A fast sequential method for polygonal approximation of digitized curves
Computer Vision, Graphics, and Image Processing
Algorithms for clustering data
Algorithms for clustering data
On the Verification of Hypothesized Matches in Model-Based Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of image registration techniques
ACM Computing Surveys (CSUR)
Intelligent scissors for image composition
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Rigid, affine and locally affine registration of free-form surfaces
International Journal of Computer Vision
A Framework for Automatic Landmark Identification Using a New Method of Nonrigid Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modal Matching for Correspondence and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hypothesis Verification in Model-Based Object Recognition with a Gaussian Error Method
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
The Softassign Procrustes Matching Algorithm
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Polyhedral Object Detection and Pose Estimation for Augmented Reality Applications
CA '02 Proceedings of the Computer Animation
Shape Matching: Similarity Measures and Algorithms
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Algorithm for computer control of a digital plotter
IBM Systems Journal
Data mining on multimedia data
Data mining on multimedia data
Automatic target recognition by matching oriented edge pixels
IEEE Transactions on Image Processing
Artificial Intelligence in Medicine
A cognitive vision approach to early pest detection in greenhouse crops
Computers and Electronics in Agriculture
MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images
MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
Image acquisition and analysis of hazardous biological material in air
MDA'06/07 Proceedings of the 2007 international conference on Advances in mass data analysis of signals and images in medicine biotechnology and chemistry
Automatic fuzzy-neural based segmentation of microscopic cell images
MDA'06/07 Proceedings of the 2007 international conference on Advances in mass data analysis of signals and images in medicine biotechnology and chemistry
MDA'06/07 Proceedings of the 2007 international conference on Advances in mass data analysis of signals and images in medicine biotechnology and chemistry
Integrating case-based reasoning with an electronic patient record system
Artificial Intelligence in Medicine
A multi-module case-based biofeedback system for stress treatment
Artificial Intelligence in Medicine
Case-Based object recognition with application to biological images
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Case-based reasoning emulation of persons for wheelchair navigation
Artificial Intelligence in Medicine
Treatment planning for supracondylar fracture in humerus in children by image processing
Intelligent Decision Technologies
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Objective: Model-based object recognition is a well-known task in Computer Vision. Usually, one object that can be generalized by a model should be detected in an image based on this model. Biomedical applications have the special quality that one object can have a great variation in appearance. Therefore the appearance of this object cannot be generalized by one model. A set of cases of the appearance of this object (sometimes 50 cases or more) is necessary to detect this object in an image. The recognition method is rather case-based object recognition than model-based object recognition. Case-based object recognition is a challenging task. Methods and material: It puts special requirements to the similarity measure and needs a matching algorithm that can work fast in a large number of cases. It also needs a case acquisition procedure that can capture the great variation in appearance of an object and generalize these data into a case description. In this paper we describe the chosen case representation, the similarity measure and the matching as well as the case acquisition procedure. We evaluate our method based on a large enough set of digital images containing biological objects such as fungi spores. Results: We can show that the similarity measure is superior to detect the objects in the images. The developed method for case acquisition and learning of generalized cases allows us to learn interactively a sufficient number of cases that are further stored into our case base. Finally, we give results on the performance of the system by calculating the recognition rate. Conclusion: These result show that we have developed a novel similarity measure for object detection in digital grey-level images and a novel procedure for case acquisition and learning that allows us to learn a sufficiently large enough case base and to generalize over a group of cases.