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MATLAB - IMAGE PROCESSING IEEE PROJECTS

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Spatial Classification of Hyperspectral Imagery Based on Mom

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Spatial Classification of Hyperspectral Imagery Based on Mom
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This paper presents a novel and efficient spectral– spatial classification method for hyperspectral images. It combines the spectral and texture features to improve the classification accuracy. The moment invariants are computed within a small window centered at the pixel to determine pixel-wise texture features. The texture and spectral features are concatenated to form a joint feature vector that is used for classification with support vector machine (SVM). The experiments are carried out on three hyperspectral datasets and results are compared with some other spectral–spatial techniques. The results indicate that the proposed method statistically significantly improved the classification accuracies over the conventional spectral method. The new method has also outperformed the other recently used spectral–spatial methods in terms of both classification accuracies and computational cost. The results also showed that the proposed method can produce good classification accuracy with smaller training sets.

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    A New Secure Image Transmission Technique via Secret-Fragmen

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    A New Secure Image Transmission Technique via Secret-Fragmen
    Approx. Rs 10,000 / student
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    A new secure image transmission technique is proposed, which transforms automatically a given large-volume secret image into a so-called secret-fragment-visible mosaic image of the same size. The mosaic image, which looks similar to an arbitrarily selected target image and may be used as a camouflage of the secret image, is yielded by dividing the secret image into fragments and transforming their color characteristics to be those of the corresponding blocks of the target image. Skillful techniques are designed to conduct the color transformation process so that the secret image may be recovered nearly losslessly. A scheme of handling the overflows/underflows in the converted pixels’ color values by recording the color differences in the untransformed color space is also proposed. The information required for recovering the secret image is embedded into the created mosaic image by a lossless data hiding scheme using a key. Good experimental results show the feasibility of the proposed method.

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    Microscopic Image Segmentation Based on Firefly Algorithm fo

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    Microscopic Image Segmentation Based on Firefly Algorithm fo
    Approx. Rs 10,000 / student
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    One third of the world is infected with tuberculosis disease. The disease is diagnosed visually by laboratory technicians. In the microscopy diagnosis with handeye control, misdiagnosis rate is quite high. In microscopic imaging, by using computer aided automatic diagnosis methods, the disease is true diagnosed. The robustness of the automatic diagnosis methods depends on accurate segmentation of microscopic images. Image segmentation methods produce a special solution for several problems. In this study, Firefly algorithm based on swarm intelligence as a novel approach in microscopic imaging is proposed to segment images. In the proposed approach, an optimum threshold value in gray-level microscopic images is determined with proposed entropy based Firefly algorithm. Microscopic images are converted to binary format by using obtained optimum threshold value. Segmentation results are compared with expert-guided segmentation results. The performance ratio of segmentation is 96% obtained by using Firefly algorithm based on swarm intelligence.

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    An Automatic Graph-Based Approach for Artery/Vein

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    An Automatic Graph-Based Approach for Artery/Vein
    Approx. Rs 10,000 / student
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    The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The results of this proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIREAVR, DRIVE, and VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for A/V classification.
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    Robust Automatic Line Scratch Detection in Films

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    Robust Automatic Line Scratch Detection in Films
    Approx. Rs 10,000 / student
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    Line scratch detection in old films is a particularly challenging problem due to the variable spatiotemporal characteristics of this defect. Some of the main problems include sensitivity to noise and texture, and false detections due to thin vertical structures belonging to the scene. We propose a robust and automatic algorithm for frame-by-frame line scratch detection in old films, as well as a temporal algorithm for the filtering of false detections. In the frame-by-frame algorithm, we relax some of the hypotheses used in previous algorithms in order to detect a wider variety of scratches. This step’s robustness and lack of external parameters is ensured by the combined use of an a contrario methodology and local statistical estimation. In this manner, over-detection in textured or cluttered areas is greatly reduced. The temporal filtering algorithm eliminates false detections due to thin vertical structures by exploiting the coherence of their motion with that of the underlying scene. Experiments demonstrate the ability of the resulting detection procedure to deal with difficult situations, in particular in the presence of noise, texture, and slanted or partial scratches. Comparisons show significant advantages over previous work

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    Texture Descriptor Based On Local Combination Adaptive Terna

