Antônio Wilson Vieira

Efficient implementation of Marching Cubes' cases with topological guarantees

Abstract Marching Cubes methods first offered visual access to experimental and theoretical volumetric data. The implementation of this method usually relies on a small look-up table; many enhancements and optimizations of Marching Cubes still use it. However, this look-up table can lead to cracks and inconsistent topology. This paper introduces a full implementation of Chernyaevs technique to ensure a topologically correct result, i.e., a manifold mesh, for any input data. It completes the original paper for the ambiguity resolution and for the feasibility of the implementation. Moreover, the cube interpolation provided here can be used in a wider range of methods. The source code is available online.

STOP: Space-Time Occupancy Patterns for 3D Action Recognition from Depth Map Sequences

This paper presents Space-Time Occupancy Patterns (STOP), a new visual representation for 3D action recognition from sequences of depth maps. In this new representation, space and time axes are divided into multiple segments to define a 4D grid for each depth map sequence. The advantage of STOP is that it preserves spatial and temporal contextual information between space-time cells while being flexible enough to accommodate intra-action variations. Our visual representation is validated with experiments on a public 3D human action dataset. For the challenging cross-subject test, we significantly improved the recognition accuracy from the previously reported 74.7% to 84.8%. Furthermore, we present an automatic segmentation and time alignment method for online recognition of depth sequences.

Real-Time Gesture Recognition from Depth Data through Key Poses Learning and Decision Forests

Human gesture recognition is a challenging task with many applications. The popularization of real time depth sensors even diversifies potential applications to end-user natural user interface (NUI). The quality of such NUI highly depends on the robustness and execution speed of the gesture recognition. This work introduces a method for real-time gesture recognition from a noisy skeleton stream, such as the ones extracted from Kinect depth sensors. Each pose is described using a tailored angular representation of the skeleton joints. Those descriptors serve to identify key poses through a multi-class classifier derived from Support Vector learning machines. The gesture is labeled on-the-fly from the key pose sequence through a decision forest, that naturally performs the gesture time warping and avoids the requirement for an initial or neutral pose. The proposed method runs in real time and shows robustness in several experiments.

On the improvement of human action recognition from depth map sequences using Space-Time Occupancy Patterns

We present a new visual representation for 3D action recognition from sequences of depth maps. In this new representation, space and time axes are divided into multiple segments to define a 4D grid for each depth map sequences. Each cell in the grid is associated with an occupancy value which is a function of the number of space-time points falling into this cell. The occupancy values of all the cells form a high dimensional feature vector, called Space-Time Occupancy Pattern (STOP). We then perform dimensionality reduction to obtain lower-dimensional feature vectors. The advantage of STOP is that it preserves spatial and temporal contextual information between space and time cells while being flexible enough to accommodate intra-action variations. Furthermore, we combine depth maps with skeletons in order to obtain view invariance and present an automatic segmentation and time alignment method for on-line recognition of depth sequences. Our visual representation is validated with experiments on a public 3D human action dataset.

Sparse Spatial Coding: A novel approach for efficient and accurate object recognition

Successful state-of-the-art object recognition techniques from images have been based on powerful methods, such as sparse representation, in order to replace the also popular vector quantization (VQ) approach. Recently, sparse coding, which is characterized by representing a signal in a sparse space, has raised the bar on several object recognition benchmarks. However, one serious drawback of sparse space based methods is that similar local features can be quantized into different visual words. We present in this paper a new method, called Sparse Spatial Coding (SSC), which combines a sparse coding dictionary learning, a spatial constraint coding stage and an online classification method to improve object recognition. An efficient new off-line classification algorithm is also presented. We overcome the problem of techniques which make use of sparse representation alone by generating the final representation with SSC and max pooling, presented for an online learning classifier. Experimental results obtained on the Caltech 101, Caltech 256, Corel 5000 and Corel 10000 databases, show that, to the best of our knowledge, our approach supersedes in accuracy the best published results to date on the same databases. As an extension, we also show high performance results on the MIT-67 indoor scene recognition dataset.

