21 research outputs found
Recognition of Tomato Late Blight by using DWT and Component Analysis
Plant disease recognition concept is one of the successful and important applications of image processing and able to provide accurate and useful information to timely prediction and control of plant diseases. In the study, the wavelet based features computed from RGB images of late blight infected images and healthy images. The extracted features submitted to Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA) and Independent Component Analysis performed (ICA) for reducing dimensions in feature data processing and classification. To recognize and classify late blight from healthy plant images are classified into two classes i.e. late blight infected or healthy. The Euclidean Distance measure is used to compute the distance by these two classes of training and testing dataset for tomato late blight recognition and classification. Finally, the three-component analysis is compared for late blight recognition accuracy. The Kernel Principal Component Analysis (KPCA) yielded overall recognition accuracy with 96.4%
Forecastable Component Analysis (ForeCA)
I introduce Forecastable Component Analysis (ForeCA), a novel dimension
reduction technique for temporally dependent signals. Based on a new
forecastability measure, ForeCA finds an optimal transformation to separate a
multivariate time series into a forecastable and an orthogonal white noise
space. I present a converging algorithm with a fast eigenvector solution.
Applications to financial and macro-economic time series show that ForeCA can
successfully discover informative structure, which can be used for forecasting
as well as classification. The R package ForeCA
(http://cran.r-project.org/web/packages/ForeCA/index.html) accompanies this
work and is publicly available on CRAN.Comment: 10 pages, 4 figures; ICML 201
Stain deconvolution using statistical analysis of multi-resolution stain colour representation
Stain colour estimation is a prominent factor of the analysis pipeline in most of histology image processing algorithms. Providing a reliable and efficient stain colour deconvolution approach is fundamental for robust algorithm. In this paper, we propose a novel method for stain colour deconvolution of histology images. This approach statistically analyses the multi-resolutional representation of the image to separate the independent observations out of the correlated ones. We then estimate the stain mixing matrix using filtered uncorrelated data. We conducted an extensive set of experiments to compare the proposed method to the recent state of the art methods and demonstrate the robustness of this approach using three different datasets of scanned slides, prepared in different labs using different scanners
Break The Spell Of Total Correlation In betaTCVAE
In the absence of artificial labels, the independent and dependent features
in the data are cluttered. How to construct the inductive biases of the model
to flexibly divide and effectively contain features with different complexity
is the main focal point of unsupervised disentangled representation learning.
This paper proposes a new iterative decomposition path of total correlation and
explains the disentangled representation ability of VAE from the perspective of
model capacity allocation. The newly developed objective function combines
latent variable dimensions into joint distribution while relieving the
independence constraints of marginal distributions in combination, leading to
latent variables with a more manipulable prior distribution. The novel model
enables VAE to adjust the parameter capacity to divide dependent and
independent data features flexibly. Experimental results on various datasets
show an interesting relevance between model capacity and the latent variable
grouping size, called the "V"-shaped best ELBO trajectory. Additionally, we
empirically demonstrate that the proposed method obtains better disentangling
performance with reasonable parameter capacity allocation