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    Texture Descriptor Based On Local Combination Adaptive Terna
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    Material recognition has several applications, such as image retrieval, object recognition and robotic manipulation. To make the material classification more suitable for real-world applications, it is fundamental to satisfy two characteristics: robustness to scale and to pose variations. In this study, the authors propose a novel discriminant descriptor for texture classification based on a new operator called local combination adaptive ternary pattern (LCATP) descriptor used to encode both colour and local information. They start by building the LCATP descriptor using a combination of three different adaptive thresholding techniques. Moreover, they present a novel operator, mean histogram (MH), used jointly with the LCATP in order to incorporate colour information into the descriptor. This approach is then extended to four different colour spaces: LC1C2, I1I2I3, LSHuv and O1O2O3. The final descriptor, LCATP fusion (LCATP_F), is produced by fusing the basic histogram (H) and MH extracted from the different colour spaces. Finally, the LCATP_F descriptor properties, such as the robustness to scale and pose changes are evaluated using the challenging KTH-textures under varying illumination, pose and scale (TIPS2b) dataset along with the least squares support vector machines classifier. The obtained experimental results, using the LCATP_F descriptor, show a significant improvement with respect to the state-of-the-art results.

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    Large- Scale Overlays And Trends Visually Mining, Panning An

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    Large- Scale Overlays And Trends Visually Mining, Panning An
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    —We introduce a web-based computing infrastructure to assist the visual integration, mining and interactive navigation of large-scale astronomy observations. Following an analysis of the application domain, we design a client-server architecture to fetch distributed image data and to partition local data into a spatial index structure that allows prefix-matching of spatial objects. In conjunction with hardware-accelerated pixel-based overlays and an online cross-registration pipeline, this approach allows the fetching, displaying, panning and zooming of gigabit panoramas of the sky in real time. To further facilitate the integration and mining of spatial and non-spatial data, we introduce interactive trend images—compact visual representations for identifying outlier objects and for studying trends within large collections of spatial objects of a given class. In a demonstration, images from three sky surveys (SDSS, FIRST and simulated LSST results) are cross-registered and integrated as overlays, allowing cross-spectrum analysis of astronomy observations. Trend images are interactively generated from catalog data and used to visually mine astronomy observations of similar type. The front-end of the infrastructure uses the web technologies WebGL and HTML5 to enable crossplatform, web-based functionality. Our approach attains interactive rendering framerates; its power and flexibility enables it to serve the needs of the astronomy community. Evaluation on three case studies, as well as feedback from domain experts emphasize the benefits of this visual approach to the observational astronomy field; and its potential benefits to large scale geospatial visualization in general.
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    A Weighted Optimization Approach to Time-of-Flight Sensor Fu

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    A Weighted Optimization Approach to Time-of-Flight Sensor Fu
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    Acquiring scenery depth is a fundamental task in computer vision, with many applications in manufacturing, surveillance, or robotics relying on accurate scenery information. Time-of-flight cameras can provide depth information in realtime and overcome short-comings of traditional stereo analysis. However, they provide limited spatial resolution and sophisticated upscaling algorithms are sought after. In this paper, we present a sensor fusion approach to time-of-flight super resolution, based on the combination of depth and texture sources. Unlike other texture guided approaches, we interpret the depth upscaling process as a weighted energy optimization problem. Three different weights are introduced, employing different available sensor data. The individual weights address object boundaries in depth, depth sensor noise, and temporal consistency. Applied in consecutive order, they form three weighting strategies for timeof-flight super resolution. Objective evaluations show advantages in depth accuracy and for depth image based rendering compared with state-of-the-art depth upscaling. Subjective view synthesis evaluation shows a significant increase in viewer preference by a factor of four in stereoscopic viewing conditions. To the best of our knowledge, this is the first extensive subjective test performed on time-of-flight depth upscaling. Objective and subjective results proof the suitability of our approach to time-of-flight super resolution approach for depth scenery capture.
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    Similarity Validation Based Nonlocal Means Image Denoising

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    Similarity Validation Based Nonlocal Means Image Denoising
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    Nonlocal means is one of the well known and mostly used image denoising methods. The conventional nonlocal means approach uses weighted version of all patches in a search neighbourhood to denoise the center patch. However, this search neighbourhood can include some dissimilar patches. In this paper, we propose a pre-processing hard thresholding algorithm that eliminates those dissimilar patches. Consequently, the method improves the performance of nonlocal means. The threshold is calculated based on the distribution of distances of noisy similar patches. The method denoted by Similarity Validation Based Nonlocal Means (NLM-SVB) shows improvement in terms of PSNR and SSIM of the retrieved image in comparison with nonlocal means and some recent variations of nonlocal means.

