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Detection and segmentation of cells is an important step for classifying the cells as cancerous or non-cancerous. Pathologists use microscopic images for analysis and further diagnosis of cancer. These images contain the microscopic structure of tissues and are stained using some staining components to facilitate the process. Staining process varies due to different stain manufacturers, staining practices...
Commonly, HoG/SVM classifier uses rectangular images for HoG feature descriptor extraction and training. This means significant additional work has to be done to process irrelevant pixels belonging to the background surrounding the object of interest. While some objects may indeed be square or rectangular, most of objects are not easily representable by simple geometric shapes. In Bitmap-HoG approach...
Classification is one step of image processing which aims to obtain information from remote sensing data. Object oriented classification is a classification algorithm which can be used to process a high resolution image because it uses the object elements as spectral, spatial, and texture. The purpose of this study is to examine the application of object oriented classification on two types of images...
In this paper, we investigate the impact of segmentation algorithms as a preprocessing step for classification of remote sensing images in a deep learning framework. Especially, we address the issue of segmenting the image into regions to be classified using pre-trained deep neural networks as feature extractors for an SVM-based classifier. An efficient segmentation as a preprocessing step helps learning...
In our previous works, we have presented methods for optimizing wavelet filter banks, which can be used for classification of image objects. The wavelet filter banks were designed to be biorthogonal, which enables a multiscale analysis on given image data. Moreover, the filters were optimized with respect to the shape, which helps the filter banks to inherit the property of the objects. This optimization...
Among the skin cancers, malignant melanoma is of by far the most deadly type. For the early diagnosis of melanoma, computer-aided diagnosis systems for the classification of dermoscopic images have been developed. For accurate image classification, the segmentation of skin lesion area is of prime importance. In this work, mathematical morphology has been used for segmentation for proper identification...
A novel Polarimetric-Texture-Structure descriptor for high-resolution PolSAR image is presented in this paper. More precisely, a PolSAR image is represented by a tree of shapes, each of which is associated with several polarimetric and texture attributes. We first extract the texture properties and polarimetric characteristics from each shape, then use the shape co-occurrence patterns (SCOPs) to characterize...
In this paper, a small set of features based on local appearance and texture is applied to the task of image recognition and classification. These features are used to train and subsequently test three different machine learning techniques, namely k-Nearest Neighbors (K-NN), Support Vector Machines (SVM) and Ensemble Learning (Bagging). A case study on a publicly available object classification dataset...
In this paper we explore the role of scale for improved feature learning in convolutional networks. We propose multi-neighborhood convolutional networks, designed to learn image features at different levels of detail. Utilizing nonlinear scale-space models, the proposed multi-neighborhood model can effectively capture fine-scale image characteristics (i.e., appearance) using a small-size neighborhood,...
Spatial pyramid (SP) representation is an extension of bag-of-feature model which embeds spatial layout information of local features by pooling feature codes over pre-defined spatial shapes. However, the uniform style of spatial pooling shapes used in standard SP is an ad-hoc manner without theoretical motivation, thus lacking the generalization power to adapt to different distribution of geometric...
Hyper spectral image processing is becoming an active topic in remote sensing and other applications in current times. Hyper spectral images can easily distinguish materials which are spectrally similar. Many techniques are available to classify hyper spectral images which are mainly deals with the curse of dimensionality and working with few training data issues which confront during classification...
The aim of this paper is to develop an effective classification approach based on Random Forest (RF) algorithm. Three fruits; i.e., apples, Strawberry, and oranges were analysed and several features were extracted based on the fruits' shape, colour characteristics as well as Scale Invariant Feature Transform (SIFT). A preprocessing stages using image processing to prepare the fruit images dataset...
Content-based image classification is such a technique which adapt to mass image data access and classification operation and it is based on the color, texture and shape feature. Image automatic classification using computer is one of the current hot topic. The traditional image classification method based on a single feature is ineffective. In this paper, we use multi-kernel SVM classifiers and the...
Since color is an important visual clue of the pornographic image, this study presents a new framework for pornographic image classification based on the fusion of color and shape information for the bag of words representation. This framework contains three fusion patterns: The early fusion, late fusion and top down color-saliency based fusion, which are compared intensively. Based on the comparison,...
Shaft orbit plays an important role in condition monitoring and fault diagnosis for hydropower unit. A novel method of shaft orbit identification based on low-level image feature representation and classification is proposed. The main characteristic is that the vibrations of the shaft in terms of displacements are used to draw points in an image panel at a fixed scale, resulting in the shaft orbit...
Rapid growth of visual data processing and analysis applications, such as content based image retrieval, augmented reality, automated inspection and defect detection, medical image understanding, and remote sensing has made the problem of developing accurate and efficient image representation and classification methods one of the key research areas. This research proposes new higher-level perceptual...
Content based image classification address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Clustering is a data mining technique to group a set of unsupervised data based on the...
In content based image retrieval (CBIR) system, target images are sorted by feature similarities in terms of related query. Image classification is the important field in applications like security, biometrics, and in medical applications. An efficient image retrieval system is Hue, Saturation and Value (HSV) color space. This Classify the image into n number of areas based on different selected ranges...
Brazilian energy distribution companies need to frequently update their database of the public lighting network. Since changes on this network are frequently not reported, it is important to monitor where they are made. An electronic device is under development for making this task easier. One component of this device uses image processing and recognition techniques to identify the luminaire models...
We propose an efficient framework for combining pixel and object-based approaches for Remote Sensing Image Classification using Support Vector Machines (SVMs) and Dempster-Shafer Theory of Evidence (DSTE). The pixel-based technique employs the multispectral information for assigning a pixel to a class according to the spectral similarities between the classes, and the object-based technique operates...
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