- Performance evaluation of 3D computer vision techniques
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- 8. TFCV 1996: Dagstuhl, Germany
On the other hand, it appears to be necessary for research groups, scientific journals, conferences and companies to present or market themselves as belonging specifically to one of these fields and, hence, various characterizations which distinguish each of the fields from the others have been presented. Computer graphics produces image data from 3D models, computer vision often produces 3D models from image data .
There is also a trend towards a combination of the two disciplines, e. Photogrammetry also overlaps with computer vision, e. Applications range from tasks such as industrial machine vision systems which, say, inspect bottles speeding by on a production line, to research into artificial intelligence and computers or robots that can comprehend the world around them. The computer vision and machine vision fields have significant overlap. Computer vision covers the core technology of automated image analysis which is used in many fields.
Machine vision usually refers to a process of combining automated image analysis with other methods and technologies to provide automated inspection and robot guidance in industrial applications. In many computer vision applications, the computers are pre-programmed to solve a particular task, but methods based on learning are now becoming increasingly common.
Examples of applications of computer vision include systems for:.
Performance evaluation of 3D computer vision techniques
One of the most prominent application fields is medical computer vision, or medical image processing, characterized by the extraction of information from image data to diagnose a patient. An example of this is detection of tumours , arteriosclerosis or other malign changes; measurements of organ dimensions, blood flow, etc. It also supports medical research by providing new information: Applications of computer vision in the medical area also includes enhancement of images interpreted by humans—ultrasonic images or X-ray images for example—to reduce the influence of noise.
A second application area in computer vision is in industry, sometimes called machine vision , where information is extracted for the purpose of supporting a manufacturing process. One example is quality control where details or final products are being automatically inspected in order to find defects. Another example is measurement of position and orientation of details to be picked up by a robot arm. Machine vision is also heavily used in agricultural process to remove undesirable food stuff from bulk material, a process called optical sorting.
Military applications are probably one of the largest areas for computer vision. The obvious examples are detection of enemy soldiers or vehicles and missile guidance. More advanced systems for missile guidance send the missile to an area rather than a specific target, and target selection is made when the missile reaches the area based on locally acquired image data. Modern military concepts, such as "battlefield awareness", imply that various sensors, including image sensors, provide a rich set of information about a combat scene which can be used to support strategic decisions.
In this case, automatic processing of the data is used to reduce complexity and to fuse information from multiple sensors to increase reliability.
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One of the newer application areas is autonomous vehicles, which include submersibles , land-based vehicles small robots with wheels, cars or trucks , aerial vehicles, and unmanned aerial vehicles UAV. The level of autonomy ranges from fully autonomous unmanned vehicles to vehicles where computer vision based systems support a driver or a pilot in various situations. Fully autonomous vehicles typically use computer vision for navigation, i.
It can also be used for detecting certain task specific events, e.
Examples of supporting systems are obstacle warning systems in cars, and systems for autonomous landing of aircraft. Several car manufacturers have demonstrated systems for autonomous driving of cars , but this technology has still not reached a level where it can be put on the market. There are ample examples of military autonomous vehicles ranging from advanced missiles, to UAVs for recon missions or missile guidance. Space exploration is already being made with autonomous vehicles using computer vision, e.
Each of the application areas described above employ a range of computer vision tasks; more or less well-defined measurement problems or processing problems, which can be solved using a variety of methods. Some examples of typical computer vision tasks are presented below. The classical problem in computer vision, image processing, and machine vision is that of determining whether or not the image data contains some specific object, feature, or activity.
Different varieties of the recognition problem are described in the literature: Currently, the best algorithms for such tasks are based on convolutional neural networks. An illustration of their capabilities is given by the ImageNet Large Scale Visual Recognition Challenge ; this is a benchmark in object classification and detection, with millions of images and hundreds of object classes.
Performance of convolutional neural networks, on the ImageNet tests, is now close to that of humans.
They also have trouble with images that have been distorted with filters an increasingly common phenomenon with modern digital cameras. By contrast, those kinds of images rarely trouble humans. Humans, however, tend to have trouble with other issues. For example, they are not good at classifying objects into fine-grained classes, such as the particular breed of dog or species of bird, whereas convolutional neural networks handle this with ease.
