Abstract: Image recognition is one of the most important fields of image processing and computer vision. 855 - 869, February, 2014. 38, issue 3, pp. Let us give you an example. This is a fundamental part of computer vision, combining image processing and pattern recognition techniques. 4458 - 4461, August, 2012. Details, Alajlan, N., and P. Fieguth, "Robust shape retrieval using maximum likelihood theory", 2004 International Conference on Image Analysis and Recognition, Portugal, 2004. This will be a problem of image (face) recognition. Image recognition is the 30, no. "Contextual" means this approach is focusing on the relationship of the nearby pixels, which is also called neighbourhood. Image recognition is the ability of AI to detect the object, classify, and recognize it. And still, others are skeptical about them thinking that AI will never exceed the capability of human intelligence. 2, pp. In 1975, Fram et al. Of course, the best way to make things work for Artificial Intelligence is to leverage the development processes. Details, amieson, M., P. Fieguth, and L. J. Lee, "Parametric contour estimation by simulated annealing", IEEE International Conference on Image Processing, vol. A lot of researchers publish papers describing their successful machine learning projects related to image recognition, but it is still hard to implement them. 261 - 268, February, 2008. In particular, digital image processing and techniques is what this article is about. Details, Schneider, M., P. Fieguth, W. C. Karl, and A. S. Willsky, "Multiscale Methods for the Segmentation of Images",ICASSP '96, vol. The experiment results show that the image processing and classification method could detect mould core apple with a … 421 - 428, September, 2005. Automatically find all the faces in an image. Some people are afraid of the consequences. Related Work Various types of techniques can be used to implement the classification and recognition of images using machine learning. Details Details For example, ask Google to find pictures of dogs and the network will fetch you hundreds of photos, illustrations and even drawings with dogs. 21-31, 2006. 383–396, 2010. Details Image recognition is a part of computer vision and a process to identify and detect an object or attribute in a digital video or image.  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. Details, Scharfenberger, C., S. Chakraborty, and G. Faerber, "Robust Image Processing for an Omnidirectional Camera-based Smart Car Door", ACM Transactions on Embedded Computing Systems, vol. Keep reading to understand what image recognition is and how it is useful in different industries. 30, 2006. It will then analyze their values upon training. That’s why Image Detection using machine learning or AI Image Recognition and Classification, are the hot topics in the dev’s world. Kumar, A., A. Wong, A. Mishra, D. A. Clausi, and P. Fieguth, "Tensor vector field based active contours", 18th IEEE International Conference on Image Processing (ICIP 2011), Brussels, Belgium, September, 2011. Details, Carter, K., S. Marschall, A. Gawish, P. Fieguth, L. Sorbara, and K. Bizheva, "Accuracy evaluation of scleral lens thickness and radius of curvature using high-resolution SD- and SS-OCT", SPIE Photonics West, 2014. Another popular application area is fake image detection. With the help of this tool, they can reduce development costs and create products quickly. 755 - 768, 2010. Details, Mishra, A., A. Wong, D. A. Clausi, and P. Fieguth, "A Bayesian information flow approach to image segmentation",7th Canadian Conference on Computer and Robot Vision, Ottawa, Ontario, Canada, March, 2010. The method extracts the local feature of the segmented image and describes the object recognition. Bias Field Correction in Endorectal Diffusion Imaging, Enhanced Decoupled Active Contour Using Structural and Textural Variation Energy Functionals, Grid Seams: A fast superpixel algorithm for real-time applications, Hybrid Structural and Texture Distinctiveness Vector Field Convolution for Region Segmentation, Multiplexed Optical High-coherence Interferometry, Statistical Textural Distinctiveness for Salient Region Detection in Natural Images, Markov-Chain Monte Carlo based Image Reconstruction for Streak Artifact Reduction on Contrast Enhanced Computed Tomography, Fully-Connected Continuous Conditional Random Field With Stochastic Cliques for Dark-spot Detection In SAR Imagery, Automatic