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IMAGE PROCESSING 2016

Category Archives

Trees Leaves Extraction In Natural Images Based On Imagesegmentation and generating Its plant details

ABSTRACT

It is always a difficult task to analyze plant leaf images by a common man because there are very minute variations in some plant leaf images & larger data set for analysis. The identification becomes much difficult if we deal with medication plants. Wrong detection of plant through leaf can cause serious catastrophes as the medicine made of these plants is directly consumed by human and animals. It is a quite difficult to develop an automated recognition system which could process on a large information and provide a correct estimation. Block Matching and Watershed Algorithm have been successfully applied to problems in leaf pattern recognition, classification and image analysis. In this paper, Multilayer feed- forward networks are trained using back propagation learning algorithm. The main objective of this paper is to develop a classification system for agriculture and Ayurveda plants by image pre-processing, leaf contour, feature extraction, and classification. In our proposed algorithm we can analyze any big database of leaves of different plants with accuracy greater than 95%.


Statistical performance analysis of a fast super-resolution technique using noisy translations

ABSTRACT

The registration process is a key step for super-resolution (SR) reconstruction. More and more devices permit to overcome this bottleneck using a controlled positioning system, e.g., sensor shifting using a piezoelectric stage. This makes possible to acquire multiple images of the same scene at different controlled positions. Then, a fast SR algorithm can be used for efficient SR reconstruction. In this case, the optimal use of r2 images for a resolution enhancement factor r is generally not enough to obtain satisfying results due to the random inaccuracy of the positioning system. Thus, we propose to take several images around each reference position. We study the error produced by the SR algorithm due to spatial uncertainty as a function of the number of images per position. We obtain a lower bound on the number of images that is necessary to ensure a given error upper bound with probability higher than some desired confidence level. Such results give precious hints to the design of SR systems.


Texture classification using Dense Micro-block Difference

ABSTRACT

This paper is devoted to the problem of texture classification. Motivated by recent advancements in the field of compressive sensing and keypoints descriptors, a set of novel features called dense micro-block difference (DMD) is proposed. These features provide highly descriptive representation of image patches by densely capturing the granularities at multiple scales and orientations. Unlike most of the earlier work on local features, the DMD does not involve any quantization, thus retaining the complete information. We demonstrate that the DMD have dimensionality much lower than Scale Invariant Feature Transform (SIFT) and can be computed using integral image much faster than SIFT. The proposed features are encoded using the Fisher vector method to obtain an image descriptor, which considers high-order statistics. The proposed image representation is combined with the linear support vector machine classifier. Extensive experiments are conducted on five texture data sets (KTH-TIPS, UMD, KTH-TIPS-2a, Brodatz, and Curet) using standard protocols. The results demonstrate that our approach outperforms the state-of-the-art in texture classification.


CASAIR: Content and Shape-Aware Image Retargeting and Its Applications

ABSTRACT

This paper proposes a novel image retargeting algorithm that can retarget images to a large family of nonrectangular shapes. Specifically, we study image retargeting from a broader perspective that includes the content as well as the shape of an image, and the proposed content and shape-aware image retargeting (CASAIR) algorithm is driven by the dual objectives of image content preservation and image domain transformation, with the latter defined by an application-specific target shape. The algorithm is based on the idea of seam segment carving that successively removes low-cost seam segments from the image to simultaneously achieve the two objectives, with the selection of seam segments determined by a cost function incorporating inputs from image content and target shape. To provide a complete characterization of shapes that can be obtained using CASAIR, we introduce the notion of bhv-convex shapes and we show that bhv-convex shapes are precisely the family of shapes that can be retargeted to by CASAIR. The proposed algorithm is simple in both its design and implementation, and in practice, it offers an efficient and effective retargeting platform that provides its users with considerable flexibility in choosing target shapes. To demonstrate the potential of CASAIR for broadening the application scope of image retargeting, this paper also proposes a smart camera-projector system that incorporates CASAIR. In the context of ubiquitous display, CASAIR equips the camera projector system with the capability to online retarget images in order to maximize the quality and fidelity of the displayed images whenever the situation demands.


Image Segmentation Using Parametric Contours With Free Endpoints

ABSTRACT

In this paper, we introduce a novel approach for active contours with free endpoints. A scheme for image segmentation is presented based on a discrete version of the Mumford-Shah functional where the contours can be both closed and open curves. Additional to a flow of the curves in normal direction, evolution laws for the tangential flow of the endpoints are derived. Using a parametric approach to describe the evolving contours together with an edge-preserving denoising, we obtain a fast method for image segmentation and restoration. The analytical and numerical schemes are presented followed by numerical experiments with artificial test images and with a real medical image.


