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| - | ====== | + | ====== |
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| + | This is a classification of techniques and algorithms, in order to give a broad view of solutions available to deal with classical problems. | ||
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| + | Other pages contain some more details about, which can be seen as memos with references to find more information (with the origin paper when possible). | ||
| - | This is a classification of techniques and algorithms, giving only keywords ... | ||
| ===== Learning ===== | ===== Learning ===== | ||
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| === Classification === | === Classification === | ||
| - | * **MLP (Multi Layers Perceptron)** - //PMC (Perceptron multicouches)// | + | * __MLP (Multi Layers Perceptron)__ - //PMC (Perceptron multicouches)// |
| * gradient backpropagation - // | * gradient backpropagation - // | ||
| * stochastic | * stochastic | ||
| Line 13: | Line 21: | ||
| * simulated annealing - //recuit simulé// | * simulated annealing - //recuit simulé// | ||
| * newton (second order) | * newton (second order) | ||
| - | * **RBFNN | + | * __RBFNN |
| * k-means then gradient descent | * k-means then gradient descent | ||
| * incremental addition of neurons then exact method | * incremental addition of neurons then exact method | ||
| - | * **SVM (Support Vectors Machine)** | + | * __SVM (Support Vectors Machine)__ |
| - | * **Decision tree** | + | * __Decision tree__ |
| * ID3 (based on entropy) | * ID3 (based on entropy) | ||
| - | * **k-nearest | + | * __k-nearest |
| + | * __Boosting__ | ||
| + | * Boosting by majority | ||
| + | * AdaBoost (ADAptive BOOSTing) | ||
| + | * Discrete AdaBoost | ||
| + | * Real AdaBoost | ||
| + | * Gentle AdaBoost | ||
| + | * LogitBoost | ||
| + | * Probabilistic AdaBoost | ||
| + | * FloatBoost | ||
| + | * AdaBoost.Reg | ||
| + | * Multiclass AdaBoost.M1 | ||
| + | * Multiclass AdaBoost.M2 | ||
| + | * Multilabel AdaBoost.MR | ||
| + | * Multilabel AdaBoost.MH | ||
| + | * Multiclass AdaBoost.MO | ||
| + | * Multiclass AdaBoost.OC | ||
| + | * Multiclass AdaBoost.ECC | ||
| + | * GrPloss | ||
| + | * BoostMA | ||
| + | * AdaBoost.M1W | ||
| + | * SAMME (Stagewise Additive Modeling using a Multi-class Exponential loss function) | ||
| + | * GAMBLE (Gentle Adaptive Multiclass Boosting Learning) | ||
| + | * UBoost | ||
| + | * LPBoost (Linear Programming BOOSTing) | ||
| + | * TotalBoost (TOTALly corrective BOOSTing) | ||
| + | * RotBoost | ||
| + | * alphaBoost | ||
| + | * MILBoost (Multiple Instance Learning BOOSting) | ||
| + | * CGBoost (Conjugate Gradient BOOSTing) | ||
| + | * Bootstrap Aggregating | ||
| + | * __Cascades of detectors__ [[classification# | ||
| + | * __Trees of detectors__ | ||
| + | |||
| === Regression === | === Regression === | ||
| - | * **MLP (Multi Layers Perceptron)** | + | * __MLP (Multi Layers Perceptron)__ |
| - | * **RBFNN | + | * __RBFNN |
| - | * **SVR (Support Vectors Regressor)** | + | * __SVR (Support Vectors Regressor)__ |
| + | === Pattern recognition === | ||
| + | |||
| + | * __Viola-Jones Detector__ [[pattern-recognition# | ||
| + | * with Extended Set of Haar features | ||
| + | * Stumps or CART trees | ||
| + | * Rotation Invariant | ||
| + | * **Multiview** | ||
| + | * Parallel Cascades | ||
| + | * Pyramid Cascade | ||
| + | * Tree Cascade | ||
| + | * Vector Boosting | ||
| ==== Unsupervised learning ==== | ==== Unsupervised learning ==== | ||
| === Vector quantization / Clustering === | === Vector quantization / Clustering === | ||
| - | [[http:// | + | |
| - | + | * k-means - // | |
| - | * **Sequential leader** | + | * GNG (Growing Neural Gas) |
| - | | + | * Auto-organizing maps (Kohonen) - //cartes auto-organisatrices de Kohonen// |
