Rostam Affendi Hamzah

Literature Survey on Stereo Vision Disparity Map Algorithms

This paper presents a literature survey on existing disparity map algorithms. It focuses on four main stages of processing as proposed by Scharstein and Szeliski in a taxonomy and evaluation of dense two-frame stereo correspondence algorithms performed in 2002. To assist future researchers in developing their own stereo matching algorithms, a summary of the existing algorithms developed for every stage of processing is also provided. The survey also notes the implementation of previous software-based and hardware-based algorithms. Generally, the main processing module for a software-based implementation uses only a central processing unit. By contrast, a hardware-based implementation requires one or more additional processors for its processing module, such as graphical processing unit or a field programmable gate array. This literature survey also presents a method of qualitative measurement that is widely used by researchers in the area of stereo vision disparity mappings.

Hybrid vibration energy harvester based on piezoelectric and electromagnetic transduction mechanism

Vibration energy harvester converts kinetic energy from ambient vibration into electrical energy. Many energy harvesters in the literature use single element transducer, either piezoelectric, electromagnetic or electrostatic for above purpose. In this paper, a hybrid based energy harvester that integrates with both, piezoelectric and electromagnetic transducers is developed and examined. The energy harvester uses four pole magnets arranged onto a piezoelectric cantilever beam free end, to produce stronger magnetic field over a stationary coil. When the harvester is excited by an external vibration, both piezoelectric and electromagnetic generates electrical energy or power. Experimental results shows that piezoelectric capable to generate optimum power of 2.3mW in a 60Ω resistive load, while electromagnetic generates 3.5mW power in a 40Ω resistive load, when vibrated at its resonant frequency 15Hz, and at 1g (1g=9.8ms-2) acceleration. By efficiently integrating both piezoelectric and electromagnetic transducers, more power could be generated as compared to a single transducer over its size.

Sum of Absolute Differences algorithm in stereo correspondence problem for stereo matching in computer vision application

This paper presents a method to solve the correspondence problem in matching the stereo image using Sum of Absolute Differences (SAD) algorithm. The computer vision application in this paper is using an autonomous vehicle which has a stereo pair on top of it. The estimation of range is using curve fitting tool (cftool) for each detected object or obstacles. This tool is provided by Matlab software. The disparity mapping is produced by block matching algorithm Sum of Absolute Differences (SAD). The range determined by cftool will be used as a reference for navigation of autonomous vehicle. The process of camera calibration, image rectification and obstacles detection is also discussed in this paper.

An obstacle detection and avoidance of a mobile robot with stereo vision camera

This paper presents an obstacle detection and avoidance of mobile robot using stereo camera for indoor environment. Block matching algorithm is solved the correspondence problem occurred in comparing stereo images (left and right sensors of the camera). The algorithm uses Sum of Absolute Differences (SAD). Left image works as a reference block to the right image and the output is disparity mapping or depth maps with the left coordinate system. A pair of camera or stereo vision baseline is based on horizontal configuration. The block matching technique is briefly described with the performance of its output. The curve fitting tool would determine the range of each obstacle detected in disparity mapping. The programming activities are using Matlab software starting from capturing images until navigation of mobile robot.

Region of Interest in Disparity Mapping for Navigation of Stereo Vision Autonomous Guided Vehicle

Stereo vision system is a practical method for depth gathering of objects and features in an environment. This paper presents the region of interest in disparity mapping for stereo vision autonomous guided vehicle using block matching algorithm. This region is a reference sight of the stereo camera and tereo vision baseline is based on horizontal configuration. The block matching technique is briefly described with the performance of its output. The disparity mapping is generated by the algorithm with the reference to the left image coordinate. The algorithm uses Sum of Absolute Differences (SAD) which is developed using Matlab software.

Stereo matching algorithm based on per pixel difference adjustment, iterative guided filter and graph segmentation ☆

Stereo matching process is a difficult and challenging task due to many uncontrollable factors that affect the results. These factors include the radiometric variations and illumination inconsistence. The absolute differences (AD) algorithms work fast, but they are too sensitive to noise and low textured areas. Therefore, this paper proposes an improved algorithm to overcome these limitations. First, the proposed algorithm utilizes per-pixel difference adjustment for AD and gradient matching to reduce the radiometric distortions. Then, both differences are combined with census transform to reduce the effect of illumination variations. Second, a new approach of iterative guided filter is introduced at cost aggregation to preserve and improve the object boundaries. The undirected graph segmentation is used at the last stage in order to smoothen the low textured areas. The experimental results on the standard indoor and outdoor datasets show that the proposed algorithm produces smooth disparity maps and accurate results.

A pixel to pixel correspondence and region of interest in stereo vision application

This paper presents an analysis of stereo images for an application of stereo vision application. The correspondence process is to determine the difference of intensities of pixel between stereo images while the region of interest ROI works as a reference area to the stereo vision application. This region is a reference view of the stereo camera and stereo vision baseline is based on horizontal configuration. The block matching technique is briefly described with the performance of its output. The disparity mapping is generated by the algorithm with the reference to the left image coordinate. The algorithm uses Sum of Absolute Differences (SAD) which is developed using Matlab software. The rectification and block matching processes are also briefly described in this paper.

The Solution of Stereo Correspondence Problem Using Block Matching Algorithm in Stereo Vision Mobile Robot

This paper presents the solution of stereo correspondence problem occur in comparing stereo images on stereo vision mobile robot using block matching algorithm. The algorithm is using Sum of Absolute Differences (SAD). Left image works as a reference block to the right image and the output is disparity mapping with the left coordinate system. The stereo vision baseline is based on horizontal configuration. The block matching technique is briefly described with the performance of its output. The curve fitting tool will determine the range of each obstacle detected in disparity mapping. The programming activities are using Matlab software starting from capturing images until navigation of mobile robot.

Disparity Mapping for Navigation of Stereo Vision Autonomous Guided Vehicle

Stereo vision system is a practical method for depth gathering of objects and features in an environment. This paper presents the disparity mapping for navigation of stereo vision autonomous guided vehicle using block matching algorithm. The stereo vision baseline is based on horizontal configuration. The block matching technique is briefly described with the performance of its output. The disparity mapping is generated by the algorithm with reference to the left image coordinate. The algorithm is using Sum of Absolute Differences (SAD) which runs in Matlab software.

Depth evaluation in selected region of disparity mapping for navigation of stereo vision mobile robot

Stereo vision system is a practical method for depth gathering of objects and features in an environment. This paper presents the depth evaluation in selected region of disparity mapping for stereo vision mobile robot using block matching algorithm. This region is a reference sight of the stereo camera and stereo vision baseline is based on horizontal configuration. The block matching technique is briefly described with the performance of its output. The disparity mapping is generated by the algorithm with the reference to the left image coordinate. The algorithm uses Sum of Absolute Differences (SAD) which is developed using Matlab software.