Learning Iteration-wise Generalized Shrinkage-Thresholding Operators for Blind Deconvolution

ABSTRACT

Salient edge selection and time-varying regularization are two crucial techniques to guarantee the success of maximum a posteriori (MAP)-based blind deconvolution. However, the existing approaches usually rely on carefully designed regularizers and handcrafted parameter tuning to obtain satisfactory estimation of the blur kernel. Many regularizers exhibit the structure-preserving smoothing capability, but fail to enhance salient edges. In this paper, under the MAP framework, we propose the iteration-wise ℓp-norm regularizers together with data-driven strategy to address these issues. First, we extend the generalized shrinkage-thresholding (GST) operator for ℓp-norm minimization with negative p value, which can sharpen salient edges while suppressing trivial details. Then, the iteration-wise GST parameters are specified to allow dynamical salient edge selection and time-varying regularization. Finally, instead of handcrafted tuning, a principled discriminative learning approach is proposed to learn the iterationwise GST operators from the training dataset. Furthermore, the multi-scale scheme is developed to improve the efficiency of the algorithm. Experimental results show that, negative p value is more effective in estimating the coarse shape of blur kernel at the early stage, and the learned GST operators can be well generalized to other dataset and real world blurry images. Compared with the state-of-the-art methods, our method achieves better deblurring results in terms of both quantitative metrics and visual quality, and it is much faster than the state-of-the-art patch-based blind deconvolution method.


Automatic Design of Color Filter Arrays in The Frequency Domain

ABSTRACT

In digital color imaging, the raw image is typically obtained through a single sensor covered by a color filter array (CFA), which allows only one color component to be measured at each pixel. The procedure to reconstruct a full color image from the raw image is known as demosaicking. Since the CFA may cause irreversible visual artifacts, the CFA and the demosaicking algorithm are crucial to the quality of demosaicked images. Fortunately, the design of CFAs in the frequency domain provides a theoretical approach to handling this issue. However, almost all the existing design methods in the frequency domain involve considerable human effort. In this paper, we present a new method to automatically design CFAs in the frequency domain. Our method is based on the frequency structure representation of mosaicked images. We utilize a multi-objective optimization approach to propose frequency structure candidates, in which the overlap among the frequency components of images mosaicked with the CFA is minimized. Then, we optimize parameters for each candidate, which is formulated as a constrained optimization problem. We use the alternating direction method to solve it. Our parameter optimization method is applicable to arbitrary frequency structures, including those with conjugate replicas of chrominance components. Experiments on benchmark images confirm the advantage of the proposed method.


A Diffusion and Clustering-based Approach for Finding Coherent Motions and Understanding Crowd Scenes

ABSTRACT

This paper addresses the problem of detecting coherent motions in crowd scenes and presents its two applications in crowd scene understanding: semantic region detection and recurrent activity mining. It processes input motion fields (e.g., optical flow fields) and produces a coherent motion filed, named as thermal energy field. The thermal energy field is able to capture both motion correlation among particles and the motion trends of individual particles which are helpful to discover coherency among them. We further introduce a two-step clustering process to construct stable semantic regions from the extracted time-varying coherent motions. These semantic regions can be used to recognize pre-defined activities in crowd scenes. Finally, we introduce a cluster-and-merge process which automatically discovers recurrent activities in crowd scenes by clustering and merging the extracted coherent motions. Experiments on various videos demonstrate the effectiveness of our approach.


Learning Invariant Color Features for Person Re-Identification

ABSTRACT

Matching people across multiple camera views known as person re-identification, is a challenging problem due to the change in visual appearance caused by varying lighting conditions. The perceived color of the subject appears to be different with respect to illumination. Previous works use color as it is or address these challenges by designing color spaces focusing on a specific cue. In this paper, we propose a data driven approach for learning color patterns from pixels sampled from images across two camera views. The intuition behind this work is that, even though pixel values of same color would be different across views, they should be encoded with the same values. We model color feature generation as a learning problem by jointly learning a linear transformation and a dictionary to encode pixel values. We also analyze different photometric invariant color spaces. Using color as the only cue, we compare our approach with all the photometric invariant color spaces and show superior performance over all of them. Combining with other learned low-level and high-level features, we obtain promising results in ViPER, Person Re-ID 2011 and CAVIAR4- REID datasets