segmentation of skin lesions from dermatological photographs using a joint probabilistic texture distinctiveness approach, Ice concentration estimation from dual-polarized SAR images using deep convolutional neural networks, Salient Region Detection Using Self-Guided Statistical Non-Redundancy in Natural Images, Structure-guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images, Extraction of Endmembers From Hyperspectral Images Using A Weighted Fuzzy Purified-Means Clustering Model, Hyperspectral Image Classification with Limited Labeled Training Samples Using Enhanced Ensemble Learning and Conditional Random, Intrinsic Representation of Hyperspectral Imagery for Unsupervised Feature Extraction, BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification, Mapping, Planning, and Sample Detection Strategies for Autonomous Exploration, A multi-scale latent Dirichlet allocation model for object-oriented clustering of VHR panchromatic satellite images, Robust Spectral Clustering using Statistical Sub-graph Affinity Model, Sorted Random Projections for Robust Rotation Invariant Texture Classification, Robust Image Processing for an Omnidirectional Camera-based Smart Car Door, Feature extraction of dual-pol SAR imagery for sea ice image segmentation, Unsupervised polarimetric SAR image segmentation and classification using region growing with edge penalty, Texture classification from random features, Extended Local Binary Patterns for Texture Classification, A robust probabilistic Braille recognition system, Monte Carlo Cluster Refinement for Noise Robust Image Segmentation, Statistical Conditional Sampling for Variable-Resolution Video Compression, Dynamic Fisher-Tippett Region Merging Approach to Transrectal Ultrasound Prostate Lesion Segmentation, Decoupled active contour (DAC) for boundary detection, Constrained watershed method to infer morphology of mammalian cells in microscopic images, KPAC: A kernel-based parametric active contour method for fast image segmentation, Multivariate image segmentation using semantic region growing with adaptive edge penalty, Interactive modeling and evaluation of tumor growth, Intra-retinal layer segmentation in optical coherence tomography images, IRGS: Image segmentation using edge penalties and region growing, Neuro-fuzzy network for the classification of buried pipe defects, Segmentation of buried concrete pipe images, Morphological segmentation and classification of underground pipe images, Preserving boundaries for image texture segmentation using grey level co-occurring probabilities, Unsupervised segmentation of synthetic aperture radar sea ice imagery using a novel Markov random field model, Multiscale statistical methods for the segmentation of signals and images, Sea ice concentration estimation from satellite SAR imagery using convolutional neural network and stochastic fully connected co, A New Mercer Sigmoid Kernel for Clinical Data Classification, Oil Spill Candidate Detection from SAR Imagery Using a Thresholding-Guided Stochastic Fully-Connected Conditional Random Field M, IMPROVED FINE STRUCTURE MODELING VIA GUIDED STOCHASTIC CLIQUE FORMATION IN FULLY CONNECTED CONDITIONAL RANDOM FIELDS, Spatio-Temporal Saliency Detection Using Abstracted Fully-Connected Graphical Models, Cross modality label fusion in multi-atlas segmentation, Return Of Grid Seams: A Superpixel Algorithm Using Discontinuous Multi-Functional Energy Seam Carving, DESIRe: Discontinuous Energy Seam Carving for Image Retargeting Via Structural and Textural Energy Functionals, Semi-Automatic Prostate Segmentation via a Hidden Markov Model with Anatomical and Textural Priors, Lung Nodule Classification Using Deep Features in CT Images, External forces for active contours using the undecimated wavelet transform, Undecimated Hierarchical Active Contours for OCT Image Segmentation, A Multi-Parametric Diffusion Magnetic Resonance Imaging Texture Feature Model for Prostate Cancer Analysis, Multiparametric MRI Prostate Cancer Analysis via a Hybrid Morphological-Textural Model, Scalable Learning for Restricted Boltzmann Machines, Evaluation of MAGIC Sea Ice Classifier on 61 Dual Polarization RADARSAT-2 Scenes, URC: Unsupervised clustering of remote sensing imagery, Semi-automatic Fisher-Tippett Guided Active Contour for Lumbar Multifidus Muscle Segmentation, Extended Local Binary Pattern Fusion for Face Recognition, EFFICIENT BAYESIAN INFERENCE USING FULLY CONNECTED