonstrate the process of face recognition in order to achieving human-computer interaction and making the process and result more intuitive.

ATS_MAT16_026 - A Cost-Effective Minutiae Disk Code For Fingerprint Recognition And Its Implementation

Fingerprint is one of the unique biometric features for the application of identity security. Minutiae cylinder code (MCC) constructs a cylinder for each minutia to record the contribution of the neighbor minutiae, which has great performance on fingerprint recognition. However, the computation time of the MCC is high. Therefore, we proposed a new disk structure to encode the local structure for each minutia. The proposed minutiae disk code (MDC) clearly illustrates the distribution of the neighbor minutiae and encodes the neighbor minutiae more efficiently by having 280.08× speed faster than the MCC encoding part on Matlab platform. The proposed MDC approach has 96.81% recognition rate on FVC2000 and FVC2002 datasets. The hardware implementation can achieve the operating frequency at 111MHz, which can process 1234 fingerprint images per second with the image size of 255 χ 255 and the maximum of 64 minutiae, under TSMC 90nm CMOS technology. The hardware implementation has 141.27× speed faster than the MCC method.

ATS_MAT16_027 - A Hands-on Application-Based Tool for STEM Students to Understand Differentiation

The main goal of this project is to illustrate to college students in science, technology, engineering, and mathematics (STEM) fields some fundamental concepts in calculus. A high-level technical computing language - MATLAB, is the core platform used in the construction of this project. A graphical user interface (GUI) is designed to interactively explain the intuition behind a key mathematical concept: differentiation. The GUI demonstrates how a derivative operation (as a form of kernel) can be applied on one-dimensional (1D) and two-dimensional (2D) images (as a form of vector). The user can interactively select from a set of predetermined operations and images in order to show how the selected kernel operates on the corresponding image. Such interactive tools in calculus courses are of great importance and need, especially for STEM students who seek an intuitive and visual understanding of mathematical notions that are often presented to them as abstract concepts. In addition to students, instructors can greatly benefit from using such tools to elucidate the use of fundamental concepts in mathematics in a real world context.

ATS_MAT16_028 - Rotation Invariant Texture Description Using Symmetric Dense Microblock Difference

This letter is devoted to the problem of rotation invariant texture classification. Novel rotation invariant feature, symmetric dense microblock difference (SDMD), is proposed which captures the information at different orientations and scales.  N -fold symmetry is introduced in the feature design configuration, while retaining the random structure that provides discriminative power. The symmetry is utilized to achieve a rotation invariance. The SDMD is extracted using an image pyramid and encoded by the Fisher vector approach resulting in a descriptor which captures variations at different resolutions without increasing the dimensionality. The proposed image representation is combined with the linear SVM classifier. Extensive experiments are conducted on four texture data sets [Brodatz, UMD, UIUC, and Flickr material data set (FMD)] using standard protocols. The results demonstrate that our approach outperforms the state of the art in texture classification. The MATLAB code is made available.11

ATS_MAT16_029 - A Novel Image Quality Assessment With Globally and Locally Consilient Visual Quality Perception

Computational models for image quality assessment (IQA) have been developed by exploring effective features that are consistent with the characteristics of a human visual system (HVS) for visual quality perception. In this paper, we first reveal that many existing features used in computational IQA methods can hardly characterize visual quality perception for local image characteristics and various distortion types. To solve this problem, we propose a new IQA method, called the structural contrast-quality index (SC-QI), by adopting a structural contrast index (SCI), which can well characterize local and global visual quality perceptions for various image characteristics with structural-distortion types. In addition to SCI, we devise some other perceptually important features for our SC-QI that can effectively reflect the characteristics of HVS for contrast sensitivity and chrominance component variation. Furthermore, we develop a modified SC-QI, called structural contrast distortion metric (SC-DM), which inherits desirable mathematical properties of valid distance metricability and quasi-convexity. So, it can effectively be used as a distance metric for image quality optimization problems. Extensive experimental results show that both SC-QI and SC-DM can very well characterize the HVS's properties of visual quality perception for local image characteristics and various distortion types, which is a distinctive merit of our methods compared with other IQA methods. As a result, both SC-QI and SC-DM have better performances with a strong consilience of global and local visual quality perception as well as with much lower computation complexity, compared with the state-of-the-art IQA methods. The MATLAB source codes of the proposed SC-QI and SC-DM are publicly available online at https://sites.google.com/site/sunghobaecv/iqa.

