DOI•
Statistical Shape Descriptors for Ancient Maya Hieroglyphs Analysis
01 Jan 2013-
TL;DR: This paper focuses on content-based image retrieval, which involves clustering, sparse coding, and histogram of orientations in the context of Maya civilization.
Abstract: Keywords: content-based image retrieval ; shape descriptor ; histogram of orientations ; clustering ; sparse coding ; image detection ; cultural heritage ; Maya civilization ; hieroglyphs These Ecole polytechnique federale de Lausanne EPFL, n° 5616 (2013)Programme doctoral Genie electriqueFaculte des sciences et techniques de l'ingenieurInstitut de genie electrique et electroniqueLaboratoire de l'IDIAPJury: S. Susstrunk (presidente), S. Marchand-Maillet, J.-Ph. Thiran, C. Wang Public defense: 2013-2-27 Reference doi:10.5075/epfl-thesis-5616Print copy in library catalog Record created on 2013-02-20, modified on 2017-05-10
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Journal Article•
TL;DR: In this paper, a strong boundary fragment model (BFM) is proposed to detect object classes using only the object's boundary, which is able to detect objects principally defined by their shape rather than their appearance.
Abstract: The objective of this work is the detection of object classes, such as airplanes or horses. Instead of using a model based on salient image fragments, we show that object class detection is also possible using only the object's boundary. To this end, we develop a novel learning technique to extract class-discriminative boundary fragments. In addition to their shape, these ''codebook entries also determine the object's centroid (in the manner of Leibe et al. [19]). Boosting is used to select discriminative combinations of boundary fragments (weak detectors) to form a strong Boundary-Fragment-Model (BFM) detector. The generative aspect of the model is used to determine an approximate segmentation. We demonstrate the following results: (i) the BFM detector is able to represent and detect object classes principally defined by their shape, rather than their appearance; and (ii) in comparison with other published results on several object classes (airplanes, cars-rear, cows) the BFM detector is able to exceed previous performances, and to achieve this with less supervision (such as the number of training images).
34 citations
18 citations
TL;DR: From experiments, the data-driven representation performs overall in par with the hand-designed representation for similar locality sizes on which the descriptor is computed, and it is observed that a larger number of hidden units, the use of average pooling, and a larger training data size in the SA representation all improved the descriptor performance.
Abstract: Shape representations are critical for visual analysis of cultural heritage materials. This article studies two types of shape representations in a bag-of-words-based pipeline to recognize Maya glyphs. The first is a knowledge-driven Histogram of Orientation Shape Context (HOOSC) representation, and the second is a data-driven representation obtained by applying an unsupervised Sparse Autoencoder (SA). In addition to the glyph data, the generalization ability of the descriptors is investigated on a larger-scale sketch dataset. The contributions of this article are four-fold: (1) the evaluation of the performance of a data-driven auto-encoder approach for shape representation; (2) a comparative study of hand-designed HOOSC and data-driven SA; (3) an experimental protocol to assess the effect of the different parameters of both representations; and (4) bridging humanities and computer vision/machine learning for Maya studies, specifically for visual analysis of glyphs. From our experiments, the data-driven representation performs overall in par with the hand-designed representation for similar locality sizes on which the descriptor is computed. We also observe that a larger number of hidden units, the use of average pooling, and a larger training data size in the SA representation all improved the descriptor performance. Additionally, the characteristics of the data and stroke size play an important role in the learned representation.
15 citations
TL;DR: Thompson's catalog represented just what it said: it was a catalogue of most of the glyphs known up to the time of its publication as discussed by the authors, which was a critical tool, for in that period few signs could be read with any certainty, and it was easier to refer to a sign as T110 rather than to something like "that squished sign with the ends marked off and parallel lines along the middle".
Abstract: The year 1962 saw the publication of a major new book in Maya studies from the University of Oklahoma Press: J. Eric S. Thompson's A Catalog of Maya Hieroglyphs. Thompson's Catalog represented just what it said: it was a catalogue of most of the glyphs known up to the time of its publication. Especially over the couple of decades after its publication it was a critical tool, for in that period few signs could be read with any certainty. With Thompson's Catalog it was easier to refer to a sign as "T110" rather than to something like "that squished sign with the ends marked off and parallel lines along the middle".
