Accurate depth-color scene modeling for 3D contents generation with low cost depth cameras

In this paper, we present a depth-color scene modeling strategy for indoors 3D contents generation. It combines depth and visual information provided by a low-cost active depth camera to improve the accuracy of the acquired depth maps considering the different dynamic nature of the scene elements. Accurate depth and color models of the scene background are iteratively built, and used to detect moving elements in the scene. The acquired depth data is continuously processed with an innovative joint-bilateral filter that efficiently combines depth and visual information thanks to the analysis of an edge-uncertainty map and the detected foreground regions. The main advantages of the proposed approach are: removing depth maps spatial noise and temporal random fluctuations; refining depth data at object boundaries, generating iteratively a robust depth and color background model and an accurate moving object silhouette.

CCD image sensor induced error in PIV applications

The readout procedure of charge-coupled device (CCD) cameras is known to generate some image degradation in different scientific imaging fields, especially in astrophysics. In the particular field of particle image velocimetry (PIV), widely extended in the scientific community, the readout procedure of the interline CCD sensor induces a bias in the registered position of particle images. This work proposes simple procedures to predict the magnitude of the associated measurement error. Generally, there are differences in the position bias for the different images of a certain particle at each PIV frame. This leads to a substantial bias error in the PIV velocity measurement (~0.1 pixels). This is the order of magnitude that other typical PIV errors such as peak-locking may reach. Based on modern CCD technology and architecture, this work offers a description of the readout phenomenon and proposes a modeling for the CCD readout bias error magnitude. This bias, in turn, generates a velocity measurement bias error when there is an illumination difference between two successive PIV exposures. The model predictions match the experiments performed with two 12-bit-depth interline CCD cameras (MegaPlus ES 4.0/E incorporating the Kodak KAI-4000M CCD sensor with 4 megapixels). For different cameras, only two constant values are needed to fit the proposed calibration model and predict the error from the readout procedure. Tests by different researchers using different cameras would allow verification of the model, that can be used to optimize acquisition setups. Simple procedures to obtain these two calibration values are also described.

Depth-based face recognition using local quantized patterns adapted for range data

A depth-based face recognition algorithm specially adapted to high range resolution data acquired by the new Microsoft Kinect 2 sensor is presented. A novel descriptor called Depth Local Quantized Pattern descriptor has been designed to make use of the extended range resolution of the new sensor. This descriptor is a substantial modification of the popular Local Binary Pattern algorithm. One of the main contributions is the introduction of a quantification step, increasing its capacity to distinguish different depth patterns. The proposed descriptor has been used to train and test a Support Vector Machine classifier, which has proven to be able to accurately recognize different people faces from a wide range of poses. In addition, a new depth-based face database acquired by the new Kinect 2 sensor have been created and made public to evaluate the proposed face recognition system.

Guidance of robot arms using depth data from RGB-D camera

Image Based Visual Servoing (IBVS) is a robotic control scheme based on vision. This scheme uses only the visual information obtained from a camera to guide a robot from any robot pose to a desired one. However, IBVS requires the estimation of different parameters that cannot be obtained directly from the image. These parameters range from the intrinsic camera parameters (which can be obtained from a previous camera calibration), to the measured distance on the optical axis between the camera and visual features, it is the depth. This paper presents a comparative study of the performance of D-IBVS estimating the depth from three different ways using a low cost RGB-D sensor like Kinect. The visual servoing system has been developed over ROS (Robot Operating System), which is a meta-operating system for robots. The experiments prove that the computation of the depth value for each visual feature improves the system performance.

Bombay Harbour, India, Nautical Chart, 1806 (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: A plan of Bombay harbour : principally illustrative of the entrance, constructed from measured bases, and a series of angles, taken in 1803 & 4 by James Horsburgh. It was published by James Horsburgh in 1806. Scale [ca.1:37,820].The image inside the map neatline is georeferenced to the surface of the earth and fit to the Kalianpur 1975 India Zone III projected coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as drainage, cities and other human settlements, fortification, shoreline features (rocks, shoals, anchorage points, ports, inlets, lighthouses, etc.), and more. Relief shown by depth soundings. Includes also profile views and navigational notes.This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of originators, ground condition dates, scales, and map purposes.

Sri Lanka, ca. 1650 (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: Insvla Zeilan, olim Taprobana, nunc incolis Tenarisim, [by] Joannes Janssonius. It was published by J. Jansson, ca. 1650. Scale [ca. 1:1,000,000]. Covers Sri Lanka. Map in Latin. The image inside the map neatline is georeferenced to the surface of the earth and fit to the Universal Transverse Mercator (UTM Zone 44N, meters, WGS 1984) projected coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as drainage, cities and other human settlements, roads, shoreline features, and more. Relief shown pictorially, depth by soundings.This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of originators, ground condition dates, scales, and map purposes.

