A Formal Design of a Tool for Static Analysis of Upper Bounds on Object Calls in Java

Abstract. This paper presents a formal design of a tool for statically establishing the upper bound on the number of executions of objects’ methods in a fragment of object-oriented code. The algorithm that our tool employs is a multi-pass interprocedural analysis consisting of data flow and region-based analyses. We describe the formalization of each of stage of the algorithm. This rigorous specification greatly aids the implementation of the tool by removing ambiguities of textual descrip-tions. There are many applications for information obtained through this method including reasoning about concurrent code, scheduling, code optimization, compositing services, etc.We concentrate on using upper bounds to instrument transactional code that uses a synchronization mechanism based on versioning, and therefore benefits from a priori knowledge about the usage of shared objects within each transaction. To this end we implement a precompiler for Java that analyzes transac-tions, and injects generated source code to initialize each transaction

On the retrieval of 3D mesh sequences of human actions

In this paper, the problem of unsupervised human action retrieval in 3D mesh sequences is addressed. An action is composed of a mesh sequence, wherein each frame is represented by a static shape descriptor. Six state-of-the-art static descriptors are used to extract meaningful information for each sequence. Firstly, these descriptors are examined in terms of frame-to-frame similarity by means of Receiver Operating Characteristic (ROC) curves. Then, they are utilized in the action retrieval problem, where the query is an entire 3D mesh sequence. Each action is a multidimensional curve which traverses the points defined by the vectors of each descriptor. The estimation of similarity between actions is achieved by calculating the Dynamic Time Warping (DTW) distance between the corresponding curves. The retrieval performance is further examined when the Sakoe band is used to constrain the search space in DTW. The experiments concerning the action retrieval problem were carried out by using a real dataset and an artificial dataset where the proposed retrieval framework is shown to achieve high performance for both datasets. © 2016, Springer Science+Business Media New York

Unsupervised human action retrieval using salient points in 3D mesh sequences

The problem of human action retrieval based on the representation of the human body as a 3D mesh is addressed. The proposed 3D mesh sequence descriptor is based on a set of trajectories of salient points of the human body: its centroid and its five protrusion ends. The extracted descriptor of the corresponding trajectories incorporates a set of significant features of human motion, such as velocity, total displacement from the initial position and direction. As distance measure, a variation of the Dynamic Time Warping (DTW) algorithm, combined with a k − means based method for multiple distance matrix fusion, is applied. The proposed method is fully unsupervised. Experimental evaluation has been performed on two artificial datasets, one of which is being made publicly available by the authors. The experimentation on these datasets shows that the proposed scheme achieves retrieval performance beyond the state of the art. © 2018, Springer Science+Business Media, LLC, part of Springer Nature

Text line and word segmentation of handwritten documents

In this paper, we present a segmentation methodology of handwritten documents in their distinct entities, namely, text lines and words. Text line segmentation is achieved by applying Hough transform on a subset of the document image connected components. A post-processing step includes the correction of possible false alarms, the detection of text lines that Hough transform failed to create and finally the efficient separation of vertically connected characters using a novel method based on skeletonization. Word segmentation is addressed as a two class problem. The distances between adjacent overlapped components in a text line are calculated using the combination of two distance metrics and each of them is categorized either as an inter- or an intra-word distance in a Gaussian mixture modeling framework. The performance of the proposed methodology is based on a consistent and concrete evaluation methodology that uses suitable performance measures in order to compare the text line segmentation and word segmentation results against the corresponding ground truth annotation. The efficiency of the proposed methodology is demonstrated by experimentation conducted on two different datasets: (a) on the test set of the ICDAR2007 handwriting segmentation competition and (b) on a set of historical handwritten documents. © 2009 Elsevier Ltd. All rights reserved

Effective Descriptors for Human Action Retrieval from 3D Mesh Sequences

Two novel methods for fully unsupervised human action retrieval using 3D mesh sequences are presented. The first achieves high accuracy but is suitable for sequences consisting of clean meshes, such as artificial sequences or highly post-processed real sequences, while the second one is robust and suitable for noisy meshes, such as those that often result from unprocessed scanning or 3D surface reconstruction errors. The first method uses a spatio-temporal descriptor based on the trajectories of 6 salient points of the human body (i.e. the centroid, the top of the head and the ends of the two upper and two lower limbs) from which a set of kinematic features are extracted. The resulting features are transformed using the wavelet transformation in different scales and a set of statistics are used to obtain the descriptor. An important characteristic of this descriptor is that its length is constant independent of the number of frames in the sequence. The second descriptor consists of two complementary sub-descriptors, one based on the trajectory of the centroid of the human body across frames and the other based on the Hybrid static shape descriptor adapted for mesh sequences. The robustness of the second descriptor derives from the robustness involved in extracting the centroid and the Hybrid sub-descriptors. Performance figures on publicly available real and artificial datasets demonstrate our accuracy and robustness claims and in most cases the results outperform the state-of-the-art. © 2019 World Scientific Publishing Company