7 citations
TL;DR: The Histogram of Orientation Shape Context (HOOSC) shape descriptor is introduced to the Digital Humanities community and a graph-based glyph visualization interface is developed to facilitate efficient exploration and analysis of hieroglyphs.
Abstract: Maya hieroglyphic analysis requires epigraphers to spend a significant amount of time browsing existing catalogs to identify individual glyphs. Automatic Maya glyph analysis provides an efficient way to assist scholars’ daily work. We introduce the Histogram of Orientation Shape Context (HOOSC) shape descriptor to the Digital Humanities community. We discuss key issues for practitioners and study the effect that certain parameters have on the performance of the descriptor. Different HOOSC parameters are tested in an automatic ancient Maya hieroglyph retrieval system with two different settings, namely, when shape alone is considered and when glyph co-occurrence information is incorporated. Additionally, we developed a graph-based glyph visualization interface to facilitate efficient exploration and analysis of hieroglyphs. Specifically, a force-directed graph prototype is applied to visualize Maya glyphs based on their visual similarity. Each node in the graph represents a glyph image; the width of an edge indicates the visual similarity between the two according glyphs. The HOOSC descriptor is used to represent glyph shape, based on which pairwise glyph similarity scores are computed. To evaluate our tool, we designed evaluation tasks and questionnaires for two separate user groups, namely, a general public user group and an epigrapher scholar group. Evaluation results and feedback from both groups show that our tool provides intuitive access to explore and discover the Maya hieroglyphic writing, and could potentially facilitate epigraphy work. The positive evaluation results and feedback further hint the practical value of the HOOSC descriptor.
6 citations
Cites methods from "Statistical Shape Descriptors for A..."
...Since 57 then, HOOSC has been successfully applied for automatic analysis of other cultural heritage 58 data, such as Oracle-Bones Inscriptions of ancient Chinese characters (Roman-Rangel, 2012), 59 and ancient Egyptian hieroglyphs (Franken et al....
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...Since 57 then, HOOSC has been successfully applied for automatic analysis of other cultural heritage 58 data, such as Oracle-Bones Inscriptions of ancient Chinese characters (Roman-Rangel, 2012), 59 and ancient Egyptian hieroglyphs (Franken et al., 2013)....
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...It has also been applied for generic 60 sketch and shape image retrieval (Roman-Rangel, 2012)....
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References
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49,597 citations
20 Jun 2005
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Abstract: We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.
31,952 citations
"Statistical Shape Descriptors for A..." refers background or methods in this paper
...The Histogram of Oriented Gradients (HOG) [Dalal and Triggs, 2005] in another global descriptor proposed for human detection, and is one of the most effective descriptors for intensity images....
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...This descriptor combines the underlying formulation of the Shape Context with the benefits that the Histogram of Oriented Gradients method provides [Dalal and Triggs, 2005], which resulted in more effective descriptors as shown by the results of a comprehensive series of retrieval experiments on…...
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TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Abstract: This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.
28,073 citations
01 Dec 2001
TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.
Abstract: This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "integral image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.
18,620 citations
01 Jan 1988
TL;DR: The problem the authors are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work.
Abstract: The problem we are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work. For example, we desire to obtain an understanding of natural scenes, containing roads, buildings, trees, bushes, etc., as typified by the two frames from a sequence illustrated in Figure 1. The solution to this problem that we are pursuing is to use a computer vision system based upon motion analysis of a monocular image sequence from a mobile camera. By extraction and tracking of image features, representations of the 3D analogues of these features can be constructed.
13,993 citations
"Statistical Shape Descriptors for A..." refers methods in this paper
...The Harris detector is similar but it uses only the first derivative [Harris and Stephens, 1988, Mikolajczyk and Schmid, 2002]....
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