There are numerous methods to classification of feature types. Imagine provides classification models in addition to texture features and convolution methods that assist in detecting various feature types. Using ESRI's ArcView and ArcGIS Feature Analyst extension, the process of feature extraction is readily accessible and user-friendly to the analyst. But, in general, road detection, specifica...

Designing a completely automatic and efficient face recognition system is a grand challenge for biometrics, computer vision and pattern recognition researchers. Generally, such a recognition system is able to perform three subtasks: face detection, feature extraction and classification. We’ll put our focus on feature extraction, the crucial step prior to classification. The key issue here is to...

Low-complexity coding and decoding (LOCOCODE) is a novel approach to sensory coding and unsupervised learning. Unlike previous methods, it explicitly takes into account the information-theoretic complexity of the code generator. It computes lococodes that convey information about the input data and can be computed and decoded by low-complexity mappings. We implement LOCOCODE by training autoass...

The last few decades has witnessed a stupendous growth of information across the internet. The giant of information are unused across the globe and it requires rigid methodology to mine and extract the text. The growth of information is increasing exponentially and it becomes more important to detect useful pattern from the data. It is very difficult for the user to retrieve the data from the d...

Popular products often have thousands of reviews that contain far too much information for customers to digest. Our goal for the project is to implement a system that extracts opinions from these reviews and summarizes them in a concise form. This allows customers to quickly get an overview of a product and manufactures to efficiently process product feedbacks. In the past, we focused on the fe...

2 Examples of feature extraction techniques 2 2.1 Image processing basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Sobel edge detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.3 Canny edge detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.4 Hough tra...

Image feature extraction is instrumental for most of the best-performing algorithms in computer vision. However, it is also expensive in terms of computational and memory resources for embedded systems due to the need of dealing with individual pixels at the earliest processing levels. In this regard, conventional system architectures do not take advantage of potential exploitation of paralleli...

In manipulating data such as in supervised learning, we often extract new features from original features for the purpose of reducing the dimensions of feature space and achieving better performances. In this paper, we propose a new feature extraction algorithm using independent component analysis (ICA) for classification problems. By using ICA in solving supervised classification problems, we ...

Time varying simulations are common in many scientific domains to study the evolution of phenomena or features. The data produced in these simulations is massive. Instead of just one dataset of 512 or 1024 (for regular gridded simulations) there could now be hundreds to thousands of timesteps. For datasets with evolving features, feature analysis and visualization tools are crucial to help inte...

Although color is commonly experienced as an indispensable quality in describing the world around us, state-of-the art local featurebased representations are mostly based on shape description, and ignore color information. The description of color is hampered by the large amount of variations which causes the measured color values to vary significantly. In this paper we aim to extend the descri...