Automated Malaria Detection fromBlood Samples Using Image Processing

ABSTRACT

Malaria is a serious disease for which the immediate diagnosis is required in order to control it. Microscopes are used to detect the disease and pathologists use the manual method due to which there is a lot of possibility of false detection being made about the disease. If the wrong detection is done then the disease can turn into more severe state. So the study about the computerized diagnosis is done in this paper, which will help in immediate detection of the disease to some extent, So that the proper treatment can be provided to the malaria patient. Also the image processing algorithm is used which will reliably detect the presence of malaria parasite from Plasmodium falciparum species in thin smears of Giemsa stained peripheral blood sample. Some image processing algorithms to automate the diagnosis of malaria on thin blood smears are developed, but the percentage of parasitaemia is often not as precise as manual count. One reason resulting in this error is ignoring the cells at the borders of images. This paper removes the human error while detecting the presence of malaria parasites in the blood sample by using image processing and automation. This is achieved by using Image Segmentation techniques to detect malaria parasites in images acquired from Giemsa stained peripheral blood samples. This is comparative study of two methods for detecting malaria parasites, first method is based on segmentation and second uses feature extraction using minimum distance classifiers. We built the malaria detection system in a robust manner so that it is unaffected by the exceptional conditions and achieved high percentages of sensitivity, specificity, positive prediction and negative prediction values


Performance Analysis of Filters on Complex Images for TextExtraction through Binarization

ABSTRACT

Text data present in image contain useful information and extraction of text from complex images is an extremely difficult task and challenging job due to variation in style, font,alignment,background intensity, illumination and various other factors. Image binarization is a technique in which text is extracted from image in dual color i.e. black and white. The extracted text will be in black color and background will be in white color. A suitable threshold value is calculated from the pixels of the input image. Existing research has been shown that different image require different method for detection or extraction of text. Filters are used for removing the noise from images and preserving the edges of text in images.. In this paper, we are doing the performance analysis of image filters on complex images for text extraction through binarization using analytical simulation in MATLAB.


A Review Paper on detection of Glaucomausing Retinal Fundus Images

Abstract

This paper describes the various techniques used for automatic detection of glaucoma. Glaucoma is a chronic eye disease in which optic nerve is progressively damaged and hence causes partial loss of vision. If not treated properly, it may lead to blindness. The current diagnosis of this neurodegenerative disease is done by extracting different features from retinal fundus images. Mostly, the features include Cup to Disc Ratio(CDR), ratio of Neuro-Retinal Rim(NRR) in Inferior, Superior, Nasal, Temporal (ISNT) quadrants, blood vessels, Para-Papillary Atrophy(PPA) and RNFL(Retinal Nerve Fibre Layer) thickness.


Predicting theForest FireUsingImage Processing

ABSTRACT

Predicting the forest fire is an important problem from many points of view. It destroys ecology and decreases the overall life quality. It is important from economical point of view as wood is a valuable resource. In this paper, we examine the problem of early fire detection using images of different parts of the forest areas. Our approach is based on image pre -processsing and segmentation of images are produced using the segmentation methods. A new approach is used to extract the features using histogram of gradient (HOG) by extract the features such as gradient, angle, magnitude and the support vector machine (SVM) are used to recognize patterns for classification is shown


Context-based prediction filtering of impulse noise images

ABSTRACT

A new image denoising method for impulse noise in greyscale images using a context-based prediction scheme is presented. The algorithm replaces the noisy pixel with the value occurring with the highest frequency, in the same context as the replaceable pixel. Since it is a context-based technique, it preserves the details in the filtered images better than other methods. In the aim of validation, the authors have compared the proposed method with several existing denoising methods, many of them being outperformed by the proposed filter.


Robust Visual Tracking via Convolutional Networks without Training

ABSTRACT

Deep networks have been successfully applied to visual tracking by learning a generic representation offline from numerous training images. However, the offline training is time-consuming and the learned generic representation may be less discriminative for tracking specific objects. In this paper, we present that, even without offline training with a large amount of auxiliary data, simple two-layer convolutional networks can be powerful enough to learn robust representations for visual tracking. In the first frame, we extract a set of normalized patches from the target region as fixed filters, which integrate a series of adaptive contextual filters surrounding the target to define a set of feature maps in the subsequent frames. These maps measure similarities between each filter and useful local intensity patterns across the target, thereby encoding its local structural information. Furthermore, all the maps together form a global representation, via which the inner geometric layout of the target is also preserved. A simple soft shrinkage method that suppresses noisy values below an adaptive threshold is employed to de-noise the global representation. Our convolutional networks have a lightweight structure and perform favorably against several state-of-the-art methods on the recent tracking benchmark data set with 50 challenging videos.