ATS_MAT16_030 - A DCT-based Total JND Profile for Spatio-Temporal and Foveated Masking Effects

In image and video processing fields, DCT-based just noticeable difference (JND) profiles have effectively been utilized to remove perceptual redundancies in pictures for compression. In this paper, we solve two problems that are often intrinsic to the conventional DCT-based JND profiles: (i) no foveated masking (FM) JND model has been incorporated in modeling the DCT-based JND profiles; and (ii) the conventional temporal masking (TM) JND models assume that all moving objects in frames can be well tracked by the eyes and that they are projected on the fovea regions of the eyes, which is not a realistic assumption and may result in poor estimation of JND values for untracked moving objects (or image regions). To solve these two problems, we first propose a generalized JND model for joint effects between TM and FM effects. With this model, called the temporal-foveated masking (TFM) JND model, JND thresholds for any tracked/untracked and moving/still image regions can be elaborately estimated. Finally, the TFM-JND model is incorporated into a total DCT-based JND profile with a spatial contrast sensitivity function, luminance masking, and contrast masking JND models. In addition, we propose a JND adjustment method for our total JND profile to avoid overestimation of JND values for image blocks of fixed sizes with various image characteristics. To validate the effectiveness of the total JND profile, an experiment involving a subjective distortionvisibility assessment has been conducted. The experiment results show that the proposed total DCT-based JND profile yields significant performance improvement with much higher capability of distortion concealment (average 5.6 dB lower PSNR) compared to state-of-the-art JND profiles. The MATLAB source code of the proposed total DCT-based JND profile is publicly available online at https://sites.google.com/site/sunghobaecv/jnd

ATS_MAT16_031 - PiCode: a New Picture-Embedding 2D Barcode

Nowadays, 2D barcodes have been widely used as an interface to connect potential customers and advertisement contents. However, the appearance of a conventional 2D barcode pattern is often too obtrusive for integrating into an aesthetically designed advertisement. Besides, no human readable information is provided before the barcode is successfully decoded. This paper proposes a new picture-embedding 2D barcode, called PiCode, which mitigates these two limitations by equipping a scannable 2D barcode with a picturesque appearance. PiCode is designed with careful considerations on both the perceptual quality of the embedded image and the decoding robustness of the encoded message. Comparisons with the existing beautified 2D barcodes show that PiCode achieves one of the best perceptual qualities for the embedded image, and maintains a better tradeoff between image quality and decoding robustness in various application conditions. PiCode has been implemented in the MATLAB on a PC and some key building blocks have also been ported to Android and iOS platforms. Its practicality for real-world applications has been successfully demonstrated.

ATS_MAT16_032 - OCR Based Feature Extraction and Template Matching Algorithms for Qatari Number Plate

There are several algorithms and methods that could be applied to perform the character recognition stage of an automatic number plate recognition system; however, the constraints of having a high recognition rate and real-time processing should be taken into consideration. In this paper four algorithms applied to Qatari number plates are presented and compared. The proposed algorithms are based on feature extraction (vector crossing, zoning, combined zoning and vector crossing) and template matching techniques. All four proposed algorithms have been implemented and tested using MATLAB. A total of 2790 Qatari binary character images were used to test the algorithms. Template matching based algorithm showed the highest recognition rate of 99.5% with an average time of 1.95 ms per character.

ATS_MAT16_033 - HD Qatari ANPR System

Recently, Automatic Number Plate Recognition (ANPR) systems have become widely used in safety, security, and commercial aspects. The whole ANPR system is based on three main stages: Number Plate Localization (NPL), Character Segmentation (CS), and Optical Character Recognition (OCR). In recent years, to provide better recognition rate, High Definition (HD) cameras have started to be used. However, most known techniques for standard definition are not suitable for real-time HD image processing due to the computationally intensive cost of localizing the number plate. In this paper, algorithms to implement the three main stages of a high definition ANPR system for Qatari number plates are presented. The algorithms have been tested using MATLAB and two databases as a proof of concept. Implementation results have shown that the system is able to process one HD image in 61 ms, with an accuracy of 98.0% in NPL, 99.75% per character in CS, and 99.5% in OCR.