| - | | + | |
| - | | + | |
| ==== Reinforcement learning ==== | ==== Reinforcement learning ==== | ||
| - | [[http:// | + | |
| - | + | ||
| - | | + | |
| * Q-learning | * Q-learning | ||
| * Value iteration | * Value iteration | ||
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| ===== Planification ===== | ===== Planification ===== | ||
| - | |||
| ==== Symbolic ==== | ==== Symbolic ==== | ||
| === State space search === | === State space search === | ||
| - | | + | |
| - | * Dijkstra | + | * WA* |
| + | * IDA* | ||
| + | * Dijkstra | ||
| === Logics === | === Logics === | ||
| - | Based on STRIPS-like languages (ADL, PDDL). | + | * __GraphPlan__ |
| - | + | * Stan | |
| - | * **GraphPlan**, | + | |
| - | * **SATplan | + | |
| + | * __SATplan | ||
| ==== Others ==== | ==== Others ==== | ||
| - | * **Genetic algorithms** | + | * __Genetic algorithms__ |
| - | * **Ant colony** | + | * __Ant colonies__ |
| ==== Specific ==== | ==== Specific ==== | ||
| === Path planning === | === Path planning === | ||
| - | * **Configurations space** | + | * __Configurations space__ |
| - | * **Potential fields** | + | * __Potential fields__ |
| ===== Perception ===== | ===== Perception ===== | ||
| ==== Vision ==== | ==== Vision ==== | ||
| - | |||
| === Color Quantization === | === Color Quantization === | ||
| - | | + | * RGB cone |
| + | | ||
| + | | ||
| + | | ||
| === Image segmentation === | === Image segmentation === | ||
| - | | + | * Floodfill |
| - | | + | * Watershed - //lignes de partage des eaux// |
| === Filters === | === Filters === | ||
| - | [[http://www.ph.tn.tudelft.nl/Courses/FIP/ | + | * **Anti-noise (smoothing)** |
| + | * Median Filter - //filtre médian// | ||
| + | * Vector Median Filter | ||
| + | * Kuwahara filter | ||
| + | * Peer Group Filtering | ||
| + | * Anisotropic Filtering | ||
| + | * **Gradient** [[vision# | ||
| + | * Prewitt [[vision# | ||
| + | * Roberts [[vision# | ||
| + | * Sobel [[vision# | ||
| + | * Laplace [[vision# | ||
| + | * Scharr [[vision# | ||
| + | * **Morphological** | ||
| + | * dilation - // | ||
| + | * erosion - // | ||
| + | * opening - // | ||
| + | * closing - // | ||
| - | | + | === Edge detection === |
| - | * gradient : prewitt, sobel, laplace | + | |
| - | * morphological : dilation, erosion, opening, closing [[http:// | + | |
| + | * Canny-Deriche | ||
| + | |||
| + | === Pattern recognition === | ||
| + | |||
| + | * Mean-Square Regression - //Régression aux moindres carrés// | ||
| + | * __Hough Transforms__ | ||
| + | * Standard Hough Transform | ||
| + | * Randomized Hough Transform | ||
| + | * Connective Randomized Hough Transform | ||
| + | * Combinatorial Hough Transform | ||
| + | * Adaptive Hough Transform | ||
| + | * Probabilistic Hough Transform | ||
| + | * Adaptive Probabilistic Hough Transform | ||
| + | * Progressive Probabilistic Hough Transform | ||
| + | * Hierarchical Hough Transform | ||
| + | * Sampling Hough Transform | ||
| + | * Generalized Hough Transform | ||
| + | * UpWrite method | ||
| + | * Curvogram | ||
| + | * **Shape Descriptors** | ||
| + | * Ankerst' | ||
| + | * Chamfer matching | ||
| + | * Contour Likelihood Measurement | ||
| + | |||
| + | |||
| + | === Tracking === | ||
| + | |||
| + | * Kalman Filter | ||
| + | * Generalized Kalman Filter | ||
| + | * Correlation Tracking | ||
| + | * ACA (Area Correlation Algorithm) | ||
| + | * KLT Tracker (Kanade-Lucas-Tomasi) | ||
| + | * IPAN Tracker | ||
| + | * MeanShift | ||
| + | * **Features detection** | ||
| + | * Harris detector | ||
| + | * Susan detector | ||
| + | * Multiresolution Contrast detector | ||
| ==== Sensors fusion ==== | ==== Sensors fusion ==== | ||
| - | | + | * Kalman filter |
| - | | + | * Particles filter (bayesian network) - //filtrage particulaire// |