Several tasks relate to motion estimation where an image sequence is processed to produce an estimate of the velocity either at each points in the image or in the 3D scene, or even of the camera that produces the images.
Examples of such tasks are:. Given one or typically more images of a scene, or a video, scene reconstruction aims at computing a 3D model of the scene. In the simplest case the model can be a set of 3D points. More sophisticated methods produce a complete 3D surface model.
The advent of 3D imaging not requiring motion or scanning, and related processing algorithms is enabling rapid advances in this field. Grid-based 3D sensing can be used to acquire 3D images from multiple angles. Algorithms are now available to stitch multiple 3D images together into point clouds and 3D models . The aim of image restoration is the removal of noise sensor noise, motion blur, etc. The simplest possible approach for noise removal is various types of filters such as low-pass filters or median filters. More sophisticated methods assume a model of how the local image structures look like, a model which distinguishes them from the noise.
By first analysing the image data in terms of the local image structures, such as lines or edges, and then controlling the filtering based on local information from the analysis step, a better level of noise removal is usually obtained compared to the simpler approaches. The organization of a computer vision system is highly application dependent. Some systems are stand-alone applications which solve a specific measurement or detection problem, while others constitute a sub-system of a larger design which, for example, also contains sub-systems for control of mechanical actuators, planning, information databases, man-machine interfaces, etc.
The specific implementation of a computer vision system also depends on if its functionality is pre-specified or if some part of it can be learned or modified during operation. Many functions are unique to the application. There are, however, typical functions which are found in many computer vision systems. Image-understanding systems IUS include three levels of abstraction as follows: Low level includes image primitives such as edges, texture elements, or regions; intermediate level includes boundaries, surfaces and volumes; and high level includes objects, scenes, or events.
Many of these requirements are really topics for further research. The representational requirements in the designing of IUS for these levels are: While inference refers to the process of deriving new, not explicitly represented facts from currently known facts, control refers to the process that selects which of the many inference, search, and matching techniques should be applied at a particular stage of processing.
Inference and control requirements for IUS are: There are many kinds of computer vision systems, nevertheless all of them contain these basic elements: In addition, a practical vision system contains software, as well as a display in order to monitor the system. Vision systems for inner spaces, as most industrial ones, contain an illumination system and may be placed in a controlled environment.
Furthermore, a completed system includes many accessories like camera supports, cables and connectors. Most computer vision systems use visible-light cameras passively viewing a scene at frame rates of at most 60 frames per second usually far slower. A few computer vision systems use image acquisition hardware with active illumination or something other than visible light or both. For example, a structured-light 3D scanner , a thermographic camera , a hyperspectral imager , radar imaging , a lidar scanner, a magnetic resonance image , a side-scan sonar , a synthetic aperture sonar , or etc.
Such hardware captures "images" that are then processed often using the same computer vision algorithms used to process visible-light images. While traditional broadcast and consumer video systems operate at a rate of 30 frames per second, advances in digital signal processing and consumer graphics hardware has made high-speed image acquisition, processing, and display possible for real-time systems on the order of hundreds to thousands of frames per second.
For applications in robotics, fast, real-time video systems are critically important and often can simplify the processing needed for certain algorithms. When combined with a high-speed projector, fast image acquisition allows 3D measurement and feature tracking to be realised.
8. TFCV 1996: Dagstuhl, Germany
Egocentric vision systems are composed of a wearable camera that automatically take pictures from a first-person perspective. As of , vision processing units are emerging as a new class of processor, to complement CPUs and graphics processing units GPUs in this role. From Wikipedia, the free encyclopedia. Vandoni, Carlo, E, ed. Image Processing, Analysis, and Machine Vision. Shapiro ; George C.
Computer Vision and Image Processing. Forsyth; Jean Ponce Computer Vision, A Modern Approach. Retrieved 2 August A History of Cognitive Science. The camera model adopted is the ' pinhole ' model, as shown in Fu and in Nalwa Camera calibration is the determination of all its inner geometric and optics features. These inner features are called intrinsic parameters. Camera calibration also means the determination of its position and orientation relative to the world co-ordinate system.
