Showing papers by "Adnan Khashman published in 2012"
TL;DR: Experimental results show that the iEm BP-based emotional neural network has improved performance when compared to the EmBP-based network, and significantly outperforms the conventional back propagation (BP)-based neural networks in recognition results and time cost.
Abstract: Emotional processes and responses are biological and psychological processes in humans. The modelling of these biological processes in machines is considered a challenging and often controversial t...
23 citations
TL;DR: Experimental results suggest that the proposed method performs well in identifying blood cell types regardless of their irregular shapes, sizes, and orientation.
Abstract: The analysis of blood cells in microscope images can provide useful information concerning the health of patients; however, manual classification of blood cells is time-consuming and susceptible to error due to the different morphological features of the cells. Therefore, a fast and automated method for identifying the different blood cells is required. In this paper, we investigate the use of different neural network models for the purpose of cell identification. The neural models are based on the back propagation learning algorithm and differ in design according to the way data features are extracted from the cell microscopic images. Three different topologies of neural networks are investigated, and a comparison between these models is drawn. Experimental results suggest that the proposed method performs well in identifying blood cell types regardless of their irregular shapes, sizes, and orientation.
17 citations
TL;DR: An automatic portion identification system that uses image processing and a neural network to identify six different chicken portions that are usually preferred by consumers, and can identify the cut portions regardless of their orientation with an overall correct identification rate.
Abstract: In poultry processing plants, the sorting of raw poultry portions into separate containers prior to packaging is performed mostly by human laborers. This sorting method has two problems: labor cost and health hazard due to possible contamination between raw meat and humans. Therefore, an automated sorting system is required to avoid these problems. In this article, we present an automatic portion identification system that uses image processing and a neural network to identify six different chicken portions that are usually preferred by consumers. The proposed rotational invariant system can identify the cut portions regardless of their orientation with an overall correct identification rate of 97.57%. Potentially, the output of the proposed identification system can be used to move robotic arms to physically separate the identified chicken portions into separate containers.
PRACTICAL APPLICATIONS
This work contributes to the development of an automated sorting system for practical use in poultry processing plants. The presented intelligent identification system is the main part of such an automated system. We train the system in this work to recognize the main chicken portions; however, other portions and other poultry birds can also be identified upon including their portion images in the training phase. This work differs from existing systems, in that it relies totally on the shape and coarse texture of a portion, using images as input data, and discards information like weight or size of the portion. Another advantage is the elimination of the need for using many birds during the development of the system; in fact, training the system can be achieved using only one bird and its portions.
7 citations