Boosting Object Recognition in Point Clouds by Saliency Detection

Object recognition in 3D point clouds is a challenging task, mainly when time is an important factor to deal with, such as in industrial applications. Local descriptors are an amenable choice whenever the 6 DoF pose of recognized objects should also be estimated. However, the pipeline for this kind of descriptors is highly time-consuming. In this work, we propose an update to the traditional pipeline, by adding a preliminary filtering stage referred to as saliency boost. We perform tests on a standard object recognition benchmark by considering four keypoint detectors and four local descriptors, in order to compare time and recognition performance between the traditional pipeline and the boosted one. Results on time show that the boosted pipeline could turn out up to 5 times faster, with the recognition rate improving in most of the cases and exhibiting only a slight decrease in the others. These results suggest that the boosted pipeline can speed-up processing time substantially with limited impacts or even benefits in recognition accuracy.Comment: International Conference on Image Analysis and Processing (ICIAP) 201

3D Object Recognition Based on ADAPTIVE-SCALE and SPCA-ALM in Cluttered Scenes

In this paper a novel 3D object recognition method which can improve the recognition accuracy of object recognition in the cluttered scenes was proposed. The proposed method use the adaptive-scale to detect the keypoint (ASDK) of 3D object in the cluttered scenes, it use the algorithm of Sparse Principal Component Analysis Augmented Lagrangian Method (SPCA-ALM) to extract the feature of object, the algorithm of SPCA-ALM has a good performance in the high dimensional due to the Spares PCA, and the ALM can raise the speed of the SPCA. The experiment shows that the proposed method can decrease the time of 3D object recognition and improve the recognition accuracy

On the digitizing and modeling of real surfaces for machining

Tato bakalářská práce se zabývá postupy digitalizace a modelování reálných povrchů pro obrábění. Práce zahrnuje technologické postupy a jednotlivé etapy procesu vedoucí k získání výsledného povrchu. Cílem této práce je seznámení se s procesy a postupy reverzního inženýrství, který je demonstrován na konkrétním případu s použitím programu Mathematica.This bachelor thesis deals with the procedures of the digitizing and modeling of real surfaces for machining. The work includes technological procedures and individual phases of the process leading to acquisition of resultant surface. The aim of this work is familiar with the procedures and phases in reverse engineering, which is demonstrated on a particular example using the Mathematica software.

Estrategias y tecnologías para la colaboración segura entre personas y robots en entornos industriales

383 p.En este trabajo se presentan diferentes contribuciones encaminadas a facilitar el desarrollo de soluciones robóticas colaborativas fáciles de usar, flexibles y seguras.Fáciles de usar mediante la utilización de tecnologías semánticas que permiten combinar dos mecanismos de interacción, los gestos y la voz, La contribución incluye, además, el desarrollo de la tecnología necesaria para el reconocimiento de gestos.La contribución en el campo de la seguridad se ha centrado en la definición de arquitecturas y estrategias de seguridad, así como en el desarrollo de tecnologías que permiten implementar el modo SSM: el seguimiento de personas y la monitorización de proximidad. Además se ha experimentado con potenciales usuarios de la robótica colaborativa para conocer el grado de aceptación de las diferentes tecnologías desarrolladas, tanto para la seguridad como para la interacción Finalmente se presentan las contribuciones encaminadas a dotar a los robots de capacidades de percepción que les doten de la flexibilidad necesaria para adaptarse a las condiciones cambiantes del entorno

Estrategias y tecnologías para la colaboración segura entre personas y robots en entornos industriales

383 p.En este trabajo se presentan diferentes contribuciones encaminadas a facilitar el desarrollo de soluciones robóticas colaborativas fáciles de usar, flexibles y seguras.Fáciles de usar mediante la utilización de tecnologías semánticas que permiten combinar dos mecanismos de interacción, los gestos y la voz, La contribución incluye, además, el desarrollo de la tecnología necesaria para el reconocimiento de gestos.La contribución en el campo de la seguridad se ha centrado en la definición de arquitecturas y estrategias de seguridad, así como en el desarrollo de tecnologías que permiten implementar el modo SSM: el seguimiento de personas y la monitorización de proximidad. Además se ha experimentado con potenciales usuarios de la robótica colaborativa para conocer el grado de aceptación de las diferentes tecnologías desarrolladas, tanto para la seguridad como para la interacción Finalmente se presentan las contribuciones encaminadas a dotar a los robots de capacidades de percepción que les doten de la flexibilidad necesaria para adaptarse a las condiciones cambiantes del entorno

