Details, Amelard, R., J. Glaister, A. Wong, and D. A. Clausi, "Melanoma decision support using lighting-corrected intuitive feature models", Computer Vision Techniques for the Diagnosis of Skin Cancer, pp. Image recognition is the To train the AI tool to detect certain objects, you have to show these objects first. The University of Waterloo acknowledges that much of our work takes place on the traditional territory of the Neutral, Anishinaabeg and Haudenosaunee peoples. Details, Shafiee, M. J., A. Chung, A. Wong, and P. Fieguth, "IMPROVED FINE STRUCTURE MODELING VIA GUIDED STOCHASTIC CLIQUE FORMATION IN FULLY CONNECTED CONDITIONAL RANDOM FIELDS", IEEE Conference on Image Processing, Accepted. 268 - 275, 2003. 77A, no. For example, Amazon’s ML-based image classification tool is called SageMaker. 15, no. Classification results are initially in raster format, but they may be generalized to polygons with further processing. It is a more advanced version of Image Detection – now the neural network has to process different images with different objects, detect them and classify by the type of the item on the picture. Details, Siva, P., and A. Wong, "Grid Seams: A fast superpixel algorithm for real-time applications", Conference on Computer and Robot Vision, 2014. 3, Kingston on Thames, Kingston University, UK, pp. Therefore, image recognition is a process of identifying and detecting an object in a digital image, and one of the uses of computer vision. Let us give you an example. Details, Siva, P., C. Scharfenberger, I. Details, Sinha, S. K., and P. Fieguth, "Morphological segmentation and classification of underground pipe images", Machine Vision and Applications, vol.  Liu, L., B. Yang, P. Fieguth, Z. Yang, and Y. Wei, "BRINT: A Binary Rotation Invariant and Noise Tolerant Texture Descriptor", International Conference on Image Processing, Melbourne, 2013. 75 - 106, 2014. Details, Clausi, D. A., and B. Yue, "Texture segmentation comparison using grey level co-occurrence probabilities and Markov random fields", 17th International Conference on Pattern Recognition (ICPR), vol. Artificial Intelligence is one of the most fascinating and controversial technologies in the modern world. 1092 - 1095, January, 2008. This tool is provided by Microsoft and offers a vast variety of AI algorithms that developers can use and alter. Details, Jobanputra, R., and D. A. Clausi, "Preserving boundaries for image texture segmentation using grey level co-occurring probabilities", Pattern Recognition, vol. A., A. Wong, P. Fieguth, and D. A. Clausi, "Robust Spectral Clustering using Statistical Sub-graph Affinity Model", Public Library of Science ONE, e82722, vol. Details, Das, A., M. Dui, C. Scharfenberger, J. Servos, A. Wong, J. S. Zelek, D. A. Clausi, and S. Waslander, "Mapping, Planning, and Sample Detection Strategies for Autonomous Exploration", Journal of Field Robotics, vol. It is a mix of Image Detection and Classification. 2247 - 2250, 1996. Details, Xu, L., J. M. Shafiee, A. Wong, and D. A. Clausi, "Fully-Connected Continuous Conditional Random Field With Stochastic Cliques for Dark-spot Detection In SAR Imagery", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, February, Accepted. Details, Eichel, J. Details, Mishra, A., P. Fieguth, and D. A. Clausi, "Robust snake convergence based on dynamic programming", 15th IEEE International Conference on Image Processing, San Diego, California, USA, pp. Details, Gawish, A., and P. Fieguth, "External forces for active contours using the undecimated wavelet transform", accepted, IEEE International Conference on Image Processing, Québec city, Québec, Canada, 2015. Details, Liu, L., P. Fieguth, and G. Kuang, "Compressed sensing for robust texture classification", 10th Asian Conference on Computer Vision (ACCV'10), pp. 375 - 378, 2008. 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