These features are called extrinsic parameters. Laudares presents in detail an extrinsic camera calibration process, which is quite suitable for the robotic application proposed in this work. The most important camera intrinsic parameter is the focal distance l , which is the distance between the centre of the lens and the image sensor plane. The following conditions must be met for the model shown in the Fig 2: According to Fu , the depth information recovery Z co-ordinate is achieved by the following expression: Some improvements on Feris technique were included in order to increase the algorithm performance and ease the correlation process, as shown in Kabayama Further information about focal distance and scale factor processes procedures and calibration results can be found in Kabayama Table 1 shows the results of some objects height measures using 30mm baseline displacement.
Disparity is the difference between respective x co-ordinates in both images and matches established is the number of correlated points. The conception of the sensorial fusion technique for 3D-vision machine is shown in the Fig. The sensor provides an analogic tension output proportional to the distance to be measured. This proportional pattern can behave in a direct or in an inverse way, depending on how the sensor is programmed rising or falling modes.
The curves of this sensor relate output tension variation as a function of distance were determined using both proportional modes for different range programs. The results showed that the ultra sound sensor has a linear behaviour in all modes and this is an important and desirable feature. The respective static calibration coefficients for each curve were calculated and they are necessary to sensitivity to establish the relationship between the output tension and the distance measured and for evaluating the sensitivity of the programmed mode for noise and the resolution.
As for ultrasound beam characteristics, as shown in Fig. The distances shown in Table 2 refer to the object top. The determination of the ultrasound beam diameter in a given level was performed in an experimental way: An object was moved on this surface towards the place that the sensor was pointing at. As soon as the object was detected, the place where that happened was marked. This procedure was repeated until a complete ultrasound profile in this level was determined. This entire process was repeated for other levels, as shown in the Table 2.
From the knowledge about the sensor features, it is possible to estimate the minimum size of the object that can be manipulated using this technique. For example, at 40cm range, the object size must be 16cm at least. The size of the objects cannot be smaller than the diameter of ultrasound beam in the object top. Besides, the object material should not absorb the ultrasound waves and the object's top must be perpendicular to the direction that the ultrasound beam may reach it.
Two different lighting patterns were studied to evaluate accuracy and check if this technique is suitable for a pick-and-place robotic application. The first pattern studied was a source of laser light from a presentation pointer device. An external DC power source was adapted to the device in order to avoid the decreasing light intensity due to batteries flattering process.
The full line and the dotted line shown in Fig. The scene is filmed twice. At the first shot, represented by the dotted line, the object is out of scene and P1 is the position of the laser beam centre area where it touches the ground. At second shot, represented by the full line, the object is in scene and P2 is the position of the laser beam centre area where it touches the top of the object.
The laser beam area centre is determined by computer vision system in both situations. P3 is the P2 projection in the horizontal plane. The laser beam reaches the ground with a q angle and d is the distance, in pixels, between area centres P1 and P2. The object h height is determined by a simple trigonometric relation see Fig. The first step was the determination of the conversion factor s.
An A4 sheet of paper has been used as a standard object shot five times. Then, the object size in pixels, in each image, were measured. The s conversion factor yields from the average rate between the standard object size measured in centimeters and in pixels. It is important to take note if this conversion factor is determined at the same direction of distance variation because the camera pixels are not square shaped. The s factor determinated was: The second step was the q angle calibration procedure.
Five different objects with known heights h were used as calibration standards and shot five times. The angle q calibration results are shown in Table 3. After completing the s and q calibration processes, the 3D structured lighting computer vision system using laser light source was ready to be operated.
Different object heights were measured and the results are shown in Table 4. The second pattern used in this experiment was a single stripe projected by an overhead projector. In this case, the object height information recovery is similar to the laser case, using the same principles and equations. The difference is that applying stripe pattern yields in three object recognition by computer vision system as shown in Fig.
Due to digitalisation errors, the alignment of O2 and O3 objects can not always be obtained. Because of this, the distance d is the average of the distance between O2 and O1 and the distance between O3 and O1. The conversion factor s used is the same as in the previous experiment. The q angle calibration process was repeated, using five standard objects. The results are shown in Table 5. After completing the s and q calibration processes, the 3D structured lighting computer vision system using the single stripe pattern was ready to use.