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    Blind Depth Quality Assessment Using Histogram Shape Analysi

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    Blind Depth Quality Assessment Using Histogram Shape Analysi
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    Multiview videos plus depth (MVD) is a popular 3D video representation where pixel depth information is exploited to generate additional views to provide 3D experience. Quality assessment of MVD data is of paramount importance since the latest research results show that existing 2D quality metrics are not suitable for MVD. This paper focuses on depth quality assessment and presents a novel algorithm to estimate the distortion in depth videos induced by compression. The proposed algorithm is noreference and does not require any prior training or modeling. The proposed method is based solely on the statistical analysis of the compression sensitive pixels of depth images. The experimental results worked out on a standard MVD dataset show that the proposed algorithm exhibits a very high correlation with conventional full-reference metrics.

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    Saliency Detection for Stereoscopic Images

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    Saliency Detection for Stereoscopic Images
    Approx. Rs 10,000 / student
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    Many saliency detection models for 2D images have been proposed for various multimedia processing applications during the past decades. Currently, the emerging applications of stereoscopic display require new saliency detection models for salient region extraction. Different from saliency detection for 2D images, the depth feature has to be taken into account in saliency detection for stereoscopic images. In this paper, we propose a novel stereoscopic saliency detection framework based on the feature contrast of color, luminance, texture, and depth. Four types of features, namely color, luminance, texture, and depth, are extracted from discrete cosine transform coefficients for feature contrast calculation. A Gaussian model of the spatial distance between image patches is adopted for consideration of local and global contrast calculation. Then, a new fusion method is designed to combine the feature maps to obtain the final saliency map for stereoscopic images. In addition, we adopt the center bias factor and human visual acuity, the important characteristics of the human visual system, to enhance the final saliency map for stereoscopic images. Experimental results on eye tracking databases show the superior performance of the proposed model over other existing methods.
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    An Approximate Spectral Clustering Ensemble for High Spatial

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    An Approximate Spectral Clustering Ensemble for High Spatial
    Approx. Rs 10,000 / student
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    Unsupervised clustering of high spatial resolution remote-sensing images plays a significant role in detailed landcover identification, especially for agricultural and environmental monitoring. A recently promising method is approximate spectral clustering (SC) which enables spectral partitioning for large datasets to extract clusters with distinct characteristics without a parametric model. It also facilitates the use of various information types via advanced similarity criteria. However, it requires an empirical selection of a similarity criterion optimal for the corresponding application. To address this challenge, we propose an approximate SC ensemble (ASCE2) which fuses partitionings obtained by different similarity representations. Contrary to existing spectral ensembles for remote-sensing applications, the proposed ASCE2 employs neural gas quantization instead of random sampling, advanced similarity criteria instead of traditional distance-based Gaussian kernel with different decay parameters, and a two-level ensemble. We evaluate the proposed ASCE2 with three measures (accuracy, adjusted Rand index, and normalized mutual information) using five remote-sensing images, two of which are commonly available. We apply the ASCE2 in two applications for agricultural monitoring: 1) land-cover identifi- cation to determine orchard fields using a WorldView-2 image (0.5-m spatial resolution) and 2) finding lands in good agricultural condition using multitemporal RapidEye images (5-m spatial resolution). Experimental results indicate a significant betterment of the resulting partitionings obtained by the proposed ensemble, with respect to the evaluation measures in these applications.

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    Coin Recognition System

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    Coin Recognition System
    Approx. Rs 10,000 / student
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    Abstract

    The objective of this paper is to classify the Indian coins of different denomination released recently. This project is framed mainly to classify the coins offered in the Hundi by the devotees of Tirumala Tirupati Devasthanam (TTD), Tirupati, India. The objective is to count money by recognizing the coins and count the total sum based on its value. The system is proposed to design coin recognition by applying heuristic approach, based on the coin table.

    This table stores parameters of each coin. This method yields 97% of result in recognizing the coin image. It is also proposed to apply HT (Houch Transform(HT)) algorithm combining the features of a) Straight line detection HT algorithm, b) Curve detection HT algorithm and c) Circle detection HT algorithm. Using these three algorithms edge of the coin is recognized. The features of old coins and new coins of different denominations are considered for classification. Some coins are used in different countries have same parameters, but it has different value. This paper concentrates on affine transformations such as simple gray level scaling, shearing, rotation etc. The coins are well recognized by zooming processes by which a coin size of the image is increased. This paper presents a coin recognition method with rotation invariance. Indian Coins are classified based on different parameters for various values of coin such as shape, size, surface design, and weight and so on. Hence, it is easy for the automatic machine to classify Indian coins.. There are many coin classification machines are available, but, the machine has to be designed for recognition of Indian coin. To increase the efficiency of the machine, they are to be embedded with proper source code. In this paper, Sobel Filter, HA and HT are used to classify the coin image. It is proposed a method for realizing a simple automatic coin recognition system more effectively. The HT technique is used to recognize almost 100% of the coin image. Comparing to Sobel edge detection method, HA the HT gives better result.
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