We present a fast algorithm for corner detection, exploiting the local features (i.e. intensities of neighbourhood pixels) around a pixel. The proposed method is simple to implement but is efficient enough to give res...
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ISBN:
(纸本)9789811021046;9789811021039
We present a fast algorithm for corner detection, exploiting the local features (i.e. intensities of neighbourhood pixels) around a pixel. The proposed method is simple to implement but is efficient enough to give results comparable to that of the state-of-the-art corner detectors. The algorithm is shown to detect corners in a given image using a learning-based framework. The algorithm simply takes the differences of the intensities of candidate pixel and pixels around its neighbourhood and processes them further to make the similar pixels look even more similar and distinct pixels even more distinct. This task is achieved by effectively training a random forest in order to classify whether the candidate pixel is a corner or not. We compare the results with several state-of-the-art techniques for corner detection and show the effectiveness of the proposed method.
The process of adapting the dynamic range of a real-world scene or a photograph in a controlled manner to suit the lower dynamic range of display devices is called tone mapping. In this paper, we present a novel local...
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ISBN:
(纸本)9789811021046;9789811021039
The process of adapting the dynamic range of a real-world scene or a photograph in a controlled manner to suit the lower dynamic range of display devices is called tone mapping. In this paper, we present a novel local tone mapping technique for high-dynamic range (HDR) images taking texture and brightness as cues. We make use of bilateral filtering to obtain base and detail layer of the luminance component. In our proposed approach, we weight the base layer using local to global brightness ratio and texture estimator, and then combine it with the detail layer to get the tone mapped image. To see the difference in contrasts between the original HDR image and the tone mapped image using our model, we make use of an online dynamic range (in)dependent metric. We present our results and compare it with other tone mapping algorithms and demonstrate that our model is better suited to compress the dynamic range of HDR images preserving visibility and information and with minimal artifacts.
Automatic personal identification system by extracting minutiae points from the thinned fingerprint image is one of the popular methods in a biometric system based on fingerprint. Due to various structural deformation...
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ISBN:
(纸本)9789811021046;9789811021039
Automatic personal identification system by extracting minutiae points from the thinned fingerprint image is one of the popular methods in a biometric system based on fingerprint. Due to various structural deformations, extracted minutiae points from a skeletonized fingerprint image may contain a large number of false minutiae points. This largely affects the overall matching performance of the system. The solution is to validate the minutiae points extracted and to select only true minutiae points for the subsequent matching process. This paper proposes several pre- and post-processing techniques which are used to enhance the fingerprint skeleton image by detecting and canceling the false minutiae points in the fingerprint image. The proposed method is tested on FVC2002 standard dataset and the experimental results show that the proposed techniques can remove false minutiae points.
Emotions are very essential for our day-to-day activities such as communication, decision-making and learning. Electroencephalography (EEG) is a non-invasive method to record electrical activity of the brain. To make ...
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ISBN:
(纸本)9789811021046;9789811021039
Emotions are very essential for our day-to-day activities such as communication, decision-making and learning. Electroencephalography (EEG) is a non-invasive method to record electrical activity of the brain. To make Human-Machine Interaction (HMI) more natural, human emotion recognition is important. Over the past decade, various signal processing methods are used for analysing EEG-based emotion recognition (ER). This paper proposes a novel technique for ER using Gray-Level Co-occurrence Matrix (GLCM)-based features. The features are validated on benchmark DEAP database upto four emotions and classified using K-nearest neighbor (K-NN) classifier.
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 capa...
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ISBN:
(纸本)9789811021046;9789811021039
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.
Capturing clear images in dim light conditions remains a critical problem in digital photography. Long exposure time inevitably leads to motion blur due to camera shake. On the other hand, short exposure time with hig...
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ISBN:
(纸本)9789811021046;9789811021039
Capturing clear images in dim light conditions remains a critical problem in digital photography. Long exposure time inevitably leads to motion blur due to camera shake. On the other hand, short exposure time with high gain yields sharp but noisy images. However, exploiting information from both the blurry and noisy images can produce superior results in image reconstruction. In this paper, we employ the image pairs to carry out a non-blind deconvolution and compare the performances of three different deconvolution methods, namely, Richardson Lucy algorithm, Algebraic deconvolution, and Basis Pursuit deconvolution. We show that the Basis Pursuit approach produces the best results in most cases.