Los Angeles, California (West), and vicinity, 1953 (Raster Image) (Image 2 of 2)

This layer is a georeferenced raster image of the United States Geological Survey sheep map set entitled: Los Angeles and vicinity, East [and West], California. Edition 1953. It was published in 1956. Compiled from 1:24,000 scale maps of the Burbank 1953, Van Nuys 1953, Canoga Park 1952, Topanga 1952, Beverly Hills 1950, Hollywood 1953, Inglewood 1952, and Venice 1950 7.5 minute quadrangles. Hydrography compiled from USC&GS Chart 5144. Scale 1:24,000. This layer is image 2 of 2 total images of the two sheet source map set representing the western portion of the map set. The image inside the map neatline is georeferenced to the surface of the earth and fit to the California State Plane Zone V Coordinate System NAD27 (in Feet) (Fipszone 0405). All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. USGS maps are typical topographic maps portraying both natural and manmade features. They show and name works of nature, such as mountains, valleys, lakes, rivers, vegetation, etc. They also identify the principal works of humans, such as roads, railroads, boundaries, transmission lines, major buildings, etc. Relief is shown with standard contour intervals of 5 and 25 feet. Depth curves in feet. Please pay close attention to map collar information on projections, spheroid, sources, dates, and keys to grid numbering and other numbers which appear inside the neatline. This layer is part of a selection of digitally scanned and georeferenced historic maps from The Harvard Map Collection as part of the Imaging the Urban Environment project. Maps selected for this project represent major urban areas and cities of the world, at various time periods. These maps typically portray both natural and manmade features at a large scale. The selection represents a range of regions, originators, ground condition dates, scales, and purposes.

Palestine, 1876 (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: Palestine ancienne & moderne d'après les sources les plus authentiques, par E. Andriveau ; gravé le trait et les montagnes par Gérin, les écritures par P. Rousset, les eaux par Mme Fontaine. It was published by E. Andriveau-Goujon in 1876. Scale 1:600,000. Covers all or portions of Israel, West Bank, Gaza Strip, Jordan, Syria and Lebanon. Map in French with place names in Latin, Arabic and Hebrew. The image inside the map neatline is georeferenced to the surface of the earth and fit to the World Miller Cylindrical projection. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as drainage, cities and other human settlements, roads, monasteries, fortification, ruines, territorial boundaries, shoreline features, and more. Relief shown by hachures. Depth shown by sounding and isolines. Includes notes and insets: [Sinai] (Scale [ca. 1:2,600,000]) -- Golfe de Suez -- [Cross section of the Palestine from the source of the Jordan to the Red Sea] -- [Panoramic view of the mountains of Palestine] -- Jérusalem d'après le plan de G. Williams (Scale [ca. 1:80,000]). This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection as part of the Open Collections Program at Harvard University project: Islamic Heritage Project. Maps selected for the project represent a range of regions, originators, ground condition dates, scales, and purposes. The Islamic Heritage Project consists of over 100,000 digitized pages from Harvard's collections of Islamic manuscripts and published materials. Supported by Prince Alwaleed Bin Talal and developed in association with the Prince Alwaleed Bin Talal Islamic Studies Program at Harvard University.

Southeast Asia, 1742 (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: Royaume de Siam, avec les royaumes qui luy sont tributaires, et les isles de Sumatra, Andemaon, etc., corrigés selon les observations des six Peres Jesuites ... ; dressé et dedie à Mr. l'abbé de Dangeau par ... le Pere Coronelli, Cosmographe de la Republique de Venisse. It was published by chez Jean Baptiste Nolin in 1742. Scale [ca. 1:4,400,000]. Covers a portion of Southeast Asia including: Indonesia, Malaysia, Thailand, India, Burma, Laos, Cambodia, and Vietnam. Map in French. The image inside the map neatline is georeferenced to the surface of the earth and fit to the Universal Transverse Mercator (UTM Zone 48N, meters, WGS 1984) projected coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as drainage, cities and other human settlements, territorial and administrative boundaries, shoreline features, and more. Relief shown pictorially. Depth shown by sounding. Includes notes.This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of originators, ground condition dates, scales, and map purposes.

Depth propagation and surface construction in 3-D vision

In stereo vision, regions with ambiguous or unspecified disparity can acquire perceived depth from unambiguous regions. This has been called stereo capture, depth interpolation or surface completion. We studied some striking induced depth effects suggesting that depth interpolation and surface completion are distinct stages of visual processing. An inducing texture (2-D Gaussian noise) had sinusoidal modulation of disparity, creating a smooth horizontal corrugation. The central region of this surface was replaced by various test patterns whose perceived corrugation was measured. When the test image was horizontal 1-D noise, shown to one eye or to both eyes without disparity, it appeared corrugated in much the same way as the disparity-modulated (DM) flanking regions. But when the test image was 2-D noise, or vertical 1-D noise, little or no depth was induced. This suggests that horizontal orientation was a key factor. For a horizontal sine-wave luminance grating, strong depth was induced, but for a square-wave grating, depth was induced only when its edges were aligned with the peaks and troughs of the DM flanking surface. These and related results suggest that disparity (or local depth) propagates along horizontal 1-D features, and then a 3-D surface is constructed from the depth samples acquired. The shape of the constructed surface can be different from the inducer, and so surface construction appears to operate on the results of a more local depth propagation process.