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Concept

Corner detection

Résumé

Corner detection is an approach used within computer vision systems to extract certain kinds of features and infer the contents of an image. Corner detection is frequently used in motion detection, , video tracking, image mosaicing, panorama stitching, 3D reconstruction and object recognition. Corner detection overlaps with the topic of interest point detection. Formalization A corner can be defined as the intersection of two edges. A corner can also be defined as a point for which there are two dominant and different edge directions in a local neighbourhood of the point. An interest point is a point in an image which has a well-defined position and can be robustly detected. This means that an interest point can be a corner but it can also be, for example, an isolated point of local intensity maximum or minimum, line endings, or a point on a curve where the curvature is locally maximal. In practice, most so-called corner detection methods detect interest points in gen

Source officielle

https://en.wikipedia.org/wiki/Corner_detection

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This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.)

Presentation of particle properties, their symmetries and interactions. Introduction to quantum electrodynamics and to the Feynman rules.

The course teaches the basics of autonomous mobile robots. Both hardware (energy, locomotion, sensors) and software (signal processing, control, localization, trajectory planning, high-level control) will be tackled. The students will apply the knowledge to program and control a real mobile robot.

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Speaker diarization of spontaneous meeting room conversations

Sree Harsha Yella

Speaker diarization is the task of identifying “who spoke when” in an audio stream containing multiple speakers. This is an unsupervised task as there is no a priori information about the speakers. Diagnostical studies on state-of-the-art diarization systems have isolated three main issues with the systems; overlapping speech, effects of background noise and speech/non- speech detection errors on clustering, and signficant performance variance between different systems. In this thesis we focuss on addressing these issues in diarization. We propose new features based on structure of a conversation such as silence and speaker change statistics for overlap detection. The features are estimated from a long-term context (3-4 seconds) and are used to estimate the probability of overlap at a given instant. These probabilities are later incorporated into acoustic feature based overlap detector as prior prob- abilities. Experiments on several meeting corpora reveal that overlap detection is improved significantly by the proposed method and this consequently reduces the diarization error. To address the issues arising from background noise, errors in speech/non-speech detection and capture speaker discriminative information in the signal, we propose two methods. In the first method, we propose Information Bottleneck with Side Information (IBSI) based diarization to supress artefacts of background noise and non-speech segments introduced into clustering. In the second method, we show that the phoneme transcript of a given recording carries useful information for speaker diarization. This obervation was used in estimation of phoneme background model which is used for diarization in Information Bottleneck (IB) framework. Both the methods achieve significant reduction in error on various meeting corpora. We train different artificial neural network (ANN) architectures to extract speaker discriminant features and use these features as input to speaker diarization systems. The ANNs are trained to perform related tasks such as speaker comparison, speaker classification and auto encod- ing. The bottleneck layer activations from these networks are used as features for speaker diarization. Experiments on different meeting corpora revealed that combination of MFCCs and ANN features reduces the diarization error. To address the issue of performance variations across different sytems, we propose feature level combination of HMM/GMM and IB diarization systems. The combination does not require any changes to the original systems. The output of IB system is used to generate features which when combined with MFCCs in a HMM/GMM system reduce diarization error.

EPFL2015

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Speaker diarization is the task of identifying ``who spoke when'' in an audio stream containing multiple speakers. This is an unsupervised task as there is no a priori information about the speakers. Diagnostical studies on state-of-the-art diarization systems have isolated three main issues with the systems; overlapping speech, effects of background noise and speech/nonspeech detection errors on clustering, and signficant performance variance between different systems. In this thesis we focuss on addressing these issues in diarization. We propose new features based on structure of a conversation such as silence and speaker change statistics for overlap detection. The features are estimated from a long-term context (3-4 seconds) and are used to estimate the probability of overlap at a given instant. These probabilities are later incorporated into acoustic feature based overlap detector as prior probabilities. Experiments on several meeting corpora reveal that overlap detection is improved significantly by the proposed method and this consequently reduces the diarization error. To address the issues arising from background noise, errors in speech/non-speech detection and capture speaker discriminative information in the signal, we propose two methods. In the first method, we propose Information Bottleneck with Side Information (IBSI) based diarization to supress artefacts of background noise and non-speech segments introduced into clustering. In the second method, we show that the phoneme transcript of a given recording carries useful information for speaker diarization. This obervation was used in estimation of phoneme background model which is used for diarization in Information Bottleneck (IB) framework. Both the methods achieve significant reduction in error on various meeting corpora. We train different artificial neural network (ANN) architectures to extract speaker discriminant features and use these features as input to speaker diarization systems. The ANNs are trained to perform related tasks such as speaker comparison, speaker classification and auto encoding. The bottleneck layer activations from these networks are used as features for speaker diarization. Experiments on different meeting corpora revealed that combination of MFCCs and ANN features reduces the diarization error. To address the issue of performance variations across different sytems, we propose feature level combination of HMM/GMM and IB diarization systems. The combination does not require any changes to the original systems. The output of IB system is used to generate features which when combined with MFCCs in a HMM/GMM system reduce diarization error.

EPFL2015

Chargement

The sliding window approach is the most widely used technique to detect objects from an image. In the past few years, classifiers have been improved in many ways to increase the scanning speed. Apart from the classifier design (such as the cascade), the scanning speed also depends on a number of different factors (such as grid spacing, and scale at which the image is searched). Scanning grid spacing controls the number of subwindows being processed, thus controlling the speed of detection.When the scanning grid spacing is larger than the tolerance of the trained classifier it can suffer from low detections. In this thesis, we propose an alternative search technique, which can improve the detections when lesser number of subwindows are processed. First, we present a technique to reduce the number of miss detections while increasing the grid spacing when using the sliding window approach for object detection. This is achieved by using a small patch to predict the location of an object within a local search area. To achieve speed, it is necessary that the time taken for location prediction is comparable or better than the time it takes in average for the object classifier to reject a subwindow. We use binary features and a decision tree as it proved to be efficient for our application. In the process we also propose a variation of an existing binary feature (Ferns) with similar performance, and requires only half the number of pixel access when compared to Fern feature. We analyze the effect of patch size on location estimation and also evaluate our approach on several face databases. Experimental evaluation shows better detection rate and speed with our proposed approach for larger grid spacing (lesser number of subwindows) when compared to standard scanning technique. We also show that by using a simple interest point detector based on quantized gradient orientation, as the front-end to the proposed location estimation technique, we can achieve better performance even when fewer number of subwindows are processed. The interest points detected can be assumed as a non-regular grid compared to regular grid in the sliding window framework. A few image patches are sampled around an interest point for estimating the probable face location and further verified using a strong face classifier. Experiment results show that using an interest point detector can reduce the number of subwindows processed while maintaining a good detection rate.

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE2012

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ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE2012

Speaker diarization of spontaneous meeting room conversations

Sree Harsha Yella

EPFL2015