Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

Traditional and recent approaches in background modeling for foreground detection: An overview

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

Entropy based segmentation of tumor from brain MR images a study with teaching learning based optimization

During document scanning, skew is inevitably introduced into the incoming document image. Presence of additional modified characters, which get plugged in as extensions and remain as disjointed protrusions of a main character is really challenging in estimating inclination in skewed documents made up of texts in south Indian languages (Kannada, Telugu, Tamil and Malayalam). In this paper, we present a novel script independent (for south Indian) skew estimation technique based on Gaussian Mixture Models (GMM). The Expectation-Maximization (EM) algorithm is used to learn the mixture of Gaussians. Subsequently the cluster means are subjected to moments to estimate the skew angle. Experiments on printed and handwritten documents corrupted by noise is done. Our method shows significantly improved performance as compared to other existing methods.

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Language independent skew estimation technique based on Gaussian mixture models: a case study on South Indian scripts

Separating text lines in handwritten documents remains a challenge because the text lines are often varying skewed and curved. In this paper, we propose a novel method for text line segmentation of unconstrained handwritten Kannada script. The proposed method consists of two phases. In the first phase, mathematical morphology technique is used to bridge the gap between character components. In the Second phase, component extension technique is used for text line extract. We experimentally evaluated our proposed method on document containing handwritten Kannada script and showed encourage results.

Text line segmentation of unconstrained handwritten Kannada script

Sentiment analysis or opinion mining is an automated process to recognize opinion, moods, emotions, attitude of individuals or communities through natural language processing, text analysis, and computational linguistics. In recent years, many studies concentrated on numerous blogs, tweets, forums and consumer review websites to identify sentiment of the communities. The information retrieved from social networking site will be in short informal text because of limited characters in blogging site or consumer review websites. Sentiment analysis in short-text is a challenging task, due to limitation of characters, user tends to shorten his/her conversation, which leads to misspellings, slang terms and shortened forms of words. Moreover, short-texts consists of more number of presence and absence of term/feature compared to regular text. In this work, our major goal is to classify sentiments into positive, negative or neutral polarity using new similarity measure. The proposed method embeds modified Similarity Measure for Text Processing (mSMTP) with K-Nearest Neighbor (KNN) classifier. The effectiveness of the proposed method is evaluated by comparing with Euclidean Distance, Cosine Similarity, Jaccard Coefficient and Correlation Coefficient. The proposed method is also compared with other classifiers like Support Vector Machine and Random Forest using benchmark dataset. The classification results are evaluated based on Accuracy, Precision, Recall and F-measure.

Classification of sentiments in short-text: an approach using mSMTP measure

Handwritten character recognition is a difficult problem due to the great variations on writing styles, different size and orientation angle of the characters. In this paper, we propose an unconstrained handwritten Kannada character recognition based on the ridgelet transforms. Ridglets are a powerful instrument in catching and representing mono-dimensional singularities in bi dimensional space [7]. Ridgelet transforms is used to extracts low pass energy of character image and is then fed to PCA for feature extraction. We conducted experiment on very large database of handwritten Kannada character. The size of the class was 200 and encouraging results are obtained.

An impact of ridgelet transform in handwritten recognition: A study on very large dataset of Kannada script

Text in an image or a video affords more precise meaning and text is a prominent source with a clear explanation of the content than any other high-level or low-level features The text detection process is a still challenging research work in the field of computer vision However, complex background and orientation of the text leads to extremely stimulating text detection tasks Multilingual text consists of different geometrical shapes than a single language In this article, a simple and yet effective approach is presented to detect the text from an arbitrary oriented multilingual image and video The proposed method employs the Laplacian of Gaussian to identify the potential text information The double line structure analysis is applied to extract the true text candidates The proposed method is evaluated on five datasets: Hua's, arbitrarily oriented, multi-script robust reading competition (MRRC), MSRA and video datasets with performance measures precision, recall and f-measure The proposed method is also tested on real-time video, and the result is promising and encouraging

V. N. Manjunath Aradhya

Papers

Language independent skew estimation technique based on Gaussian mixture models: a case study on South Indian scripts

Text line segmentation of unconstrained handwritten Kannada script

Classification of sentiments in short-text: an approach using mSMTP measure

An impact of ridgelet transform in handwritten recognition: A study on very large dataset of Kannada script

LoG and Structural Based Arbitrary Oriented Multilingual Text Detection in Images/Video