Fast and Efficient Foveated Video Compression Schemes for H.264/AVC Platform

Some fast and efficient foveated video compression schemes for H.264/AVC platform are presented in this dissertation. The exponential growth in networking technologies and widespread use of video content based multimedia information over internet for mass communication applications like social networking, e-commerce and education have promoted the development of video coding to a great extent. Recently, foveated imaging based image or video compression schemes are in high demand, as they not only match with the perception of human visual system (HVS), but also yield higher compression ratio. The important or salient regions are compressed with higher visual quality while the non-salient regions are compressed with higher compression ratio. From amongst the foveated video compression developments during the last few years, it is observed that saliency detection based foveated schemes are the keen areas of intense research. Keeping this in mind, we propose two multi-scale saliency detection schemes. (1) Multi-scale phase spectrum based saliency detection (FTPBSD); (2) Sign-DCT multi-scale pseudo-phase spectrum based saliency detection (SDCTPBSD). In FTPBSD scheme, a saliency map is determined using phase spectrum of a given image/video with unity magnitude spectrum. On the other hand, the proposed SDCTPBSD method uses sign information of discrete cosine transform (DCT) also known as sign-DCT (SDCT). It resembles the response of receptive field neurons of HVS. A bottom-up spatio-temporal saliency map is obtained by linear weighted sum of spatial saliency map and temporal saliency map. Based on these saliency detection techniques, foveated video compression (FVC) schemes (FVC-FTPBSD and FVC-SDCTPBSD) are developed to improve the compression performance further.Moreover, the 2D-discrete cosine transform (2D-DCT) is widely used in various video coding standards for block based transformation of spatial data. However, for directional featured blocks, 2D-DCT offers sub-optimal performance and may not able to efficiently represent video data with fewer coefficients that deteriorates compression ratio. Various directional transform schemes are proposed in literature for efficiently encoding such directional featured blocks. However, it is observed that these directional transform schemes suffer from many issues like ‘mean weighting defect’, use of a large number of DCTs and a number of scanning patterns. We propose a directional transform scheme based on direction-adaptive fixed length discrete cosine transform (DAFL-DCT) for intra-, and inter-frame to achieve higher coding efficiency in case of directional featured blocks.Furthermore, the proposed DAFL-DCT has the following two encoding modes. (1) Direction-adaptive fixed length ― high efficiency (DAFL-HE) mode for higher compression performance; (2) Direction-adaptive fixed length ― low complexity (DAFL-LC) mode for low complexity with a fair compression ratio. On the other hand, motion estimation (ME) exploits temporal correlation between video frames and yields significant improvement in compression ratio while sustaining high visual quality in video coding. Block-matching motion estimation (BMME) is the most popular approach due to its simplicity and efficiency. However, the real-world video sequences may contain slow, medium and/or fast motion activities. Further, a single search pattern does not prove efficient in finding best matched block for all motion types. In addition, it is observed that most of the BMME schemes are based on uni-modal error surface. Nevertheless, real-world video sequences may exhibit a large number of local minima available within a search window and thus possess multi-modal error surface (MES). Hence, the following two uni-modal error surface based and multi-modal error surface based motion estimation schemes are developed. (1) Direction-adaptive motion estimation (DAME) scheme; (2) Pattern-based modified particle swarm optimization motion estimation (PMPSO-ME) scheme. Subsequently, various fast and efficient foveated video compression schemes are developed with combination of these schemes to improve the video coding performance further while maintaining high visual quality to salient regions. All schemes are incorporated into the H.264/AVC video coding platform. Various experiments have been carried out on H.264/AVC joint model reference software (version JM 18.6). Computing various benchmark metrics, the proposed schemes are compared with other existing competitive schemes in terms of rate-distortion curves, Bjontegaard metrics (BD-PSNR, BD-SSIM and BD-bitrate), encoding time, number of search points and subjective evaluation to derive an overall conclusion