Weakly Supervised Fine-Grained Categorization with Part-Based Image Representation

Abstract

In this paper, we propose a fine-grained image categorization system with easy deployment. We do not use any object/part annotation (weakly supervised) in the training or in the testing stage, but only class labels for training images. Finegrained image categorization aims to classify objects with only subtle distinctions (e.g., two breeds of dogs that look alike). Most existing works heavily rely on object/part detectors to build the correspondence between object parts, which require accurate object or object part annotations at least for training images. The need for expensive object annotations prevents the wide usage of these methods. Instead, we propose to generate multiscale part proposals from object proposals, select useful part proposals, and use them to compute a global image representation for categorization. This is specially designed for the weakly supervised fine-grained categorization task, because useful parts have been shown to play a critical role in existing annotation dependent works, but accurate part detectors are hard to acquire. With the proposed image representation, we can further detect and visualize the key (most discriminative) parts in objects of different classes. In the experiments, the proposed weakly supervised method achieves comparable or better accuracy than the state-of-the-art weakly supervised methods and most existing annotation-dependent methods on three challenging datasets. Its success suggests that it is not always necessary to learn expensive object/part detectors in fine-grained image categorization.


Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation

ABSTRACT

Positron emission tomography (PET) images are widely used in many clinical applications, such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic quality, a sufficient amount of radioactive tracer has to be injected into a living body, which will inevitably increase the risk of radiation exposure. On the other hand, if the tracer dose is considerably reduced, the quality of the resulting images would be significantly degraded. It is of great interest to estimate a standard-dose PET (S-PET) image from a low-dose one in order to reduce the risk of radiation exposure and preserve image quality. This may be achieved through mapping both S-PET and low-dose PET data into a common space and then performing patch-based sparse representation. However, a one-size-fits-all common space built from all training patches is unlikely to be optimal for each target S-PET patch, which limits the estimation accuracy. In this paper, we propose a data-driven multi-level canonical correlation analysis scheme to solve this problem. In particular, a subset of training data that is most useful in estimating a target S-PET patch is identified in each level, and then used in the next level to update common space and improve estimation. In addition, we also use multi-modal magnetic resonance images to help improve the estimation with complementary information. Validations on phantom and real human brain data sets show that our method effectively estimates S-PET images and well preserves critical clinical quantification measures, such as standard uptake value.


Quaternion Collaborative and Sparse Representation With Application to Color Face Recognition

ABSTRACT

Collaborative representation-based classification (CRC) and sparse RC (SRC) have recently achieved great success in face recognition (FR). Previous CRC and SRC are originally designed in the real setting for grayscale image-based FR. They separately represent the color channels of a query color image and ignore the structural correlation information among the color channels. To remedy this limitation, in this paper, we propose two novel RC methods for color FR, namely, quaternion CRC (QCRC) and quaternion SRC (QSRC) using quaternion ℓ1 minimization. By modeling each color image as a quaternion signal, they naturally preserve the color structures of both query and gallery color images while uniformly coding the query channel images in a holistic manner. Despite the empirical success of CRC and SRC on FR, a few theoretical results are developed to guarantee their effectiveness. Another purpose of this paper is to establish the theoretical guarantee for QCRC and QSRC under mild conditions. Comparisons with competing methods on benchmark real-world databases consistently show the superiority of the proposed methods for both color FR and reconstruction.


Double-Tip Artefact Removal from Atomic Force Microscopy Images

ABSTRACT

The atomic force microscopy (AFM) allows the measurement of interactions at interfaces with nanoscale resolution. Imperfections in the shape of the tip often lead to the presence of imaging artifacts, such as the blurring and repetition of objects within images. In general, these artifacts can only be avoided by discarding data and replacing the probe. Under certain circumstances (e.g., rare, high-value samples, or extensive chemical/physical tip modification), such an approach is not feasible. Here, we apply a novel deblurring technique, using a Bayesian framework, to yield a reliable estimation of the real surface topography without any prior knowledge of the tip geometry (blind reconstruction). A key contribution is to leverage the significant recently successful body of work in natural image deblurring to solve this problem. We focus specifically on the double-tip effect, where two asperities1 are present on the tip, each contributing to the image formation mechanism. Finally, we demonstrate that the proposed technique successfully removes the double-tip effect from high-resolution AFM images, which demonstrate this artifact while preserving feature resolution.


Data-driven Soft Decoding of Compressed Images in Dual Transform-Pixel Domain

ABSTRACT

In the large body of research literature on image restoration, very few papers were concerned with compression-induced degradations, although in practice, the most common cause of image degradation is compression. This paper presents a novel approach to restoring JPEG-compressed images. The main innovation is in the approach of exploiting residual redundancies of JPEG code streams and sparsity properties of latent images. The restoration is a sparse coding process carried out jointly in the DCT and pixel domains. The prowess of the proposed approach is directly restoring DCT coefficients of the latent image to prevent the spreading of quantization errors into the pixel domain, and at the same time, using online machine-learned local spatial features to regulate the solution of the underlying inverse problem. Experimental results are encouraging and show the promise of the new approach in significantly improving the quality of DCT-coded images.


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