Oskar Linde

Composed complex-cue histograms: An investigation of the information content in receptive field base

Journal of Mathematical Imaging and Vision, 2013

Scale-invariant interest points have found several highly successful applications in computer vis... more Scale-invariant interest points have found several highly successful applications in computer vision, in particular for image-based matching and recognition. This paper presents a theoretical analysis of the scale selection properties of a generalized framework for detecting interest points from scale-space features presented in Lindeberg (Int. J. Comput. Vis. 2010, under revision) and comprising: - an enriched set of differential interest

Composed complex-cue histograms: An investigation of the information content in receptive field based image descriptors for object recognition

Computer Vision and Image Understanding, Apr 1, 2012

Recent work has shown that effective methods for recognizing objects and spatio-temporal events c... more Recent work has shown that effective methods for recognizing objects and spatio-temporal events can be constructed based on histograms of receptive field like image operations. This paper presents the results of an extensive study of the performance of different types of receptive field like image descriptors for histogram-based object recognition, based on different combinations of image cues in terms of Gaussian derivatives or differential invariants applied to either intensity information, colour-opponent channels or both. A rich set of composed complex-cue image descriptors is introduced and evaluated with respect to the problems of (i) recognizing previously seen object instances from previously unseen views, and (ii) classifying previously unseen objects into visual categories. It is shown that there exist novel histogram descriptors with significantly better recognition performance compared to previously used histogram features within the same class. Specifically, the experiments show that it is possible to obtain more discriminative features by combining lower-dimensional scale-space features into composed complexcue histograms. Furthermore, different types of image descriptors have different relative advantages with respect to the problems of object instance recognition vs. object category classification. These conclusions are obtained from extensive evaluations on two mutually independent data sets. For the task of recognizing specific object instances, combined histograms of spatial and spatio-chromatic derivatives are highly discriminative, and several image descriptors in terms rotationally invariant (intensity and spatiochromatic) differential invariants up to order two lead to very high recognition rates. For category classification, primary information is contained in both first-and second-order derivatives, where second-order partial derivatives constitute the most discriminative cue. Dimensionality reduction by principal component analysis and variance normalization prior to training and recognition can in many cases lead to a significant increase in recognition or classification performance. Surprisingly high recognition rates can even be obtained with binary histograms that reveal the polarity of local scale-space features, and which can be expected to be particularly robust to illumination variations. An overall conclusion from this study is that compared to previously used lower-dimensional histograms, the use of composed complex-cue histograms of higher dimensionality reveals the co-variation of multiple cues and enables much better recognition performance, both with regard to the problems of recognizing previously seen objects from novel views and for classifying previously unseen objects into visual categories.

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Object recognition using composed receptive field histograms of higher dimensionality

International Conference on Pattern Recognition, Aug 23, 2004

Recent work has shown that effective methods for recognising objects or spatio-temporal events ca... more Recent work has shown that effective methods for recognising objects or spatio-temporal events can be constructed based on receptive field responses summarised into histograms or other histogram-like image descriptors. This paper presents a set of composed histogram features of higher dimensionality, which give significantly better recognition performance compared to the histogram descriptors of lower dimensionality that were used in the original papers by Swain & Ballard (1991) or Schiele & Crowley (2000). The use of histograms of higher dimensionality is made possible by a sparse representation for efficient computation and handling of higher-dimensional histograms. Results of extensive experiments are reported, showing how the performance of histogram-based recognition schemes depend upon different combinations of cues, in terms of Gaussian derivatives or differential invariants applied to either intensity information, chromatic information or both. It is shown that there exist composed higher-dimensional histogram descriptors with much better performance for recognising known objects than previously used histogram features. Experiments are also reported of classifying unknown objects into visual categories. * The support from the Swedish Research Council and from the Royal Swedish Academy of Sciences as well as the Knut and Alice Wallenberg Foundation is gratefully acknowledged. We would also like to thank Barbara Caputo for valuable discussions and for granting access to her modified version of libSVM.

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Composed complex-cue histograms: An investigation of the information content in receptive field base

Journal of Mathematical Imaging and Vision, 2013

Scale-invariant interest points have found several highly successful applications in computer vis... more Scale-invariant interest points have found several highly successful applications in computer vision, in particular for image-based matching and recognition. This paper presents a theoretical analysis of the scale selection properties of a generalized framework for detecting interest points from scale-space features presented in Lindeberg (Int. J. Comput. Vis. 2010, under revision) and comprising: - an enriched set of differential interest

Examensarbete Closing the Loop Mobile Visual Location Recognition

Visual simultaneous localization and mapping (SLAM) as field has been researched for ten years, b... more Visual simultaneous localization and mapping (SLAM) as field has been researched for ten years, but with recent advances in mobile performance visual SLAM is entering the consumer market in a completely new way. A visual SLAM system will however be sensitive to non cautious use that may result in severe motion, occlusion or poor surroundings in terms of visual features that will cause the system to temporarily fail. The procedure of recovering from such a fail is called relocalization. Together with two similar problems localization, to find your position in an existing SLAM session, and loop closing, the online reparation and perfection of the map in an active SLAM session, these can be grouped as visual location recognition (VLR). This thesis presents novel results by combining the scalability of FabMap and the precision of 13th Lab’s tracking yielding high-precision VLR, ±10 cm, while maintaining above 99 % precision and 60 % recall for sessions containing thousands of images. Ev...

