Indoor Place Recognition using Online Independent Support Vector Machines

In the framework of indoor mobile robotics, place recognition is a challenging task, where it is crucial that self-localization be enforced precisely, notwithstanding the changing conditions of illumination, objects being shifted around and/or people affecting the appearance of the scene. In this scenario online learning seems the main way out, thanks to the possibility of adapting to changes in a smart and flexible way. Nevertheless, standard machine learning approaches usually suffer when confronted with massive amounts of data and when asked to work online. Online learning requires a high training and testing speed, all the more in place recognition, where a continuous flow of data comes from one or more cameras. In this paper we follow the Support Vector Machines-based approach of Pronobis et al., proposing an improvement that we call Online Independent Support Vector Machines. This technique exploits linear independence in the image feature space to incrementally keep the size of the learning machine remarkably small while retaining the accuracy of a standard machine. Since the training and testing time crucially depend on the size of the machine, this solves the above stated problems. Our experimental results prove the effectiveness of the approach.

On-line Independent Support Vector Machines for Cognitive Systems

Barbara Caputo, Jie Luo

Learning from experience and adapting to changing stimuli are fundamental capabilities for artificial cognitive systems. This calls for on-line learning methods able to achieve high accuracy while at the same time using limited computer power. Research on autonomous agents has been actively investigating these issues, mostly using probabilistic frameworks and within the context of navigation and learning by imitation. Still, recent results on robot localization have clearly pointed out the potential of discriminative classifiers for cognitive systems. In this paper we follow this approach and propose an on-line version of the Support Vector Machine (SVM) algorithm. Our method, that we call On-line Independent SVM, builds a solution on-line, achieving an excellent accuracy vs.~compactness trade-off. In particular the size of the obtained solution is always bounded, implying a bounded testing time. At the same time, the algorithm converges to the optimal solution at each incremental step, as opposed to similar approaches where optimality is achieved in the limit of infinite number of training data. These statements are supported by experiments on standard benchmark databases as well as on two real-world applications, namely

(a)

place recognition by a mobile robot in an indoor environment, and

(b)

human grasping posture classification.

IDIAP2007

Text detection and recognition in images and video sequences

Datong Chen

Text characters embedded in images and video sequences represents a rich source of information for content-based indexing and retrieval applications. However, these text characters are difficult to be detected and recognized due to their various sizes, grayscale values and complex backgrounds. This thesis investigates methods for building an efficient application system for detecting and recognizing text of any grayscale values embedded in images and video sequences. Both empirical image processing methods and statistical machine learning and modeling approaches are studied in two sub-problems: text detection and text recognition. Applying machine learning methods for text detection encounters difficulties due to character size, grayscale variations and heavy computation cost. To overcome these problems, we propose a two-step localization/verification approach. The first step aims at quickly localizing candidate text lines, enabling the normalization of characters into a unique size. In the verification step, a trained support vector machine or multi-layer perceptrons is applied on background independent features to remove the false alarms. Text recognition, even from the detected text lines, remains a challenging problem due to the variety of fonts, colors, the presence of complex backgrounds and the short length of the text strings. Two schemes are investigated addressing the text recognition problem: bi-modal enhancement scheme and multi-modal segmentation scheme. In the bi-modal scheme, we propose a set of filters to enhance the contrast of black and white characters and produce a better binarization before recognition. For more general cases, the text recognition is addressed by a text segmentation step followed by a traditional optical character recognition (OCR) algorithm within a multi-hypotheses framework. In the segmentation step, we model the distribution of grayscale values of pixels using a Gaussian mixture model or a Markov Random Field. The resulting multiple segmentation hypotheses are post-processed by a connected component analysis and a grayscale consistency constraint algorithm. Finally, they are processed by an OCR software. A selection algorithm based on language modeling and OCR statistics chooses the text result from all the produced text strings. Additionally, methods for using temporal information of video text are investigated. A Monte Carlo video text segmentation method is proposed for adapting the segmentation parameters along temporal text frames. Furthermore, a ROVER (Recognizer Output Voting Error Reduction) algorithm is studied for improving the final recognition text string by voting the characters through temporal frames. The whole system was successfully evaluated on large databases of camera-based images and video sequences obtained in context of two European projects ASSAVID1 and CIMWOS2. ---------- 1 European project ASSAVID: Automatic Segmentation and Semantic Annotation of Sports Videos, 5th Framework Programme, Information Society Technology, supported by OFES. Web site: http://www.bpe-rnd.co.uk/assavid/ 2 European project CIMWOS: Combined IMages and WOrd Spotting , 5th Framework Programme, Information Society Technology, supported by OFES.Web site: http://www.xanthi.ilsp.gr/cimwos/

EPFL2003

Text detection and recognition in images and video sequences

Datong Chen

2003

Text detection and recognition in images and video sequences

Datong Chen

2003

On-line Independent Support Vector Machines for Cognitive Systems

Barbara Caputo, Jie Luo

(a)

place recognition by a mobile robot in an indoor environment, and

(b)

human grasping posture classification.

IDIAP2007

Text detection and recognition in images and video sequences

Datong Chen

EPFL2003