Online gesture recognition from pose kernel learning and decision forests

The recent popularization of real time depth sensors has diversified the potential applications of online gesture recognition to end-user natural user interface (NUI). This requires significant robustness of the gesture recognition to cope with the noisy data from the popular depth sensor, while the quality of the final NUI heavily depends on the recognition execution speed. This work introduces a method for real-time gesture recognition from a noisy skeleton stream, such as those extracted from Kinect depth sensors. Each pose is described using an angular representation of the skeleton joints. Those descriptors serve to identify key poses through a Support Vector Machine multi-class classifier, with a tailored pose kernel. The gesture is labeled on-the-fly from the key pose sequence with a decision forest, which naturally performs the gesture time control/warping and avoids the requirement for an initial or neutral pose. The proposed method runs in real time and its robustness is evaluated in several experiments.

BRAND: A robust appearance and depth descriptor for RGB-D images

This work introduces a novel descriptor called Binary Robust Appearance and Normals Descriptor (BRAND), that efficiently combines appearance and geometric shape information from RGB-D images, and is largely invariant to rotation and scale transform. The proposed approach encodes point information as a binary string providing a descriptor that is suitable for applications that demand speed performance and low memory consumption. Results of several experiments demonstrate that as far as precision and robustness are concerned, BRAND achieves improved results when compared to state of the art descriptors based on texture, geometry and combination of both information. We also demonstrate that our descriptor is robust and provides reliable results in a registration task even when a sparsely textured and poorly illuminated scene is used.

Efficient Edgebreaker for surfaces of arbitrary topology

The typical surface models handled by contemporary computer graphics applications have millions of triangles and numerous connected component, handles and boundaries. Edgebreaker and Spirale Reversi are examples of efficient schemes to compress and decompress their connectivity. A surprisingly simple linear-time implementation has been proposed for triangulated surfaces homeomorphic to a sphere and was subsequently extended to surfaces with handles. Here, we further extend its scope to surfaces with multiple components, handles, and multiple boundaries. The result is a simple and efficient compression/decompression solution for the broad class of orientable manifold surfaces.

On the development of a robust, fast and lightweight keypoint descriptor

Abstract In this paper we introduce BRAND—Binary Robust Appearance and Normal Descriptor, a novel descriptor which efficiently combines appearance and geometric information from RGB-D images, that is largely invariant to rotation and scale transformations. Based on relevant characteristics of successful image only descriptors, we define a set of eight fundamental requirements to guide the design and evaluation of descriptors that also use depth information. We then describe the design of BRAND, followed by the evaluation of its performance according to those requirements. We also show how BRAND can be simplified in order to obtain a higher performance version, that we named BASE, for applications that require speed performance, but do not demand rigorous scale and rotation invariance. We compare the performance of BRAND against three standard descriptors on real world data. Results of several experiments demonstrate that as far as precision and robustness is concerned, BRAND compares favorably to SIFT and SURF for textured images, and to Spin-Image, for geometrical shape information. Furthermore, BRAND attains improved results when compared to state of the art descriptors that are based either on texture or geometry alone, or on their combination. Finally, we report on the use of BRAND in two applications for which we show that it provides reliable results for the registration of indoor textured depth maps and for object recognition in tasks that require the extraction of semantic knowledge.

Stellar Mesh Simplification Using Probabilistic Optimization

This paper proposes the stellar mesh simplification method, a fast implementation of the Four‐Face Cluster (FFC) algorithm. In this method, a probabilistic optimization heuristic substitutes the priority queue of the original method, which results in a 40% faster algorithm with the same order of distortion. It extends naturally to a progressive and/or multiresolution scheme for combinatorial surfaces. This work also presents a simple way to encode the hierarchy of the resulting multiresolution meshes. This work also focuses on important aspects for the development of a practical and robust implementation of this simplification technique, and on the analysis of the influence of the parameters.