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Composed complex-cue histograms: An investigation of the information content in receptive field based image descriptors for object recognition

Computer Vision and Image Understanding, 2012

Recent work has shown that effective methods for recognizing objects and spatio-temporal events c... more Recent work has shown that effective methods for recognizing objects and spatio-temporal events can be constructed based on histograms of receptive field like image operations. This paper presents the results of an extensive study of the performance of different types of receptive field like image descriptors for histogram-based object recognition, based on different combinations of image cues in terms of Gaussian derivatives or differential invariants applied to either intensity information, colour-opponent channels or both. A rich set of composed complex-cue image descriptors is introduced and evaluated with respect to the problems of (i) recognizing previously seen object instances from previously unseen views, and (ii) classifying previously unseen objects into visual categories. It is shown that there exist novel histogram descriptors with significantly better recognition performance compared to previously used histogram features within the same class. Specifically, the experiments show that it is possible to obtain more discriminative features by combining lower-dimensional scale-space features into composed complexcue histograms. Furthermore, different types of image descriptors have different relative advantages with respect to the problems of object instance recognition vs. object category classification. These conclusions are obtained from extensive evaluations on two mutually independent data sets. For the task of recognizing specific object instances, combined histograms of spatial and spatio-chromatic derivatives are highly discriminative, and several image descriptors in terms rotationally invariant (intensity and spatiochromatic) differential invariants up to order two lead to very high recognition rates. For category classification, primary information is contained in both first-and second-order derivatives, where second-order partial derivatives constitute the most discriminative cue. Dimensionality reduction by principal component analysis and variance normalization prior to training and recognition can in many cases lead to a significant increase in recognition or classification performance. Surprisingly high recognition rates can even be obtained with binary histograms that reveal the polarity of local scale-space features, and which can be expected to be particularly robust to illumination variations. An overall conclusion from this study is that compared to previously used lower-dimensional histograms, the use of composed complex-cue histograms of higher dimensionality reveals the co-variation of multiple cues and enables much better recognition performance, both with regard to the problems of recognizing previously seen objects from novel views and for classifying previously unseen objects into visual categories.

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Local Histogram Based Descriptors for Recognition

Proceedings of the Fourth International Conference on Computer Vision Theory and Applications, 2009

This paper proposes a set of new image descriptors based on local histograms of basic operators. ... more This paper proposes a set of new image descriptors based on local histograms of basic operators. These descriptors are intended to serve in a first-level stage of an hierarcical representation of image structures. For reasons of efficiency and scalability, we argue that descriptors suitable for this purpose should be able to capture and separate invariant and variant properties. Unsupervised clustering of the image descriptors from training data gives a visual vocabulary, which allow for compact representations. We demonstrate the representational power of the proposed descriptors and vocabularies on image categorization tasks using wellknown datasets. We use image representations via statistics in form of global histograms of the underlying visual words, and compare our results to earlier reported work.

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Object recognition using composed receptive field histograms of higher dimensionality

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004

Recent work has shown that effective methods for recognising objects or spatio-temporal events ca... more Recent work has shown that effective methods for recognising objects or spatio-temporal events can be constructed based on receptive field responses summarised into histograms or other histogram-like image descriptors. This paper presents a set of composed histogram features of higher dimensionality, which give significantly better recognition performance compared to the histogram descriptors of lower dimensionality that were used in the original papers by Swain & Ballard (1991) or Schiele & Crowley (2000). The use of histograms of higher dimensionality is made possible by a sparse representation for efficient computation and handling of higher-dimensional histograms. Results of extensive experiments are reported, showing how the performance of histogram-based recognition schemes depend upon different combinations of cues, in terms of Gaussian derivatives or differential invariants applied to either intensity information, chromatic information or both. It is shown that there exist composed higher-dimensional histogram descriptors with much better performance for recognising known objects than previously used histogram features. Experiments are also reported of classifying unknown objects into visual categories. * The support from the Swedish Research Council and from the Royal Swedish Academy of Sciences as well as the Knut and Alice Wallenberg Foundation is gratefully acknowledged. We would also like to thank Barbara Caputo for valuable discussions and for granting access to her modified version of libSVM.

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