CONDITIONAL RANDOM FIELDS WITH STOCHASTIC CLIQUES, Accuracy evaluation of scleral lens thickness and radius of curvature using high-resolution SD- and SS-OCT, BRINT: A Binary Rotation Invariant and Noise Tolerant Texture Descriptor, Extracting Morphological High-Level Intuitive Features (HLIF) for Enhancing Skin Lesion Classification, Extracting High-Level Intuitive Features (HLIF) For Classifying Skin Lesions Using Standard Camera Images, Multi-scale tensor vector field active contour, SALIENCY DETECTION VIA STATISTICAL NON-REDUNDANCY, Tensor vector field based active contours, Generalized Local Binary Patterns for Texture Classification, Sorted Random Projections for Robust Texture Classification, Combining Sorted Random Features for Texture Classification, Automated 3D reconstruction and segmentation from optical coherence tomography, A Bayesian information flow approach to image segmentation, Decoupled active surface for volumetric image segmentation, A cellular automata based semi-automatic algorithm for segmentation of choroidal blood vessels from ultrahigh, Nonlinear scale-space theory in texture classification using multiple classifier systems, Compressed sensing for robust texture classification, Texture classification using compressed sensing, SAR sea ice image segmentation using an edge-preserving region-based MRF, A novel algorithm for extraction of the layers of the cornea, SEC: Stochastic ensemble consensus approach to unsupervised SAR sea-ice segmentation, A robust modular wavelet network based symbol classifier, Probabilistic Estimation of Braille Document Parameters, Robust snake convergence based on dynamic programming, Accurate boundary localization using dynamic programming on snakes, Improved interactive medical image segmentation using Enhanced Intelligent Scissors (EIS), Shape-guided active contour based segmentation and tracking of lumbar vertebrae in video fluoroscopy using complex wavelets, Watershed deconvolution for cell segmentation, SAR sea ice image segmentation based on edge-preserving watersheds, Improving sea ice classification using the MAGSIC system, Filament preserving segmentation for SAR sea ice imagery using a new statistical model, Joint image segmentation and interpretation using iterative semantic region growing on SAR sea ice imagery, Hierarchical region mean-based image segmentation, Pixel-based sea ice classification using the MAGSIC system, Comparing classification metrics for labeling segmented remote sensing images, Combining local and global features for image segmentation using iterative classification and region merging, A narrow-band level-set method with dynamic velocity for neural stem cell cluster segmentation, Texture segmentation comparison using grey level co-occurrence probabilities and Markov random fields, Feature fusion for image texture segmentation, A new Gabor filter based kernel for texture classification with SVM, Hierarchical regions for image segmentation, Robust shape retrieval using maximum likelihood theory, Phase-based methods for Fourier shape matching, Operational segmentation and classification of SAR sea ice imagery, A probabilistic framework for image segmentation, Parametric contour estimation by simulated annealing, Image segmentation using MRI vertebral cross-sections, Color image segmentation using a region growing method, Sea ice segmentation using Markov random fields, Highlight and shading invariant color image segmentation using simulated annealing, Fast retrieval methods for images with significant variations, Towards a Novel Approach for Texture Segmentation of SAR Sea Ice Imagery, Multiscale Methods for the Segmentation of Images, Melanoma decision support using lighting-corrected intuitive feature models, Mixture of Latent Variable Models for Remotely Sensed Image Processing, Automated Ice-Water Classification using Dual Polarization SAR Imagery, High-Level Intuitive Features (HLIFs) for Melanoma Detection, Automatic segmentation of skin lesions from dermatological photographs, Illumination and Noise-Based Scene Classification - Application to SAR Sea Ice Imagery, Segmentation of RADARSAT-2 Dual-Polarization Sea Ice Imagery, Preserving Texture Boundaries for SAR Sea Ice Segmentation, Automated Underground Pipe Inspection Using a Unified Image Processing and Artificial Intelligence Methodology, Texture Segmentation of SAR Sea Ice Imagery. 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