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International Journal of Computer and Applications

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

Advances in computing technology have fostered the development of new and powerful deep learning (DL) techniques, which have demonstrated promising results in a wide range of applications. Particularly, DL methods have been successfully used to classify remotely sensed data collected by Earth Observation (EO) instruments. Hyperspectral imaging (HSI) is a hot topic in remote sensing data analysis due to the vast amount of information comprised by this kind of images, which allows for a better characterization and exploitation of the Earth surface by combining rich spectral and spatial information. However, HSI poses major challenges for supervised classification methods due to the high dimensionality of the data and the limited availability of training samples. These issues, together with the high intraclass variability (and interclass similarity) –often present in HSI data– may hamper the effectiveness of classifiers. In order to solve these limitations, several DL-based architectures have been recently developed, exhibiting great potential in HSI data interpretation. This paper provides a comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature. For each discussed method, we provide quantitative results using several well-known and widely used HSI scenes, thus providing an exhaustive comparison of the discussed techniques. The paper concludes with some remarks and hints about future challenges in the application of DL techniques to HSI classification. The source codes of the methods discussed in this paper are available from: https://github.com/mhaut/hyperspectral_deeplearning_review .

Deep learning classifiers for hyperspectral imaging: A review

PhysioBank是一个大型的逐渐扩增的生理学信号和相关数据的数字化记录文档。目前包含多参数的心肺、神经和其他生物医学信号，尤以心电图（ECG）为主。信号来自健康受试者和各种疾病的患者。涉及的疾病包括心脏猝死、充血性心力衰竭、癫痫、步态不稳、睡眠呼吸暂停和衰老等。

PhysioBank

This work presents a method for identifying plant leaf disease and an approach for careful detection of diseases. The goal of proposed work is to diagnose the disease of brinjal leaf using image processing and artificial neural techniques. The diseases on the brinjal are critical issue which makes the sharp decrease in the production of brinjal. The study of interest is the leaf rather than whole brinjal plant because about 85–95 % of diseases occurred on the brinjal leaf like, Bacterial Wilt, Cercospora Leaf Spot, Tobacco mosaic virus (TMV). The methodology to detect brinjal leaf disease in this work includes K-means clustering algorithm for segmentation and Neural-network for classification. The proposed detection model based artiifical neural networks are very effective in recognizing leaf diseases.

An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method

This project briefs the detection of diseases present in a tomato leaf using Convolutional Neural Networks (CNNs) which is a class under a deep neural network. As an initial step, the dataset is segregated before the detection of tomato leaves. The concept of transfer learning is used where a pre-trained model (ResNet-50) is imported and adjusted according to our classification problem. To increase the quality of the ResNet model and to enhance the result as close to the actual prevailing disease, data augmentation has been implemented. Taking all these into consideration, a tomato leaf disease detection model has been developed using PyTorch that uses deep - CNNs. Finally, the testing dataset is processed for validation based on the learned parameters from the ResNet 50 model. Six most prevailing diseases in tomato crops have been taken for classification. Data augmentation has been introduced to increase the data set to 4 times the actual data and the model has shown an accuracy of 97%.

Tomato Leaf Disease Detection using Convolutional Neural Network with Data Augmentation

In India, smart organic farming is gaining importance. There may be problems due to environment, temperature, humidity or nutrient deficiency in this farming. If we have a monitoring system for this farming it is possible to produce healthy plant. The aim is to address this issue using computer aided image processing technique. Main solution is to create an automation system which can detect the disease present in the leaf of the plant. In this paper, a first level attempt is made to detect diseases present in the leaf of salad cucumber. The most common diseases which are present in salad cucumber are Alternaria leaf blight, Bacterial wilt, Cucumber green mottle mosaic, Leaf Miner, Leaf spot, Cucumber Mosaic Virus (CMV) disease and so on. K-means clustering, an unsupervised algorithm along with Support Vector Machine(SVM) is used in this work to address this problem.

Leaf disease detection on cucumber leaves using multiclass Support Vector Machine

Hyperspectral image classification is an emerging and interesting research area that has attracted several researchers to contribute to this field. Hyperspectral images have multiple narrow bands for a single image that enable the development of algorithms to extract diverse features. Three-dimensional discrete wavelet transform (3D-DWT) has the advantage of extracting the spatial and spectral information simultaneously. Decomposing an image into a set of spatial–spectral components is an important characteristic of 3D-DWT. It has motivated us to perform the proposed research work. The novelty of this work is to bring out the features of 3D-DWT applicable to hyperspectral images classification using Haar, Fejer-Korovkin and Coiflet filters. Three-dimensional-DWT is implemented with the help of three stages of 1D-DWT. The first two stages of 3D-DWT are extracting spatial resolution, and the third stage is extracting the spectral content. In this work, the 3D-DWT features are extracted and fed to the following classifiers (i) random forest (ii) K-nearest neighbor (KNN) and (iii) support vector machine (SVM). Exploiting both spectral and spatial features help the classifiers to provide a better classification accuracy. A comparison of results was performed with the same classifiers without DWT features. The experiments were performed using Salinas Scene and Indian Pines hyperspectral datasets. From the experiments, it has been observed that the SVM with 3D-DWT features performs better in terms of the performance metrics such as overall accuracy, average accuracy and kappa coefficient. It has shown significant improvement compared to the state of art techniques. The overall accuracy of 3D-DWT+SVM is 88.3%, which is 14.5% larger than that of traditional SVM (77.1%) for the Indian Pines dataset. The classification map of 3D-DWT + SVM is more closely related to the ground truth map.

Robust Classification Technique for Hyperspectral Images Based on 3D-Discrete Wavelet Transform

Hexagonal sampling grid offers less storage time, less computation time, increased coding efficiency, less quantization error, equidistant property and consistent connectivity, etc., Edge detection is one of the important preprocessing steps in many of the image processing applications. Various edge detection techniques are available for images on rectangular grids such as classical operators, CLAP algorithm based edge detection, wavelet based edge detection etc. Wavelet based edge detection is found to be a better technique for specific application such as iris recognition system, 3D vertebrae shape recognition, infrared target recognition etc. These techniques are not suitable for hexagonal domain. For these we need to choose proper addressing scheme. In this paper, (i) Edge detection using masks on hexagonal grid (using spiral addressing scheme) is performed (ii) CLAP algorithm is performed on rectangular domain and hexagonal domain (using alternate pixel suppressal and half-pixel shift method ) (iii) Wavelet based edge detection was performed on rectangular domain. For hexagonal domain, we need to choose proper directional wavelet. Gabor wavelet based edge detection is proposed for the hexagonal grid and we were able to get good results. For performance evaluation we are considering Mean Square Error, Peak Signal to Noise Ratio and ratio of edge pixels to size of image. For all operations, edge detection on hexagonal domain gives better results and better visual appeal of images compared to edge detection on rectangular domain.

/pdf/performance-analysis-of-edge-detection-methods-on-hexagonal-3i3q0pr5m9.pdf

S. Veni

Papers

An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method

Tomato Leaf Disease Detection using Convolutional Neural Network with Data Augmentation

Leaf disease detection on cucumber leaves using multiclass Support Vector Machine

Robust Classification Technique for Hyperspectral Images Based on 3D-Discrete Wavelet Transform

Performance Analysis of Edge Detection Methods on Hexagonal Sampling Grid