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The ongoing development autonomous vehicles and adaptive vehicle dynamics present in many modern vehicles has generated a need for road environment classification - i.e., the ability to determine the nature of the current road or terrain environment from an onboard vehicle sensor. In this paper, we investigate the use of a low-cost camera vision solution capable of urban, rural, or off-road classification...
Breast cancer is the most frequent cancer and the most frequent cause of cancer induced death in women in the world. Diagnosis and prognosis of this cancer can be done through the radiological, surgical, and pathologic assessments of breast tissue samples. In developing countries, testing for detection of this cancer involves visual microscopic test of cytology samples such as Fine Needle Aspiration...
The paper presents an upgrading process of rubber tree seed clones identification model using image processing techniques. Sample of rubber tree seeds are captured using digital camera where the RGB color image are processed involving segmentation algorithm which includes thresholding and morphological technique. Texture patterns from seed clones images are then analysed through wavelet's Daubechies...
Answering to a query like when a particular document was printed is quite helpful in practice especially forensic purposes. This study attempts to develop a general framework that makes use of image processing and pattern recognition principles for ink age determination in printed documents. The approach, at first, computationally extracts a set of suitable color features and then analyzes them to...
This paper presents an efficient indoor-outdoor scene classification algorithm based on neural network learning. To facilitate the hardware implementation, efficient image (composite) feature extraction based on image color, entropy, DCT coefficients, and edge orientation was developed. The trained scene classifiers demonstrate robust performance.
In this paper, we propose a face detection framework that combines both feature, and skin pixel approaches, while making the framework self adaptive which is important for non controlled environmental conditions. The framework uses skin color information to reduce the search space for faces by localizing the probable skin regions using a mixture of multivariate Gaussians whose parameters are first...
Quality measurement of face localization using neural networks is presented in this communication. First, neural network was trained with Zernike moments feature parameters vectors. Coordinate vectors of pixels surrounding faces in images were used as target vectors on the supervised training procedure. Thus, trained neural network provides on its output layer a coordinate's vector (p, Theta) representing...
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