Computed Tomography (CT) is one of the significant research areas in medical image analysis. One of the main aspects of CT that researchers remain focused, is on reducing the dosage as Xrays are generally harmful to h...
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ISBN:
(纸本)9783319681245;9783319681238
Computed Tomography (CT) is one of the significant research areas in medical image analysis. One of the main aspects of CT that researchers remain focused, is on reducing the dosage as Xrays are generally harmful to human bodies. In order to reduce radiation dosage, compressed sensing (CS) based methodologies appear to be promising. The basic premise is that medical images have inherent sparsity in some transformation domain. As a result, CS provides the possibility of recovering a high quality image from fewer projection data. In general, the sensing matrix in CT is generated from Radon projections by appropriately sampling the radial and angular parameters. In our work, by restricting the number of such parameters, we generate an under-determined linear system involving projection (Radon) data and a sparse sensing matrix, bringing thereby the problem into CS framework. Among various recent solvers, the Split-Bregman iterative scheme has of late become popular due to its suitability for solving a wide variety of optimization problems. Intending to exploit the underlying structure of sensing matrix, the present work analyzes its properties and finds a banded structure for an associated intermediate matrix. Using this observation, we simplify the Split-Bregman solver, proposing thereby a CT-specific solver of low complexity. We also provide the efficacy of proposed method empirically.
On different devices images are often viewed with different resolutions which require image resizing. Resizing images often affects the quality of the images. To better resize images to different resolutions content a...
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ISBN:
(纸本)9789811021046;9789811021039
On different devices images are often viewed with different resolutions which require image resizing. Resizing images often affects the quality of the images. To better resize images to different resolutions content aware image resizing should be done so that important features are preserved. Seam carving is one such content aware image resizing technique. In this work seam carving is used to downsize an image. This is achieved by carving out an optimal seam (either vertical or horizontal), which contains less information. Each seam removes a pixel from every row (or column) to reduce the height (or width) of the image. To prevent distortion resulting from uniform seam carving, we propose an algorithm that uses a new energy gradient function. In this method minimum of three neighboring pixels is calculated in both energy map and cumulative map and these values are added to find the value of pixel for the new cost matrix.
The cancelable biometric system is susceptible to a variety of attacks aimed at deteriorating the integrity of the authentication procedure. These attacks are intended to either ruin the security afforded by the syste...
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ISBN:
(纸本)9789811021046;9789811021039
The cancelable biometric system is susceptible to a variety of attacks aimed at deteriorating the integrity of the authentication procedure. These attacks are intended to either ruin the security afforded by the system or deter the normal functioning of the authentication system. This paper describes various threats that can be encountered by a cancelable biometric system. It specifically focuses on preventing the attacks designed to extract information about the transformed biometric data of an individual, from the template database. Furthermore, we provide experimental results pertaining to a system combining the cancelable biometrics with segment-based visual cryptography, which converts traditional biocode into novel structures called shares.
image databases are getting larger and diverse with the coming up of new imaging devices and advancements in technology. Content-based image classification (CBIC) is a method to classify images from large databases in...
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ISBN:
(纸本)9789811021046;9789811021039
image databases are getting larger and diverse with the coming up of new imaging devices and advancements in technology. Content-based image classification (CBIC) is a method to classify images from large databases into different categories, on the basis of image content. An efficient image representation is an important component of a CBIC system. In this paper, we demonstrate that Self-Organizing Maps (SOM)-based clustering can be used to form an efficient representation of an image for a CBIC system. The proposed method first extracts Scale-Invariant Feature Transform (SIFT) features from images. Then it uses SOM for clustering of descriptors and forming a Bag of Features (BOF) or Vector of Locally Aggregated Descriptors (VLAD) representation of image. The performance of proposed method has been compared with systems using k-means clustering for forming VLAD or BOF representations of an image. The classification performance of proposed method is found to be better in terms of F-measure (FM) value and execution time.
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