ATS_MAT16_034 - Template Matching of Aerial Images using GPU

During the last decade, processor architectures have emerged with hundreds and thousands of high speed processing cores in a single chip. These cores can work in parallel to share a work load for faster execution. This paper presents performance evaluations on such multicore and many-core devices by mapping a computationally expensive correlation kernel of a template matching process using various programming models. The work builds a base performance case by a sequential mapping of the algorithm on an Intel processor. In the second step, the performance of the algorithm is enhanced by parallel mapping of the kernel on a shared memory multicore machine using OpenMP programming model. Finally, the Normalized Cross-Correlation (NCC) kernel is scaled to map on a many-core K20 GPU using CUDA programming model. In all steps, the correctness of the implementation of algorithm is taken care by comparing computed data with reference results from a high level implementation in MATLAB. The performance results are presented with various optimization techniques for MATLAB, Sequential, OpenMP and CUDA based implementations. The results show that GPU based implementation achieves 32x and 5x speed-ups respectively to the base case and multicore implementations respectively. Moreover, using inter-block sub-sampling on an 8-bit 4000×4000 reference gray-scale image achieves the execution time upto 2.8sec with an error growth less than 20% for the selected templates of size 96×96.

ATS_MAT16_035 - Analysis of Adaptive Filter and ICA for Noise Cancellation from a Video Frame

Noise cancellation algorithms have been frequently applied in many fields including image/video processing. Adaptive noise cancellation algorithms exploit the correlation property of noise and remove the noise from the input signal more effectively than non-adaptive algorithms. In this paper different noise cancellation techniques are applied to de-noise a video frame. Three different variants of gradient based adaptive filtering algorithms and independent component analysis (ICA) procedure are implemented and compared on the basis of signal to noise ratio (SNR) and computational time. The common algorithms used in adaptive filters are least mean square (LMS), normalized least means square (NLMS), and recursive least mean square (RLS). The simulation results demonstrates that NLMS algorithm is computationally efficient but cannot handle impulsive noise whereas LMS and RLS can perform better for long duration noise signals. The comparative analysis of adaptive filtering algorithms and ICA shows that ICA can perform better then all three iterative gradient based algorithms because of its non-iterative nature. For testing and simulations, three variants of white Gaussian noise (WGN) are used to corrupt the video frame.

ATS_MAT16_036 - Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval

Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes. However, they face difficulties in coping with large training data sets. With the increasing amount of optical remote sensing data made available for analysis and the possibility of using a large amount of simulated data from radiative transfer models (RTMs) to train kernel MLRAs, efficient data reduction techniques will need to be implemented. Active learning (AL) methods enable to select the most informative samples in a data set. This letter introduces six AL methods for achieving optimized biophysical variable estimation with a manageable training data set, and their implementation into a Matlab-based MLRA toolbox for semiautomatic use. The AL methods were analyzed on their efficiency of improving the estimation accuracy of the leaf area index and chlorophyll content based on PROSAIL simulations. Each of the implemented methods outperformed random sampling, improving retrieval accuracy with lower sampling rates. Practically, AL methods open opportunities to feed advanced MLRAs with RTM-generated training data for the development of operational retrieval models.

ATS_MAT16_037 - Development of a Brain-Computer Interface Based on Visual Stimuli for the Movement of a Robot Joints

This paper presents a brain computer interface (BCI) to control a robotic arm by brain signals from visual stimuli. The following signal processing steps were established; acquisition of brain signals by electroencephalography (EEG) electrodes; noise reduction; extraction of signal characteristics and signal classification. Reliable brain signals were obtained by the use of the Emotiv EPOC® commercial hardware. The OpenViBE® commercial software was used to program the signal processing algorithms. By using Matlab® together with an Arduino® electronic board, two servo motors were controlled to drive two joints of a 5 degrees-of-freedom robot commanded by P300-type evoked potential brain signals from visual stimulation when a subject concentrates on particular images from an image matrix displayed in the computer screen. The experiments were conducted with and without auditive and visual noise (artifacts) to find out the noise influence in the signal classification outcome. The obtained experimental results presented an efficiency in the identification stage up to 100% with and without hearing noise conditions. However, under visual noise conditions a maximum efficiency of 50% was reached. The experiments for the servomotors control were carried out without noise, reaching an efficiency of 100% in the identification stage.