Refereed full papers (journals, book chapters, international conferences)

1997

@inproceedings{Cas1997,
	vgclass =	{refpap},
	vgproject =	{nn},
	author =	{David Casasent},
	title =	{New Techniques for Object Detection and Recognition},
	booktitle =	{The 10th Scandinavian Conference on Image Analysis (SCIA'97)},
	address =	{Lappeenranta, Finland},
	pages =	{597--604},
	month =	{June},
	year =	{1997},
	abstract =	{In image processing, the first step is detection (location
	of all regions of interest (ROI's) in a scene, where candidate objects
	may exist) with high detection probability $P_\mathrm{D}$ and low false
	alarm rate $P_\mathrm{FA}$. This must be achieved when multiple objects
	in different classes in different distortions are present. We present
	several algorithms to achieve this using morphological and wavelet
	transform techniques. No one detection algorithm is best and thus we
	introduce the concept of algorithm fusion to reduce $P_\mathrm{FA}$;
	i.e. we apply several different detection algorithms to the same scene
	and we nonlinearly combine the results of different algorithms using
	fuzzy logic concepts. Case study test results are presented.

	Distortion-invariant correlation filters represent an attractive
	technique to recognize different distorted aspect views of one objects
	and distinguish them from another object. We review algorithms to
	achieve this and present initial test results.

	Once ROI's have been located, they are further processed by image
	enhancement, features of each ROI are then extracted and fed to a
	classifier. We describe a new neural net (NN) classifier that uses a
	new feature space trajectory (FST) representation of distorted objects.
	We show case study distortion-invariant multi-class object recognition
	using this classifier and we note how it overcomes various problems
	with standard classifiers.

	Finally, we consider a product-inspection case study in Agriculture
	inspection. This involves segmentation of touching input objects,
	processing and feature extraction of each item, and classification of
	the class (good, minor or major defect) of each input item. This
	utilizes various new image processing techniques, feature extraction
	and use of our neural net classifiers.},
}