Classification algorithms based sparse coding have formed a mature system for visual recognition. Recent studies suggest collaborative representation is a much more effective method for classification, compared with s...
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Classification algorithms based sparse coding have formed a mature system for visual recognition. Recent studies suggest collaborative representation is a much more effective method for classification, compared with sparse representation,the objective function of collaborative representation is constrained by 2-norm. Traditional collaborative representation based classification always uses a set of training samples to construct a dictionary directly, which causes high residual error and thus reduces the correct rate of classification. To handle the problem, we propose an innovative method, which integrates centralized image coding and class specific dictionary learning algorithm with collaborative representation based classification together, namely class specific centralized dictionary learning based collaborative representation(CSCDL-CRC). Meanwhile,kernel method can obtain nonlinear information between data points through mapping feature space to kernel space, especially when it is applied to image classification. We extended our proposed CSCDL-CRC to the kernel space to improve the classification performance. We make plenty of experiments on three frequently-used fine-grained image datasets, including Caltech-UCSD Birds-200-2011(CUB-200-2011) dataset, Oxford 102-Flowers dataset and Stanford Dogs dataset, to validate the effectiveness of the proposed approach.
Lung cancer is the uncontrolled growth of abnormal cells that starts off in one or both images. People who smoke have the greatest risk of lung cancer. The overall 5 year survival rate for lung cancer combining all st...
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ISBN:
(纸本)9781479939152
Lung cancer is the uncontrolled growth of abnormal cells that starts off in one or both images. People who smoke have the greatest risk of lung cancer. The overall 5 year survival rate for lung cancer combining all stages is roughly 15%. Early detection of lung cancer can increase the chance of survival among people. Lung cancer may be found by imaging tests such as chest computed tomography scan as it provides more detailed picture. To classify the stages of lung cancer, imageprocessing technique is developed. In this work, new algorithm is developed using imageprocessing technique to detect the cancer at early stage with more accuracy. imageprocessing involves the pre- processing, feature extraction and finally classification steps.
For any automated image analysis process, the segmentation is an important task because all subsequent tasks in imageprocessing heavily rely on the quality of image segmentation. It determines the eventual success or...
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ISBN:
(纸本)9781479939152
For any automated image analysis process, the segmentation is an important task because all subsequent tasks in imageprocessing heavily rely on the quality of image segmentation. It determines the eventual success or failure of the analysis. The problem in image segmentation occurs when an image has a varying gray level background. There are several algorithms and methods are available for image segmentation, but there is a need to develop a unique method for it. In this paper, some of the image segmentation algorithms are compared to segment the diseased portion of rice leaves.
Polycystic Ovarian Syndrome (PCOS) is one of the most common hormonal disorder present in females in reproductive age group. Early detection and treatment of PCOS is important since it is often associated with obesity...
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ISBN:
(纸本)9781479939152
Polycystic Ovarian Syndrome (PCOS) is one of the most common hormonal disorder present in females in reproductive age group. Early detection and treatment of PCOS is important since it is often associated with obesity, type 2 diabetes mellitus, and high cholesterol levels. In this paper, automated detection of PCOS is done by calculating no of follicles in ovarian ultrasound image and then incorporating clinical, biochemical and imaging parameters to classify patients in two groups i.e. normal and PCOS affected. Number of follicles are detected by ovarian ultrasound imageprocessing using preprocessing which includes contrast enhancement and filtering, feature extraction using Multiscale morphological approach and segmentation. Support Vector Machine algorithm is used for classification which takes into account all the parameters such as body mass index (BMI), hormonal levels, menstrual cycle length and no of follicles detected in ovarian ultrasound imageprocessing. The results obtained are verified by doctors and compared with manual detection. The accuracy obtained for the proposed method is 95%.
Ischemic stroke is a condition in which brain stops working due to lack of blood supply resulting in death of brain cells. Magnetic Resonance Imaging is widely used to detect Ischemic Strokes in brain. This paper give...
