- Property Tax Assessment System (PTAS?) January 27, 2012
- Business Permit and Licensing System January 22, 2012
- Insect Identification December 18, 2011
- Legislative Information Management and Tracking System September 6, 2011
- People Counting - Patent finally approved! July 20, 2011
research
August 28, 2008 | 
Author We are currently doing research in people counting. The goal is to develop an algorithm that would solve the problem of occlusion. More info later….
Posted in research | 
Tags: background subtraction, people counting, people for, surveillance | 
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Prior to the previous post, I also finished a paper entitled “Automatic milled rice quality evaluation” that’s about to appear in one international conference proceedings. The idea is to build a low-cost, efficient, and reliable alternative for the milled rice quality evaluation. As you know, some rice quality standards require the grading factors or defectives in terms weight (grams) , making this problem a very challenging and interesting one! Read on, some results are discussed in the succeeding paragraphs.
This is the block diagram of the milled rice quality evaluation system, comprised of 6 different stages.
Posted in research | Tags: automatic, evaluation, image processing, Milled Rice, PNN, quality, rice, Weight estimation | No Comments »
This article presents a method for weight estimation and classification of milled rice kernels using supervised learning algorithm. Shape descriptors are used as geometric features for determining the grade factors such as headrice, broken kernel, and brewer. Color histogram is extracted from milled rice image to obtain 24 color features in RGB and Cielab color spaces. A support vector machines (SVM) is adopted for addressing the classification and regression problem in milled rice quality evaluation. We built a support vector regression (SVR) model for estimating rice kernel weight and support vector classifier (SVC) for rice defectives.
Results showed that in real data, the performance of SVR is better than linear regression (LR) with a mean square error (MSE), mean absolute error (MAE) and correlation coefficient of 0.078, 0.21 and 99.4%, respectively. For classification of rice defectives using SVC, the accuracy is 98.9% outperforming the general regression neural network (GRNN) model.
Posted in research | Tags: classification, grain, GRNN, Milled Rice, milled rice classification, milled rice weight estimation, SVM, Weight estimation | No Comments »