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ISBN:
(纸本)9781479939152
Ischemic stroke is a condition in which brain stops working due to lack of blood supply resulting in death of brain cells. Magnetic Resonance Imaging is widely used to detect Ischemic Strokes in brain. This paper gives an automated algorithm to detect the stroke using imageprocessing techniques. The algorithm consists of six phases. Data in the form of MRI images is collected in first phase. The preprocessing is performed including filtering on the raw data collected. Midline is traced in third phase for acquiring a symmetrical image. It is followed by bifurcation of image in fourth phase. Finally the image quality matrix is formed for texture analysis in fifth phase and neural network is applied in sixth phase for classification of normal and infected brain. The advantage is that the strokes can be detected in its early stage. The algorithm proposed is simple, efficient and less time consuming. The efficiency is found to be 98%.
Object extraction is important field of imageprocessing. In this paper we will discuss about feature extraction and performance evaluation to detect object using varying threshold values. The fundamental concept of v...
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ISBN:
(纸本)9781479939152
Object extraction is important field of imageprocessing. In this paper we will discuss about feature extraction and performance evaluation to detect object using varying threshold values. The fundamental concept of varying threshold and frequency based method provide a new aid to computer vision. This paper proposes a new object extraction varying threshold method with the combination of existing priori research paper method. Object extraction of a given image involves detection, feature extraction on the basis of area comparison of pixels, size, enrichment and mining. The purpose of this paper is to resolves the object extraction problems to some extent but still involve lots of tinkering problems, to point out the promising directions for future research.
Gesture recognition is the fast growing field in imageprocessing and artificial technology. The gesture recognition is a process in which the gestures or postures of human body parts are identified and are used to co...
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ISBN:
(纸本)9781479939152
Gesture recognition is the fast growing field in imageprocessing and artificial technology. The gesture recognition is a process in which the gestures or postures of human body parts are identified and are used to control computers and other electronic appliances. The most contributing reason for the emerging gesture recognition is that they can create a simple communication path between human and computer called HCI (Human Computer Interaction). This paper is confined to identification of hand postures and to establish a man-machine interaction. The hand region in the image is detected and the number of active fingers is determined. In this approach, the input which is an image or a frame from a video can be obtained from web camera or any other camera. This color image is converted into binary image and preprocessed and the number of fingers is counted using scanning method in MATLAB. This is a simple yet efficient approach. The main reason to employ scanning method is to make the code to recognize the finger count independent of size and rotation of the hand
The proposed algorithm describes the problem of Magnetic Resonance (MR) brain image segmentation using the tree-metric graph cuts (TM) algorithm, a novel segmentation algorithm and introducing a "tree-cutting&quo...
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ISBN:
(纸本)9781479939152
The proposed algorithm describes the problem of Magnetic Resonance (MR) brain image segmentation using the tree-metric graph cuts (TM) algorithm, a novel segmentation algorithm and introducing a "tree-cutting" method to interpret the labeling returned by the TM algorithm as tissue classification for the input brain MR brain image. The algorithm has three sequential steps: 1) pre-processing, which generates a tree of labels as key to the TM algorithm;2) a sweep of the TM algorithm, which proceeds a globally optimal labeling with respect to the tree of labels;3) post-processing, which involves running the "tree-cutting" method to generate a mapping from labels to brain tissues such as Grey Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF) producing a meaningful MR brain image segmentation. On comparison with the current approaches, the result obtained shows that the tree metrics graph cut algorithm is faster and the overall segmentation accuracy is better for segmenting both T1 and T2 weighted MR axial brain slice images.
Wavelet Transforms is a part of large community of mathematical function approximation method, they are being increasing and being deployed in imageprocessing for segmentation, filtering, classification etc. This wor...
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ISBN:
(纸本)9781479939152
Wavelet Transforms is a part of large community of mathematical function approximation method, they are being increasing and being deployed in imageprocessing for segmentation, filtering, classification etc. This work is based on image classification with the use of single level Discrete Wavelet Transform (DWT). Wavelets have been employed in many applications of signal *** texture features within images are extracted for accurate and efficient Glaucoma Classification. Energy is distributed over the wavelet sub-bands to find these important texture features. The discriminatory potential of wavelet features obtained from the daubechies (db3), symlets (sym3), and reverse biorthogonal (rbio3.3, rbio3.5, and rbio3.7) wavelet filters. We propose a technique to extract energy features obtained using 2-D discrete wavelet transform. The energy features obtained from the detailed coefficients can be used to distinguish between normal and glaucomatous images with very high accuracy. The effectiveness is evaluated using K- NN classifier by taking 30 normal and glaucoma images, 15 images are used for training and 15 images for testing.
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